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src/ground_slam/.gitignore vendored Executable file
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build/
bin/
lib/
debug/
msg_gen/
srv_gen/
msg/*Action.msg
msg/*ActionFeedback.msg
msg/*ActionGoal.msg
msg/*ActionResult.msg
msg/*Feedback.msg
msg/*Goal.msg
msg/*Result.msg
msg/_*.py
# Generated by dynamic reconfigure
*.cfgc
/cfg/cpp/
/cfg/*.py
# Ignore generated docs
*.dox
*.wikidoc
# eclipse stuff
.project
.cproject
# qcreator stuff
CMakeLists.txt.user
srv/_*.py
*.pcd
*.pyc
qtcreator-*
*.user
*~
# Emacs
.#*
# Catkin custom files
CATKIN_IGNORE
bag
.vscode/*
log/*
saving/*
debug/*
test/*

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src/ground_slam/CMakeLists.txt Executable file
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cmake_minimum_required(VERSION 3.8)
project(ground_slam)
# Default to C++17 (ROS2 Humble standard)
if(NOT CMAKE_CXX_STANDARD)
set(CMAKE_CXX_STANDARD 17)
endif()
set(CMAKE_CXX_STANDARD_REQUIRED ON)
set(CMAKE_BUILD_TYPE Release)
set(CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR}/cmake)
# ── ROS2 packages ──────────────────────────────────────
find_package(ament_cmake REQUIRED)
find_package(rclcpp REQUIRED)
find_package(geometry_msgs REQUIRED)
find_package(nav_msgs REQUIRED)
find_package(std_msgs REQUIRED)
find_package(sensor_msgs REQUIRED)
find_package(cv_bridge REQUIRED)
find_package(tf2 REQUIRED)
find_package(tf2_geometry_msgs REQUIRED)
find_package(tf2_ros REQUIRED)
find_package(visualization_msgs REQUIRED)
# ── System dependencies ────────────────────────────────
find_package(OpenCV 4.2 REQUIRED)
find_package(OpenMP REQUIRED)
find_package(Eigen3 REQUIRED)
find_package(FFTW3 REQUIRED)
find_package(Ceres REQUIRED)
find_package(yaml-cpp REQUIRED)
find_package(Boost REQUIRED)
find_package(VTK REQUIRED)
# ── Compiler flags ─────────────────────────────────────
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -Wall -O3 -march=native")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall -O3 -march=native -Wno-reorder")
if(OPENMP_FOUND)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} ${OpenMP_C_FLAGS}")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
endif()
# ── Include directories ────────────────────────────────
include_directories(
${PROJECT_SOURCE_DIR}/include
${OpenCV_INCLUDE_DIRS}
${EIGEN3_INCLUDE_DIR}
${FFTW3_INCLUDE_DIRS}
${YAML_CPP_INCLUDE_DIR}
${CERES_INCLUDE_DIRS}
${Boost_INCLUDE_DIRS}
${VTK_INCLUDE_DIRS}
)
# ── Core library ───────────────────────────────────────
add_library(${PROJECT_NAME}_lib SHARED
src/optimization_2d/pose_graph_2d.cc
src/utils.cc
src/dataset.cc
src/camera.cc
src/frame.cc
src/edge.cc
src/correlation_flow.cc
src/loop_closure.cc
src/map.cc
src/map_builder.cc
src/thread_publisher.cc
src/map_stitcher.cc
src/visualization.cc
src/timer.cc
)
target_link_libraries(${PROJECT_NAME}_lib
-lfftw3
-lfftw3f
${OpenCV_LIBS}
${EIGEN3_LIBS}
${CERES_LIBRARIES}
yaml-cpp
${Boost_LIBRARIES}
${VTK_LIBRARIES}
)
# ROS2 deps (header-only via include_directories above, link where needed)
ament_target_dependencies(${PROJECT_NAME}_lib
rclcpp
geometry_msgs
nav_msgs
std_msgs
sensor_msgs
cv_bridge
tf2
tf2_geometry_msgs
tf2_ros
visualization_msgs
)
# ── Executable ─────────────────────────────────────────
add_executable(${PROJECT_NAME} main.cpp)
target_link_libraries(${PROJECT_NAME} ${PROJECT_NAME}_lib)
ament_target_dependencies(${PROJECT_NAME}
rclcpp
)
# ── Install ────────────────────────────────────────────
install(TARGETS ${PROJECT_NAME} ${PROJECT_NAME}_lib
ARCHIVE DESTINATION lib
LIBRARY DESTINATION lib
RUNTIME DESTINATION lib/${PROJECT_NAME}
)
install(DIRECTORY configs/
DESTINATION share/${PROJECT_NAME}/configs
)
ament_package()

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src/ground_slam/LICENSE.md Normal file
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If the disclaimer of warranty and limitation of liability provided
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reviewing courts shall apply local law that most closely approximates
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END OF TERMS AND CONDITIONS
How to Apply These Terms to Your New Programs
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possible use to the public, the best way to achieve this is to make it
free software which everyone can redistribute and change under these terms.
To do so, attach the following notices to the program. It is safest
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state the exclusion of warranty; and each file should have at least
the "copyright" line and a pointer to where the full notice is found.
{one line to give the program's name and a brief idea of what it does.}
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This program is free software: you can redistribute it and/or modify
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Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short
notice like this when it starts in an interactive mode:
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This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
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138
src/ground_slam/README.md Normal file
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<h1 align="center">GroundSLAM: A Robust Visual SLAM System for Warehouse Robots Using Ground Textures</h1><p align="center"><strong>
<a href = "https://scholar.google.com/citations?user=-p7HvCMAAAAJ&hl=zh-CN">Kuan Xu</a><sup>1</sup>,
<a href = "https://github.com/yangzheng-yz">Zheng Yang</a><sup>1</sup>,
<a href = "https://scholar.google.com.sg/citations?user=Fmrv3J8AAAAJ&hl=en">Lihua Xie</a><sup>1</sup>,
<a href = "https://sairlab.org/team/chenw/">Chen Wang</a><sup>2</sup>
</strong></p>
<p align="center"><strong>
<a href = "https://www.ntu.edu.sg/cartin">1: Centre for Advanced Robotics Technology Innovation (CARTIN), Nanyang Technological University</a><br>
<a href = "https://sairlab.org/">2: Spatial AI & Robotics (SAIR) Lab, Computer Science and Engineering, University at Buffalo</a><br>
</strong></p>
<p align="center"><strong>
<!-- <a href = "https://arxiv.org/pdf/2408.03520">&#128196; [PDF]</a> |
<a href = "https://xukuanhit.github.io/airslam/">&#128190; [Project Site]</a> | -->
<a href = "https://youtu.be/2sI76SDptDA">&#127909; [Youtube]</a> |
<a href = "https://www.bilibili.com/video/BV1vWh8zTE77">&#127909; [Bilibili]</a>
<a href = "supplementary/GroundSLAM_Supplement_Materia.pdf">&#128202; [Supplementary]</a>
<!-- &#128214; [OpenAccess] -->
</strong></p>
<img src="figures/pipeline.png" width = "800" alt="pipeline" />
GroundSLAM is a novel **feature-free** and **ground-texture-based** SLAM system for **the warehouse robot**. Our system can provide robust pose estimation and localization in environments with many **dynamic** objects or **open spaces**, such as warehouses, which is very challenging for localization systems with a forward-facing camera or LiDAR. GroundSLAM consists of three components: feature-free visual odometry, ground-texture-based loop detection and map optimization, and map reuse. Specifically, we introduce a kernel cross-correlator for **image-level pose tracking**, loop detection, and map reuse to improve localization accuracy and robustness, and incorporate adaptive pruning strategies to enhance efficiency. Due to these specific designs, GroundSLAM more **robust** and **accurate** when dealing with ground images with **few textures** or with many **repetitive patterns** than the feature-based methods.
**Video:**
<p align="middle">
<a href="https://youtu.be/2sI76SDptDA" target="_blank"><img src="figures/video.png" width="600" border="10"/></a>
</p>
## Test Environment
### Dependencies
* OpenCV 4.2
* Eigen 3
* Ceres 2.0.0
* FFTW3
* ROS noetic
* Boost
* yaml-cpp
* VTK
## Build
```
cd ~/catkin_ws/src
git clone https://github.com/sair-lab/GroundSLAM.git
cd ../
catkin_make
source ~/catkin_ws/devel/setup.bash
```
## Run
Modify the configuration file in [configs](configs) and then run
```
rosrun ground_slam ground_slam src/GroundSLAM/configs/your_config.yaml
```
## Data
### PathTex Dataset
<img src="figures/sample_images.jpg" width = "600" alt="pathtex_dataset" />
Our data collection platform is a modified Weston SCOUT Robot. The robot is equipped with an IDS uEye monocular camera, which is positioned at the bottom and facing downward, placed at a height of 0.1m above the ground. To ensure constant illumination, a set of LED lights are arranged around the camera. For ground truth, a prism is installed on the top of the robot, and its position is tracked by a Leica Nova MS60 MultiStation laser tracker.
We collect the data of 10 common ground textures, including 6 $\color{lightblue}{outdoor}$ textures and 4 $\color{red}{indoor}$ textures. The table below provides detailed information and download links for each sequence. The **camera parameters** can be found [here](https://entuedu-my.sharepoint.com/:u:/g/personal/kuan_xu_staff_main_ntu_edu_sg/EQqXW2eDcXNAkBWlSGgq6bgBe2yYdjdcuSJ8HEFey3haGg?e=nOg2ym).
<!-- <div align=center> -->
Sequence Name|Total Size|Total Images|Download Link
:--:|:--:|:--:|:--:
Brick_seq1|1.0g|3119|[Link](https://entuedu-my.sharepoint.com/:u:/g/personal/kuan_xu_staff_main_ntu_edu_sg/EUMENxrPbQBFmzAQmfhMw4YB4df6_uq6ua7H1fo9A7NC7g?e=5Mzy9Q)
Brick_seq2|0.9g|5328|[Link](https://entuedu-my.sharepoint.com/:u:/g/personal/kuan_xu_staff_main_ntu_edu_sg/EY1-hFr2taZJqjxE4k6-W6oB-XenzRAk_RW8DME6VvqUmg?e=PSQEi6)
Carpet1_seq1|1.7g|8458|[Link](https://entuedu-my.sharepoint.com/:u:/g/personal/kuan_xu_staff_main_ntu_edu_sg/ETuQX1ePORdKmQJ_wFXT1mUBS6MqJ3LoZ-eeUsDhhXouug?e=AmMgBX)
Carpet1_seq2|1.7g|8499|[Link](https://entuedu-my.sharepoint.com/:u:/g/personal/kuan_xu_staff_main_ntu_edu_sg/EQwzCVxFMT1Hh9BMMNljwSgBM27RXd7v8gdM3KMNWm5mDQ?e=aSoH1v)
Carpet2_seq1|3.0g|15481|[Link](https://entuedu-my.sharepoint.com/:u:/g/personal/kuan_xu_staff_main_ntu_edu_sg/EZ6Y6xsmk6xBjkkU7JNrVQIB_zTovWY8-AOPdW7AN7XJLw?e=GvlK03)
Carpet3_seq1|0.7g|4500|[Link](https://entuedu-my.sharepoint.com/:u:/g/personal/kuan_xu_staff_main_ntu_edu_sg/EXxxHyPOvsFBh8judFQqhtEB-kI6uBzFmKcCiNDZZBzBIw?e=917b5c)
Carpet3_seq2|0.7g|4385|[Link](https://entuedu-my.sharepoint.com/:u:/g/personal/kuan_xu_staff_main_ntu_edu_sg/EQHoNL0xtpdKj6e6mhBBLyEBl-7s5N0cJ6U47jzT1tJheg?e=ot4hSH)
Carpet3_seq3|1.0g|6428|[Link](https://entuedu-my.sharepoint.com/:u:/g/personal/kuan_xu_staff_main_ntu_edu_sg/EcE3o-HN0UJEpc57_EJM3kcBSVSIQ8M7oCjkWsZlUQRKLw?e=qG8MUM)
Coarse_asphalt_seq1|1.2g|5897|[Link](https://entuedu-my.sharepoint.com/:u:/g/personal/kuan_xu_staff_main_ntu_edu_sg/EQbDBVemPH9Cp7bqV2ZwfFIBD_gsHQ-d3atPfSY8__6DKQ?e=ASludI)
Concrete_seq1|1.0g|5850|[Link](https://entuedu-my.sharepoint.com/:u:/g/personal/kuan_xu_staff_main_ntu_edu_sg/EcIYh8RIhClKs7ogwgFmS_QBlflxcx-eR33BwRVerNXFgg?e=8McLDA)
Concrete_seq2|0.9g|5975|[Link](https://entuedu-my.sharepoint.com/:u:/g/personal/kuan_xu_staff_main_ntu_edu_sg/EbNJ5FdpQSJMqK-2zyoEPNoBNCO4ZzhuzjzTL2m1AYZQKQ?e=feQNUX)
Fine_asphalt_seq1|1.1g|5119|[Link](https://entuedu-my.sharepoint.com/:u:/g/personal/kuan_xu_staff_main_ntu_edu_sg/EY22RWOcM89ApAYEr0E5I5EB6QhINnL5iGsbgli8INjuXg?e=HArfqb)
Fine_asphalt_seq2|1.3g|11897|[Link](https://entuedu-my.sharepoint.com/:u:/g/personal/kuan_xu_staff_main_ntu_edu_sg/ERBTClOvkpBHgNeAEc5co4sBI5E-ubQ2MqNFwlfys07mbQ?e=WDd0uP)
Granite_tiles_seq1|1.2g|7194|[Link](https://entuedu-my.sharepoint.com/:u:/g/personal/kuan_xu_staff_main_ntu_edu_sg/ERNkKMy_nvFFmORT7LU_MxEB7TDzhAsNKEEfBf_cNG4Zbg?e=sjDkYW)
Granite_tiles_seq2|1.6g|10633|[Link](https://entuedu-my.sharepoint.com/:u:/g/personal/kuan_xu_staff_main_ntu_edu_sg/EU-8ocb82WZDpErJuOGfR6oBbQwXEpAzR-nykA6wXk9zeg?e=Vx4P1t)
Gravel_road1_seq1|0.8g|4883|[Link](https://entuedu-my.sharepoint.com/:u:/g/personal/kuan_xu_staff_main_ntu_edu_sg/ERRQQR0VTCVEqSdo9pM85zwBNMQFRBcgev4VVIZtjg68Vg?e=KFxJST)
Gravel_road2_seq1|2.1g|11776|[Link](https://entuedu-my.sharepoint.com/:u:/g/personal/kuan_xu_staff_main_ntu_edu_sg/EZS9BNL-ct1Nu3_Ag1Z0YuIBt6ah02i8vojro4eHexs4ww?e=hQOLIF)
<!-- </div> -->
### Run with Your Data
The data should be organized in the following format:
```
dataroot
├── image_names.txt
├── rgb
│   ├── 00001.png
│   ├── 00002.png
│   ├── 00003.png
│   └── ......
└── times.txt
```
where `image_names.txt` contains the image names in `/dataroot/rgb` and `times.txt` contains the corresponding double type timestamps.
## Experiments
### Data Association
<img src="figures/features_small.jpg" width = "700" alt="data_association" />
We compare the data association of our system with ORB and SIFT on the HD Ground dataset. The numbers of features and matching inliers are given. For our method, the correction results are projected to three coordinate axes and represent the estimation of the 3-DOF movement. The vertical axis is the confidence of estimated movement on the horizontal axis. The higher the value of the peak relative to other positions, the greater the confidence of motion estimation. The results show that the data association of our system is more stable for various ground texture images.
### Visual Odometry
<div style="display:flex; justify-content:flex-start;">
<img src="figures/trajectory.jpg" height="200" style="margin-right: 20px;" />
<img src="figures/rmse_curve.jpg" height="200" />
</div>
This experiment is conducted on our PathTex dataset. The left figure shows the trajectories produced by our system and GT-SLAM on 4 sequences. The right figure provides the comparison of error distributions of different systems on the Gravel_road2_seq1 sequence, where the vertical axis is the proportion of pose errors that are less than the given error threshold on the horizontal axis.
### Loop Closure
<div style="display:flex; justify-content:flex-start;">
<img src="figures/loop_trajectory.jpg" height="135" style="margin-right: 20px;" />
<img src="figures/loop_error.jpg" height="135" />
</div>
These two figures show the performance difference of GroundSLAM with and without loop correction on the Fine_asphalt_seq2 sequence. It is seen that the pose errors are significantly decreased after the loop correction.

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src/ground_slam/cmake/FindEigen3.cmake Executable file
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# - Try to find Eigen3 lib
#
# This module supports requiring a minimum version, e.g. you can do
# find_package(Eigen3 3.1.2)
# to require version 3.1.2 or newer of Eigen3.
#
# Once done this will define
#
# EIGEN3_FOUND - system has eigen lib with correct version
# EIGEN3_INCLUDE_DIR - the eigen include directory
# EIGEN3_VERSION - eigen version
# Copyright (c) 2006, 2007 Montel Laurent, <montel@kde.org>
# Copyright (c) 2008, 2009 Gael Guennebaud, <g.gael@free.fr>
# Copyright (c) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
# Redistribution and use is allowed according to the terms of the 2-clause BSD license.
if(NOT Eigen3_FIND_VERSION)
if(NOT Eigen3_FIND_VERSION_MAJOR)
set(Eigen3_FIND_VERSION_MAJOR 2)
endif(NOT Eigen3_FIND_VERSION_MAJOR)
if(NOT Eigen3_FIND_VERSION_MINOR)
set(Eigen3_FIND_VERSION_MINOR 91)
endif(NOT Eigen3_FIND_VERSION_MINOR)
if(NOT Eigen3_FIND_VERSION_PATCH)
set(Eigen3_FIND_VERSION_PATCH 0)
endif(NOT Eigen3_FIND_VERSION_PATCH)
set(Eigen3_FIND_VERSION "${Eigen3_FIND_VERSION_MAJOR}.${Eigen3_FIND_VERSION_MINOR}.${Eigen3_FIND_VERSION_PATCH}")
endif(NOT Eigen3_FIND_VERSION)
macro(_eigen3_check_version)
file(READ "${EIGEN3_INCLUDE_DIR}/Eigen/src/Core/util/Macros.h" _eigen3_version_header)
string(REGEX MATCH "define[ \t]+EIGEN_WORLD_VERSION[ \t]+([0-9]+)" _eigen3_world_version_match "${_eigen3_version_header}")
set(EIGEN3_WORLD_VERSION "${CMAKE_MATCH_1}")
string(REGEX MATCH "define[ \t]+EIGEN_MAJOR_VERSION[ \t]+([0-9]+)" _eigen3_major_version_match "${_eigen3_version_header}")
set(EIGEN3_MAJOR_VERSION "${CMAKE_MATCH_1}")
string(REGEX MATCH "define[ \t]+EIGEN_MINOR_VERSION[ \t]+([0-9]+)" _eigen3_minor_version_match "${_eigen3_version_header}")
set(EIGEN3_MINOR_VERSION "${CMAKE_MATCH_1}")
set(EIGEN3_VERSION ${EIGEN3_WORLD_VERSION}.${EIGEN3_MAJOR_VERSION}.${EIGEN3_MINOR_VERSION})
if(${EIGEN3_VERSION} VERSION_LESS ${Eigen3_FIND_VERSION})
set(EIGEN3_VERSION_OK FALSE)
else(${EIGEN3_VERSION} VERSION_LESS ${Eigen3_FIND_VERSION})
set(EIGEN3_VERSION_OK TRUE)
endif(${EIGEN3_VERSION} VERSION_LESS ${Eigen3_FIND_VERSION})
if(NOT EIGEN3_VERSION_OK)
message(STATUS "Eigen3 version ${EIGEN3_VERSION} found in ${EIGEN3_INCLUDE_DIR}, "
"but at least version ${Eigen3_FIND_VERSION} is required")
endif(NOT EIGEN3_VERSION_OK)
endmacro(_eigen3_check_version)
if (EIGEN3_INCLUDE_DIR)
# in cache already
_eigen3_check_version()
set(EIGEN3_FOUND ${EIGEN3_VERSION_OK})
else (EIGEN3_INCLUDE_DIR)
# specific additional paths for some OS
if (WIN32)
set(EIGEN_ADDITIONAL_SEARCH_PATHS ${EIGEN_ADDITIONAL_SEARCH_PATHS} "C:/Program Files/Eigen/include" "C:/Program Files (x86)/Eigen/include")
endif(WIN32)
find_path(EIGEN3_INCLUDE_DIR NAMES signature_of_eigen3_matrix_library
PATHS
${CMAKE_INSTALL_PREFIX}/include
${EIGEN_ADDITIONAL_SEARCH_PATHS}
${KDE4_INCLUDE_DIR}
PATH_SUFFIXES eigen3 eigen
)
if(EIGEN3_INCLUDE_DIR)
_eigen3_check_version()
endif(EIGEN3_INCLUDE_DIR)
include(FindPackageHandleStandardArgs)
find_package_handle_standard_args(Eigen3 DEFAULT_MSG EIGEN3_INCLUDE_DIR EIGEN3_VERSION_OK)
mark_as_advanced(EIGEN3_INCLUDE_DIR)
endif(EIGEN3_INCLUDE_DIR)
if(EIGEN3_FOUND)
include_directories(${EIGEN3_INCLUDE_DIR})
link_directories(${EIGEN3_LIBRARIES})
else(EIGEN3_FOUND)
MESSAGE(FATAL_ERROR "Eigen3 library not found")
endif(EIGEN3_FOUND)

18
src/ground_slam/cmake/FindFFTW3.cmake Executable file
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# - Find FFTW3
# Find the native FFTW3 includes and library
#
# FFTW3_INCLUDE_DIRS - where to find fftw3.h
# FFTW3_LIBRARIES - List of libraries when using FFTW3.
# FFTW3_FOUND - True if FFTW3 found.
if (FFTW3_INCLUDE_DIRS)
set(FFTW3_FIND_QUIETLY TRUE)
endif()
find_path(FFTW3_INCLUDE_DIRS fftw3.h)
find_library(FFTW3_LIBRARIES NAMES fftw3)
include(FindPackageHandleStandardArgs)
find_package_handle_standard_args(FFTW3 DEFAULT_MSG FFTW3_LIBRARIES FFTW3_INCLUDE_DIRS)
mark_as_advanced(FFTW3_LIBRARIES FFTW3_INCLUDE_DIRS)

49
src/ground_slam/configs/config_HD.yaml Normal file
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dataset:
camera_config: /media/zheng/xukuanssd/NTU/ground_texture/HD_ground/camera.yaml
dataroot: /media/zheng/xukuanssd/NTU/ground_texture/HD_ground/ramp_rubber/database/seq0033
image_dir_name: rgb
correlation_flow:
gaussian:
sigma: 0.2
height: 1200
kernel: 0 # {0: "polynomial", 1: "gaussian"}
lambda: 0.1
polynomial:
offset: 0.1
power: 3
rotation_channel: 480
rotation_divisor: 720
width: 1600
keyframe_selection:
lower_response_thr: 30
max_angle: 6.3
max_distance: 0.01
upper_response_thr: 90
map:
grid_scale: 0.1 # the true size of one grid
loop_closure:
to_find_loop: true
angle_response_thr: 60
distance_thr: 5
frame_gap_thr: 100
position_response_thr: 60
map_sticther:
cell_size: 1000
stitch_map: true
visualization:
frame_id: map
topic:
image: /kcc_slam/image
frame_pose: /kcc_slam/frame_pose
kcc_pose: /kcc_slam/kcc_pose
map: /kcc_slam/occupancy_map
saving:
save_pose: true
saving_root: /home/xukuan/debug/kcc

50
src/ground_slam/configs/config_geekplus.yaml Normal file
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dataset:
dataroot: "/home/zheng/datasets/loopclosure/"
camera_config: "/home/zheng/datasets/loopclosure/camera.yaml"
image_dir_name: ""
correlation_flow:
width: 448
height: 448
rotation_divisor: 720 #720 # Higher means higher resolution, but slower
rotation_channel: 480 #480 # Lower means faster. Too lower, e.g., less than 10, may produce error about 1/rotation_divisor*360 degrees. 64 may work well.
lambda: 0.1 # For regularization
kernel: 0 #1 # {0: "polynomial", 1: "gaussian"}
polynomial: # (X^T Y + offset).pow(power)
offset: 0.1
power: 3
gaussian: # exp(-(|X-Y|^2/(2*sigma^2)))
sigma: 0.2 # For Gaussian Kernel
keyframe_selection:
to_find_loop: true
max_distance: 0.2 # in normalized plane, real size equal max_distance*camera_height
max_angle: 0.5 # rad
lower_response_thr: 30
upper_response_thr: 60
map:
grid_scale: 0.1 # the true size of one grid
loop_closure:
to_find_loop: true
position_response_thr: 60 # this should be tuned together with width and height: higher width*height -> higher threshold
angle_response_thr: 60 # this should be tuned together with rotation_divisor and rotation_channel: higher divisor*channel -> higher threshold
frame_gap_thr: 100
distance_thr: 5 # in normalized plane, real size equal distance_thr*camera_height
map_sticther:
stitch_map: true # whether to stitch map
cell_size: 1000
visualization:
frame_id: "map"
topic:
image: /kcc_slam/undistort_image
kcc_pose: "/kcc_slam/kcc_pose"
frame_pose: "/kcc_slam/frame_pose"
map: "/kcc_slam/occupancy_map"
saving:
save_pose: true
saving_root: "/home/zheng/projects/ros_ws/src/kcc_slam/saving/experiments"

49
src/ground_slam/configs/config_ntu.yaml Normal file
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dataset:
camera_config: /media/data/datasets/ntu/NTU_Ground_Texture_Dataset/camera.yaml
dataroot: /media/data/datasets/ntu/NTU_Ground_Texture_Dataset/brick-V3
image_dir_name: rgb
correlation_flow:
gaussian:
sigma: 0.2
height: 480
kernel: 0 # {0: "polynomial", 1: "gaussian"}
lambda: 0.1
polynomial:
offset: 0.1
power: 3
rotation_channel: 480
rotation_divisor: 720
width: 640
keyframe_selection:
lower_response_thr: 30
max_angle: 0.052359877
max_distance: 0.4
upper_response_thr: 90
map:
grid_scale: 0.1 # the true size of one grid
loop_closure:
to_find_loop: true
angle_response_thr: 60
distance_thr: 5
frame_gap_thr: 100
position_response_thr: 60
map_sticther:
cell_size: 1000
stitch_map: true
visualization:
frame_id: map
topic:
image: /kcc_slam/image
frame_pose: /kcc_slam/frame_pose
kcc_pose: /kcc_slam/kcc_pose
map: /kcc_slam/occupancy_map
saving:
save_pose: true
saving_root: /home/xukuan/debug/kcc

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#ifndef CAMERA_H_
#define CAMERA_H_
#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/opencv.hpp>
#include <Eigen/Core>
#include <Eigen/Dense>
#include <Eigen/SparseCore>
class Camera{
public:
Camera();
Camera(const std::string& camera_file);
Camera& operator=(const Camera& camera);
void UndistortImage(cv::Mat& image, cv::Mat& undistort_image);
void GetNewCameraMatrix(cv::Mat& camera_matrix);
double GetImageHeight();
double GetImageWidth();
double GetHeight();
bool HeightIsAccurate();
void GetExtrinsics(Eigen::Matrix3d& extrinsics);
double GetLengthOfPixel();
Eigen::Vector3d ConvertPrincipalToCenter(const Eigen::Vector3d& image_plane_pose);
Eigen::Vector3d ConvertCenterToPrincipal(const Eigen::Vector3d& image_center_pose);
// Image plane: pixel plane, principal point on the image is the origin
// Camera: normalized plane,
// Robot: robot body coordinate system.
bool ConvertImagePlanePoseToCamera(Eigen::Vector3d& image_plane_pose, Eigen::Vector3d& camera_pose);
bool ConvertCameraPoseToImagePlane(Eigen::Vector3d& image_plane_pose, Eigen::Vector3d& camera_pose);
bool ConvertCameraPoseToRobot(Eigen::Vector3d& camera_pose, Eigen::Vector3d& robot_pose);
bool ConvertRobotPoseToCamera(Eigen::Vector3d& camera_pose, Eigen::Vector3d& robot_pose);
bool ConvertImagePlanePoseToRobot(Eigen::Vector3d& image_plane_pose, Eigen::Vector3d& robot_pose);
bool ConvertRobotPoseToImagePlane(Eigen::Vector3d& image_plane_pose, Eigen::Vector3d& robot_pose);
private:
int _image_height;
int _image_width;
double _scale;
double _new_scale;
int _new_width;
int _new_height;
double _height;
bool _accurate_height;
cv::Mat _K;
cv::Mat _new_K;
cv::Mat _D;
cv::Mat _map1;
cv::Mat _map2;
Eigen::Matrix3d _extrinsics;
};
typedef std::shared_ptr<Camera> CameraPtr;
#endif // CAMERA_H_

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// circ_shift.h
// https://stackoverflow.com/questions/46077242/eigen-modifyable-custom-expression/46301503#46301503
// this file implements circShift, fftshift, and ifftshift for Eigen vectors/matrices.
//
#pragma once
#include <Eigen/Core>
template <bool B> using bool_constant = std::integral_constant<bool, B>;
namespace helper
{
namespace detail
{
template <typename T>
constexpr std::true_type is_matrix(Eigen::MatrixBase<T>);
std::false_type constexpr is_matrix(...);
template <typename T>
constexpr std::true_type is_array(Eigen::ArrayBase<T>);
std::false_type constexpr is_array(...);
}
template <typename T>
struct is_matrix : decltype(detail::is_matrix(std::declval<std::remove_cv_t<T>>()))
{
};
template <typename T>
struct is_array : decltype(detail::is_array(std::declval<std::remove_cv_t<T>>()))
{
};
template <typename T>
using is_matrix_or_array = bool_constant<is_array<T>::value || is_matrix<T>::value>;
/*
* Index something if it's not an scalar
*/
template <typename T, typename std::enable_if<is_matrix_or_array<T>::value, int>::type = 0>
auto index_if_necessary(T&& thing, Eigen::Index idx)
{
return thing(idx);
}
/*
* Overload for scalar.
*/
template <typename T, typename std::enable_if<std::is_scalar<std::decay_t<T>>::value, int>::type = 0>
auto index_if_necessary(T&& thing, Eigen::Index)
{
return thing;
}
}
namespace Eigen
{
template <typename XprType, typename RowIndices, typename ColIndices>
class CircShiftedView;
namespace internal
{
template <typename XprType, typename RowIndices, typename ColIndices>
struct traits<CircShiftedView<XprType, RowIndices, ColIndices>>
: traits<XprType>
{
enum
{
RowsAtCompileTime = traits<XprType>::RowsAtCompileTime,
ColsAtCompileTime = traits<XprType>::ColsAtCompileTime,
MaxRowsAtCompileTime = (RowsAtCompileTime != Dynamic
? int(RowsAtCompileTime)
: int(traits<XprType>::MaxRowsAtCompileTime)),
MaxColsAtCompileTime = (ColsAtCompileTime != Dynamic
? int(ColsAtCompileTime)
: int(traits<XprType>::MaxColsAtCompileTime)),
XprTypeIsRowMajor = (int(traits<XprType>::Flags) & RowMajorBit) != 0,
IsRowMajor = ((MaxRowsAtCompileTime == 1 && MaxColsAtCompileTime != 1) ? 1
: (MaxColsAtCompileTime == 1 && MaxRowsAtCompileTime != 1) ? 0
: XprTypeIsRowMajor),
FlagsRowMajorBit = IsRowMajor ? RowMajorBit : 0,
FlagsLvalueBit = is_lvalue<XprType>::value ? LvalueBit : 0,
Flags = (traits<XprType>::Flags & HereditaryBits) | FlagsLvalueBit | FlagsRowMajorBit
};
};
}
template <typename XprType, typename RowShift, typename ColShift, typename StorageKind>
class CircShiftedViewImpl;
template <typename XprType, typename RowShift, typename ColShift>
class CircShiftedView : public CircShiftedViewImpl<XprType, RowShift, ColShift,
typename internal::traits<XprType>::StorageKind>
{
public:
typedef typename CircShiftedViewImpl<XprType, RowShift, ColShift,
typename internal::traits<XprType>::StorageKind>::Base Base;
EIGEN_GENERIC_PUBLIC_INTERFACE(CircShiftedView)
EIGEN_INHERIT_ASSIGNMENT_OPERATORS(CircShiftedView)
typedef typename internal::ref_selector<XprType>::non_const_type MatrixTypeNested;
typedef typename internal::remove_all<XprType>::type NestedExpression;
template <typename T0, typename T1>
CircShiftedView(XprType& xpr, const T0& rowShift, const T1& colShift)
: m_xpr(xpr), m_rowShift(rowShift), m_colShift(colShift)
{
for (auto c = 0; c < xpr.cols(); ++c)
assert(std::abs(helper::index_if_necessary(m_rowShift, c)) < m_xpr.rows()); // row shift must be within +- rows()-1
for (auto r = 0; r < xpr.rows(); ++r)
assert(std::abs(helper::index_if_necessary(m_colShift, r)) < m_xpr.cols()); // col shift must be within +- cols()-1
}
/** \returns number of rows */
Index rows() const { return m_xpr.rows(); }
/** \returns number of columns */
Index cols() const { return m_xpr.cols(); }
/** \returns the nested expression */
const typename internal::remove_all<XprType>::type&
nestedExpression() const { return m_xpr; }
/** \returns the nested expression */
typename internal::remove_reference<XprType>::type&
nestedExpression() { return m_xpr.const_cast_derived(); }
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Index getRowIdx(Index row, Index col) const
{
Index R = m_xpr.rows();
assert(row >= 0 && row < R && col >= 0 && col < m_xpr.cols());
Index r = row - helper::index_if_necessary(m_rowShift, col);
if (r >= R)
return r - R;
if (r < 0)
return r + R;
return r;
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Index getColIdx(Index row, Index col) const
{
Index C = m_xpr.cols();
assert(row >= 0 && row < m_xpr.rows() && col >= 0 && col < C);
Index c = col - helper::index_if_necessary(m_colShift, row);
if (c >= C)
return c - C;
if (c < 0)
return c + C;
return c;
}
protected:
MatrixTypeNested m_xpr;
RowShift m_rowShift;
ColShift m_colShift;
};
// Generic API dispatcher
template <typename XprType, typename RowIndices, typename ColIndices, typename StorageKind>
class CircShiftedViewImpl
: public internal::generic_xpr_base<CircShiftedView<XprType, RowIndices, ColIndices>>::type
{
public:
typedef typename internal::generic_xpr_base<CircShiftedView<XprType, RowIndices, ColIndices>>::type Base;
};
namespace internal
{
template <typename ArgType, typename RowIndices, typename ColIndices>
struct unary_evaluator<CircShiftedView<ArgType, RowIndices, ColIndices>, IndexBased>
: evaluator_base<CircShiftedView<ArgType, RowIndices, ColIndices>>
{
typedef CircShiftedView<ArgType, RowIndices, ColIndices> XprType;
enum
{
CoeffReadCost = (evaluator<ArgType>::CoeffReadCost
+ NumTraits<Index>::AddCost /* for comparison */
+ NumTraits<Index>::AddCost) /* for addition */,
Flags = (evaluator<ArgType>::Flags & HereditaryBits),
Alignment = 0
};
EIGEN_DEVICE_FUNC explicit unary_evaluator(const XprType& xpr) : m_argImpl(xpr.nestedExpression()), m_xpr(xpr)
{
EIGEN_INTERNAL_CHECK_COST_VALUE(CoeffReadCost);
}
typedef typename XprType::Scalar Scalar;
typedef typename XprType::CoeffReturnType CoeffReturnType;
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index row, Index col) const
{
return m_argImpl.coeff(m_xpr.getRowIdx(row, col), m_xpr.getColIdx(row, col));
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
CoeffReturnType coeff(Index idx) const
{
if (m_xpr.cols() == 1)
return m_argImpl.coeff(m_xpr.getRowIdx(idx, 1), 1);
if (m_xpr.rows() == 1)
return m_argImpl.coeff(1, m_xpr.getColIdx(1, idx));
assert(m_xpr.cols() == 1 || m_xpr.rows() == 1);
// default no-assert case - assume col vector
return m_argImpl.coeff(m_xpr.getRowIdx(idx, 1), 1);
}
EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
Scalar& coeffRef(Index row, Index col)
{
assert(row >= 0 && row < m_xpr.rows() && col >= 0 && col < m_xpr.cols());
return m_argImpl.coeffRef(m_xpr.getRowIdx(row, col), m_xpr.getColIdx(row, col));
}
protected:
evaluator<ArgType> m_argImpl;
const XprType& m_xpr;
};
} // end namespace internal
} // end namespace Eigen
template <typename XprType, typename RowShift, typename ColShift>
auto circShift(Eigen::DenseBase<XprType>& x, RowShift r, ColShift c)
{
return Eigen::CircShiftedView<XprType, RowShift, ColShift>(x.derived(), r, c);
}
template <typename XprType>
auto fftshift(Eigen::DenseBase<XprType>& x)
{
Eigen::Index rs = x.rows() / 2;
Eigen::Index cs = x.cols() / 2;
return Eigen::CircShiftedView<XprType, Eigen::Index, Eigen::Index>(x.derived(), rs, cs);
}
template <typename XprType>
auto ifftshift(Eigen::DenseBase<XprType>& x)
{
Eigen::Index rs = (x.rows() + 1) / 2;
Eigen::Index cs = (x.cols() + 1) / 2;
return Eigen::CircShiftedView<XprType, Eigen::Index, Eigen::Index>(x.derived(), rs, cs);
}

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#ifndef CORRELATION_FLOW_H
#define CORRELATION_FLOW_H
#include <fftw3.h>
#include "utils.h"
#include "read_configs.h"
class CorrelationFlow{
public:
CorrelationFlow(CFConfig& cf_config, double &image_height, double &image_width);
void ComputeIntermedium(const Eigen::ArrayXXf&, Eigen::ArrayXXcf&, Eigen::ArrayXXcf&);
Eigen::Vector3d ComputePose(const Eigen::ArrayXXcf&, const Eigen::ArrayXXf&, const Eigen::ArrayXXcf&, const Eigen::ArrayXXcf&, Eigen::Vector3d&, bool);
private:
CFConfig cfg;
Eigen::ArrayXXcf target_fft;
Eigen::ArrayXXcf target_rotation_fft;
Eigen::ArrayXXcf FFT(const Eigen::ArrayXXf&);
Eigen::ArrayXXf IFFT(const Eigen::ArrayXXcf&);
Eigen::ArrayXXcf GetTargetFFT(int, int);
inline Eigen::ArrayXXf RemoveZeroComponent(const Eigen::ArrayXXf&);
inline Eigen::ArrayXXcf gaussian_kernel(const Eigen::ArrayXXcf&, int, int);
inline Eigen::ArrayXXcf gaussian_kernel(const Eigen::ArrayXXcf&, const Eigen::ArrayXXcf&, int, int);
inline Eigen::ArrayXXcf polynomial_kernel(const Eigen::ArrayXXcf&, int, int);
inline Eigen::ArrayXXcf polynomial_kernel(const Eigen::ArrayXXcf&, const Eigen::ArrayXXcf&, int, int);
float EstimateTrans(const Eigen::ArrayXXcf&, const Eigen::ArrayXXcf&, const Eigen::ArrayXXcf&, int, int, Eigen::Vector2d&);
inline Eigen::ArrayXXf polar(const Eigen::ArrayXXf&);
inline float GetInfo(const Eigen::ArrayXXf&, float);
// void PrintArrayToFile (const Eigen::ArrayXXf target);
};
typedef std::shared_ptr<CorrelationFlow> CorrelationFlowPtr;
#endif // CORRELATION_FLOW_H

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#ifndef DATASET_H_
#define DATASET_H_
#include <vector>
#include <Eigen/Dense>
#include <Eigen/SparseCore>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/opencv.hpp>
#include "utils.h"
class Dataset{
public:
Dataset(const std::string& dataroot, const std::string& image_dir_name);
size_t GetDatasetLength();
bool GetImage(cv::Mat& image, size_t idx);
double GetTimestamp(size_t idx);
private:
std::string _dataroot;
std::string _image_dir;
std::string _image_name_file_path;
std::vector<std::string> _image_names;
std::string _time_file_path;
std::vector<double> _timestamps;
};
#endif // DATASET_H_

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#ifndef EDGE_H_
#define EDGE_H_
#include <string>
#include <Eigen/Dense>
#include <Eigen/SparseCore>
#include "utils.h"
struct Edge{
enum Type {
Odom = 0,
KCC = 1,
Loop = 2,
Others = 3,
};
int _edge_id;
Type _type;
int _from;
int _to;
Eigen::Vector3d _T;
Eigen::Matrix3d _information;
Edge();
Edge(int edge_id, Type type, int from, int to, Eigen::Vector3d& T, Eigen::Matrix3d& information);
};
typedef std::shared_ptr<Edge> EdgePtr;
#endif // EDGE_H_

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#ifndef FRAME_H_
#define FRAME_H_
#include <string>
#include <Eigen/Dense>
#include <Eigen/SparseCore>
#include "utils.h"
class Frame{
public:
Frame();
Frame(int frame_id);
Frame(int frame_id, double timestamp, Eigen::ArrayXXf&, Eigen::ArrayXXcf&, Eigen::ArrayXXcf&);
Frame& operator=(const Frame& other);
void SetFrameId(int frame_id);
int GetFrameId();
double GetTimestamp();
Eigen::ArrayXXf GetFrame();
void SetFFTResult(Eigen::ArrayXXcf& fft_result);
void SetFFTResult(Eigen::ArrayXXcf& fft_result, Eigen::ArrayXXcf& depth_fft_result);
void GetFFTResult(Eigen::ArrayXXcf& fft_result);
void GetFFTResult(Eigen::ArrayXXcf& fft_result, Eigen::ArrayXXcf& depth_fft_result);
void SetPose(Eigen::Vector3d& pose);
void GetPose(Eigen::Vector3d& pose);
void AddEdge(int edge_id);
void GetEdgeIds(std::vector<int>& edge_ids);
void SaveToDisk(const std::string root_dir);
private:
int _frame_id;
double _timestamp;
Eigen::ArrayXXf _frame;
Eigen::ArrayXXcf _fft_result;
Eigen::ArrayXXcf _fft_polar;
Eigen::ArrayXXcf _depth_fft_result;
Eigen::Vector3d _pose;
std::vector<int> _edge_ids;
};
typedef std::shared_ptr<Frame> FramePtr;
#endif // FRAME_H_

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#ifndef LOOP_CLOSURE_H_
#define LOOP_CLOSURE_H_
#include "read_configs.h"
#include "correlation_flow.h"
#include "map.h"
struct LoopClosureResult{
bool found;
Eigen::Vector3d response;
FramePtr current_frame;
FramePtr loop_frame;
Eigen::Vector3d relative_pose;
LoopClosureResult(): found(false), response(-1.0, -1.0, -1.0) {}
LoopClosureResult(bool _found, Eigen::Vector3d _resopnse): found(_found), response(_resopnse) {}
LoopClosureResult& operator=(const LoopClosureResult& other){
found = other.found;
response = other.response;
current_frame = other.current_frame;
loop_frame = other.loop_frame;
relative_pose = other.relative_pose;
return *this;
}
};
class LoopClosure{
public:
LoopClosure(LoopClosureConfig& loop_closure_config, CorrelationFlowPtr correlation_flow, MapPtr map);
LoopClosureResult FindLoopClosure(Eigen::ArrayXXf& image, FramePtr& current_frame);
LoopClosureResult FindLoopClosure(Eigen::ArrayXXf& image, FramePtr& current_frame, Eigen::Vector3d& prior_pose);
LoopClosureResult FindLoopClosure(Eigen::ArrayXXf& image, FramePtr& current_frame, std::vector<FramePtr>& frames);
private:
LoopClosureConfig _loop_thr;
CorrelationFlowPtr _correlation_flow;
MapPtr _map;
};
typedef std::shared_ptr<LoopClosure> LoopClosurePtr;
#endif // LOOP_CLOSURE_H_

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#ifndef MAP_H_
#define MAP_H_
#include <vector>
#include <map>
#include <unordered_set>
#include <unordered_map>
#include "read_configs.h"
#include "camera.h"
#include "frame.h"
#include "edge.h"
#include "utils.h"
struct GridLocation{
GridLocation() : x(0), y(0) {}
GridLocation(int x_, int y_) : x(x_), y(y_) {}
GridLocation& operator=(const GridLocation& other){
x = other.x;
y = other.y;
return *this;
}
friend std::ostream & operator<<(std::ostream &out, GridLocation &loc){
out << "(" << loc.x << ", " << loc.y << ")";
return out;
}
int x;
int y;
};
struct GridLocationHash{
std::size_t operator() (const GridLocation& loc) const{
return std::hash<int>()(loc.x) ^ std::hash<int>()(loc.y);
}
};
struct GridLocationEqual{
bool operator()(const GridLocation& l1, const GridLocation& l2) const{
return l1.x == l2.x && l1.y == l2.y;
}
};
typedef std::unordered_set<FramePtr> FrameSet;
typedef std::unordered_map<GridLocation, FrameSet, GridLocationHash, GridLocationEqual> GridMap;
class Map{
public:
Map();
Map(MapConfig& map_config);
void AddFrame(FramePtr& frame);
void SetFrameDistance(FramePtr& frame, double distance);
void AddEdge(EdgePtr& edge);
int GetAllFrames(std::vector<FramePtr>& frames);
double GetFrameDistance(FramePtr& frame);
int GetAllEdges(std::vector<EdgePtr>& edges);
void UpdatePoses(AlignedMap<int, Eigen::Vector3d> frame_poses);
GridLocation ComputeGridLocation(double x, double y);
GridLocation ComputeGridLocation(Eigen::Vector3d pose);
int GetFramesInGrids(std::vector<FramePtr>& frames, std::vector<GridLocation>& grid_locations);
FramePtr GetBaseframe();
private:
CameraPtr _camera;
std::map<int, FramePtr> _frames;
std::map<FramePtr, double> _frame_distanses;
std::map<int, EdgePtr> _edges;
double _grid_scale;
GridMap _grid_map;
FramePtr _baseframe;
};
typedef std::shared_ptr<Map> MapPtr;
#endif // MAP_H_

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#ifndef MAP_BUILDER_H_
#define MAP_BUILDER_H_
#include "read_configs.h"
#include "camera.h"
#include "frame.h"
#include "correlation_flow.h"
#include "map.h"
#include "loop_closure.h"
#include "map_stitcher.h"
class MapBuilder{
public:
MapBuilder(Configs& configs);
bool AddNewInput(cv::Mat& image, double timestamp = -1);
void ComputeFFTResult(cv::Mat& image);
void ConstructFrame(double timestamp);
void SetCurrentFramePose();
bool Initialize();
void UpdateIntermedium();
void UpdateCurrentPose();
bool Tracking(Eigen::Vector3d& response);
void AddCFEdge();
void AddCFEdgeToMap(Eigen::Vector3d& relative_pose, int from, int to,
int edge_id, Edge::Type edge_type, Eigen::Matrix3d& info);
Eigen::Vector2d ComputeRelativeDA();
void SetFrameDistance();
bool FindLoopClosure();
void AddLoopEdges();
bool OptimizeMap();
void CheckAndOptimize();
void UpdateValueAfterLoop();
// for visualization
bool GetCFPose(Eigen::Vector3d& pose);
bool GetFramePoses(Aligned<std::vector, Eigen::Vector3d>& poses, std::vector<double>& timestamps);
bool GetOccupancyMapOrigin(
Eigen::Vector3d& pixel_origin, Eigen::Matrix<double, 7, 1>& real_origin); // [qw, qx, qy, qz, x, y, z]
double GetMapResolution();
OccupancyData& GetMapData();
private:
bool _init;
int _frame_id;
int _edge_id;
bool _last_lost;
// tmp
FramePtr _last_frame;
FramePtr _current_frame;
// image plane pose
Eigen::Vector3d _last_cf_pose;
Eigen::Vector3d _current_cf_pose;
// real scale pose
Eigen::Vector3d _last_cf_real_pose;
Eigen::Vector3d _current_cf_real_pose;
// robot pose
Eigen::Vector3d _last_pose;
Eigen::Vector3d _current_pose;
// distance
double _distance;
// intermedium rsults
Eigen::ArrayXXf _image_array;
Eigen::ArrayXXcf _last_fft_result;
Eigen::ArrayXXcf _last_fft_polar;
Eigen::ArrayXXcf _fft_result;
Eigen::ArrayXXcf _fft_polar;
std::vector<LoopClosureResult> _loop_matches;
Configs _configs;
CameraPtr _camera;
CorrelationFlowPtr _correlation_flow;
KeyframeSelectionConfig _kfs_config;
MapPtr _map;
LoopClosurePtr _loop_closure;
MapStitcherPtr _map_stitcher;
};
#endif // MAP_BUILDER_H

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#ifndef MAP_STITCHER_H_
#define MAP_STITCHER_H_
#include "optimization_2d/pose_graph_2d_error_term.h"
#include "camera.h"
#include "frame.h"
#include "map.h"
#include "read_configs.h"
struct Cell{
int size;
Eigen::ArrayXXi data;
Eigen::ArrayXXi weight;
Cell(): size(0) {}
Cell(int _size): size(_size), data(Eigen::ArrayXXi::Zero(_size, _size)), weight(Eigen::ArrayXXi::Zero(_size, _size)) {}
};
typedef std::unordered_map<GridLocation, Cell, GridLocationHash, GridLocationEqual> OccupancyData;
class MapStitcher{
public:
MapStitcher(MapStitcherConfig config, CameraPtr camera);
void InsertFrame(FramePtr frame, cv::Mat& image);
// input is the posion in one axis, output is the cell and the position in cell
Eigen::Vector2i ComputeCellPosition(int x);
void AddImageToOccupancy(FramePtr frame);
void RecomputeOccupancy();
OccupancyData& GetOccupancyData();
private:
int _cell_size;
bool _to_stitch;
CameraPtr _camera;
std::unordered_map<FramePtr, Eigen::MatrixXi> _raw_images;
OccupancyData _occupancy_data;
};
typedef std::shared_ptr<MapStitcher> MapStitcherPtr;
#endif // MAP_STITCHER_H_

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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2016 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * Neither the name of Google Inc. nor the names of its contributors may be
// used to endorse or promote products derived from this software without
// specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Author: vitus@google.com (Michael Vitus)
#ifndef OPTIMIZATION_2D_ANGLE_LOCAL_PARAMETERIZATION_H_
#define OPTIMIZATION_2D_ANGLE_LOCAL_PARAMETERIZATION_H_
#include <ceres/local_parameterization.h>
#include "normalize_angle.h"
namespace ceres {
namespace optimization_2d {
// Defines a local parameterization for updating the angle to be constrained in
// [-pi to pi).
class AngleLocalParameterization {
public:
template <typename T>
bool operator()(const T* theta_radians,
const T* delta_theta_radians,
T* theta_radians_plus_delta) const {
*theta_radians_plus_delta =
NormalizeAngle(*theta_radians + *delta_theta_radians);
return true;
}
static ceres::LocalParameterization* Create() {
return (new ceres::AutoDiffLocalParameterization<AngleLocalParameterization,
1,
1>);
}
};
} // namespace optimization_2d
} // namespace ceres
#endif // OPTIMIZATION_2D_ANGLE_LOCAL_PARAMETERIZATION_H_

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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2016 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * Neither the name of Google Inc. nor the names of its contributors may be
// used to endorse or promote products derived from this software without
// specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Author: vitus@google.com (Michael Vitus)
#ifndef OPTIMIZATION_2D_NORMALIZE_ANGLE_H_
#define OPTIMIZATION_2D_NORMALIZE_ANGLE_H_
#include <cmath>
#include <ceres/ceres.h>
namespace ceres {
namespace optimization_2d {
// Normalizes the angle in radians between [-pi and pi).
template <typename T>
inline T NormalizeAngle(const T& angle_radians) {
// Use ceres::floor because it is specialized for double and Jet types.
T two_pi(2.0 * M_PI);
return angle_radians -
two_pi * ceres::floor((angle_radians + T(M_PI)) / two_pi);
}
} // namespace optimization_2d
} // namespace ceres
#endif // OPTIMIZATION_2D_NORMALIZE_ANGLE_H_

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#ifndef OPTIMIZATION_2D_POSE_GRAPH_2D_H_
#define OPTIMIZATION_2D_POSE_GRAPH_2D_H_
#include <fstream>
#include <iostream>
#include <map>
#include <string>
#include <vector>
#include <ceres/ceres.h>
#include "angle_local_parameterization.h"
#include "pose_graph_2d_error_term.h"
#include "types.h"
namespace ceres {
namespace optimization_2d {
void BuildOptimizationProblem(const std::vector<Constraint2d>& constraints,
std::map<int, Pose2d>* poses,
ceres::Problem* problem);
void BuildOptimizationProblemWithScale(const std::vector<Constraint2d>& constraints,
std::vector<ScaleData>& scale_data,
const std::vector<int>& scale_data_idx,
std::map<int, Pose2d>* poses,
ceres::Problem* problem);
bool SolveOptimizationProblem(ceres::Problem* problem);
} // namespace optimization_2d
} // namespace ceres
#endif // OPTIMIZATION_2D_POSE_GRAPH_2D_H_

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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2016 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * Neither the name of Google Inc. nor the names of its contributors may be
// used to endorse or promote products derived from this software without
// specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Author: vitus@google.com (Michael Vitus)
//
// Cost function for a 2D pose graph formulation.
#ifndef OPTIMIZATION_2D_POSE_GRAPH_2D_ERROR_TERM_H_
#define OPTIMIZATION_2D_POSE_GRAPH_2D_ERROR_TERM_H_
#include <Eigen/Core>
#include "optimization_2d/normalize_angle.h"
namespace ceres {
namespace optimization_2d {
template <typename T>
Eigen::Matrix<T, 2, 2> RotationMatrix2D(T yaw_radians) {
const T cos_yaw = ceres::cos(yaw_radians);
const T sin_yaw = ceres::sin(yaw_radians);
Eigen::Matrix<T, 2, 2> rotation;
rotation << cos_yaw, -sin_yaw, sin_yaw, cos_yaw;
return rotation;
}
// Computes the error term for two poses that have a relative pose measurement
// between them. Let the hat variables be the measurement.
//
// residual = information^{1/2} * [ r_a^T * (p_b - p_a) - \hat{p_ab} ]
// [ Normalize(yaw_b - yaw_a - \hat{yaw_ab}) ]
//
// where r_a is the rotation matrix that rotates a vector represented in frame A
// into the global frame, and Normalize(*) ensures the angles are in the range
// [-pi, pi).
class PoseGraph2dErrorTerm {
public:
PoseGraph2dErrorTerm(double x_ab,
double y_ab,
double yaw_ab_radians,
const Eigen::Matrix3d& sqrt_information)
: p_ab_(x_ab, y_ab),
yaw_ab_radians_(yaw_ab_radians),
sqrt_information_(sqrt_information) {}
template <typename T>
bool operator()(const T* const x_a,
const T* const y_a,
const T* const yaw_a,
const T* const x_b,
const T* const y_b,
const T* const yaw_b,
T* residuals_ptr) const {
const Eigen::Matrix<T, 2, 1> p_a(*x_a, *y_a);
const Eigen::Matrix<T, 2, 1> p_b(*x_b, *y_b);
Eigen::Map<Eigen::Matrix<T, 3, 1>> residuals_map(residuals_ptr);
residuals_map.template head<2>() =
RotationMatrix2D(*yaw_a).transpose() * (p_b - p_a) - p_ab_.cast<T>();
residuals_map(2) = NormalizeAngle(
(*yaw_b - *yaw_a) - static_cast<T>(yaw_ab_radians_));
// Scale the residuals by the square root information matrix to account for
// the measurement uncertainty.
residuals_map = sqrt_information_.template cast<T>() * residuals_map;
return true;
}
static ceres::CostFunction* Create(double x_ab,
double y_ab,
double yaw_ab_radians,
const Eigen::Matrix3d& sqrt_information) {
return (new ceres::
AutoDiffCostFunction<PoseGraph2dErrorTerm, 3, 1, 1, 1, 1, 1, 1>(
new PoseGraph2dErrorTerm(
x_ab, y_ab, yaw_ab_radians, sqrt_information)));
}
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
private:
// The position of B relative to A in the A frame.
const Eigen::Vector2d p_ab_;
// The orientation of frame B relative to frame A.
const double yaw_ab_radians_;
// The inverse square root of the measurement covariance matrix.
const Eigen::Matrix3d sqrt_information_;
};
class PoseGraph2dErrorTermWithScale {
public:
PoseGraph2dErrorTermWithScale(double x_ab,
double y_ab,
double yaw_ab_radians,
const Eigen::Matrix3d& sqrt_information)
: p_ab_(x_ab, y_ab),
yaw_ab_radians_(yaw_ab_radians),
sqrt_information_(sqrt_information) {}
template <typename T>
bool operator()(const T* const x_a,
const T* const y_a,
const T* const yaw_a,
const T* const x_b,
const T* const y_b,
const T* const yaw_b,
const T* const scale,
T* residuals_ptr) const {
const Eigen::Matrix<T, 2, 1> p_a(*x_a, *y_a);
const Eigen::Matrix<T, 2, 1> p_b(*x_b, *y_b);
Eigen::Map<Eigen::Matrix<T, 3, 1>> residuals_map(residuals_ptr);
Eigen::Matrix<T, 2, 2> scale_matrix = Eigen::Matrix<T, 2, 2>::Identity();
scale_matrix *= (*scale);
residuals_map.template head<2>() =
RotationMatrix2D(*yaw_a).transpose() * (p_b - p_a) - scale_matrix * p_ab_.cast<T>();
residuals_map(2) = NormalizeAngle(
(*yaw_b - *yaw_a) - static_cast<T>(yaw_ab_radians_));
// Scale the residuals by the square root information matrix to account for
// the measurement uncertainty.
residuals_map = sqrt_information_.template cast<T>() * residuals_map;
return true;
}
static ceres::CostFunction* Create(double x_ab,
double y_ab,
double yaw_ab_radians,
const Eigen::Matrix3d& sqrt_information) {
return (new ceres::
AutoDiffCostFunction<PoseGraph2dErrorTermWithScale, 3, 1, 1, 1, 1, 1, 1, 1>(
new PoseGraph2dErrorTermWithScale(
x_ab, y_ab, yaw_ab_radians, sqrt_information)));
}
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
private:
// The position of B relative to A in the A frame.
const Eigen::Vector2d p_ab_;
// The orientation of frame B relative to frame A.
const double yaw_ab_radians_;
// The inverse square root of the measurement covariance matrix.
const Eigen::Matrix3d sqrt_information_;
};
} // namespace optimization_2d
} // namespace ceres
#endif // OPTIMIZATION_2D_POSE_GRAPH_2D_ERROR_TERM_H_

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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2016 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * Neither the name of Google Inc. nor the names of its contributors may be
// used to endorse or promote products derived from this software without
// specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Author: vitus@google.com (Michael Vitus)
//
// Defines the types used in the 2D pose graph SLAM formulation. Each vertex of
// the graph has a unique integer ID with a position and orientation. There are
// delta transformation constraints between two vertices.
#ifndef OPTIMIZATION_2D_TYPES_H_
#define OPTIMIZATION_2D_TYPES_H_
#include <fstream>
#include <Eigen/Core>
#include "optimization_2d/normalize_angle.h"
namespace ceres {
namespace optimization_2d {
struct ScaleData{
double scale;
bool fixed;
ScaleData() {}
ScaleData(double _scale, bool _fixed): scale(_scale), fixed(_fixed) {}
};
inline std::istream& operator>>(std::istream& input, ScaleData& scale_data) {
input >> scale_data.scale >> scale_data.fixed;
// Normalize the angle between -pi to pi.
return input;
}
// The state for each vertex in the pose graph.
struct Pose2d {
double x;
double y;
double yaw_radians;
// The name of the data type in the g2o file format.
static std::string name() { return "VERTEX_SE2"; }
};
inline std::istream& operator>>(std::istream& input, Pose2d& pose) {
input >> pose.x >> pose.y >> pose.yaw_radians;
// Normalize the angle between -pi to pi.
pose.yaw_radians = NormalizeAngle(pose.yaw_radians);
return input;
}
// The constraint between two vertices in the pose graph. The constraint is the
// transformation from vertex id_begin to vertex id_end.
struct Constraint2d {
int id_begin;
int id_end;
double x;
double y;
double yaw_radians;
// The inverse of the covariance matrix for the measurement. The order of the
// entries are x, y, and yaw.
Eigen::Matrix3d information;
// The name of the data type in the g2o file format.
static std::string name() { return "EDGE_SE2"; }
};
inline std::istream& operator>>(std::istream& input, Constraint2d& constraint) {
input >> constraint.id_begin >> constraint.id_end >> constraint.x >>
constraint.y >> constraint.yaw_radians >> constraint.information(0, 0) >>
constraint.information(0, 1) >> constraint.information(0, 2) >>
constraint.information(1, 1) >> constraint.information(1, 2) >>
constraint.information(2, 2);
// Set the lower triangular part of the information matrix.
constraint.information(1, 0) = constraint.information(0, 1);
constraint.information(2, 0) = constraint.information(0, 2);
constraint.information(2, 1) = constraint.information(1, 2);
// Normalize the angle between -pi to pi.
constraint.yaw_radians = NormalizeAngle(constraint.yaw_radians);
return input;
}
} // namespace optimization_2d
} // namespace ceres
#endif // OPTIMIZATION_2D_TYPES_H_

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#ifndef READ_CONFIGS_H_
#define READ_CONFIGS_H_
#include <iostream>
#include <yaml-cpp/yaml.h>
#include "utils.h"
struct DatasetConfig{
std::string dataroot;
std::string image_dir_name;
std::string camera_file;
};
struct CFConfig{
int width;
int height;
float lambda;
int kernel;
float sigma;
float offset;
int power;
int rotation_divisor;
int rotation_channel;
};
struct KeyframeSelectionConfig{
double max_distance;
double max_angle;
double lower_response_thr;
double upper_response_thr;
};
struct MapConfig{
double grid_scale;
};
struct LoopClosureConfig{
bool to_find_loop;
double position_response_thr;
double angle_response_thr;
int frame_gap_thr;
double distance_thr;
};
struct MapStitcherConfig{
bool stitch_map;
int cell_size;
};
struct VisualizationConfig{
std::string frame_id;
std::string kcc_pose_topic;
std::string frame_pose_topic;
std::string map_topic;
std::string image_topic;
};
struct SavingConfig{
std::string saving_root;
bool save_pose;
};
struct Configs{
DatasetConfig dataset_config;
CFConfig cf_config;
KeyframeSelectionConfig keyframe_selection_config;
MapConfig map_config;
LoopClosureConfig loop_closure_config;
MapStitcherConfig map_stitcher_config;
VisualizationConfig visualization_config;
SavingConfig saving_config;
Configs(const std::string& config_file){
if(!FileExists(config_file)){
std::cout << "config file: " << config_file << " doesn't exist" << std::endl;
return;
}
YAML::Node file_node = YAML::LoadFile(config_file);
YAML::Node dataset_node = file_node["dataset"];
dataset_config.dataroot = dataset_node["dataroot"].as<std::string>();
dataset_config.image_dir_name = dataset_node["image_dir_name"].as<std::string>();
dataset_config.camera_file = dataset_node["camera_config"].as<std::string>();
YAML::Node cf_node = file_node["correlation_flow"];
cf_config.width = cf_node["width"].as<int>();
cf_config.height = cf_node["height"].as<int>();
cf_config.lambda = cf_node["lambda"].as<float>();
cf_config.rotation_divisor = cf_node["rotation_divisor"].as<int>();
cf_config.rotation_channel = cf_node["rotation_channel"].as<int>();
cf_config.kernel = cf_node["kernel"].as<int>();
cf_config.offset = cf_node["polynomial"]["offset"].as<float>();
cf_config.power = cf_node["polynomial"]["power"].as<int>();
cf_config.sigma = cf_node["gaussian"]["sigma"].as<float>();
YAML::Node kfs_node = file_node["keyframe_selection"];
keyframe_selection_config.max_distance = kfs_node["max_distance"].as<double>();
keyframe_selection_config.max_angle = kfs_node["max_angle"].as<double>();
keyframe_selection_config.lower_response_thr = kfs_node["lower_response_thr"].as<double>();
keyframe_selection_config.upper_response_thr = kfs_node["upper_response_thr"].as<double>();
YAML::Node map_node = file_node["map"];
map_config.grid_scale = map_node["grid_scale"].as<double>();
YAML::Node loop_closure_node = file_node["loop_closure"];
loop_closure_config.to_find_loop =
loop_closure_node["to_find_loop"].as<bool>();
loop_closure_config.position_response_thr =
loop_closure_node["position_response_thr"].as<double>();
loop_closure_config.angle_response_thr =
loop_closure_node["angle_response_thr"].as<double>();
loop_closure_config.frame_gap_thr =
loop_closure_node["frame_gap_thr"].as<int>();
loop_closure_config.distance_thr =
loop_closure_node["distance_thr"].as<double>();
YAML::Node map_stitcher_node = file_node["map_sticther"];
map_stitcher_config.stitch_map = map_stitcher_node["stitch_map"].as<bool>();
map_stitcher_config.cell_size = map_stitcher_node["cell_size"].as<int>();
YAML::Node visualization_node = file_node["visualization"];
visualization_config.frame_id = visualization_node["frame_id"].as<std::string>();
visualization_config.kcc_pose_topic = visualization_node["topic"]["kcc_pose"].as<std::string>();
visualization_config.frame_pose_topic = visualization_node["topic"]["frame_pose"].as<std::string>();
visualization_config.map_topic = visualization_node["topic"]["map"].as<std::string>();
visualization_config.image_topic = visualization_node["topic"]["image"].as<std::string>();
YAML::Node saving_node = file_node["saving"];
saving_config.saving_root = saving_node["saving_root"].as<std::string>();
saving_config.save_pose = saving_node["save_pose"].as<bool>();
}
};
#endif // READ_CONFIGS_H_

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#ifndef THREAD_PUBLISHER_H_
#define THREAD_PUBLISHER_H_
#include <string>
#include <thread>
#include <mutex>
#include <condition_variable>
#include <vector>
#include <queue>
#include <functional>
template <typename T>
class ThreadPublisher {
public:
ThreadPublisher();
~ThreadPublisher();
void Register(std::function<void(const std::shared_ptr<const T>&)> cb);
void Start();
void Publish(const std::shared_ptr<const T> msg);
void Process();
void ShutDown();
private:
std::mutex msg_mutex_;
std::condition_variable msg_cond_;
std::queue<std::shared_ptr<const T>> msgs_;
std::thread publish_thread_;
std::vector<std::function<void(const std::shared_ptr<const T>&)>> callbacks_;
bool shutdown_requested_;
};
#endif // THREAD_PUBLISHER_H_

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#ifndef _TIMER_H_
#define _TIMER_H_
#include <sys/time.h>
#include <stdio.h>
typedef struct{
struct timeval start;
struct timeval stop;
}Timer;
void startTimer(Timer *pTimer);
void stopTimer(Timer *pTimer);
double getElapsedTime(Timer *pTimer);
void writeTimeToFile(double arrTime[], int nCount, int nFrameNo, char *filename);
#define INITIALIZE_TIMER Timer stTimer; double arrTime[100]
#define START_TIMER startTimer(&stTimer)
#define STOP_TIMER(text) stopTimer(&stTimer); printf(text); printf(": %.1f\n",getElapsedTime(&stTimer))
#define END_TIMER(nIndex) stopTimer(&stTimer); arrTime[nIndex] = getElapsedTime(&stTimer)
#define ACC_TIMER(nIndex) stopTimer(&stTimer); arrTime[nIndex] += getElapsedTime(&stTimer)
#define WRITE_TIME_FILE(nCount, nFrameNo, filename) writeTimeToFile(arrTime, nCount, nFrameNo, (char *)filename)
#endif//_TIMER_H_

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#ifndef UTILS_H_
#define UTILS_H_
#include <iostream>
#include <fstream>
#include <string>
#include <vector>
#include <sys/types.h>
#include <sys/stat.h>
#include <functional>
#include <map>
#include <limits.h>
#include <memory>
#include <type_traits>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <Eigen/Core>
#include <Eigen/StdVector>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/opencv.hpp>
#include <opencv2/core/eigen.hpp>
// Eigen type
template <template <typename, typename> class Container, typename Type>
using Aligned = Container<Type, Eigen::aligned_allocator<Type>>;
template <typename KeyType, typename ValueType>
using AlignedMap =
std::map<KeyType, ValueType, std::less<KeyType>,
Eigen::aligned_allocator<std::pair<const KeyType, ValueType>>>;
template <typename KeyType, typename ValueType>
using AlignedUnorderedMap = std::unordered_map<
KeyType, ValueType, std::hash<KeyType>, std::equal_to<KeyType>,
Eigen::aligned_allocator<std::pair<const KeyType, ValueType>>>;
template <typename KeyType, typename ValueType>
using AlignedUnorderedMultimap = std::unordered_multimap<
KeyType, ValueType, std::hash<KeyType>, std::equal_to<KeyType>,
Eigen::aligned_allocator<std::pair<const KeyType, ValueType>>>;
template <typename Type>
using AlignedUnorderedSet =
std::unordered_set<Type, std::hash<Type>, std::equal_to<Type>,
Eigen::aligned_allocator<Type>>;
// files
bool FileExists(const std::string& file);
bool PathExists(const std::string& path);
void ConcatenateFolderAndFileName(
const std::string& folder, const std::string& file_name,
std::string* path);
std::string ConcatenateFolderAndFileName(
const std::string& folder, const std::string& file_name);
void MakeDir(const std::string& path);
void ReadTxt(const std::string& file_path,
std::vector<std::vector<std::string> >& lines, std::string seq);
void WriteTxt(const std::string file_path,
std::vector<std::vector<std::string> >& lines, std::string seq);
// correlation flow
void ConvertMatToNormalizedArray(cv::Mat& image, Eigen::ArrayXXf& array);
Eigen::ArrayXXf ConvertMatToArray(const cv::Mat&);
cv::Mat ConvertArrayToMat(const Eigen::ArrayXXf&);
Eigen::ArrayXXf RotateArray(const Eigen::ArrayXXf&, float);
Eigen::ArrayXXf WarpArray(const Eigen::ArrayXXf&, float tx, float ty, float degree);
double NormalizeDegree(double angle_degree);
// Eigen pose
Eigen::Vector3d ComputeRelativePose(Eigen::Vector3d& pose1, Eigen::Vector3d& pose2);
Eigen::Vector3d ComputeAbsolutePose(Eigen::Vector3d& pose1, Eigen::Vector3d& relative_pose);
void ShowArray(const Eigen::ArrayXXf&, std::string window="debug", int waitKey=1);
#endif // UTILS_H_

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#ifndef VISUALIZATION_H_
#define VISUALIZATION_H_
#include <iostream>
#include <fstream>
#include <string>
#include <vector>
#include <functional>
#include <map>
#include <memory>
#include <Eigen/Core>
#include <rclcpp/rclcpp.hpp>
#include <nav_msgs/msg/path.hpp>
#include <geometry_msgs/msg/quaternion.hpp>
#include <geometry_msgs/msg/pose_stamped.hpp>
#include <nav_msgs/msg/occupancy_grid.hpp>
#include <nav_msgs/msg/odometry.hpp>
#include <sensor_msgs/msg/image.hpp>
#include <sensor_msgs/msg/point_cloud2.hpp>
#include <cv_bridge/cv_bridge.h>
#include "read_configs.h"
#include "map_stitcher.h"
#include "map_builder.h"
class Visualizer {
public:
enum class TrajectoryType {
Frame = 0,
KCC = 1,
Odom = 2,
};
Visualizer(rclcpp::Node::SharedPtr node, VisualizationConfig& config);
void AddNewPoseToPath(
Eigen::Vector3d& pose, double time_double,
nav_msgs::msg::Path& path, std::string& frame_id);
void UpdateOdomPose(Eigen::Vector3d& pose, double time_double);
void UpdateKccPose(Eigen::Vector3d& pose, double time_double);
void UpdateFramePose(Aligned<std::vector, Eigen::Vector3d>& frame_poses,
std::vector<double>& timestamps);
void ConvertMapToOccupancyMsgs(OccupancyData& map,
nav_msgs::msg::OccupancyGrid& msgs);
void UpdateMap(MapBuilder& map_builder);
void PublishImage(cv::Mat& image, double time_double);
void GetTrajectoryTxt(std::vector<std::vector<std::string>>& lines,
TrajectoryType trajectory_type);
private:
rclcpp::Node::SharedPtr node_;
std::string frame_id_;
rclcpp::Publisher<nav_msgs::msg::Path>::SharedPtr kcc_pose_pub_;
rclcpp::Publisher<nav_msgs::msg::Path>::SharedPtr frame_pose_pub_;
rclcpp::Publisher<nav_msgs::msg::OccupancyGrid>::SharedPtr map_pub_;
rclcpp::Publisher<sensor_msgs::msg::Image>::SharedPtr image_pub_;
nav_msgs::msg::Path odom_pose_msgs_;
nav_msgs::msg::Path kcc_pose_msgs_;
nav_msgs::msg::Path frame_pose_msgs_;
nav_msgs::msg::OccupancyGrid occupancy_map_msgs_;
};
#endif // VISUALIZATION_H_

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#include <iostream>
#include <iomanip>
#include <queue>
#include <string>
#include <unistd.h>
#include <Eigen/Dense>
#include <opencv2/highgui/highgui.hpp>
#include <rclcpp/rclcpp.hpp>
#include <cv_bridge/cv_bridge.h>
#include "read_configs.h"
#include "dataset.h"
#include "camera.h"
#include "frame.h"
#include "map_stitcher.h"
#include "map_builder.h"
#include "thread_publisher.h"
#include "visualization.h"
#include <typeinfo>
#include <time.h>
using namespace std;
class GroundSlamNode : public rclcpp::Node {
public:
GroundSlamNode(const std::string& config_file)
: Node("build_map"), configs_(config_file) {}
void run() {
DatasetConfig dataset_config = configs_.dataset_config;
Dataset dataset(dataset_config.dataroot, dataset_config.image_dir_name);
MapBuilder map_builder(configs_);
Visualizer visualizer(this->shared_from_this(), configs_.visualization_config);
Aligned<std::vector, Eigen::Vector3d> frame_poses;
std::vector<double> timestamps;
Eigen::Vector3d new_kcc_pose;
size_t dataset_length = dataset.GetDatasetLength();
for (size_t i = 0; i < dataset_length; ++i) {
if (!rclcpp::ok()) break;
std::cout << i << std::endl;
cv::Mat image;
if (!dataset.GetImage(image, i)) {
std::cout << "can not get image " << i << std::endl;
break;
}
double time_double = dataset.GetTimestamp(i);
visualizer.PublishImage(image, time_double);
auto t1 = std::chrono::high_resolution_clock::now();
bool insert_keyframe = map_builder.AddNewInput(image, time_double);
auto t2 = std::chrono::high_resolution_clock::now();
auto compute_time = std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1).count() / 1e3;
std::cout << "processing for one frame is " << compute_time << "ms" << std::endl;
if ((i + 1) >= dataset_length) {
map_builder.CheckAndOptimize();
} else if (!insert_keyframe) {
continue;
}
std::cout << "Insert a keyframe !" << std::endl;
if (map_builder.GetCFPose(new_kcc_pose)) {
visualizer.UpdateKccPose(new_kcc_pose, time_double);
}
if (map_builder.GetFramePoses(frame_poses, timestamps)) {
visualizer.UpdateFramePose(frame_poses, timestamps);
}
visualizer.UpdateMap(map_builder);
rclcpp::spin_some(this->shared_from_this());
}
// save trajectories
std::string saving_root = configs_.saving_config.saving_root;
MakeDir(saving_root);
std::string trajectory_KCC = saving_root + "/KCC_Keyframe.txt";
std::string trajectory_frame = saving_root + "/optimized_keyframe.txt";
std::vector<std::vector<std::string>> kcc_keyframe_lines, optimized_keyframe_lines;
visualizer.GetTrajectoryTxt(kcc_keyframe_lines, Visualizer::TrajectoryType::KCC);
visualizer.GetTrajectoryTxt(optimized_keyframe_lines, Visualizer::TrajectoryType::Frame);
WriteTxt(trajectory_KCC, kcc_keyframe_lines, " ");
WriteTxt(trajectory_frame, optimized_keyframe_lines, " ");
}
private:
Configs configs_;
};
int main(int argc, char** argv) {
rclcpp::init(argc, argv);
if (argc < 2) {
std::cerr << "Usage: ground_slam <config_yaml>" << std::endl;
rclcpp::shutdown();
return 1;
}
auto node = std::make_shared<GroundSlamNode>(argv[1]);
node->run();
rclcpp::shutdown();
return 0;
}

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<?xml version="1.0"?>
<?xml-model href="http://download.ros.org/schema/package_format3.xsd" schematypens="http://www.w3.org/2001/XMLSchema"?>
<package format="3">
<name>ground_slam</name>
<version>0.1.0</version>
<description>GroundSLAM: Feature-free ground-texture-based visual SLAM for warehouse robots (ROS2 port)</description>
<maintainer email="sunrise@todo.todo">sunrise</maintainer>
<license>MIT</license>
<buildtool_depend>ament_cmake</buildtool_depend>
<depend>rclcpp</depend>
<depend>geometry_msgs</depend>
<depend>visualization_msgs</depend>
<depend>nav_msgs</depend>
<depend>std_msgs</depend>
<depend>sensor_msgs</depend>
<depend>cv_bridge</depend>
<depend>tf2</depend>
<depend>tf2_geometry_msgs</depend>
<depend>tf2_ros</depend>
<export>
<build_type>ament_cmake</build_type>
</export>
</package>

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#include <yaml-cpp/yaml.h>
#include <math.h>
#include <unistd.h>
#include <iostream>
#include "camera.h"
#include "utils.h"
#include "optimization_2d/pose_graph_2d_error_term.h"
using namespace std;
Camera::Camera(){
}
Camera::Camera(const std::string& camera_file){
if(!FileExists(camera_file)){
std::cout << "camera file: " << camera_file << " doesn't exist" << std::endl;
return;
}
YAML::Node file_node = YAML::LoadFile(camera_file);
_image_width = file_node["image_size"][0].as<int>();
_image_height = file_node["image_size"][1].as<int>();
_height = file_node["height"].as<double>();
_accurate_height = file_node["accurate_height"].as<bool>();
_new_scale = 0;
_new_width = 0;
_new_height = 0;
YAML::Node K_node = file_node["intrinsics"]["data"];
double fx = K_node[0].as<double>();
double cx = K_node[1].as<double>();
double fy = K_node[2].as<double>();
double cy = K_node[3].as<double>();
_K = (cv::Mat_<double>(3, 3) << fx, 0.0, cx, 0.0, fy, cy, 0.0, 0.0, 1.0);
YAML::Node D_node = file_node["distortion"]["data"];
double D_data[5];
for(size_t i = 0; i < 5; i++){
D_data[i] = D_node[i].as<double>();
}
_D = (cv::Mat_<double>(5, 1) << D_data[0], D_data[1], D_data[2], D_data[3], D_data[4]);
cv::Size image_size(_image_width, _image_height);
_new_K = getOptimalNewCameraMatrix(_K, _D, image_size, 0, image_size);
initUndistortRectifyMap(_K, _D, cv::Mat(), _new_K, image_size, CV_16SC2, _map1, _map2);
// // update _new_K
// if(_image_width > _image_height)
// {
// _scale = min(_new_K.at<double>(0,0), _new_K.at<double>(1,1)) / max(_new_K.at<double>(0,0), _new_K.at<double>(1,1));
// _new_width = round(_image_width * _scale);
// _new_scale = double(_new_width) / double(_image_width);
// cout << "_new width: " << _new_width << endl;
// _new_K.at<double>(0,0) = _new_K.at<double>(0,0) / _new_scale;
// _new_K.at<double>(0,2) = _new_K.at<double>(0,2) / _new_scale;
// _image_width = _new_width;
// }
// else{
// _scale = min(_new_K.at<double>(0,0), _new_K.at<double>(1,1)) / max(_new_K.at<double>(0,0), _new_K.at<double>(1,1));
// _new_height = round(_image_height * _scale);
// _new_scale = _new_height / _image_height;
// _new_K.at<double>(1,1) = _new_K.at<double>(1,1) / _new_scale;
// _new_K.at<double>(1,2) = _new_K.at<double>(1,2) / _new_scale;
// _image_height = _new_height;
// }
YAML::Node E_node = file_node["extrinsics"]["data"];
for(size_t i = 0; i < 3; i++){
for(size_t j = 0; j < 3; j++){
size_t idx = 3 * i + j;
_extrinsics(i, j) = E_node[idx].as<double>();
}
}
}
Camera& Camera::operator=(const Camera& camera){
_image_height = camera._image_height;
_image_width = camera._image_width;
_height = camera._height;
_accurate_height = camera._accurate_height;
_K = camera._K.clone();
_new_K = camera._new_K;
_D = camera._D.clone();
_map1 = camera._map1.clone();
_map2 = camera._map2.clone();
_extrinsics = camera._extrinsics;
return *this;
}
void Camera::UndistortImage(cv::Mat& image, cv::Mat& undistort_image){
remap(image, undistort_image, _map1, _map2, cv::INTER_LINEAR);
// cv::Mat _new_undistort_image;
// if(_new_height == 0 && _new_scale != 0){
// resize(undistort_image, _new_undistort_image, cv::Size(), _new_scale, 1.0, cv::INTER_LINEAR);
// }
// else if(_new_width == 0 && _new_scale != 0){
// resize(undistort_image, _new_undistort_image, cv::Size(), 1.0, _new_scale, cv::INTER_LINEAR);
// }
// _new_undistort_image.copyTo(undistort_image);
}
void Camera::GetNewCameraMatrix(cv::Mat& camera_matrix){
camera_matrix = _new_K.clone();
}
double Camera::GetImageHeight(){
return _image_height;
}
bool Camera::HeightIsAccurate(){
return _accurate_height;
}
double Camera::GetImageWidth(){
return _image_width;
}
double Camera::GetHeight(){
return _height;
}
void Camera::GetExtrinsics(Eigen::Matrix3d& extrinsics){
extrinsics = _extrinsics;
}
double Camera::GetLengthOfPixel(){
Eigen::Vector3d pixel(1.0, 1.0, 0.0);
Eigen::Vector3d real;
ConvertImagePlanePoseToRobot(pixel, real);
return (real(0) + real(1)) / 2;
}
Eigen::Vector3d Camera::ConvertPrincipalToCenter(const Eigen::Vector3d& image_plane_pose){
Eigen::Vector3d image_center_pose;
const Eigen::Matrix2d I2 = Eigen::Matrix2d::Identity();
image_center_pose(2) = image_plane_pose(2);
Eigen::Matrix2d R = ceres::optimization_2d::RotationMatrix2D(image_plane_pose(2));
Eigen::Vector2d O_bias;
O_bias << (_image_width * 0.5 - _new_K.at<double>(0, 2)), (_image_height * 0.5 - _new_K.at<double>(1, 2));
image_center_pose.head(2) = image_plane_pose.head(2) - (I2 - R) * O_bias;
return image_center_pose;
}
Eigen::Vector3d Camera::ConvertCenterToPrincipal(const Eigen::Vector3d& image_center_pose){
Eigen::Vector3d image_plane_pose;
const Eigen::Matrix2d I2 = Eigen::Matrix2d::Identity();
image_plane_pose(2) = image_center_pose(2);
Eigen::Matrix2d R = ceres::optimization_2d::RotationMatrix2D(image_center_pose(2));
Eigen::Vector2d O_bias;
O_bias << (_image_width * 0.5 - _new_K.at<double>(0, 2)), (_image_height * 0.5 - _new_K.at<double>(1, 2));
image_plane_pose.head(2) = image_center_pose.head(2) + (I2 - R) * O_bias;
return image_plane_pose;
}
bool Camera::ConvertImagePlanePoseToCamera(
Eigen::Vector3d& image_plane_pose, Eigen::Vector3d& camera_pose){
double u = image_plane_pose(0);
double v = image_plane_pose(1);
double angle = image_plane_pose(2);
double fx = _new_K.at<double>(0, 0);
// double cx = _new_K.at<double>(0, 2);
double fy = _new_K.at<double>(1, 1);
// double cy = _new_K.at<double>(1, 2);
double x = u / fx;
double y = v / fy;
camera_pose << x, y, angle;
return true;
}
bool Camera::ConvertCameraPoseToImagePlane(
Eigen::Vector3d& image_plane_pose, Eigen::Vector3d& camera_pose){
double x = camera_pose(0);
double y = camera_pose(1);
double angle = camera_pose(2);
double fx = _new_K.at<double>(0, 0);
// double cx = _new_K.at<double>(0, 2);
double fy = _new_K.at<double>(1, 1);
// double cy = _new_K.at<double>(1, 2);
double u = fx * x;
double v = fy * y;
image_plane_pose << u, v, angle;
return true;
}
bool Camera::ConvertCameraPoseToRobot(
Eigen::Vector3d& camera_pose, Eigen::Vector3d& robot_pose){
if(_height < 0){
std::cout << "camera height: " << _height << " < 0" << std::endl;
return false;
}
double x = _height * camera_pose(0);
double y = _height * camera_pose(1);
robot_pose << x, y, camera_pose(2);
robot_pose = _extrinsics * robot_pose;
return true;
}
bool Camera::ConvertRobotPoseToCamera(
Eigen::Vector3d& camera_pose, Eigen::Vector3d& robot_pose){
if(_height < 0){
std::cout << "camera height: " << _height << " < 0" << std::endl;
return false;
}
camera_pose = _extrinsics.inverse() * robot_pose;
camera_pose(0) /= _height;
camera_pose(1) /= _height;
return true;
}
bool Camera::ConvertImagePlanePoseToRobot(
Eigen::Vector3d& image_plane_pose, Eigen::Vector3d& robot_pose){
Eigen::Vector3d camera_pose;
bool c1 = ConvertImagePlanePoseToCamera(image_plane_pose, camera_pose);
bool c2 = ConvertCameraPoseToRobot(camera_pose, robot_pose);
return (c1 && c2);
}
bool Camera::ConvertRobotPoseToImagePlane(
Eigen::Vector3d& image_plane_pose, Eigen::Vector3d& robot_pose){
Eigen::Vector3d camera_pose;
bool c1 = ConvertRobotPoseToCamera(camera_pose, robot_pose);
bool c2 = ConvertCameraPoseToImagePlane(image_plane_pose, camera_pose);
return (c1 && c2);
}

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#include <math.h>
#include <opencv2/imgproc.hpp>
#include "correlation_flow.h"
#include "read_configs.h"
#include "circ_shift.h"
#include "optimization_2d/pose_graph_2d_error_term.h"
#include <iostream>
#include <fstream>
using namespace std;
#include "unistd.h"
#include <chrono>
using namespace std::chrono;
// // void PrintArrayToFile (const Eigen::ArrayXXf target, int rows, int cols) {
// void PrintArrayToFile (const auto& target, int rows, int cols) {
// ofstream myfile ("example.txt");
// if (myfile.is_open())
// {
// for(int count_row = 0; count_row < rows; count_row ++){
// for(int count_col = 0; count_col < cols; count_col ++){
// myfile << target(count_row, count_col) << " " ;
// if(count_col == cols - 1){
// myfile << "\n";
// }
// }
// }
// myfile.close();
// }
// else std::cout << "Unable to open file";
// }
CorrelationFlow::CorrelationFlow(CFConfig& cf_config, double &image_height, double &image_width):cfg(cf_config){
cfg.height = int(image_height);
cfg.width = int(image_width);
target_fft = GetTargetFFT(cfg.height, cfg.width);
target_rotation_fft = GetTargetFFT(cfg.rotation_divisor, cfg.rotation_channel);
}
Eigen::ArrayXXcf CorrelationFlow::GetTargetFFT(int rows, int cols){
Eigen::ArrayXXf target = Eigen::ArrayXXf::Zero(rows, cols);
target(rows/2, cols/2) = 1;
return FFT(target);
}
Eigen::ArrayXXcf CorrelationFlow::FFT(const Eigen::ArrayXXf& x){
Eigen::ArrayXXcf xf = Eigen::ArrayXXcf(x.rows()/2+1, x.cols()); // xf [225, 448]
fftwf_plan fft_plan = fftwf_plan_dft_r2c_2d(x.cols(), x.rows(), (float(*))(x.data()),
(float(*)[2])(xf.data()), FFTW_ESTIMATE); // reverse order for column major
fftwf_execute(fft_plan);
fftwf_destroy_plan(fft_plan);
fftw_cleanup();
return xf;
}
Eigen::ArrayXXf CorrelationFlow::IFFT(const Eigen::ArrayXXcf& xf){
Eigen::ArrayXXf x = Eigen::ArrayXXf((xf.rows()-1)*2, xf.cols());
Eigen::ArrayXXcf cxf = xf;
fftwf_plan fft_plan = fftwf_plan_dft_c2r_2d(xf.cols(), (xf.rows()-1)*2, (float(*)[2])(cxf.data()),
(float(*))(x.data()), FFTW_ESTIMATE);
fftwf_execute(fft_plan);
fftwf_destroy_plan(fft_plan);
fftw_cleanup();
return x/x.size();
}
inline Eigen::ArrayXXf CorrelationFlow::RemoveZeroComponent(const Eigen::ArrayXXf& x){
Eigen::ArrayXXf y(x);
unsigned int cols = x.cols();
unsigned int rows = x.rows();
y.block(0, 0, 1, cols) = (x.block(1, 0, 1, cols) + x.block(rows-1, 0, 1, cols))/2.0;
y.block(0, 0, rows, 1) = (x.block(0, 1, rows, 1) + x.block(0, cols-1, rows, 1))/2.0;
return y;
}
void CorrelationFlow::ComputeIntermedium(const Eigen::ArrayXXf& image, Eigen::ArrayXXcf& fft_result, Eigen::ArrayXXcf& fft_polar){
fft_result = FFT(image);
Eigen::ArrayXXf power = IFFT(fft_result.abs());
auto high_power = RemoveZeroComponent(power);
fft_polar = FFT(polar(fftshift(high_power)));
}
Eigen::Vector3d CorrelationFlow::ComputePose(const Eigen::ArrayXXcf& last_fft_result, const Eigen::ArrayXXf& image,
const Eigen::ArrayXXcf& last_fft_polar, const Eigen::ArrayXXcf& fft_polar,
Eigen::Vector3d& pose, bool not_large_rotation){
Eigen::Vector2d trans, trans_orig, trans_veri, rots; Eigen::Vector3d info; float info_trans;
auto info_rots = EstimateTrans(last_fft_polar, fft_polar, target_rotation_fft, cfg.rotation_divisor, cfg.rotation_channel, rots);
float degree = rots[0]*(2.0/cfg.rotation_divisor)*180;
degree = NormalizeDegree(degree);
if(not_large_rotation){
degree = std::abs(degree) > 90 ? degree - 180 : degree;
auto fft_rot_orig = FFT(RotateArray(image, -degree));
float info_trans_orig = EstimateTrans(last_fft_result, fft_rot_orig, target_fft, cfg.height, cfg.width, trans_orig);
info_trans = info_trans_orig;
trans = trans_orig;
degree = degree;
}else{
auto fft_rot_orig = FFT(RotateArray(image, -degree));
auto fft_rot_veri = FFT(RotateArray(image, -degree+180));
float info_trans_orig = EstimateTrans(last_fft_result, fft_rot_orig, target_fft, cfg.height, cfg.width, trans_orig); //error
float info_trans_veri = EstimateTrans(last_fft_result, fft_rot_veri, target_fft, cfg.height, cfg.width, trans_veri);
if (info_trans_orig > info_trans_veri)
{
info_trans = info_trans_orig;
trans = trans_orig;
degree = degree;
}
else{
info_trans = info_trans_veri;
trans = trans_veri;
degree = degree + 180;
}
}
(degree>180)? degree=degree-360 : degree=degree;
float theta = degree/180*M_PI;
info[0] = info_trans; pose[0] = trans[1];
info[1] = info_trans; pose[1] = trans[0];
info[2] = info_rots; pose[2] = theta;
std::cout<<"X, Y, \u0398: "<<pose.transpose()<<" Rad = "<< degree <<"Degree"<<std::endl;
std::cout<<"Info: "<<info.transpose()<<std::endl;
auto rectify = WarpArray(IFFT(last_fft_result),-pose[0],-pose[1], degree);
return info;
}
float CorrelationFlow::EstimateTrans(const Eigen::ArrayXXcf& last_fft_result, const Eigen::ArrayXXcf& fft_result,
const Eigen::ArrayXXcf& output_fft, int height, int width, Eigen::Vector2d& trans){
/// @brief can esrimate translation, rotation, and scale
/// @param last_fft_result fft result of key frame
/// @param fft_result current frame's fft result
/// @param output_fft target_rotation_fft generate from GetTargetFFT(720, 480)
/// @param height cfg.rotation_divisor
/// @param width cfg.rotation_channel
/// @param trans the result, ie. rotation degree, notice that the trans length is two, trans[0] is actually the degree we need
Eigen::ArrayXXcf Kzz, Kxz;
switch(cfg.kernel) {
case 0:
Kzz = polynomial_kernel(last_fft_result, height, width);
Kxz = polynomial_kernel(fft_result, last_fft_result, height, width);
break;
case 1:
Kzz = gaussian_kernel(last_fft_result, height, width);
Kxz = gaussian_kernel(fft_result, last_fft_result, height, width);
break;
default:
throw std::invalid_argument( "Received invalid kernel type" );
}
auto H = output_fft/(Kzz + cfg.lambda);
Eigen::ArrayXXcf G = H*Kxz;
Eigen::ArrayXXf g = IFFT(G);
Eigen::ArrayXXf::Index row, col;
float response = g.maxCoeff(&(row), &(col));
trans[0] = -(row-height/2);
trans[1] = -(col-width/2);
return GetInfo(g, response);
}
inline Eigen::ArrayXXcf CorrelationFlow::gaussian_kernel(const Eigen::ArrayXXcf& xf, const Eigen::ArrayXXcf& zf, int height, int width){
unsigned int N = height * width;
auto xx = xf.square().abs().sum()/N; // Parseval's Theorem
auto zz = zf.square().abs().sum()/N;
auto zfc = zf.conjugate();
Eigen::ArrayXXcf xzf = xf * zfc;
auto xz = IFFT(xzf);
auto xxzz = (xx+zz-2*xz)/N;
Eigen::ArrayXXf kernel = (-1/(cfg.sigma*cfg.sigma)*xxzz).exp();
kernel = kernel/kernel.abs().maxCoeff();
return FFT(kernel);
}
inline Eigen::ArrayXXcf CorrelationFlow::gaussian_kernel(const Eigen::ArrayXXcf& xf, int height, int width){
unsigned int N = height * width;
auto xx = xf.square().abs().sum()/N; // Parseval's Theorem
auto zfc = xf.conjugate();
Eigen::ArrayXXcf xzf = xf * zfc;
auto xz = IFFT(xzf);
auto xxzz = (xx+xx-2*xz)/N;
Eigen::ArrayXXf kernel = (-1/(cfg.sigma*cfg.sigma)*xxzz).exp();
kernel = kernel/kernel.abs().maxCoeff();
return FFT(kernel);
}
inline Eigen::ArrayXXcf CorrelationFlow::polynomial_kernel(const Eigen::ArrayXXcf& xf, const Eigen::ArrayXXcf& zf, int height, int width){
auto zfc = zf.conjugate();
Eigen::ArrayXXcf xzf = xf * zfc;
auto xz = IFFT(xzf);
Eigen::ArrayXXf kernel = (xz+cfg.offset).pow(cfg.power);
kernel = kernel/kernel.abs().maxCoeff();
return FFT(kernel);
}
inline Eigen::ArrayXXcf CorrelationFlow::polynomial_kernel(const Eigen::ArrayXXcf& xf, int height, int width){
auto zfc = xf.conjugate();
Eigen::ArrayXXcf xzf = xf * zfc;
auto xz = IFFT(xzf);
Eigen::ArrayXXf kernel = (xz+cfg.offset).pow(cfg.power);
kernel = kernel/kernel.abs().maxCoeff();
return FFT(kernel);
}
inline Eigen::ArrayXXf CorrelationFlow::polar(const Eigen::ArrayXXf& array){
cv::Mat polar_img, img=ConvertArrayToMat(array);
cv::Point2f center((float)img.cols/2, (float)img.rows/2);
double radius = (double)std::min(img.rows/2, img.cols/2);
cv::Size dsize = cv::Size(cfg.rotation_channel, cfg.rotation_divisor);
cv::warpPolar(img, polar_img, dsize, center, radius, cv::INTER_LINEAR + cv::WARP_FILL_OUTLIERS);
return ConvertMatToArray(polar_img);
}
inline float CorrelationFlow::GetInfo(const Eigen::ArrayXXf& output, float response){
float side_lobe_mean = (output.sum()-response)/(output.size()-1);
float std = sqrt((output-side_lobe_mean).square().mean());
return (response - side_lobe_mean)/(std+1e-7);
}

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#include "dataset.h"
#include "utils.h"
#include<iostream>
#include <unistd.h>
using namespace std;
Dataset::Dataset(const std::string& dataroot, const std::string& image_dir_name): _dataroot(dataroot){
if(!PathExists(dataroot)){
std::cout << "dataroot : " << dataroot << " doesn't exist" << std::endl;
exit(0);
}
_image_dir = ConcatenateFolderAndFileName(_dataroot, image_dir_name);
_image_name_file_path = ConcatenateFolderAndFileName(_dataroot, "image_names.txt");
_time_file_path = ConcatenateFolderAndFileName(_dataroot, "times.txt");
std::vector<std::vector<std::string> > image_names_data;
ReadTxt(_image_name_file_path, image_names_data, ",");
for(std::vector<std::string>& line : image_names_data){
_image_names.emplace_back(line[0]);
}
std::vector<std::vector<std::string> > timestamps_data;
if(FileExists(_time_file_path)){
ReadTxt(_time_file_path, timestamps_data, ",");
for(std::vector<std::string>& line : timestamps_data){
_timestamps.push_back(atof(line[0].c_str()));
}
}
}
size_t Dataset::GetDatasetLength(){
return _image_names.size();
}
bool Dataset::GetImage(cv::Mat& image, size_t idx){
if(idx >= _image_names.size()){
return false;
}
std::string image_name = _image_names[idx];
std::string image_path = ConcatenateFolderAndFileName(_image_dir, image_name);
image = cv::imread(image_path, cv::ImreadModes::IMREAD_GRAYSCALE);
return true;
}
double Dataset::GetTimestamp(size_t idx){
if(idx < _timestamps.size()){
return _timestamps[idx];
}else{
return -1.0;
}
}

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#include "edge.h"
Edge::Edge(){
}
Edge::Edge(int edge_id, Type type, int from, int to, Eigen::Vector3d& T, Eigen::Matrix3d& information):
_edge_id(edge_id), _type(type), _from(from), _to(to), _T(T), _information(information){
}

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#include "frame.h"
Frame::Frame(){
}
Frame::Frame(int frame_id): _frame_id(frame_id){
}
Frame::Frame(int frame_id, double timestamp, Eigen::ArrayXXf& frame, Eigen::ArrayXXcf& fft_result, Eigen::ArrayXXcf& fft_polar):
_frame_id(frame_id), _timestamp(timestamp), _frame(frame), _fft_result(fft_result), _fft_polar(fft_polar){
}
Frame& Frame::operator=(const Frame& other){
_frame_id = other._frame_id;
_timestamp = other._timestamp;
_fft_result = other._fft_result;
_fft_polar = other._fft_polar;
_pose = other._pose;
_edge_ids = other._edge_ids;
return *this;
}
void Frame::SetFrameId(int frame_id){
_frame_id = frame_id;
}
int Frame::GetFrameId(){
return _frame_id;
}
double Frame::GetTimestamp(){
return _timestamp;
}
Eigen::ArrayXXf Frame::GetFrame(){
return _frame;
}
void Frame::SetFFTResult(Eigen::ArrayXXcf& fft_result){
_fft_result = fft_result;
}
void Frame::SetFFTResult(Eigen::ArrayXXcf& fft_result,
Eigen::ArrayXXcf& fft_polar){
_fft_result = fft_result;
_fft_polar = fft_polar;
}
void Frame::GetFFTResult(Eigen::ArrayXXcf& fft_result){
fft_result = _fft_result;
}
void Frame::GetFFTResult(Eigen::ArrayXXcf& fft_result,
Eigen::ArrayXXcf& fft_polar){
fft_result = _fft_result;
fft_polar = _fft_polar;
}
void Frame::SetPose(Eigen::Vector3d& pose){
_pose = pose;
}
void Frame::GetPose(Eigen::Vector3d& pose){
pose = _pose;
}
void Frame::AddEdge(int edge_id){
_edge_ids.emplace_back(edge_id);
}
void Frame::GetEdgeIds(std::vector<int>& edge_ids){
edge_ids = _edge_ids;
}
void Frame::SaveToDisk(const std::string root_dir){
//TODO
}

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#include "loop_closure.h"
#include <cmath>
LoopClosure::LoopClosure(LoopClosureConfig& loop_closure_config,
CorrelationFlowPtr correlation_flow, MapPtr map):
_loop_thr(loop_closure_config), _correlation_flow(correlation_flow), _map(map){
}
LoopClosureResult LoopClosure::FindLoopClosure(Eigen::ArrayXXf& image, FramePtr& current_frame){
std::vector<FramePtr> frames;
_map->GetAllFrames(frames);
LoopClosureResult result = FindLoopClosure(image, current_frame, frames);
return result;
}
LoopClosureResult LoopClosure::FindLoopClosure(
Eigen::ArrayXXf& image, FramePtr& current_frame, Eigen::Vector3d& prior_pose){
std::vector<GridLocation> grid_locations;
GridLocation grid_location = _map->ComputeGridLocation(prior_pose);
for(int i = -1; i <= 1; i++){
for(int j = -1; j <= 1; j++){
GridLocation gl = grid_location;
gl.x += i;
gl.y += j;
grid_locations.emplace_back(gl);
}
}
std::vector<FramePtr> frames;
_map->GetFramesInGrids(frames, grid_locations);
LoopClosureResult result = FindLoopClosure(image, current_frame, frames);
return result;
}
LoopClosureResult LoopClosure::FindLoopClosure(
Eigen::ArrayXXf& image, FramePtr& current_frame, std::vector<FramePtr>& frames){
Eigen::ArrayXXcf current_fft_result, current_fft_polar;
current_frame->GetFFTResult(current_fft_result, current_fft_polar);
LoopClosureResult result;
result.current_frame = current_frame;
for(FramePtr frame : frames){
if(_loop_thr.frame_gap_thr > 0 &&
std::abs((current_frame->GetFrameId() - frame->GetFrameId())) < _loop_thr.frame_gap_thr){
continue;
}
if(_loop_thr.distance_thr > 0){
double d1 = _map->GetFrameDistance(current_frame); // distance is accumulation distance
double d2 = _map->GetFrameDistance(frame);
if(std::abs((d1 - d2)) < _loop_thr.distance_thr){
continue;
}
}
Eigen::ArrayXXcf fft_result, fft_polar;
frame->GetFFTResult(fft_result, fft_polar);
Eigen::Vector3d relative_pose;
Eigen::Vector3d response =
_correlation_flow->ComputePose(fft_result, image, fft_polar, current_fft_polar, relative_pose, false);
if(response.sum() > result.response.sum()){
result.response = response;
result.loop_frame = frame;
result.relative_pose = relative_pose;
}
}
bool c1 = (result.response(0) > _loop_thr.position_response_thr);
bool c2 = (result.response(2) > _loop_thr.angle_response_thr);
result.found = (c1 && c2);
return result;
}

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#include <iostream>
#include <unistd.h>
#include <map>
#include <vector>
#include "map.h"
using namespace std;
Map::Map(): _grid_scale(1.0){
}
Map::Map(MapConfig& map_config): _grid_scale(map_config.grid_scale){
}
void Map::AddFrame(FramePtr& frame){
if(_frames.size() < 1){
_baseframe = frame;
frame->SetFrameId(0);
}
int frame_id = frame->GetFrameId();
_frames[frame_id] = frame;
Eigen::Vector3d pose;
frame->GetPose(pose);
GridLocation grid_location = ComputeGridLocation(pose);
_grid_map[grid_location].insert(frame);
}
void Map::SetFrameDistance(FramePtr& frame, double distance){
_frame_distanses[frame] = distance;
}
void Map::AddEdge(EdgePtr& edge){
int edge_id = edge->_edge_id;
int from = edge->_from;
int to = edge->_to;
_edges[edge_id] = edge;
if(_frames.count(from) > 0){
_frames[from]->AddEdge(edge_id);
}
if(_frames.count(to) > 0){
_frames[to]->AddEdge(edge_id);
}
}
int Map::GetAllFrames(std::vector<FramePtr>& frames){
for(auto kv : _frames){
frames.emplace_back(kv.second);
}
return frames.size();
}
double Map::GetFrameDistance(FramePtr& frame){
if(_frame_distanses.count(frame) > 0){
return _frame_distanses[frame];
}else{
return -1;
}
}
int Map::GetAllEdges(std::vector<EdgePtr>& edges){
for(auto kv : _edges){
edges.emplace_back(kv.second);
}
return edges.size();
}
void Map::UpdatePoses(AlignedMap<int, Eigen::Vector3d> frame_poses){
for(auto kv : frame_poses){
if(_frames.count(kv.first) > 0){
_frames[kv.first]->SetPose(kv.second);
}
}
}
GridLocation Map::ComputeGridLocation(double x, double y){
int grid_x = static_cast<int>((x / _grid_scale));
int grid_y = static_cast<int>((y / _grid_scale));
return GridLocation(grid_x, grid_y);
}
GridLocation Map::ComputeGridLocation(Eigen::Vector3d pose){
double x = pose(0);
double y = pose(1);
return ComputeGridLocation(x, y);
}
int Map::GetFramesInGrids(
std::vector<FramePtr>& frames, std::vector<GridLocation>& grid_locations){
for(auto grid_location : grid_locations){
if(_grid_map.count(grid_location) > 0){
frames.insert(frames.end(), _grid_map[grid_location].begin(), _grid_map[grid_location].end());
}
}
return frames.size();
}
FramePtr Map::GetBaseframe(){
return _baseframe;
}

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#include "map_builder.h"
#include <set>
#include <string>
#include <cmath>
#include <iostream>
#include <Eigen/Dense>
#include <Eigen/SparseCore>
#include "edge.h"
#include "utils.h"
#include "optimization_2d/types.h"
#include "optimization_2d/pose_graph_2d.h"
using namespace std;
MapBuilder::MapBuilder(Configs& configs):_init(false), _frame_id(0), _edge_id(0),
_configs(configs), _kfs_config(configs.keyframe_selection_config){
_camera = std::shared_ptr<Camera>(new Camera(configs.dataset_config.camera_file));
double _image_height = _camera->GetImageHeight();
double _image_width = _camera->GetImageWidth();
_correlation_flow = std::shared_ptr<CorrelationFlow>(new CorrelationFlow(configs.cf_config, _image_height, _image_width));
_map = std::shared_ptr<Map>(new Map(configs.map_config));
_loop_closure = std::shared_ptr<LoopClosure>(
new LoopClosure(configs.loop_closure_config, _correlation_flow, _map));
_map_stitcher = std::shared_ptr<MapStitcher>(new MapStitcher(configs.map_stitcher_config, _camera));
}
bool MapBuilder::AddNewInput(cv::Mat& image, double timestamp){
cv::Mat undistort_image;
_camera->UndistortImage(image, undistort_image);
ComputeFFTResult(undistort_image);
ConstructFrame(timestamp);
if(!_init){
Initialize();
_map_stitcher->InsertFrame(_current_frame, undistort_image);
UpdateIntermedium();
return true;
}
Eigen::Vector3d response;
bool good_tracking = Tracking(response);
if(good_tracking){
UpdateCurrentPose();
SetCurrentFramePose();
Eigen::Vector2d da = ComputeRelativeDA();
bool c1 = da(0) > _kfs_config.max_distance;
bool c2 = da(1) > _kfs_config.max_angle;
bool c3 = ((response(0) > _kfs_config.lower_response_thr) && (response(0) < _kfs_config.upper_response_thr));
bool c4 = ((response(2) > _kfs_config.lower_response_thr) && (response(2) < _kfs_config.upper_response_thr));
bool to_insert = (c1 || c2 || c3 || c4);
if(!to_insert) return false;
_distance += da(0);
}else{
return false;
}
if(good_tracking) AddCFEdge();
_map->AddFrame(_current_frame);
SetFrameDistance();
_map_stitcher->InsertFrame(_current_frame, undistort_image);
bool loop_found = _configs.loop_closure_config.to_find_loop ? FindLoopClosure() : false;
if(!loop_found){
CheckAndOptimize();
}
UpdateIntermedium();
return true;
}
void MapBuilder::ComputeFFTResult(cv::Mat& image){
ConvertMatToNormalizedArray(image, _image_array);
_correlation_flow->ComputeIntermedium(_image_array, _fft_result, _fft_polar);
}
void MapBuilder::ConstructFrame(double timestamp){
_current_frame = std::shared_ptr<Frame>(
new Frame(_frame_id++, timestamp, _image_array, _fft_result, _fft_polar));
}
void MapBuilder::SetCurrentFramePose(){
_current_frame->SetPose(_current_pose);
}
bool MapBuilder::Initialize(){
_current_cf_pose << 0.0, 0.0, 0.0;
_camera->ConvertImagePlanePoseToCamera(_current_cf_pose, _current_cf_real_pose);
_camera->ConvertCameraPoseToRobot(_current_cf_real_pose, _current_pose);
SetCurrentFramePose();
_map->AddFrame(_current_frame);
_distance = 0;
_map->SetFrameDistance(_current_frame, _distance);
_init = true;
_last_lost = false;
return true;
}
void MapBuilder::UpdateIntermedium(){
_last_frame = _current_frame;
_last_cf_pose = _current_cf_pose;
_last_cf_real_pose = _current_cf_real_pose;
_last_pose = _current_pose;
_last_fft_result = _fft_result;
_last_fft_polar = _fft_polar;
}
void MapBuilder::CheckAndOptimize(){
if(_loop_matches.size() >= 2){
AddLoopEdges();
OptimizeMap();
UpdateValueAfterLoop();
_map_stitcher->RecomputeOccupancy();
}
_loop_matches.clear();
}
void MapBuilder::UpdateCurrentPose(){
Eigen::Vector3d last_robot_pose_from_cf, current_robot_pose_from_cf;
_camera->ConvertImagePlanePoseToRobot(_last_cf_pose, last_robot_pose_from_cf);
_camera->ConvertImagePlanePoseToRobot(_current_cf_pose, current_robot_pose_from_cf);
Eigen::Vector3d relativate_pose_from_cf = ComputeRelativePose(last_robot_pose_from_cf, current_robot_pose_from_cf);
Eigen::Vector3d relative_pose = relativate_pose_from_cf;
_current_pose = ComputeAbsolutePose(_last_pose, relative_pose);
}
bool MapBuilder::Tracking(Eigen::Vector3d& response){
Eigen::Vector3d relative_pose;
response = _correlation_flow->ComputePose(
_last_fft_result, _image_array, _last_fft_polar, _fft_polar, relative_pose, true);
relative_pose = _camera->ConvertCenterToPrincipal(relative_pose);
bool good_tracking = ((response(0) > _kfs_config.lower_response_thr) && ((response(2) > _kfs_config.lower_response_thr)));
if(good_tracking){
_current_cf_pose = ComputeAbsolutePose(_last_cf_pose, relative_pose);
_camera->ConvertImagePlanePoseToCamera(_current_cf_pose, _current_cf_real_pose);
}
return good_tracking;
}
void MapBuilder::AddCFEdge(){
Eigen::Vector3d relative_cf_real_pose = ComputeRelativePose(_last_cf_real_pose, _current_cf_real_pose);
Eigen::Matrix3d info = Eigen::Matrix3d::Identity();
AddCFEdgeToMap(relative_cf_real_pose, _last_frame->GetFrameId(), _current_frame->GetFrameId(), _edge_id++, Edge::Type::KCC, info);
}
void MapBuilder::AddCFEdgeToMap(Eigen::Vector3d& relative_pose, int from, int to,
int edge_id, Edge::Type edge_type, Eigen::Matrix3d& info){
EdgePtr cf_edge = std::make_shared<Edge>();
cf_edge->_edge_id = edge_id;
cf_edge->_type = edge_type;
cf_edge->_from = from;
cf_edge->_to = to;
cf_edge->_T = relative_pose;
cf_edge->_information = info;
_map->AddEdge(cf_edge);
}
Eigen::Vector2d MapBuilder::ComputeRelativeDA(){
Eigen::Vector3d rlt_camera_pose;
Eigen::Vector3d rlt_pose = _current_cf_pose - _last_cf_pose;
_camera->ConvertImagePlanePoseToCamera(rlt_pose, rlt_camera_pose);
double d = sqrt(rlt_camera_pose.head(2).dot(rlt_camera_pose.head(2)));
Eigen::Vector2d result;
result << d, std::abs(rlt_camera_pose(2));
return result;
}
void MapBuilder::SetFrameDistance(){
_map->SetFrameDistance(_current_frame, _distance);
}
bool MapBuilder::FindLoopClosure(){
LoopClosureResult loop_closure_result = _loop_closure->FindLoopClosure(_image_array, _current_frame, _current_pose);
if(loop_closure_result.found){
loop_closure_result.relative_pose = _camera->ConvertCenterToPrincipal(loop_closure_result.relative_pose);
_loop_matches.emplace_back(loop_closure_result);
std::cout << "Find a loop edge, current frame = " << loop_closure_result.current_frame->GetFrameId()
<< ", loop frame = " << loop_closure_result.loop_frame->GetFrameId() << std::endl;
}
return loop_closure_result.found;
}
void MapBuilder::AddLoopEdges(){
for(auto loop_match : _loop_matches){ // greater than two matches
int edge_id = _edge_id++;
int from = loop_match.loop_frame->GetFrameId();
int to = loop_match.current_frame->GetFrameId();
Eigen::Vector3d relative_pose;
_camera->ConvertImagePlanePoseToCamera(loop_match.relative_pose, relative_pose);
Eigen::Matrix3d info = Eigen::Matrix3d::Identity();
AddCFEdgeToMap(relative_pose, from, to, edge_id, Edge::Type::Loop, info);
}
}
bool MapBuilder::OptimizeMap(){
std::map<int, ceres::optimization_2d::Pose2d> poses;
std::vector<ceres::optimization_2d::Constraint2d> constraints;
std::vector<FramePtr> frames;
std::set<int> frame_ids;
std::vector<EdgePtr> edges;
_map->GetAllFrames(frames);
_map->GetAllEdges(edges);
for(FramePtr& frame : frames){
int frame_id = frame->GetFrameId();
Eigen::Vector3d pose_vec;
frame->GetPose(pose_vec);
frame_ids.insert(frame_id);
ceres::optimization_2d::Pose2d pose_2d;
pose_2d.x = pose_vec(0);
pose_2d.y = pose_vec(1);
pose_2d.yaw_radians = pose_vec(2);
poses.insert(std::pair<int, ceres::optimization_2d::Pose2d>(frame_id, pose_2d));
}
std::vector<ceres::optimization_2d::ScaleData> scale_data;
ceres::optimization_2d::ScaleData cf_scale;
cf_scale.scale = _camera->GetImageHeight();
if(!_camera->HeightIsAccurate()){
cf_scale.fixed = false;
}else{
cf_scale.fixed = true;
}
scale_data.emplace_back(cf_scale);
std::vector<int> scale_data_idx;
for(EdgePtr& edge : edges){
int from = edge->_from;
int to = edge->_to;
if((frame_ids.count(from) < 1) || (frame_ids.count(to) < 1)){
continue;
}
Eigen::Vector3d relative_pose;
if(edge->_type == Edge::Type::KCC || edge->_type == Edge::Type::Loop){
scale_data_idx.emplace_back(0);
_camera->ConvertCameraPoseToRobot(edge->_T, relative_pose);
}else{
continue;
}
ceres::optimization_2d::Constraint2d constraint;
constraint.id_begin = from;
constraint.id_end = to;
constraint.x = relative_pose(0);
constraint.y = relative_pose(1);
constraint.yaw_radians = relative_pose(2);
constraint.information = edge->_information;
constraints.emplace_back(constraint);
}
ceres::Problem problem;
ceres::optimization_2d::BuildOptimizationProblem(constraints, &poses, &problem);
// ceres::optimization_2d::BuildOptimizationProblemWithScale(constraints, scale_data, scale_data_idx, &poses, &problem);
CHECK(ceres::optimization_2d::SolveOptimizationProblem(&problem))
<< "The solve was not successful, exiting.";
// copy back frame pose
AlignedMap<int, Eigen::Vector3d> frame_poses;
for(auto kv : poses){
Eigen::Vector3d frame_pose;
frame_pose << kv.second.x, kv.second.y, kv.second.yaw_radians;
frame_poses[kv.first] = frame_pose;
}
_map->UpdatePoses(frame_poses);
std::cout << "Map optimization is done !" << std::endl;
return true;
}
void MapBuilder::UpdateValueAfterLoop(){
_current_frame->GetPose(_current_pose);
_camera->ConvertRobotPoseToCamera(_current_cf_real_pose, _current_pose);
_camera->ConvertCameraPoseToImagePlane(_current_cf_pose, _current_cf_real_pose);
}
// for visualization
bool MapBuilder::GetCFPose(Eigen::Vector3d& pose){
Eigen::Vector3d cf_base_pose(0.0, 0.0, 0.0);
Eigen::Vector3d robot_base_pose;
_camera->ConvertImagePlanePoseToRobot(cf_base_pose, robot_base_pose);
Eigen::Vector3d robot_current_pose;
_camera->ConvertImagePlanePoseToRobot(_current_cf_pose, robot_current_pose);
pose = ComputeRelativePose(robot_base_pose, robot_current_pose);
return true;
}
bool MapBuilder::GetFramePoses(Aligned<std::vector, Eigen::Vector3d>& poses, std::vector<double>& timestamps){
poses.clear();
timestamps.clear();
std::vector<FramePtr> frames;
_map->GetAllFrames(frames);
for(auto frame : frames){
Eigen::Vector3d pose;
frame->GetPose(pose);
poses.emplace_back(pose);
timestamps.emplace_back(frame->GetTimestamp());
}
return true;
}
bool MapBuilder::GetOccupancyMapOrigin(
Eigen::Vector3d& pixel_origin, Eigen::Matrix<double, 7, 1>& real_origin){
Eigen::Matrix3d Rbc;
_camera->GetExtrinsics(Rbc);
Eigen::Quaterniond qbc(Rbc);
real_origin(0, 0) = qbc.w();
real_origin(1, 0) = qbc.x();
real_origin(2, 0) = qbc.y();
real_origin(3, 0) = qbc.z();
Eigen::Vector3d robot_origin;
_camera->ConvertImagePlanePoseToRobot(pixel_origin, robot_origin);
real_origin(4, 0) = robot_origin(0);
real_origin(5, 0) = robot_origin(1);
real_origin(6, 0) = robot_origin(2);
return true;
}
double MapBuilder::GetMapResolution(){
return _camera->GetLengthOfPixel();
}
OccupancyData& MapBuilder::GetMapData(){
return _map_stitcher->GetOccupancyData();
}

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#include "map_stitcher.h"
#include "utils.h"
#include <algorithm>
#include <iostream>
using namespace std;
MapStitcher::MapStitcher(MapStitcherConfig config, CameraPtr camera):
_cell_size(config.cell_size), _to_stitch(config.stitch_map), _camera(camera){
}
void MapStitcher::InsertFrame(FramePtr frame, cv::Mat& image){
if(!_to_stitch) return;
const double scale = 100.0 / 255.0;
cv::Mat norm_image = image * scale;
Eigen::MatrixXi matrix;
cv::cv2eigen(norm_image, matrix);
_raw_images[frame] = matrix;
AddImageToOccupancy(frame);
}
Eigen::Vector2i MapStitcher::ComputeCellPosition(int x){
Eigen::Vector2i result;
if(x >=0 ){
result(0) = x / _cell_size;
}else{
result(0) = (x - _cell_size + 1) / _cell_size;
}
result(1) = x - result(0) * _cell_size;
return result;
}
void MapStitcher::AddImageToOccupancy(FramePtr frame){
Eigen::MatrixXi& data = _raw_images[frame];
Eigen::Vector3d robot_pose, image_pose;
frame->GetPose(robot_pose);
_camera->ConvertRobotPoseToImagePlane(image_pose, robot_pose);
image_pose = _camera->ConvertPrincipalToCenter(image_pose);
int W = data.cols();
int H = data.rows();
Eigen::Matrix2d R = ceres::optimization_2d::RotationMatrix2D(image_pose(2));
Eigen::VectorXd W_idx(W);
Eigen::VectorXd H_idx(H);
Eigen::VectorXd X(W);
Eigen::VectorXd Y(W);
double cx = (double)W / 2;
double cy = (double)H / 2;
for(int i = 0; i < W; i++){
W_idx(i) = i - cx;
X(i) = image_pose(0);
Y(i) = image_pose(1);
}
for(int i = 0; i < H; i++) H_idx(i) = i - cy;
Eigen::VectorXd Wx = R(0, 0) * W_idx + X;
Eigen::VectorXd Wy = R(1, 0) * W_idx + Y;
Eigen::VectorXd Hx = R(0, 1) * H_idx;
Eigen::VectorXd Hy = R(1, 1) * H_idx;
// find which cells may be used
int x00 = static_cast<int>((Wx(0) + Hx(0)));
int y00 = static_cast<int>((Wy(0) + Hy(0)));
int x01 = static_cast<int>((Wx(0) + Hx((H-1))));
int y01 = static_cast<int>((Wy(0) + Hy((H-1))));
int x10 = static_cast<int>((Wx((W-1)) + Hx(0)));
int y10 = static_cast<int>((Wy((W-1)) + Hy(0)));
int x11 = static_cast<int>((Wx((W-1)) + Hx((H-1))));
int y11 = static_cast<int>((Wy((W-1)) + Hy((H-1))));
int min_x = std::min(x00, std::min(x01, std::min(x10, x11)));
int max_x = std::max(x00, std::max(x01, std::max(x10, x11)));
int min_y = std::min(y00, std::min(y01, std::min(y10, y11)));
int max_y = std::max(y00, std::max(y01, std::max(y10, y11)));
Eigen::Vector2i min_cell_x = ComputeCellPosition(min_x);
Eigen::Vector2i max_cell_x = ComputeCellPosition(max_x);
Eigen::Vector2i min_cell_y = ComputeCellPosition(min_y);
Eigen::Vector2i max_cell_y = ComputeCellPosition(max_y);
std::unordered_map<GridLocation, bool, GridLocationHash, GridLocationEqual> locations;
OccupancyData tmp_data;
for(int i = min_cell_x(0); i <= max_cell_x(0); i++){
for(int j = min_cell_y(0); j <= max_cell_y(0); j++){
GridLocation grid_location(i, j);
tmp_data[grid_location] = Cell(_cell_size);
locations[grid_location] = false;
}
}
for(int i = 0; i < W; i++){
for(int j = 0; j < H; j ++){
int x = static_cast<int>((Wx(i) + Hx(j)));
int y = static_cast<int>((Wy(i) + Hy(j)));
Eigen::Vector2i cell_x = ComputeCellPosition(x);
Eigen::Vector2i cell_y = ComputeCellPosition(y);
GridLocation grid_location(cell_x(0), cell_y(0));
// cout << data.size() << endl;
// sleep(1000);
tmp_data[grid_location].data(cell_y(1), cell_x(1)) += data(j, i);
tmp_data[grid_location].weight(cell_y(1), cell_x(1)) += 1;
locations[grid_location] = true;
}
}
for(auto& kv : tmp_data){
GridLocation loc = kv.first;
if(!locations[loc]) continue;
if(_occupancy_data.count(loc) > 0){
_occupancy_data[loc].size = _cell_size;
_occupancy_data[loc].data =
_occupancy_data[loc].data * _occupancy_data[loc].weight + tmp_data[loc].data * tmp_data[loc].weight;
_occupancy_data[loc].weight = _occupancy_data[loc].weight + tmp_data[loc].weight;
for(int i = 0; i < _cell_size; i++){
for(int j = 0; j < _cell_size; j++){
if(_occupancy_data[loc].weight(i, j) < 1) continue;
_occupancy_data[loc].data(i, j) = _occupancy_data[loc].data(i, j) / _occupancy_data[loc].weight(i, j);
}
}
}else{
_occupancy_data[loc].size = _cell_size;
_occupancy_data[loc].data = tmp_data[loc].data;
_occupancy_data[loc].weight = tmp_data[loc].weight;
}
}
}
void MapStitcher::RecomputeOccupancy(){
std::cout << "Recompute occupancy map ........" << std::endl;
_occupancy_data.clear();
for(auto kv : _raw_images){
AddImageToOccupancy(kv.first);
Eigen::Vector3d pose;
kv.first->GetPose(pose);
}
std::cout << "Occupancy map has been updated !" << std::endl;
}
OccupancyData& MapStitcher::GetOccupancyData(){
return _occupancy_data;
}

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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2016 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * Neither the name of Google Inc. nor the names of its contributors may be
// used to endorse or promote products derived from this software without
// specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Author: vitus@google.com (Michael Vitus)
//
// An example of solving a graph-based formulation of Simultaneous Localization
// and Mapping (SLAM). It reads a 2D pose graph problem definition file in the
// g2o format, formulates and solves the Ceres optimization problem, and outputs
// the original and optimized poses to file for plotting.
#include <fstream>
#include <iostream>
#include <map>
#include <string>
#include <vector>
#include <gflags/gflags.h>
#include <glog/logging.h>
#include "optimization_2d/pose_graph_2d.h"
namespace ceres {
namespace optimization_2d {
// Constructs the nonlinear least squares optimization problem from the pose
// graph constraints.
void BuildOptimizationProblem(const std::vector<Constraint2d>& constraints,
std::map<int, Pose2d>* poses,
ceres::Problem* problem) {
CHECK(poses != NULL);
CHECK(problem != NULL);
if (constraints.empty()) {
LOG(INFO) << "No constraints, no problem to optimize.";
return;
}
ceres::LossFunction* loss_function = NULL;
ceres::LocalParameterization* angle_local_parameterization =
AngleLocalParameterization::Create();
for (std::vector<Constraint2d>::const_iterator constraints_iter =
constraints.begin();
constraints_iter != constraints.end();
++constraints_iter) {
const Constraint2d& constraint = *constraints_iter;
std::map<int, Pose2d>::iterator pose_begin_iter =
poses->find(constraint.id_begin);
CHECK(pose_begin_iter != poses->end())
<< "Pose with ID: " << constraint.id_begin << " not found.";
std::map<int, Pose2d>::iterator pose_end_iter =
poses->find(constraint.id_end);
CHECK(pose_end_iter != poses->end())
<< "Pose with ID: " << constraint.id_end << " not found.";
const Eigen::Matrix3d sqrt_information =
constraint.information.llt().matrixL();
// Ceres will take ownership of the pointer.
ceres::CostFunction* cost_function = PoseGraph2dErrorTerm::Create(
constraint.x, constraint.y, constraint.yaw_radians, sqrt_information);
problem->AddResidualBlock(cost_function,
loss_function,
&pose_begin_iter->second.x,
&pose_begin_iter->second.y,
&pose_begin_iter->second.yaw_radians,
&pose_end_iter->second.x,
&pose_end_iter->second.y,
&pose_end_iter->second.yaw_radians);
problem->SetParameterization(&pose_begin_iter->second.yaw_radians,
angle_local_parameterization);
problem->SetParameterization(&pose_end_iter->second.yaw_radians,
angle_local_parameterization);
}
// fix base frame
std::map<int, Pose2d>::iterator baseframe_pose_iter = poses->find(0);
CHECK(baseframe_pose_iter != poses->end());
problem->SetParameterBlockConstant(&baseframe_pose_iter->second.x);
problem->SetParameterBlockConstant(&baseframe_pose_iter->second.y);
problem->SetParameterBlockConstant(&baseframe_pose_iter->second.yaw_radians);
}
void BuildOptimizationProblemWithScale(const std::vector<Constraint2d>& constraints,
std::vector<ScaleData>& scale_data,
const std::vector<int>& scale_data_idx,
std::map<int, Pose2d>* poses,
ceres::Problem* problem) {
CHECK(poses != NULL);
CHECK(problem != NULL);
CHECK_EQ(constraints.size(), scale_data_idx.size());
if (constraints.empty()) {
LOG(INFO) << "No constraints, no problem to optimize.";
return;
}
ceres::LossFunction* loss_function = NULL;
ceres::LocalParameterization* angle_local_parameterization =
AngleLocalParameterization::Create();
size_t i = 0;
for (std::vector<Constraint2d>::const_iterator constraints_iter =
constraints.begin();
constraints_iter != constraints.end();
++constraints_iter) {
const Constraint2d& constraint = *constraints_iter;
std::map<int, Pose2d>::iterator pose_begin_iter =
poses->find(constraint.id_begin);
CHECK(pose_begin_iter != poses->end())
<< "Pose with ID: " << constraint.id_begin << " not found.";
std::map<int, Pose2d>::iterator pose_end_iter =
poses->find(constraint.id_end);
CHECK(pose_end_iter != poses->end())
<< "Pose with ID: " << constraint.id_end << " not found.";
int scale_idx = scale_data_idx[i++];
const Eigen::Matrix3d sqrt_information =
constraint.information.llt().matrixL();
// Ceres will take ownership of the pointer.
ceres::CostFunction* cost_function = PoseGraph2dErrorTermWithScale::Create(
constraint.x, constraint.y, constraint.yaw_radians, sqrt_information);
problem->AddResidualBlock(cost_function,
loss_function,
&pose_begin_iter->second.x,
&pose_begin_iter->second.y,
&pose_begin_iter->second.yaw_radians,
&pose_end_iter->second.x,
&pose_end_iter->second.y,
&pose_end_iter->second.yaw_radians,
&(scale_data[scale_idx].scale));
problem->SetParameterization(&pose_begin_iter->second.yaw_radians,
angle_local_parameterization);
problem->SetParameterization(&pose_end_iter->second.yaw_radians,
angle_local_parameterization);
}
// fix some scales
for(size_t j = 0; j < scale_data.size(); j++){
if(scale_data[j].fixed){
problem->SetParameterBlockConstant(&(scale_data[j].scale));
}
}
// fix base frame
std::map<int, Pose2d>::iterator baseframe_pose_iter = poses->find(0);
CHECK(baseframe_pose_iter != poses->end());
problem->SetParameterBlockConstant(&baseframe_pose_iter->second.x);
problem->SetParameterBlockConstant(&baseframe_pose_iter->second.y);
problem->SetParameterBlockConstant(&baseframe_pose_iter->second.yaw_radians);
}
// Returns true if the solve was successful.
bool SolveOptimizationProblem(ceres::Problem* problem) {
CHECK(problem != NULL);
ceres::Solver::Options options;
options.max_num_iterations = 300;
options.linear_solver_type = ceres::SPARSE_NORMAL_CHOLESKY;
ceres::Solver::Summary summary;
ceres::Solve(options, problem, &summary);
std::cout << summary.FullReport() << '\n';
return summary.IsSolutionUsable();
}
// Output the poses to the file with format: ID x y yaw_radians.
bool OutputPoses(const std::string& filename,
const std::map<int, Pose2d>& poses) {
std::fstream outfile;
outfile.open(filename.c_str(), std::istream::out);
if (!outfile) {
std::cerr << "Error opening the file: " << filename << '\n';
return false;
}
for (std::map<int, Pose2d>::const_iterator poses_iter = poses.begin();
poses_iter != poses.end();
++poses_iter) {
const std::map<int, Pose2d>::value_type& pair = *poses_iter;
outfile << pair.first << " " << pair.second.x << " " << pair.second.y << ' '
<< pair.second.yaw_radians << '\n';
}
return true;
}
} // namespace optimization_2d
} // namespace ceres

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#include "thread_publisher.h"
template <typename T>
ThreadPublisher<T>::ThreadPublisher() {
shutdown_requested_ = false;
}
template <typename T>
ThreadPublisher<T>::~ThreadPublisher() {
}
template <typename T>
void ThreadPublisher<T>::Register(
std::function<void(const std::shared_ptr<const T>&)> cb) {
callbacks_.push_back(cb);
}
template <typename T>
void ThreadPublisher<T>::Start() {
publish_thread_ = std::thread(std::bind(&ThreadPublisher::Process, this));
}
template <typename T>
void ThreadPublisher<T>::Publish(const std::shared_ptr<const T> msg) {
std::unique_lock<std::mutex> locker(msg_mutex_);
msgs_.push(msg);
locker.unlock();
msg_cond_.notify_one();
}
template <typename T>
void ThreadPublisher<T>::Process() {
while (!shutdown_requested_) {
std::shared_ptr<const T> msg;
std::unique_lock<std::mutex> locker(msg_mutex_);
while (msgs_.empty()) {
if (shutdown_requested_) {
locker.unlock();
break;
} else {
msg_cond_.wait(locker);
}
}
if (shutdown_requested_) {
break;
} else {
msg = msgs_.back();
while (!msgs_.empty()) {
msgs_.pop();
}
}
locker.unlock();
for (auto callback : callbacks_) {
callback(msg);
}
}
}
template <typename T>
void ThreadPublisher<T>::ShutDown() {
shutdown_requested_ = true;
msg_cond_.notify_one();
if (publish_thread_.joinable()) {
publish_thread_.join();
}
}

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#include <stdlib.h>
#include "timer.h"
void startTimer(Timer *pTimer){
gettimeofday(&pTimer->start, NULL);
}
void stopTimer(Timer *pTimer){
gettimeofday(&pTimer->stop, NULL);
}
double getElapsedTime(Timer *pTimer){
return ((pTimer->stop.tv_sec - pTimer->start.tv_sec) * 1000.0 + (pTimer->stop.tv_usec - pTimer->start.tv_usec) / 1000.0);
}
void writeTimeToFile(double arrTime[], int nCount, int nFrameNo, char *filename){
FILE *pFile;
int i;
pFile = fopen(filename, "a+");
if(NULL == pFile)
return;
fprintf(pFile, "FrameNo, %d", nFrameNo);
for (i = 0; i < nCount; i++)
{
fprintf(pFile, ",%f", arrTime[i]);
}
fprintf(pFile, "\n");
fclose(pFile);
}

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#include "utils.h"
#include "optimization_2d/pose_graph_2d_error_term.h"
bool FileExists(const std::string& file) {
struct stat file_status;
if (stat(file.c_str(), &file_status) == 0 &&
(file_status.st_mode & S_IFREG)) {
return true;
}
return false;
}
bool PathExists(const std::string& path) {
struct stat file_status;
if (stat(path.c_str(), &file_status) == 0 &&
(file_status.st_mode & S_IFDIR)) {
return true;
}
return false;
}
void ConcatenateFolderAndFileName(
const std::string& folder, const std::string& file_name,
std::string* path) {
*path = folder;
if (path->back() != '/') {
*path += '/';
}
*path = *path + file_name;
}
std::string ConcatenateFolderAndFileName(
const std::string& folder, const std::string& file_name) {
std::string path;
ConcatenateFolderAndFileName(folder, file_name, &path);
return path;
}
void MakeDir(const std::string& path){
if(!PathExists(path)){
mkdir(path.c_str(), S_IRWXU | S_IRWXG | S_IROTH | S_IXOTH);
}
}
void ReadTxt(const std::string& file_path,
std::vector<std::vector<std::string> >& lines, std::string seq){
if(!FileExists(file_path)){
std::cout << "file: " << file_path << " dosen't exist" << std::endl;
exit(0);
}
std::ifstream infile(file_path, std::ifstream::in);
if(!infile.is_open()){
std::cout << "open file: " << file_path << " failure" << std::endl;
exit(0);
}
std::string line;
while (getline(infile, line)){
std::string whitespaces(" \t\f\v\n\r");
std::size_t found = line.find_last_not_of(whitespaces);
if (found!=std::string::npos){
line.erase(found+1);
std::vector<std::string> line_data;
while (true){
int index = line.find(seq);
std::string sub_str = line.substr(0, index);
if (!sub_str.empty()){
line_data.push_back(sub_str);
}
line.erase(0, index + seq.size());
if (index == -1){
break;
}
}
lines.emplace_back(line_data);
}
else{
line.clear(); // str is all whitespace
}
}
}
void WriteTxt(const std::string file_path,
std::vector<std::vector<std::string> >& lines, std::string seq){
std::fstream file;
file.open(file_path.c_str(), std::ios::out|std::ios::app);
if(!file.good()){
std::cout << "Error: cannot open file " << file_path << std::endl;
exit(0);
}
for(std::vector<std::string>& line : lines){
size_t num_in_line = line.size();
if(num_in_line < 1) continue;
std::string line_txt = line[0];
for(size_t i = 1; i < num_in_line; ++i){
line_txt = line_txt + seq + line[i];
}
line_txt += "\n";
file << line_txt;
}
file.close();
}
void ConvertMatToNormalizedArray(cv::Mat& image, Eigen::ArrayXXf& array){
cv::Mat dst;
// auto clahe = cv::createCLAHE(2, cv::Size(4, 4));
// clahe->apply(image, dst);
dst = image;
Eigen::MatrixXf matrix;
cv::cv2eigen(dst, matrix);
array = matrix.array()/255.0;
}
Eigen::ArrayXXf ConvertMatToArray(const cv::Mat& image){
Eigen::MatrixXf matrix;
cv::cv2eigen(image, matrix);
return matrix.array();
}
cv::Mat ConvertArrayToMat(const Eigen::ArrayXXf& array){
cv::Mat image;
Eigen::MatrixXf matrix(array);
cv::eigen2cv(matrix, image);
return image;
}
// Eigen pose
Eigen::Vector3d ComputeRelativePose(
Eigen::Vector3d& pose1, Eigen::Vector3d& pose2){
Eigen::Vector3d result;
Eigen::Matrix2d Rw1 = ceres::optimization_2d::RotationMatrix2D(pose1(2));
result.head(2) = Rw1.transpose() * (pose2.head(2) - pose1.head(2));
result(2) = pose2(2) - pose1(2);
result(2) = ceres::optimization_2d::NormalizeAngle(result(2));
return result;
}
Eigen::Vector3d ComputeAbsolutePose(
Eigen::Vector3d& pose1, Eigen::Vector3d& relative_pose){
Eigen::Vector3d result;
Eigen::Matrix2d Rw1 = ceres::optimization_2d::RotationMatrix2D(pose1(2));
result.head(2) = pose1.head(2) + Rw1 * relative_pose.head(2);
result(2) = pose1(2) + relative_pose(2);
result(2) = ceres::optimization_2d::NormalizeAngle(result(2));
return result;
}
Eigen::ArrayXXf RotateArray(const Eigen::ArrayXXf& array, float degree)
{
cv::Mat dst, src=ConvertArrayToMat(array);
cv::Point2f pc(src.cols/2., src.rows/2.);
cv::Mat r = cv::getRotationMatrix2D(pc, degree, 1.0);
cv::warpAffine(src, dst, r, src.size(), cv::INTER_LINEAR, cv::BORDER_WRAP);
return ConvertMatToArray(dst);
}
Eigen::ArrayXXf WarpArray(const Eigen::ArrayXXf& array, float tx, float ty, float degree)
{
cv::Mat dst, src=ConvertArrayToMat(array);
float warp_values[] = {1, 0, tx, 0, 1, ty};
cv::Mat warp = cv::Mat(2, 3, CV_32F, warp_values);
cv::warpAffine(src, dst, warp, src.size(), cv::INTER_LINEAR, cv::BORDER_WRAP);
// ShowArray(ConvertMatToArray(dst), "test", 0);
return RotateArray(ConvertMatToArray(dst), degree);
}
double NormalizeDegree(double angle_degree) {
return angle_degree - 360 * floor((angle_degree + 180) / 360);
}
void ShowArray(const Eigen::ArrayXXf& array, std::string window, int waitkey)
{
auto img = ConvertArrayToMat(array);
cv::imshow(window, img);
cv::waitKey(waitkey);
}

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#include "visualization.h"
#include <iostream>
#include <algorithm>
#include <numeric>
#include <time.h>
#include <tf2/LinearMath/Quaternion.h>
#include <tf2_geometry_msgs/tf2_geometry_msgs.hpp>
#include "utils.h"
using namespace std;
Visualizer::Visualizer(rclcpp::Node::SharedPtr node, VisualizationConfig& config)
: node_(node), frame_id_(config.frame_id) {
kcc_pose_pub_ = node_->create_publisher<nav_msgs::msg::Path>(config.kcc_pose_topic, 10);
frame_pose_pub_ = node_->create_publisher<nav_msgs::msg::Path>(config.frame_pose_topic, 10);
map_pub_ = node_->create_publisher<nav_msgs::msg::OccupancyGrid>(config.map_topic, 1);
image_pub_ = node_->create_publisher<sensor_msgs::msg::Image>(config.image_topic, 1);
auto current_time = node_->get_clock()->now();
odom_pose_msgs_.header.stamp = current_time;
odom_pose_msgs_.header.frame_id = frame_id_;
kcc_pose_msgs_.header.stamp = current_time;
kcc_pose_msgs_.header.frame_id = frame_id_;
frame_pose_msgs_.header.stamp = current_time;
frame_pose_msgs_.header.frame_id = frame_id_;
occupancy_map_msgs_.header.stamp = current_time;
occupancy_map_msgs_.header.frame_id = frame_id_;
}
void Visualizer::AddNewPoseToPath(
Eigen::Vector3d& pose, double time_double,
nav_msgs::msg::Path& path, std::string& id) {
auto current_time = node_->get_clock()->now();
geometry_msgs::msg::PoseStamped pose_stamped;
if (time_double < 0) {
pose_stamped.header.stamp = current_time;
} else {
auto sec = static_cast<int64_t>(time_double);
auto nsec = static_cast<uint32_t>((time_double - sec) * 1e9);
pose_stamped.header.stamp = rclcpp::Time(sec, nsec);
}
pose_stamped.header.frame_id = id;
pose_stamped.pose.position.x = pose(0);
pose_stamped.pose.position.y = pose(1);
pose_stamped.pose.position.z = 0;
// tf2 equivalent of tf::createQuaternionMsgFromYaw
tf2::Quaternion q;
q.setRPY(0, 0, pose(2));
pose_stamped.pose.orientation = tf2::toMsg(q);
path.poses.push_back(pose_stamped);
}
void Visualizer::UpdateOdomPose(Eigen::Vector3d& pose, double time_double) {
AddNewPoseToPath(pose, time_double, odom_pose_msgs_, frame_id_);
// odom_pose_pub not used currently
}
void Visualizer::UpdateKccPose(Eigen::Vector3d& pose, double time_double) {
AddNewPoseToPath(pose, time_double, kcc_pose_msgs_, frame_id_);
kcc_pose_pub_->publish(kcc_pose_msgs_);
}
void Visualizer::UpdateFramePose(
Aligned<std::vector, Eigen::Vector3d>& frame_poses,
std::vector<double>& timestamps) {
frame_pose_msgs_.poses.clear();
for (size_t i = 0; i < frame_poses.size(); i++) {
AddNewPoseToPath(frame_poses[i], timestamps[i], frame_pose_msgs_, frame_id_);
}
frame_pose_pub_->publish(frame_pose_msgs_);
}
void Visualizer::ConvertMapToOccupancyMsgs(
OccupancyData& map, nav_msgs::msg::OccupancyGrid& msgs) {
if (map.size() < 1) return;
int max_x = std::numeric_limits<int>::min();
int max_y = std::numeric_limits<int>::min();
int min_x = std::numeric_limits<int>::max();
int min_y = std::numeric_limits<int>::max();
for (auto& kv : map) {
int x = kv.first.x;
int y = kv.first.y;
max_x = std::max(max_x, x);
max_y = std::max(max_y, y);
min_x = std::min(min_x, x);
min_y = std::min(min_y, y);
}
if ((min_x > max_x) || (min_y > max_y)) return;
int size = map.begin()->second.size;
msgs.info.width = (max_x - min_x + 1) * size;
msgs.info.height = (max_y - min_y + 1) * size;
msgs.info.origin.position.x = (min_x * size);
msgs.info.origin.position.y = (min_y * size);
msgs.info.origin.position.z = 0;
int num_grids = msgs.info.width * msgs.info.height;
std::vector<int8_t> data(num_grids, -1);
for (auto& kv : map) {
for (int i = 0; i < kv.second.size; i++) {
for (int j = 0; j < kv.second.size; j++) {
if (kv.second.weight(i, j) < 1) continue;
int x = (kv.first.x - min_x) * kv.second.size + j;
int y = (kv.first.y - min_y) * kv.second.size + i;
int idx = y * msgs.info.width + x;
int pixel = static_cast<int>(kv.second.data(i, j));
data[idx] = 100 - static_cast<int8_t>(pixel);
}
}
}
msgs.data = data;
}
void Visualizer::UpdateMap(MapBuilder& map_builder) {
occupancy_map_msgs_.info.resolution = map_builder.GetMapResolution();
OccupancyData map_data = map_builder.GetMapData();
if (map_data.size() < 1) return;
ConvertMapToOccupancyMsgs(map_data, occupancy_map_msgs_);
Eigen::Matrix<double, 7, 1> real_origin;
Eigen::Vector3d pixel_origin;
pixel_origin << occupancy_map_msgs_.info.origin.position.x,
occupancy_map_msgs_.info.origin.position.y,
occupancy_map_msgs_.info.origin.position.z;
map_builder.GetOccupancyMapOrigin(pixel_origin, real_origin);
occupancy_map_msgs_.info.origin.orientation.w = real_origin(0, 0);
occupancy_map_msgs_.info.origin.orientation.x = real_origin(1, 0);
occupancy_map_msgs_.info.origin.orientation.y = real_origin(2, 0);
occupancy_map_msgs_.info.origin.orientation.z = real_origin(3, 0);
occupancy_map_msgs_.info.origin.position.x = real_origin(4, 0);
occupancy_map_msgs_.info.origin.position.y = real_origin(5, 0);
occupancy_map_msgs_.info.origin.position.z = real_origin(6, 0);
map_pub_->publish(occupancy_map_msgs_);
}
void Visualizer::PublishImage(cv::Mat& image, double time_double) {
auto image_msg = cv_bridge::CvImage(std_msgs::msg::Header(), "mono8", image).toImageMsg();
if (time_double > 0) {
auto sec = static_cast<int64_t>(time_double);
auto nsec = static_cast<uint32_t>((time_double - sec) * 1e9);
image_msg->header.stamp = rclcpp::Time(sec, nsec);
}
image_pub_->publish(*image_msg);
}
void Visualizer::GetTrajectoryTxt(
std::vector<std::vector<std::string>>& lines,
TrajectoryType trajectory_type) {
nav_msgs::msg::Path* path_ptr;
switch (trajectory_type) {
case TrajectoryType::Frame:
path_ptr = &frame_pose_msgs_;
break;
case TrajectoryType::KCC:
path_ptr = &kcc_pose_msgs_;
break;
case TrajectoryType::Odom:
path_ptr = &odom_pose_msgs_;
break;
default:
std::cout << "please select trajectory_type from Frame, KCC and Odom !" << std::endl;
return;
}
for (geometry_msgs::msg::PoseStamped& pose_stamped : (*path_ptr).poses) {
std::vector<std::string> line;
double time_double = static_cast<double>(pose_stamped.header.stamp.sec) +
static_cast<double>(pose_stamped.header.stamp.nanosec) / 1e9;
std::string s_time = std::to_string(time_double);
line.emplace_back(s_time);
line.emplace_back(std::to_string(pose_stamped.pose.position.x));
line.emplace_back(std::to_string(pose_stamped.pose.position.y));
line.emplace_back(std::to_string(pose_stamped.pose.position.z));
line.emplace_back(std::to_string(pose_stamped.pose.orientation.x));
line.emplace_back(std::to_string(pose_stamped.pose.orientation.y));
line.emplace_back(std::to_string(pose_stamped.pose.orientation.z));
line.emplace_back(std::to_string(pose_stamped.pose.orientation.w));
lines.push_back(line);
}
}

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