Efficient Traversability Mapping for Service Robots Using a Pointcloud Fast Filter This paper proposes Pointcloud Fast Filter (PFF), an algorithm to process efficiently 3D data from pointcloud sensors in order to build traversability maps for service robots. Our method is intended to be integrated with a 2D mapping algorithm, enhancing 2D standard maps with enough traversability information for robot navigation in indoor structured environments. The method is agnostic to the 3D sensor or mapping algorithm used, and keeps computational requirements low. Thus, we enable middleclass computers and a wide variety of sensors to be employed for service robots, reducing the costs of the platform. We evaluate the performance of PFF with different 3D sensors on a real robot and its impact on mapping, comparing it with alternative 2D and 3D mapping approaches.

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.