Abstract:

In this paper, a new localization method for mobile robot is developed based on a laser range finder. The scanned data points in each frame are first divided into clusters. The current and previous scans are matched
according to the clusters to obtain two types of match clusters, holonomic matches and nonholonomic. For a pair
of holonomically matched clusters, both pairs of endpoints and centroids are considered as match points, while
for nonholonomically matched clusters, only endpoints are considered as match points. Random sample consensus (RANSAC) algorithm is then used to remove outliers, and nonlinear least squares method is adopted to estimate the motion parameters of the mobile robot. Experimental results demonstrate that the approach has satisfactory performance in both static and dynamic indoor environments.

Key words: mobile robot, localization, clustering, RANSAC, laser range finder