Image Map Correspondence for Mobile Robot Self-Location Using Computer Graphics

This paper discusses the use of computer graphics in estimating the position of an autonomous mobile robot navigating in an outdoor, mountainous environment, which is fundamentally a computer vision task A digital elevation map (DEM) of the area in which the robot is to navigate is given, and the robot is equipped with a camera that can be panned and tilted, a compass, and an altimeter. The position of the robot is estimated by establishing a correspondence between the images acquired by the camera on the robot (actual images) and the images generated from the DEM (predicted images) using computer graphics techniques. Features are extracted from the predicted images, and the actual images that are used in establishing the correspondence. The features used are the horizon line contours (HLC’s) in the images. In order to reduce the search space (the set of possible robot locations) to be considered in generating the images, a constrained search paradigm is used. Geometric constraints help prune the search space significantly. The novel feature of this work is the collaboration between computer graphics and computer vision techniques to establish the position of the robot in its environment. The approach is tested using real terrain data of areas in Colorado and simulated images. The method is suitable for use in outdoor mobile robots and planetary rovers.