This chapter proposes to generalize the Kalman filter to cases where the functions are nonlinear and the noise is non‐Gaussian. The resulting observer will be called the Bayes filter. In the linear and Gaussian case, the Bayes filter is equivalent to the Kalman filter. A Bayes network is a probabilistic graphical model that represents a set of random vectors and their conditional dependencies. By increasing the level of abstraction, the Bayes filter will allow people to have a better understanding of the Kalman filter, and some proofs become easier and more intuitive. The smoothing process consists of a state estimation when all the measurements are available. The chapter considers the smoothing problem where the estimation is made more accurate by taking all future measurements, when available. The Kalman smoother is used to improve the precision of the landmark positions by taking into account the past as well as the future.
Bayes Filter
Mobile Robotics ; 329-357
30.09.2019
29 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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