Abstract
This chapter presents essential algorithms that are normally used in mobile robotics for solving the problems of optimum state estimation, sensor data fusion, robot localization, etc. After a short review of probability theory, the basic concepts of Bayesian filtering are introduced. The algorithms are broken down into the basic parts that are studied separately to allow the reader to understand how the algorithm works and to show the influence of the parameters on algorithm performance. The most influential algorithms, Bayesian filter, (extended) Kalman filter, and particle filter, are described in more detail and several simple examples are used to demonstrate the applicability of the algorithms. State estimation algorithms are accompanied with a discussion on observability analysis, estimate convergence, and bias.
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