Mobile Robot Localization Based on Improved Model Matching in Hough Space

Abstract

Perceiving the position and orientation of the mobile robot in environment is an important element for an autonomous robot. This paper presents a novel method in which the classical Hough transform is introduced into localization of the mobile robot. To reduce ambiguity significantly, an improved more detailed sonar model is utilized. Firstly a local geometric map in the Hough space is built via the sonar system. Then the matching between a known map of the environment and a local map is performed in the Hough space. Finally this matching result is fused with odometry information by means of the extended Kalman filtering. The technique is especially adapted to indoor polygonal environments. Experimental results validate the favorable performance of this approach