To the Bookstore! Autonomous Wheelchair Navigation in an Urban Environment.

In this paper, we demonstrate reliable navigation of a smart wheelchair system (SWS) in an urban environment. Urban environments present unique challenges for service robots. They require localization accuracy at the sidewalk level, but compromise GPS position estimates through significant multi-path effects. However, they are also rich in landmarks that can be leveraged by feature-based localization approaches. To this end, our SWS employed a map-based localization approach. A map of the environment was acquired using a server vehicle, synthesized a priori, and made accessible to the SWS. The map embedded not only the locations of landmarks, but also semantic data delineating 7 different landmark classes to facilitate robust data association. Landmark segmentation and tracking by the SWS was then accomplished using both 2D and 3D LIDAR systems. The resulting localization method has demonstrated decimeter level positioning accuracy in a global coordinate frame. The localization package was integrated into a ROS framework with a sample based motion planner and control loop running at 5 Hz to enable autonomous navigation. For validation, the SWS repeatedly navigated autonomously between Lehigh University’s Packard Laboratory and the University bookstore, a distance of approximately 1.0 km roundtrip.