Sonar windows and geometrically represented objects for mobile robot self-referencing

When navigating through an environment, a mobile robot updates its position and orientation by searching for known objects called geometric beacons. This operation of comparing predicted sonar ranges with observed ones is referred to as self-referencing. There are two fundamental steps necessary for solving the self-referencing problem: how to represent the objects in the environment and how to search for landmarks based on this representation. This paper presents a method of mobile robot self-referencing where every object in the environment can be used as a landmark beacon. First, it shows how to interpret and predict sonar range readings through regional sampling and sensor window construction. Second, it justifies geometrically representing obstacles on the basis of surface information quality, compatibility with global motion planning, and the computer storage necessary to satisfy both criteria. Finally, it justifies the use of traversability vectors and configuration-space-time based on how they can be simultaneously used to plan motion globally and facilitate the search for geometric beacons