Deterministic and Stochastic Bayesian Methods in Terrain Navigation

Terrain navigation is an application where inference be tween conceptually di erent sensors is performed re cursively on line In this work the Bayesian framework of statistical inference is applied to this recursive esti mation problem Three algorithms for approximative Bayesian estimation are evaluated in simulations one deterministic algorithm and two stochastic The deter ministic method solve the Bayesian inference problem by numerical integration while the stochastic methods simulate several candidate solutions and evaluates the integral by averaging between these candidates Simu lations show that all three algorithms are e cient and approximately reach the Cram er Rao bound However the stochastic methods are sensitive to outliers and the deterministic method has the limitation of being hard to implement in higher dimensions