Friday, 22 February 2013

Linear Gaussian Model for robot localisation

The task is to predict the location of a robot given noisy observations of its current position 1. In a static localisation setup, it's assumed that robot is not moving while subsequent measurements of a robot location are taken. Whereas in a dynamic variant, robot's position is changing over the time. There are 4 different solutions presented to 1D localisation problem, including Kalman Filter 1, Canonical Gaussian 2,4, Bayes's theorem for Gaussian Variables 3 and Expectation Propagation 3,5.

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