Efficient RGB-D data processing for feature-based self-localization of mobile robots

Marek Kraft; Michał Nowicki; Rudi Penne; Adam Schmidt; Piotr Skrzypczyński

International Journal of Applied Mathematics and Computer Science (2016)

  • Volume: 26, Issue: 1, page 63-79
  • ISSN: 1641-876X

Abstract

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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.

How to cite

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Marek Kraft, et al. "Efficient RGB-D data processing for feature-based self-localization of mobile robots." International Journal of Applied Mathematics and Computer Science 26.1 (2016): 63-79. <http://eudml.org/doc/276568>.

@article{MarekKraft2016,
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.},
author = {Marek Kraft, Michał Nowicki, Rudi Penne, Adam Schmidt, Piotr Skrzypczyński},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {visual odometry; simultaneous localization and mapping; RGB-D; tracking; point features},
language = {eng},
number = {1},
pages = {63-79},
title = {Efficient RGB-D data processing for feature-based self-localization of mobile robots},
url = {http://eudml.org/doc/276568},
volume = {26},
year = {2016},
}

TY - JOUR
AU - Marek Kraft
AU - Michał Nowicki
AU - Rudi Penne
AU - Adam Schmidt
AU - Piotr Skrzypczyński
TI - Efficient RGB-D data processing for feature-based self-localization of mobile robots
JO - International Journal of Applied Mathematics and Computer Science
PY - 2016
VL - 26
IS - 1
SP - 63
EP - 79
AB - 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.
LA - eng
KW - visual odometry; simultaneous localization and mapping; RGB-D; tracking; point features
UR - http://eudml.org/doc/276568
ER -

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