Cost-based Registration using A Priori Data for Mobile Robot Localization

Abstract

A major challenge facing outdoor navigation is the localization of a mobile robot as it traverses a particular terrain. Inaccuracies in dead-reckoning and the loss of global positioning information (GPS) often lead to unacceptable uncertainty in vehicle position. We propose a localization algorithm that utilizes cost-based registration and particle filtering techniques to localize a robot in the absence of GPS. We use vehicle sensor data to provide terrain information similar to that stored in an overhead satellite map. This raw sensor data is converted to mobility costs to normalize for perspective disparities and then matched against overhead cost maps. Cost-based registration is particularly suited for localization in the navigation domain because these normalized costs are directly used for path selection. To improve the robustness of the algorithm, we use particle filtering to handle multi-modal distributions. Results of our algorithm applied to real field data from a mobile robot show higher localization certainty compared to that of dead-reckoning alone.