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A new research paper from the CMU Robotics Institute describes a new variation on the Simultaneous Localization and Mapping (SLAM) algorithm that is commonly used in mobile robots. The traditional approach to SLAM is based on extended Kalman filters. FastSLAM uses a particle filter that allows the robot to assimilate more landmarks into its internal map representation faster than traditional SLAM. The FastSLAM algorithms described in the paper were tested on the ACFR High Speed Vehicle, a standard pickup truck that has been converted into an autonomous robot capable of speeds up to 90 Km/h. |