Mobile robot self-location using constrained search

Presents a technique for estimating the position and pose of an autonomous mobile robot navigating in an outdoor, urban environment consisting of polyhedral buildings. The 3D descriptions of the rooftops of the buildings are assumed to be given as a world model. The robot is assumed to be equipped with a visual camera. The position and pose are estimated by establishing a correspondence between the lines that constitute the rooftops of the buildings (world model features) and their images. A constrained search paradigm is used to isolate a consistent set of correspondences. The geometric constraints between the world model features and their images are used to prune the search space. To effectively capture the geometric relations between the world model features with respect to their visibility from various positions of the robot in the plane in which it navigates (visibility plane), the free space of the robot is partitioned into a set of distinct, nonoverlapping regions called the edge visibility regions (EVRs). The use of these EVRs in isolating a consistent set of correspondences between the world model and the image features is discussed