Method

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


Submitted on 23 Oct. 2017 03:38 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/channel/UCEEqrCpT8lnp0q1HBzS3rtQ

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

Test Set Average


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Sequence 11


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Sequence 12


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Sequence 13


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Sequence 14


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Sequence 15


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