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Article

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Title

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

Authors

[ 1 ] Instytut Automatyki i Inżynierii Informatycznej, Wydział Elektryczny, Politechnika Poznańska | [ P ] employee

Year of publication

2016

Published in

International Journal of Applied Mathematics and Computer Science

Journal year: 2016 | Journal volume: vol. 26 | Journal number: no. 1

Article type

scientific article

Publication language

english

Keywords
EN
  • visual odometry
  • simultaneous localization and mapping
  • tracking
  • point features
Abstract

EN The problem of position and orientation estimation for an active vision sensor that moves with respect to the full six degreesof freedom is considered. The proposed approach is based on point features extracted from RGB-D data. This workfocuses on efficient point feature extraction algorithms and on methods for the management of a set of features in a singleRGB-D data frame. While the fast, RGB-D-based visual odometry system described in this paper builds upon our previousresults as to the general architecture, the important novel elements introduced here are aimed at improving the precisionand 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 andmapping solution. The proposed solutions are tested on publicly available data sets to ensure that the results are scientificallyverifiable. 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.

Pages (from - to)

63 - 79

DOI

10.1515/amcs-2016-0005

URL

https://sciendo.com/pl/article/10.1515/amcs-2016-0005

License type

CC BY-NC-ND (attribution - noncommercial - no derivatives)

Open Access Mode

open journal

Open Access Text Version

final published version

Full text of article

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Access level to full text

public

Ministry points / journal

25

Impact Factor

1,42

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