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[RAL/IROS 2022] OverlapTransformer: An Efficient and Yaw-Angle-Invariant Transformer Network for LiDAR-Based Place Recognition.
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# OverlapTransformer
The code for our paper for **RAL/IROS 2022**:
**OverlapTransformer: An Efficient and Yaw-Angle-Invariant Transformer Network for LiDAR-Based Place Recognition.** [[paper](https://arxiv.org/pdf/2203.03397.pdf)]
OverlapTransformer (OT) is a novel lightweight neural network exploiting the LiDAR range images to achieve fast execution with **less than 4 ms per frame using python, less than 2 ms per frame using C++** in LiDAR similarity estimation.
It is a newer version of our previous [OverlapNet](https://github.com/PRBonn/OverlapNet), which is faster and more accurate in LiDAR-based loop closure detection and place recognition.
Developed by [Junyi Ma](https://github.com/BIT-MJY), [Xieyuanli Chen](https://github.com/Chen-Xieyuanli) and [Jun Zhang](https://github.com/zhangjun-xyz).
OverlapTransformer is not a sophisticated model but holds natural mathematical properties in a lightweight style for surround-view observations. It can be seamlessly integrated into any range-image-based approach as a backbone, e.g., [EINet](https://arxiv.org/abs/2402.17264) (IROS 2024). **Welcome to post results in issues if you have tried other input types (e.g., RGBD camera, Livox, 16/32-beam LiDAR).**
## News!
**[2024-06]** [EINet](https://arxiv.org/abs/2402.17264) successfully integrates OT into its framework as a powerful submodule, which is accepted by IROS 2024!
**[2023-09]** **The multi-view extension of OT, [CVTNet](https://github.com/BIT-MJY/CVTNet)**, is accepted by IEEE Transactions on Industrial Informatics (TII)! A better long-term recognition performance is available :star:
**[2022-12]** SeqOT is accepted by IEEE Transactions on Industrial Electronics (TIE)!
**[2022-09]** We further develop **a sequence-enhanced version of OT named as SeqOT**, which can be found [here](https://github.com/BIT-MJY/SeqOT).
## Haomo Dataset
<img src="https://github.com/haomo-ai/OverlapTransformer/blob/master/query_database_haomo.gif" >
Fig. 1 An online demo for finding the top1 candidate with OverlapTransformer on sequence 1-1 (database) and 1-3 (query) of [Haomo Dataset](https://github.com/haomo-ai/OverlapTransformer/tree/master/Haomo_Dataset).
<div align=center>
<img src="https://github.com/haomo-ai/OverlapTransformer/blob/master/Haomo_Dataset/haomo_dataset.png" width="98%"/>
</div>
Fig. 2 [Haomo Dataset](https://github.com/haomo-ai/OverlapTransformer/tree/master/Haomo_Dataset) which is collected by **HAOMO.AI**.
More details of Haomo Dataset can be found in dataset description ([link](https://github.com/haomo-ai/OverlapTransformer/tree/master/Haomo_Dataset)).
## Table of Contents
1. [Introduction and Haomo Dataset](#OverlapTransformer)
2. [Publication](#Publication)
3. [Dependencies](#Dependencies)
4. [How to Use](#How-to-Use)
5. [Datasets Used by OT](#Datasets-Used-by-OT)
6. [Related Work](#Related-Work)
7. [License](#License)
## Publication
If you use the code or the Haomo dataset in your academic work, please cite our paper ([PDF](https://arxiv.org/pdf/2203.03397.pdf)):
```
@ARTICLE{ma2022ral,
author={Ma, Junyi and Zhang, Jun and Xu, Jintao and Ai, Rui and Gu, Weihao and Chen, Xieyuanli},
journal={IEEE Robotics and Automation Letters},
title={OverlapTransformer: An Efficient and Yaw-Angle-Invariant Transformer Network for LiDAR-Based Place Recognition},
year={2022},
volume={7},
number={3},
pages={6958-6965},
doi={10.1109/LRA.2022.3178797}}
```
## Dependencies
We use pytorch-gpu for neural networks.
An nvidia GPU is needed for faster retrival.
OverlapTransformer is also fast enough when using the neural network on CPU.
To use a GPU, first you need to install the nvidia driver and CUDA.
- CUDA Installation guide: [link](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
We use CUDA 11.3 in our work. Other versions of CUDA are also supported but you should choose the corresponding torch version in the following Torch dependences.
- System dependenciesExcerpt of 17,883 characters
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