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Paper details
Number 1 - March 2016
Volume 26 - 2016
Efficient RGB-D data processing for feature-based self-localization of mobile robots
Marek Kraft, Michał Nowicki, Rudi Penne, Adam Schmidt, Piotr Skrzypczyński
Abstract
The problem of position and orientation estimation for an active vision sensor that moves with respect to the full six degrees
of freedom is considered. The proposed approach is based on point features extracted from RGB-D data. This work
focuses on efficient point feature extraction algorithms and on methods for the management of a set of features in a single
RGB-D data frame. While the fast, RGB-D-based visual odometry system described in this paper builds upon our previous
results as to the general architecture, the important novel elements introduced here are aimed at improving the precision
and robustness of the motion estimate computed from the matching point features of two RGB-D frames. Moreover,
we demonstrate that the visual odometry system can serve as the front-end for a pose-based simultaneous localization and
mapping solution. The proposed solutions are tested on publicly available data sets to ensure that the results are scientifically
verifiable. The experimental results demonstrate gains due to the improved feature extraction and management mechanisms,
whereas the performance of the whole navigation system compares favorably to results known from the literature.
Keywords
visual odometry, simultaneous localization and mapping, RGB-D, tracking, point features