摘要: 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 current robot position with previously environment in order probabilistically maximize agreement between maps. This able operate both indoor and outdoor environments using either discrete features or an occupancy grid represent world map. may be any detect robot's surroundings, including vision, sonar, laser range-finder. perform efficient global search pose space guarantees best found according measure discretized space. In addition, subpixel localization uncertainty estimation performed by fitting likelihood function parameterized surface. application these several experiments.
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