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“Direct Visual Odometry in Low Light using Binary Descriptors” by H. Alismail, M. Kaess, B. Browning, and S. Lucey. IEEE Robotics and Automation Letters, RA-L, vol. 2, no. 2, Apr. 2017, pp. 444-451. Presented at ICRA 2017.
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.
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BibTeX entry:
@article{Alismail17ral,
author = {H. Alismail and M. Kaess and B. Browning and S. Lucey},
title = {Direct Visual Odometry in Low Light using Binary Descriptors},
journal = {IEEE Robotics and Automation Letters, RA-L},
volume = {2},
number = {2},
pages = {444-451},
month = apr,
year = {2017},
note = {Presented at ICRA 2017.}
}