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Autonomous navigation is an essential capability for mobile robots. In order to operate robustly, a robot needs to know what the environment looks like, where it is in its environment, and how to navigate it. This book summarizes approaches that address these problems and that use particle filters as the underlying model for representing beliefs.
This book examines simultaneous localization and mapping (SLAM) for autonomous robots. Solutions include uncertainty-driven exploration, coordination of multiple robots, learning and incorporating background knowledge, and dealing with dynamic environments.
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