The KITTI Vision Benchmark Suite

Method

Vision-lidar Odometry and Mapping [la] [V-LOAM]

Submitted on 25 Jul. 2020 18:20 by
Ji Zhang (Carnegie Mellon University)

Running time:		0.1 s
Environment:		2 cores @ 2.5 Ghz (C/C++)

Method Description:

Visual odometry (DEMO) for ego-motion estimation, lidar
odometry (LOAM) for refinement

Research videos are available at:
www.youtube.com/c/JiZhang_CMU

Latest, improved method is commercialized by:
www.kaarta.com

Full-length paper:
J. Zhang and S. Singh. Laser-visual-inertial Odometry and
Mapping with High Robustness and Low Drift. Journal of
Field Robotics. vol. 35, no. 8, pp. 1242–1264, 2018.

Parameters:

Visual odometry: Harris corner features
Lidar odometry: Point to line/plane scan matching

Latex Bibtex:

@inproceedings{Zhang2015ICRA,
title = {Visual-lidar Odometry and Mapping: Low drift,
Robust, and Fast},
author = {Ji Zhang and Sanjiv Singh},
booktitle = {IEEE International Conference on Robotics and
Automation(ICRA)},
month = {May},
year={2015},
address={Seattle, WA}
}

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.