Fuzzy Control Simultaneous Localization and Mapping Strategy Based on Iterative Closest Point and k-Dimensional Tree Algorithms

Abstract

In this study, we apply laser and infrared sensors to a wheeled mobile robot (WMR) for simultaneous localization and mapping (SLAM). The robot utilizes a laser measurement sensor to detect obstacles and identify unknown environments. Fuzzy theory and the iterative closest point (ICP) algorithm are applied to control design. The proposed control scheme can control the WMR movement along walls and avoid obstacles. In addition, the k-dimensional (k-D) tree is used to reduce the computation time and achieve real-time positioning. By calculating the rotation and translation matrices among different sets of measured points, distance and angle information of the moving robot can be recorded. Furthermore, the worst point rejection method is applied to delete less corresponding points that can prevent the ICP process convergence to a local optimum.