init fusion lcd orin config

README.md

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+# FUSION
+
+## Table of Contents
+- [Paper](#paper)
+- [Overview](#overview)
+- [Prerequisites](#prerequisites)
+- [Running the Code](#running-the-code)
+- [Evaluation](#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](https://www.cvlibs.net/datasets/kitti/eval_odometry.php) and [KITTI-360](https://www.cvlibs.net/datasets/kitti-360/download.php).
+
+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.