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

Download Publication

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.


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.

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},
} 2017-09-13T10:38:07-04:00