BEVPlace++: Fast, Robust, and Lightweight LiDAR Global Localization for Unmanned Ground Vehicles

BEVPlace++ is a LiDAR-based global localization method. It projects point clouds into Bird's-eye View (BEV) images and generates global features with a rotation equivariant module and the NetVLAD. It sequentially performs place recognition and pose estimation to achieve complete global localization. Experiments show that BEVPlace++ significantly outperforms the state-of-the-art (SOTA) methods and generalizes well to previously unseen environments. BEVPlace++ will certainly benefit various applications, including loop closure detection, global localization, and SLAM. Please feel free to use and enjoy it!

More details can be found in our pre-print paper https://arxiv.org/pdf/2408.01841.

Results

Loop results on KITTI 08.

Global localization demo on NCLT.

Quick Start

1. Download the dataset from google drive. Unzip and move the files into the "data" directory.

2. Create a conda environment and install Pytorch according to your Cuda version. Then install the dependencies by

   pip install -r requirements.txt

3. You can train and evaluate BEVPlace++ by simply running

   python main.py --mode=train
   python main.py --mode=test --load_from=/path/to/your/checkpoint/directory

Evaluate your own data

Organize your data following the description in data.md and customize your dataloader following kitti_dataset.py. Then evaluate the performance with the script main.py

News

• 2024-08-04: BEVPlace++ is released. Compared to BEVPlace, it achieves complete 3DoF global localization.
• 2023-08-31: Update the pre-trained weights and the bev dataset of KITTI to reproduce the numbers in the paper.
• 2023-07-14: Our paper is accepted by ICCV 2023!
• 2023-03-14: Initial version.
• 2022-09-02: Our method ranked 2nd in the General Place Recognition Competition of ICRA 2022!

Cite

@misc{luo2024bevplacefastrobustlightweight,
      title={BEVPlace++: Fast, Robust, and Lightweight LiDAR Global Localization for Unmanned Ground Vehicles}, 
      author={Lun Luo and Si-Yuan Cao and Xiaorui Li and Jintao Xu and Rui Ai and Zhu Yu and Xieyuanli Chen},
      year={2024},
      eprint={2408.01841},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2408.01841}, 
}
@INPROCEEDINGS{luo2023bevplace,
  author={Luo, Lun and Zheng, Shuhang and Li, Yixuan and Fan, Yongzhi and Yu, Beinan and Cao, Si-Yuan and Li, Junwei and Shen, Hui-Liang},
  booktitle={2023 IEEE/CVF International Conference on Computer Vision (ICCV)}, 
  title={BEVPlace: Learning LiDAR-based Place Recognition using Bird’s Eye View Images}, 
  year={2023},
  pages={8666-8675},
  doi={10.1109/ICCV51070.2023.00799}}