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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A The Kalman Filter,The 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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C , Points,A set of . + 1 sigma points . are deterministically chosen to satisfy a condition set of the form...where .(.) is the . of ., not necessarily Gaussian, and .(⋅,⋅) determines the information that should be captured about ..

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Marginal Filter Stability, Unfortunately, in SLAM, the state space constructed by appending the robot pose and the landmark locations is fully correlated; a situation that hinders full observability. Moreover, the modeling of map states as static landmarks yields a partially controllable state vector. The identification of t

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Suboptimal Filter Stability, state space. The diagonal elements of . corresponding to these incorruptible states will be driven to zero by the Kalman filter, and once this happens, these estimates will remain fixed and no further observations will alter their values. The dynamics of the model assume the landmarks are fixed ele

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Simultaneous Localization, Control and Mapping,ion. 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

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Marginal Filter Stability,ers full observability. Moreover, the modeling of map states as static landmarks yields a partially controllable state vector. The identification of these problems, and the steps taken to palliate them, constitute one of the main topics of this monograph. The bulk of which is covered in this chapter.