Semantic Probabilistic Traversable Map Generation For Robot Path Planning 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 elevationbased traversable map cannot represent such complex environment well. Thus, it may cause navigation failure. To address this limitation, this paper proposes a novel semanticelevation mapping approach for navigation task. We first build a multilayer semantic map from continuous semantic segmentation images. Then, this multilayer 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 semanticelevation mapping approach works more reliably and safely than that only with elevationbased approach.

Real Time Obstacle Avoidance and Navigation with Mobile Robot via Local Elevation Information This paper considers the realtime navigation and obstacle avoidance of mobile robots in complex environments. A novel local environment perception and navigation framework for mobile robot navigation based on the elevation information is proposed. A local elevation map is introduced to perceive and map obstacles in the local environment. Calculating obstaclesxe2x80x99 heights and shapes by local elevation maps, the terrain traversability of the surrounding environment of a robot is determined, and a twodimensional local occupancy grid map for robot path planning and navigation in local environment is generated. The effectiveness of our approach is validated experimentally through realtime navigation and obstacle avoidance in the artificial environment. The experimental results show that using the proposed framework, a mobile robot can safely reach its target position while avoiding the noncrossable area. For indoor mobile robots with limited computing power, in complex environments, it could provides more safe navigation and pathplanning.