Terrain Classification for Autonomous Robot Mobility from Safety, Security, Rescue Robotics to Planetary Exploration

Mobile robots are increasingly used in unstructured domains without permanent supervision by a human operator. One example is Safety, Security and Rescue Robotics (SSRR) where human operators are a scarce resource. There are additional motivations in this domain to increase robot autonomy, e.g., the desire to decrease the cognitive load on the operator or to allow robot operations when communication to a operator’s station fails. Planetary exploration has in this respect much in common with SSRR. Namely, it takes place in unstructured environments and it requires high amounts of autonomy due to the significant delay in communication. Here we present efforts to carry over results from research within SSRR, especially work on terrain classification for autonomous mobility, to planetary exploration. The simple yet efficient approach to terrain classification is based on the Hough transform of planes. The idea is to design a parameter space such that drivable surfaces lead to a strong single response, whereas non-drivable ones lead to data-points spread over the parameter space. The distinction between negotiable and nonnegotiable as well as other terrain type is then done by a decision tree. The algorithm is applied in the SSRR domain to 3D data obtained from two different sensors, namely, a near infra-red time of flight camera and a stereo camera. Experimental results are presented for typical indoor as well as outdoor terrains, demonstrating robust realtime detection of drivable ground. The work is then carried over to the planetary exploration domain by using data from the Mars Exploration Rover Mission (MER).