- Title
- Research on EKF-based localization method of tracked mobile robot
- Creator
- Qu, Junsuo; Zhang, Qipeng; Hou, Leichao; Zhang, Ruijun; Ting, Kaiming
- Date
- 2017
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165091
- Identifier
- vital:13249
- Identifier
-
https://doi.org/10.2991/iccia-17.2017.98
- Identifier
- ISBN:978-94-6252-361-6 ISBN; 2352-538X ISSN
- Abstract
- To estimate the position and heading angle of mobile robot precisely, an measurement variable estimation model was proposed to adapt any angle. Fusing the predictive value of odometry and measurement data of multiple sensors by the Extended Kalman Filtering (EKF) for reducing the accumulative error by using only traditional odometry. The proposed models is verified by Matlab simulation and experimental results.
- Publisher
- Atlantis Press
- Relation
- 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017); Wuhan, China; 8th-9th July 2017; published in ACSR-Advances in Computer Science Research series Vol. 74, p. 175-180
- Rights
- https://creativecommons.org/licenses/by-nc/4.0/
- Rights
- Copyright © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/
- Rights
- Open Access
- Rights
- This metadata is freely available under a CCO license
- Subject
- Mobile robot; Position; Heading angle; Extended Kalman Filtering; Odometry
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