Detection and tracking of road networks in rural terrain by fusing vision and LIDAR

The ability to perceive a robot's local environment is one of the main challenges in the development of mobile ground robots. Here, we present a robust model-based approach for detection and tracking of road networks in rural terrain. To get a rich environment representation, we fuse the complementary data provided by a 3D LIDAR and an active camera platform into an accumulated, colored 3D elevation map of the terrain. Additionally, we use commercially available GIS data to get a rough idea about the geometry of the road network ahead of the robot. This way, the system is able to dynamically adjust the geometric model used within a particle filter framework for both detection and estimation of the road network's geometry. The estimation process makes use of edge- and region-based image features as well as obstacle information, all supplied by the dense terrain map. Instead of tuning the likelihood functions used within the particle filter's cue fusion concept by hand, as commonly done, we apply supervised learning techniques to derive an appropriate weighting of all features. We finally show that the proposed approach enables our ground robot MuCAR-3 to autonomously navigate on rural- and dirt-road networks.