Using Active Vision to Simplify Perception for Robot Driving

As mobile robots attempt more difficult tasks in more complex environments, they are faced with combinatorially harder perceptual problems. In fact, computation costs for perception can easily dominate the costs for planning in a mobile robot. Existing perception systems on mobile robots are potentially many orders of magnitude too slow for real-world domains. In this paper we show active vision at the system level can make perception more tractable. We describe how our planning system for a complex domain, tactical driving, makes specific perceptual requests to find objects of interest. The perception system then scans the scene using routines to search for these objects in limited areas. This selective vision is based on an understanding and analysis of the driving task. We illustrate the effectiveness of request-driven routines by comparing the computational cost of general scene analysis with that of selective vision in simulated driving situations. This research was sponsored by the Avionics Lab, Wright Research and Development Center, Aeronautical Systems Division (AFSC), U.S. Air Force, Wright-Patterson AFB, OH 45433-6543 under Contract F33615-90-C-1465, Arpa Order No. 7597 and Contract DACA76-85-C-0003. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government.