Ego-location and situational awareness in semistructured environments

The success of any potential application for mobile robots depends largely on the specific environment where the application takes place. Practical applications are rarely found in highly structured environments, but unstructured environments (such as natural terrain) pose major challenges to any mobile robot. We believe that semi-structured environments-such as parking lots-provide a good opportunity for successful mobile robot applications. Parking lots tend to be flat and smooth, and cars can be uniquely identified by their license plates. Our scenario is a parking lot where only known vehicles are supposed to park. The robot looks for vehicles that do not belong in the parking lot. It checks both license plates and vehicle types, in case the plate is stolen from an approved vehicle. It operates autonomously, but reports back to a guard who verifies its performance. Our interest is in developing the robot's vision system, which we call Scene Estimation & Situational Awareness Mapping Engine (SESAME). In this paper, we present initial results from the development of two SESAME subsystems, the ego-location and license plate detection systems. While their ultimate goals are obviously quite different, our design demonstrates that by sharing intermediate results, both tasks can be significantly simplified. The inspiration for this design approach comes from the basic tenets of Situational Awareness (SA), where the benefits of holistic perception are clearly demonstrated over the more typical designs that attempt to solve each sensing/perception problem in isolation.