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What is Posterior Belief

Encyclopedia of Artificial Intelligence
It refers to the probability distribution of the robot pose estimate conditioned upon information such as control and sensor measurement data. The extended Kalman filter and particle filter are two different methods for computing the posterior belief.
Published in Chapter:
Mobile Robots Navigation, Mapping, and Localization Part II
Lee Gim Hee (DSO National Laboratories, Singapore) and Marcelo H. Ang Jr. (National University of Singapore, Singapore)
Copyright: © 2009 |Pages: 9
DOI: 10.4018/978-1-59904-849-9.ch159
Abstract
In addition to the capability to navigate from a point of origin to a given goal and avoiding all static and dynamic obstacles, a mobile robot must posses another two competencies: map building and localization in order to be useful. A mobile robot acquires information of its environment via the process of map building. Map building for mobile robots are commonly divided into occupancy grid and topological maps. Occupancy-grid maps seek to represent the geometric properties of the environment. Occupancy-grid mapping was first suggested by Elfes in 1987 and the idea was published in his Ph.D. thesis (A. Elfes, 1989) in 1989. Topological mapping was first introduced in 1985 as an alternative to the occupancy- grid mapping by R. Chatila and J.-P. Laumond (R. Chatila, & J.-P. Laumond, 1985). Topological maps describe the connectivity of different locations in the environment. The pose of a mobile robot must be known at all times for it to navigation and build a map accurately. This is the problem of localization and it was first described in the late 1980’s by R. Smith et al (R. Smith et al, 1980). Some key algorithms for map building and localization will be discussed in this article.
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