SLAM problem via H∞ filter with compensation for intermittent observation

This paper deals with the Simultaneous Localization and Mapping (SLAM) problem via the H∞ filter with compensations for intermittent observations. In order to estimate positions of a robot and landmarks accurately under intermittent observations, we propose the method with a novel filter which detects and compensates intermittent observations by comparing the obtained data with their estimations. This paper also shows the convergence of the estimated error covariance matrices. With simulation and experimental results, we confirm that the state of the robot and the environmental conditions are estimated accurately via the proposed filter under intermittent observations and that the derived theorems of the convergence are correct.