fusion_LCD/README.md

FUSION

Table of Contents

• Paper
• Overview
• Prerequisites
• Running the Code
• Evaluation

Paper

If you find the poject helps you, you can cite our paper:

Cao D, Yue H, Liu Z, et al. BEVLCD+: Real-Time and Rotation-Invariant Loop Closure Detection Based on BEV of Point Cloud[J]. IEEE Transactions on Instrumentation and Measurement, 2023.

Yue H, Cao D, Liu Z, et al. Cross Fusion of Point Cloud and Learned Image for Loop Closure Detection[J]. IEEE Robotics and Automation Letters, 2024.

Overview

We provide code for BEV mode and fusion mode, so you can easily train and test.

Prerequisites

Before you can use this project, you'll need to do the following:

1. Download Datasets: Download the KITTI and KITTI-360.

2. Prepare Dataset Structure: Use preparedataset.py to construct a dataset structure that complies with the project's requirements. Make sure to update the necessary paths in the code.

3. Prepare environment: Use the commonds on env.txt to create your environment. Windows and Ubuntu is OK.

Running the Code

To run the code, follow these steps:

1. Configure the code to run in either BEV mode or fusion mode using the settings in config.yaml.

2. If you want to load a trained model used in the paper, ensure that you update the file path accordingly.

3. Run python train.py

Evaluation

Evaluate the saved data using the evaluation script.

Others

If you have any questions please feel free to contact us.