The KITTI Vision Benchmark Suite

Method

Depth Enhanced Monocular Odometry [la] [DEMO]

Submitted on 8 Jan. 2014 03:11 by
Ji Zhang (Carnegie Mellon University)

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

Method Description:

Monocular visual odometry assisted by depth from
Velodyne data

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:

Harris corner features

Latex Bibtex:

@inproceedings{Zhang2014IROS,
title = {Real-time Depth Enhanced Monocular Odometry},
author = {Ji Zhang and Michael Kaess and Sanjiv Singh},
booktitle = {IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS)},
month = {Sept.},
year={2014},
address={Chicago, IL}
}

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.