CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature Description

@article{yin2020caelo,
    title={CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature Description},
    author={Deyu Yin and Qian Zhang and Jingbin Liu and Xinlian Liang and Yunsheng Wang and Jyri Maanpää and Hao Ma and Juha Hyyppä and Ruizhi Chen},
    journal={arXiv preprint arXiv:2001.01354},
    year={2020}
}
@article{
    title={Interest Point Detection from Multi-Beam LiDAR Point Cloud Using Unsupervised CNN},
    author={Deyu Yin, Qian Zhang, Jingbin Liu, Xinlian Liang, Yunsheng Wang, Shoubin Chen, Jyri Maanpää, Juha Hyyppä, Ruizhi Chen},
    journal={IET Image Processing},
    year={2020}
}

See the rankings in KITTI, our method's name is "CAE-LO".

1. Basic enviornments for python3 and Keras. Simple networks. No worries. Package requirements can be found in requirements.txt.
2. Dirs.py to modify dictionaries.
3. BatchProcess.py projects PCs to spherical rings with multi-thread processing.
4. BatchVoxelization.py project PCs into multi-solution voxel model with multi-thread processing.
5. SphericalRing.py to do basic function about spherical ring model, especially the function of getting keypts.
6. You can try Match.py to see some demos using trained models.
7. PoseEstimation.py to generate initial odometry.
8. RefinePoses.py to generate refined odometry based on extended interest points & ground normals, and also to show generated trajectories. (The code for generating ground normals is currently commented. Uncomment it if you want to use.)

1. Generated interest points and features for sequence 00 and 01 can be found in GoogleDrive.
2. The data arragement format is simple. Just serveral folders like "KeyPts", "Features", "InliersIdx", "SphericalRing", etc.
3. If you have any problems or confunsions, please post them in ISSUES or contact me by email.