CAE-LO

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}
}

The work based on this method is currently ranked 13th in KITTI named as "CAE-LO". And our paper is avialable on arXiv.

Now, only the evaluation data and some demos are published. The source code will be here until our paper is accepted. Generated interest points and features for sequence 00 and 01 can be found in GoogleDrive.