Mobile robot simultaneous localization and mapping in unstructured environments

Abstract

This paper describes a method of mobile robot simultaneous localization and mapping (SLAM) based on laser range finder in unstructured environments. Considering drawbacks of the traditional iterative closest point (ICP) algorithm, matching laser range scans gets into local extrema when severe occlusions occur, the iterative dual closest point based on clustering (IDCP BoC) algorithm is proposed. Scan data are filtered and divided into clusters firstly, and then a procedure of reducing of data is conducted. The closest point (CP) rule is modified and dual closest point (DCP) rule is advanced when choosing the closest points. The reduced data is used for iterative computation before the error of two consecutive iterations' residual errors less than a preset threshold to speed up the algorithm, after that the data which is not reduced is used to guarantee the accuracy. Experimental results show that the method improves the accuracy of localization and mapping.