Semantic Probabilistic Traversable Map Generation For Robot Path Planning

Abstract

Probabilistic traversable map plays a critical role for mobile robot in safe and reliable navigation. Different from structured environment, traversable region in unstructured environment such as grass and sidewalk is relatively more complex. Traditional elevation-based traversable map cannot represent such complex environment well. Thus, it may cause navigation failure. To address this limitation, this paper proposes a novel semantic-elevation mapping approach for navigation task. We first build a multi-layer semantic map from continuous semantic segmentation images. Then, this multi-layer semantic map is fused and converted into a probabilistic map by a distance transform approach. Generated semantic probabilistic map is then fused to an elevation map at path planning stage. The proposed approach is tested on an Unmanned Ground Vehicle (UGV) platform. The results show that our semantic-elevation mapping approach works more reliably and safely than that only with elevation-based approach.