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The Markov Localization Algorithm


Update Equations (9) and (16) form the core of the Markov localization algorithm. The full algorithm is shown in Table 1. Following [Basye et al. 1992] and [Russell & Norvig1995], we denote tex2html_wrap_inline2917 as the robot's motion model, since it models how motion effect the robot's position. The conditional probability tex2html_wrap_inline2919 is called perceptual model, because it models the outcome of the robot's sensors.


In the Markov localization algorithm tex2html_wrap_inline2935 , which initializes the belief tex2html_wrap_inline2937 , reflects the prior knowledge about the starting position of the robot. This distribution can be initialized arbitrarily, but in practice two cases prevail: If the position of the robot relative to its map is entirely unknown, tex2html_wrap_inline2939 is usually uniformly distributed. If the initial position of the robot is approximately known, then tex2html_wrap_inline2939 is typically a narrow Gaussian distribution centered at the robot's position.

Dieter Fox
Fri Nov 19 14:29:33 MET 1999