TransLO: A Window-Based Masked Point Transformer Framework for Large-Scale LiDAR Odometry (AAAI2023)

This is official implementation for our AAAI2023 paper "TransLO: A Window-Based Masked Point Transformer Framework for Large-Scale LiDAR Odometry" created by Jiuming Liu, Guangming Wang, Chaokang Jiang, Zhe Liu, and Hesheng Wang.

Installation

Our model only depends on the following commonly used packages.

Package	Version
CUDA	1.11.3
Python	3.8.10
PyTorch	1.12.0
h5py	not specified
tqdm	not specified
openpyxl	not specified
numpy	not specified
scipy	not specified

Install the pointnet2 library

Compile the furthest point sampling, grouping and gathering operation for PyTorch with following commands.

cd pointnet2
python setup.py install

Install the CUDA-based KNN searching and random searching

We leverage CUDA-based operator for parallel computing, please compile them with following commands.

cd ops_pytorch
cd fused_conv_random_k
python setup.py install
cd ../
cd fused_conv_select_k
python setup.py install
cd ../

Datasets

Datasets are available at KITTI Odometry benchmark website: https://drive.google.com/drive/folders/1Su0hCuGFo1AGrNb_VMNnlF7qeQwKjfhZ The data of the KITTI odometry dataset should be organized as follows:

data_root
├── 00
│   ├── velodyne
│   ├── calib.txt
├── 01
├── ...

Training

Train the network by running :

python train.py 

Please reminder to specify the GPU, data_root,log_dir, train_list(sequences for training), val_list(sequences for validation) in the scripts. You may specify the value of arguments. Please find the available arguments in the configs.py.

Testing

Evaluate the network by running :

python train.py

Please reminder to specify the GPU, data_root,log_dir, test_list(sequences for testing) in the scripts.

Quantitative results:

Citation

@inproceedings{liu2023translo,
  title={TransLO: A Window-Based Masked Point Transformer Framework for Large-Scale LiDAR Odometry},
  author={Liu, Jiuming and Wang, Guangming and Jiang, Chaokang and Liu, Zhe and Wang, Hesheng},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={37},
  number={2},
  pages={1683--1691},
  year={2023}
}

Acknowledgments

We thank the following open-source project for the help of the implementations:

• PointNet++
• KITTI_odometry_evaluation_tool
• [PWCLONet] (https://github.com/IRMVLab/PWCLONet)