Particle Filter Based Robot Self-localization Using RGBD Cues and Wheel Odometry Measurements

DOI

10.2991/emim-16.2016.309How to use a DOI?

Keywords

Mobile robot; Self-localization; RGBD; Wheel odometry; Particle filter

Abstract

Mobile robot localization in the GPS denied environments is increasingly exerting fundamental roles in a wide range of applications such as SFM and SLAM. However, the traditional single sensor based positioning methods are either unreliable or inaccurate in the long term. This paper presents a novel moving agent localizing approach that combines both RGBD cues and wheel odometry measurements within the particle filter based probabilistic framework. Unlike the traditional RGBD localization methods which are computationally expensive and non-robust, we took advantage of wheel odomery measurements as the prior information or say the initial values during the RBGD pose optimization process. Additionally, the optimal pose derived from visual sensor is, in turn, able to determine the reliability of the wheel odometry inputs. This verifying process is considerably useful in the presence of wheel slip. Experimental results validate that our approach is effective and reliable in wheel robot localization.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Enyang Gao
AU  - Zhaohua Chen
AU  - Qizhuhui Gao
PY  - 2016/04
DA  - 2016/04
TI  - Particle Filter Based Robot Self-localization Using RGBD Cues and Wheel Odometry Measurements
BT  - Proceedings of the 6th International Conference on Electronic, Mechanical, Information and Management Society
PB  - Atlantis Press
SP  - 1523
EP  - 1529
SN  - 2352-538X
UR  - https://doi.org/10.2991/emim-16.2016.309
DO  - 10.2991/emim-16.2016.309
ID  - Gao2016/04
ER  -