Environment representation using multiple abstraction levels

This paper describes an approach to building a hierarchy of maplike descriptions of an initially unknown environment using a mobile robot. High-level tasks require a symbolic and qualitative description of space, yet this remains a stable and consistent method for defining specific regions of interest. The paper describes a hierarchy of spatial abstractions and a classification scheme for spatial regions to be used in navigation and mapping. In particular, the problem of maintaining a stable description of the environment that can be used for symbol extraction receives attention. By combining signal-domain analysis for initial processing of sonar and video data in conjunction with geometric processing for symbolic map data constructed from the sonar data, robust navigation becomes possible. Geometric map construction is based on range data, with sonar data used as an example. Recalibration of the robot´s position, however, is critical to this task. Two different and complementary forms, of localization algorithm are described. One of these is based on simple geometric modeling of range data points, followed by data-to-object matching. The second method depends less strongly on a simple environment geometry and is based on image data