Probabilistic self-localization for mobile robots

Abstract

We describe probabilistic self-localization techniques for mobile robots that are based on the principle of maximum-likelihood estimation. The basic method is to compare a map generated at the current robot position with a previously generated map of the environment in order to probabilistically maximize the agreement between the maps. This method is able to operate in both indoor and outdoor environments using either discrete features or an occupancy grid to represent the world map. The map may be generated using any method to detect features in the robot's surroundings, including vision, sonar, and laser range-finder. We perform an efficient global search of the pose space that guarantees that the best position is found according to the probabilistic map agreement measure in a discretized pose space. In addition, subpixel localization and uncertainty estimation are performed by fitting the likelihood function with a parameterized surface. We describe the application of these techniques in several experiments.