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 as the robot's *
motion model*, since it models how motion effect the robot's
position. The conditional probability is called *
perceptual model*, because it models the outcome of the robot's
sensors.

In the Markov localization algorithm , which initializes
the belief , 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, is usually
uniformly distributed. If the initial position of the robot is
approximately known, then is typically a narrow Gaussian
distribution centered at the robot's position.

Fri Nov 19 14:29:33 MET 1999