Local elevation mapping for automated vehicles using lidar ray geometry and particle filters

Two-dimensional occupancy grid mapping is a common approach for environment mapping and sensor data fusion but non-planar environments are still a remaining issue. The ground shape has to be considered in such environments, especially when low obstacles on the road need to be recognized. Elevation maps are a suitable model because the height is not discretized. This paper presents an improved lidar-based approach for elevation mapping that uses particle filters to estimate the height indirectly by a fusion of lower and upper height boundaries. These boundaries are extracted from lidar reflections and ray geometry of different time steps. Furthermore, a tailored interpolation algorithm is presented that takes the statistics of the particle population of each cell into account. A conclusive qualitative and quantitative evaluation highlights the performance of the presented approach.