| Title: | GS9-4 Simultaneous Localization and Mapping (SLAM) algorithm base on EKF and SPKF. |
|---|---|
| Publication: | ICAROB2016 |
| Volume: | 21 |
| Pages: | 217-220 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2016.GS9-4 |
| Author(s): | Zolghadr Javad, Yuanli Cai, Yekkehfallah Majid |
| Publication Date: | January 29, 2016 |
| Keywords: | Extended Kalman Filter, Sigma Point Kalman Filter, SLAM, instability, Mobile Robot,Nonlinear |
| Abstract: | Simultaneous Localization and Mapping (SLAM) is the problem in which a sensor-enabled mobile robot incrementally builds a map for an unknown environment, while localizing itself within this map. The Kalman Filter's linearized error propagation can result in big errors and instability in the SLAM problem. One approach to reduce this situation is using of iteration in Extended Kalman Filter (EKF) and Sigma Point Kalman Filter (SPKF). We will show that the recapitulate versions of kalman filters can improve the estimation accuracy and robustness of these filters beside of linear error propagation. Simulation results are presented to validate this improvement of state estimate convergence through repetitive linearization of the nonlinear model in EKF and SPKF for SLAM algorithms. Results of this evaluation are introduced by computer simulations and verified by offline implementation of the SLAM algorithm on mobile robot in MRL Robotic Lab. |
| PDF File: | https://alife-robotics.co.jp/members2016/icarob/data/papers/GS/GS9-4.pdf |
| 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/ |
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