Learning a Terrain Model for Autonomous Navigation in Rough Terrain

Current approaches to local rough-terrain navigation are limited by their ability to build a terrain model from sensor data. Available sensors make very indirect measurements of quantities of interest such as the supporting ground surface and the location of obstacles. This is especially true in domains where vegetation may hide the ground surface or partially obscure obstacles. This thesis presents two related approaches for automatically learning how to use sensor data to build a local terrain model that includes the height of the supporting ground surface and the location of obstacles in challenging rough-terrain environments that include vegetation. The first approach uses an online learning method that directly learns the mapping between sensor data and ground height through experience with the world. The system can be trained by simply driving through representative areas. The second approach includes a terrain model that encodes structure in the world such as ground smoothness, class continuity, and similarity in vegetation height. This structure helps constrain the problem to better handle dense vegetation. Results from an autonomous tractor show that the mapping from sensor data to a terrain model can be automatically learned, and that exploiting structure in the environment improves ground height estimates in vegetation.