Sufficient Condition for Estimation in DesigningH∞Filter-Based SLAM

Nur Aqilah Othman,Hamzah Ahmad,Toru Namerikawa

doi:10.1155/2015/238131

Abstract

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 implementingH∞filter in SLAM instead of EKF. In implementingH∞filter in SLAM, the parameters of the filter especiallyγneed 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.

Highlights

The existence of uncertainties in various forms has caused numerous applications to function not as intended
Comparison between H∞ filter- and extended Kalman filter (EKF)-based simultaneous localization and mapping (SLAM) regarding map construction analysis, state error covariance update, and root-mean-square error (RMSE) evaluation is given for each case that has been analysed in the preceding section
This study has demonstrated that the H∞ filter may be considered as one of the best candidates to mitigate navigation issues for SLAM especially for an environment with unknown noise characteristics

Summary

Introduction

The existence of uncertainties in various forms has caused numerous applications to function not as intended. Until today, their existence is almost inevitable; some devices have been proposed to mitigate such a problem. Owing to the existence of this problem, a robotics application known as simultaneous localization and mapping (SLAM) has experienced difficulties in increasing the efficiency of its estimation. SLAM provides a condition where a mobile robot is assigned to observe an unknown environment and incrementally constructs a map showing the environment that it has recognized. The mobile robot attempts to localize itself on the constructed map recursively until its task is achieved.

Objectives

Results

Conclusion