Recursive Bayesian estimation: Difference between revisions

Revision as of 20:45, 5 October 2024

Latest revision as of 17:14, 30 October 2024

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==In robotics==

A Bayes filter is an algorithm used in [[computer science]] for calculating the probabilities of multiple beliefs to allow a [[robot]] to infer its position and orientation. Essentially, Bayes filters allow robots to continuously update their most likely position within a coordinate system, based on the most recently acquired sensor data. This is a recursive algorithm. It consists of two parts: prediction and innovation. If the variables are [[Normal Distribution|normally distributed]] and the transitions are linear, the Bayes filter becomes equal to the [[Kalman filter]].

In a simple example, a robot moving throughout a grid may have several different sensors that provide it with information about its surroundings. The robot may start outbegin with certainty that it is at position (0,0). However, as it moves fartherfurther and fartherfurther from its original position, the robot has continuously less certainty about its position; using a Bayes filter, a probability can be assigned to the robot's belief about its current position, and that probability can be continuously updated from additional sensor information.

== Model ==