Algorithmic Solution for Autonomous Vision-Based O -Road Navigation

A vision based navigation system is a basic tool to provide autonomous operations of unmanned vehicles. For o road navigation that means that the vehicle equipped with a stereo vision system and perhaps a laser ranging device shall be able to maintain a high level of autonomy under various illumination conditions and with little a priori information about the underlying scene. The task becomes particularly important for unmanned planetary exploration with the help of autonomous rovers. For example in the LEDA Moon exploration project currently under focus by the European Space Agency (ESA), during the autonomous mode the vehicle (rover) should perform the following operations: on-board absolute localization, elevation model (DEM) generation, obstacle detection and relative localization, global path planning and execution. Focus of this article is a computational solution for fully autonomous path planning and path execution. An operational DEM generation method based on stereoscopy is introduced. Self-localization on the DEM and robust natural feature tracking are used as basic navigation steps, supported by inertial sensor systems. The following operations are performed on the basis of stereo image sequences: 3D scene reconstruction, risk map generation, local path planning, camera position update during the motion on the basis of landmarks tracking, obstacle avoidance. Experimental veri cation is done with the help of a laboratory terrain mockup and a high precision camera mounting device. It is shown that standalone tracking using automatically identi ed landmarks is robust enough to give navigation data for further stereoscopic reconstruction of the surrounding terrain. Iterative tracking and reconstruction leads to a complete description of the vehicle path and its surrounding with an accuracy high enough to meet the speci cations for autonomous outdoor navigation.