Map Building using Mobile Robots

Abstract: In this thesis two methods to solve the Simultaneous Localization And Mapping problem are presented. The classical extended Kalman filter is used as a reference from where an efficient particle filter is examined, which uses deterministic samples called sigma points. Most of the effort is put on implementing these algorithms together with the Symmetries and Permutations Model, but a preliminary comparison of the methods has been done as well. Experiments show that linearization errors make the map inaccurate over long periods of time, and methods are discussed which decrease these effects.