Robust feature detection using sonar sensors for mobile robots

Abstract

Sonar sensor is an attractive tool for the SLAM of mobile robot because of their economic aspects. This cheap sensor gives relatively accurate range readings if disregarding angular uncertainty and specular reflections. However, these defects make feature detection difficult for the most part of the SLAM. This paper proposed a robust sonar feature detection algorithm. This algorithm gives feature detection methods for both point features and line features. The point feature detection method was based on the TBF scheme. Moreover, three additional processes improved the performance of feature detection as follows; 1) stable intersections, 2) efficient sliding window update and 3) removal of the false point features on the wall. The line feature detection method was based on the basic property of adjacent sonar sensors. Along the line feature, three adjacent sonar sensors gave similar range readings. Using this sensor property, it proposed a novel algorithm for line feature detection, which is simple and the feature can be obtained by using only current sensor data. The proposed feature detection algorithm gives a good solution for the SLAM of mobile robots because it gives accurate feature information for both the point and line features even with sensor errors. Furthermore, a sufficient number of features are available to correct mobile robot pose. Experimental results for point feature and line feature detection demonstrate the performance of the proposed algorithm in a home-like environment.