Statistical Classification Based Fast Drivable Region Detection for Indoor Mobile Robot

Abstract

A fast method based on binocular vision is proposed for mobile robot to detect drivable regions. At first, the image is segmented into regions, and some obstacles are determined by the ground vanishing line. Then, according to the different distribution of feature points extracted from the left regions, we propose two approaches to classify regions: region determination based on feature statistical classification for regions with rich feature points and region determination based on area statistical classification for regions with sparse feature points. Finally, we get the drivable regions by combination of the two approaches. The results of indoor experiments show that the method can perform quickly and robustly.