Sufficient Condition for Estimation in Designing H∞ Filter-Based SLAM - Hamzah, Ahmad; Nur Aqilah, Othman; Namerikawa, Toru (Hindawi Publishing Corporation, 2015)
Extended Kalman filter (EKF) is often employed in determining the position of mobile robot and landmarks in Simultaneous Localization and Mapping (SLAM). Nonetheless, there are some disadvantages of using EKF, namely the requirement of Gaussian distribution for the state and noises, as well as the fact that it requires the smallest possible initial state covariance. This has led researchers to find alternative ways to mitigate the aforementioned shortcomings. Therefore, this study is conducted to propose an alternative technique by implementing HÃ¢ÂÂ filter in SLAM instead of EKF. In implementing HÃ¢ÂÂ filter in SLAM, the parameters of the filter especially Ã¯ÂÂ§ needs to be properly defined to prevent finite escape time problem. Hence, this study proposes a sufficient condition for the estimation purposes. Two distinct cases of initial state covariance are analysed considering an indoor environment to ensure the best solution for SLAM problem exists along with considerations of process and measurement noises statistical behaviour. If the prescribed conditions are not satisfied, then the estimation would exhibit unbounded uncertainties and consequently results in erroneous inference about the robot and landmarks estimation. The simulation results have shown the reliability and consistency as suggested by the theoretical analysis and our previous findings.