/Direct Visual Odometry in Low Light using Binary Descriptors

Direct Visual Odometry in Low Light using Binary Descriptors

Hatem Alismail, Michael Kaess, Brett Browning and Simon Lucey
Journal Article, IEEE Robotics and Automation Letters, RA-L, Vol. 2, pp. 444-451, April, 2017

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Abstract

Feature descriptors are powerful tools for photometrically and geometrically invariant image matching. To date, however, their use has been tied to sparse interest point detection, which is susceptible to noise under adverse imaging conditions. In this work, we propose to use binary feature descriptors in a direct tracking framework without relying on sparse interest points. This novel combination of feature descriptors and direct tracking is shown to achieve robust and efficient visual odometry with applications to poorly lit subterranean environments.

BibTeX Reference
@article{Alismail-2017-27302,
author = {Hatem Alismail and Michael Kaess and Brett Browning and Simon Lucey},
title = {Direct Visual Odometry in Low Light using Binary Descriptors},
journal = {IEEE Robotics and Automation Letters, RA-L},
year = {2017},
month = {April},
volume = {2},
pages = {444-451},
}
2017-09-13T10:38:07+00:00