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Probabilistic Localization for Mobile Robots
Clark F. Olson JPL internal report D-16543, Jet Propulsion Laboratory, California Institute of Technology, 1999. Localization is a critical issue in mobile robotics. If the robot does not know where it is, it cannot effectively plan movements, locate objects, or reach goals. In this paper, we describe probabilistic self-localization techniques for mobile robots that are based on the principal of maximum-likelihood estimation. The basic method is to compare a map generated at the current robot position to a previously generated map of the environment 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. A global search of the pose space is performed that guarantees that the best position in a discretized pose space is found according to the probabilistic map agreement measure. In addition, fitting the likelihood function with a parameterized surface allows both subpixel localization and uncertainty estimation to be performed. The application of these techniques in several experiments is described, including experimental localization results for the Sojourner Mars rover. |
