43 lines
1.6 KiB
Markdown
43 lines
1.6 KiB
Markdown
# FUSION
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## Table of Contents
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- [Paper](#paper)
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- [Overview](#overview)
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- [Prerequisites](#prerequisites)
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- [Running the Code](#running-the-code)
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- [Evaluation](#evaluation)
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## Paper
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If you find the poject helps you, you can cite our paper:
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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.
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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.
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## Overview
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We provide code for BEV mode and fusion mode, so you can easily train and test.
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## Prerequisites
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Before you can use this project, you'll need to do the following:
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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).
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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.
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3. **Prepare environment**: Use the commonds on `env.txt` to create your environment. Windows and Ubuntu is OK.
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## Running the Code
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To run the code, follow these steps:
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1. Configure the code to run in either BEV mode or fusion mode using the settings in `config.yaml`.
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2. If you want to load a trained model used in the paper, ensure that you update the file path accordingly.
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3. Run `python train.py`
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## Evaluation
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Evaluate the saved data using the evaluation script.
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## Others
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If you have any questions please feel free to contact us.
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