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FastSLAM: A Factored Solution to the Simultaneous ...

FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem Michael Montemerlo and Sebastian Thrun Daphne Koller and Ben Wegbreit School of Computer Science Computer Science Department Carnegie Mellon University Stanford University Pittsburgh, PA 15213 Stanford, CA 94305-9010. Abstract A key limitation of EKF-based approaches is their compu- tational complexity. Sensor updates require time quadratic The ability to simultaneously localize a robot and ac- in the number of landmarks to compute. This complex- . curately map its surroundings is considered by many to ity stems from the fact that the covariance matrix maintained be a key prerequisite of truly autonomous robots. How- by the Kalman filters has elements, all of which must ever, few approaches to this problem scale up to handle be updated even if just a single landmark is observed. The the very large number of landmarks present in real envi- ronments.

FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem Michael Montemerlo and Sebastian Thrun School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 mmde@cs.cmu.edu, thrun@cs.cmu.edu Daphne Koller and Ben Wegbreit Computer Science Department Stanford University Stanford, CA 94305-9010

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