The KITTI Vision Benchmark Suite

Method

CAE based LiDAR Odometry [la] [CAE-LO]
https://github.com/SRainGit/CAE-LO

Submitted on 27 Aug. 2019 16:41 by
Deyu Yin (Wuhan University)

Running time:		2 s
Environment:		8 cores @ 3.5 Ghz (Python)

Method Description:

Using Convolutional Auto-Encoder to extract
keypoints and features from LiDAR point cloud.
A refine ICP-matching is applied for keyframes.

Parameters:

VoxelSize = 0.02m

Latex Bibtex:

@misc{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},
year={2020},
eprint={2001.01354},
archivePrefix={arXiv},
primaryClass={cs.CV}
}

Detailed Results

From all test sequences (sequences 11-21), our benchmark computes translational and rotational errors for all possible subsequences of length (5,10,50,100,150,...,400) meters. Our evaluation ranks methods according to the average of those values, where errors are measured in percent (for translation) and in degrees per meter (for rotation). Details for different trajectory lengths and driving speeds can be found in the plots underneath. Furthermore, the first 5 test trajectories and error plots are shown below.