Probabilistic semantic occupancy grid mapping using IPM of semantic segmentation for traversable area recognition

Abstract

Recognition of the traversable area is a vital function for autonomous mobile robots. This paper proposes a probabilistic generation of a semantic occupancy grid map. The proposed method uses inverse perspective mapping (IPM) of semantic segmentation and prior occupancy grid map to recognize the traversable area. We apply the binary Bayes filter and the truncated normal distribution spatial filter to deal with the uncertainty of semantic segmentation and IPM distortion to the far side. The experiment result shows these filters improve the recognition accuracy of the traversable area.