A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
Information-Driven Direct RGB-D Odometry
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
This paper presents an information-theoretic approach to point selection for direct RGB-D odometry. The aim is to select only the most informative measurements, in order to reduce the optimization problem with a minimal impact in the accuracy. It is usual practice in visual odometry/SLAM to track several hundreds of points, achieving real-time performance in high-end desktop PCs. Reducing their computational footprint will facilitate the implementation of odometry and SLAM in low-end platforms
doi:10.1109/cvpr42600.2020.00498
dblp:conf/cvpr/FontanCT20
fatcat:jyfa2vobwff5jk6wxq53ghegsa