Multi-modal local terrain maps from vision and LiDAR

In this paper, we present a method to build precise local terrain maps for an autonomous vehicle from vision and LiDAR that surpass most existing maps used in the field, both in the details they represent and in efficiency of construction. The high level of detail is obtained by spatio-temporal fusion of data from multiple, complementary sensors in a grid map. The map not only consists of obstacle probabilities, but contains different features of the environment: elevation, color, infrared reflectivity, terrain slopes and surface roughness. Still, an efficient way to manage the map's memory allows us to build the maps online on-board our autonomous vehicle. As we demonstrate by describing some of its applications, the maps can serve as a unified representation to solve further perception and navigation problems without having to resort to individual sensor data again. The proposed terrain maps have proven their robustness and precision in many real-world scenarios, leading to award-winning performances at international robotics competitions.