Particle Filter Based Robot Self-localization Using RGBD Cues and Wheel Odometry Measurements
Enyang Gao, Zhaohua Chen, Qizhuhui Gao
Available Online April 2016.
- https://doi.org/10.2991/emim-16.2016.309How to use a DOI?
- Mobile robot; Self-localization; RGBD; Wheel odometry; Particle filter
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - 6th International Conference on Electronic, Mechanical, Information and Management Society PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/emim-16.2016.309 DO - https://doi.org/10.2991/emim-16.2016.309 ID - Gao2016/04 ER -