Investigating State Covariance Properties During Finite Escape Time in H∞ Filter SLAM
This paper deals with the investigation of finite escape time problem in H∞ Filter based localization and mapping. Finite escape time in H∞ Filter has restricted the technique to be applied as the mobile robot cannot determine its location effectively due to inconsistent information. Therefore, an analysis to improved the current H∞ Filter is proposed to investigate the state covariance behavior during mobile robot estimation. Three main factors are being considered in this research namely the initial state covariance, the γ values and the type of noises. This paper also proposed a modified H∞ Filter to reduce the finite escape time problem in the estimation. The analysis and simulation results determine that the modified H∞ Filter has better performance compared to the normal H∞ Filter as well as to Kalman Filter for different γ, initial state covariance and works well in non-gaussian noise environment.
KeywordsH∞ filter Finite escape time Estimation
The authors would like to thank Ministry of Higher Education and Universiti Malaysia Pahang for supporting this research under RDU160145 and RDU160379.
- 4.Porta, J.M.: CuikSLAM: a kinematics-based approach to SLAM. In: Proceeding of the 2005 IEEE International Conference on Robotics and Automation, pp. 2425–2431, Spain (2005)Google Scholar
- 5.Thallas, A., Tsardoulias, E., Petrou, L.: Particle filter-scan matching SLAM recovery under kinematic model failures. In: 24th Mediterranean Conference on Control and Automation, pp. 232–237, Greece (2016)Google Scholar
- 6.Johansen, T.A., Brekke, E.: Globally exponential stable Kalman Filtering for SLAM with AHRS. In: 19th International Conference on Information Fusion (FUSION), pp. 909–916, Germany (2016)Google Scholar
- 9.Kurt-Yavuz, Z., Yavuz, S.: A comparison of EKF, UKF, FastSLAM2.0, and UKF based FastSLAM algorithm. In: IEEE 16th Conference on Intelligent Engineering System (INES), pp. 37–43, Portugal (2012)Google Scholar
- 10.Buonocore, L., Barros dos Santos, S.R., Neto, A.A., Nascimento, C.L,: FastSLAM filter implementation for indoor autonomous robot. In: 2016 IEEE Intelligent Vehicles Symposium, pp. 484–489, Sweden (2016)Google Scholar
- 11.Guo, L., Song, C., Mao, Y.: H infinity filter in maneuvering target tracking of military guidance field. In: International Conference on Automatic Control and Artificial Intelligence (ACAI2012), pp. 1114–1116, China (2012)Google Scholar
- 18.Ahmad, H., Namerikawa, T.: Feasibility study of partial observability in H∞ filtering for robot localization and mapping problem. In: American Control Conference (ACC) 2010, pp. 3980–3985 (2010)Google Scholar