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资源说明：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 in it. This work summarizes approaches that address these three problems and that use particle filters as their main underlying model for representing beliefs. We illustrate that these filters are powerful tools that can robustly estimate the state of the robot and its environment and that it is also well-suited to make decisions about how to navigate in order to minimize the uncertainty of the joint belief about the robot's position and the state of the environment.
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