<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

Institute of Control and Information Engineering, Poznań University of Technology, ul. Piotrowo 3A, 60-965 Poznań, Poland

Faculty of Applied Engineering, University of Antwerp, Salesianenlaan 90, 2660 Hoboken, Belgium

Department of Mathematics, University of Antwerp, Middelheimlaan 1, 2020 Antwerp, Belgium

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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...

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

Published Online: Mar 31, 2016

Page range: 63 - 79

Received: Sep 28, 2014

DOI: https://doi.org/10.1515/amcs-2016-0005

Keywords
visual odometry, simultaneous localization and mapping, RGB-D, tracking, point features

© 2016 Marek Kraft et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Published Online: Mar 31, 2016

Page range: 63 - 79

Received: Sep 28, 2014

DOI: https://doi.org/10.1515/amcs-2016-0005

Keywordsvisual odometry, simultaneous localization and mapping, RGB-D, tracking, point features

© 2016 Marek Kraft et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
visual odometry, simultaneous localization and mapping, RGB-D, tracking, point features