Robust sonar feature detection for the SLAM of mobile robot
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 proposes a robust sonar feature detection algorithm. This algorithm gives feature detection methods for both point features and line features. The point feature detection method is based on the TBF (Wijk and Christensen, 2000) scheme. Moreover, three additional processes improve 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 is based on the basic property of adjacent sonar sensors. Along the line feature, three adjacent sonar sensors give similar range readings. Using this sensor property, we propose 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 an 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 of the EKF-based SLAM demonstrate the performance of the proposed feature detection algorithm in a home-like environment.