Titlebook: Environment Learning for Indoor Mobile Robots; A Stochastic State E Juan Andrade-Cetto,Alberto Sanfeliu Book 2006 Springer-Verlag Berlin He

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https://doi.org/10.1057/9781137455307ion. For example, by studying which vehicle maneuvers would most effectively reduce localization uncertainty [25, 78], or what maneuvers would provide the greatest reward in terms of exploration gain [31]; by incorporating visual servoing techniques [20], or by implementing a PD controller over an A* searched trajectory [86].

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https://doi.org/10.1057/978-1-137-58909-5Perception happens locally, in the egocentric frame of reference of the robot. In order to ensure correspondence between the local representation of the environment built by the landmark extraction processes, and the global representation contained in a map, the robot must estimate its own position with respect to this map.

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Spiritual Approaches to Living with CancerThe Kalman Filter developed in the early sixties by R.E. Kalman [57, 58] is a recursive state estimator for partially observed non-stationary stochastic processes. It gives an optimal estimate in the least squares sense of the actual value of a state vector from noisy observations.

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Simultaneous Localization and Map Building,Perception happens locally, in the egocentric frame of reference of the robot. In order to ensure correspondence between the local representation of the environment built by the landmark extraction processes, and the global representation contained in a map, the robot must estimate its own position with respect to this map.