Comparing EKF and SPKF Algorithms for Simultaneous Localization and Mapping (SLAM)
- 10.2991/jrnal.2017.3.4.2How to use a DOI?
- Extended Kalman Filter, Sigma Point Kalman Filter, SLAM, instability, Mobile Robot, Nonlinear Estimation.
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. A problem with detection of correct path of moving objects is the received noisy data. Therefore, it is possible that the information is incorrectly detected. 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.
- © 2013, 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/).
Cite this article
TY - JOUR AU - Zolghadr Javad AU - Yuanli Cai AU - Yekkehfallah Majid PY - 2017 DA - 2017/03/01 TI - Comparing EKF and SPKF Algorithms for Simultaneous Localization and Mapping (SLAM) JO - Journal of Robotics, Networking and Artificial Life SP - 217 EP - 220 VL - 3 IS - 4 SN - 2352-6386 UR - https://doi.org/10.2991/jrnal.2017.3.4.2 DO - 10.2991/jrnal.2017.3.4.2 ID - Javad2017 ER -