GPU accelerated realtime traversability mapping The navigation of autonomous mobile robots requires effective localization and mapping modules. Dense map representation of the robot surroundings, which contains detailed information of the drivable region can be easily used for motion planning. To build a dense map on mobile robots, the main challenge is that the system has to be efficient due to the limited computational resources. In this paper, we propose a novel approach to generate a dense map with drivable information. First, the dense map with elevation information is generated by the proprioceptive localization results acquired from kinematic and inertial measurement, as well as the accumulated raw data from the range sensor. Then, we calculate slope and roughness of each grid on the map to assess whether this area is accessible. Combining the data in these two steps, we can form the dense map with drivable information. The entire system accelerated by GPU performs well in handling dynamic obstacles. For implementations, we demonstrate the effectiveness of our approach with mobile robot in a complex outdoor environment and have a detailed comparison with other methods.

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.