This work presents a design of an indoor/outdoor SLAM technique combined with navigation for mobile robots. The system does not use any external beacons and relies on only one 2D range finder. This work focuses mainly on an implementation of already established algorithms which were significantly improved (which in effect helped also to overcome the set sensory limitations). To localize the robot and create a map of an unknown environment, we are using a variant of a Rao-Blackwell's particle filter. We also present techniques for navigating in the map and recognizing terrain types. The method for recognizing terrain types creates a much more unique map and also improves the outdoor localization. The outdoor environment that we focused on are city parks where the robot has to stay on designated paths.

In this thesis, we introduce a Rao-Blackwellized Particle Filter (RBPF) for the algorithm Simultaneously Localizing and Mapping (SLAM) to be used on an Autonomous Underwater Vehicle (AUV) with a Stochastic Variational Gaussian Process (SVGP) algorithm. With a positive result, the combination was proven to be working. The main limitation has been the complexity of the two algorithms. Even though we got the two working together in a dynamic environment, it has only worked in a simulation. Before testing the solution on a real AUV in a natural environment, modifications need to be added, speeding up the whole process. / I det här examensarbetet har vi introducerat ett Rao-Blackwellized Partikel Filter (RBPF) med lokaliserings och kartläggnings algoritmen (SLAM). Detta för att användas på en autonom undervattensfarkost (AUV), tillsammans med algoritmen för att förutspå hittills osedda platser (SVGP). Kombinationen av de två algoritmerna på farkosten visade sig fungera. Den största begränsningen har varit hur tunga de båda algoritmerna är, vilket har lett till att färre partiklar har kunnat användas och med mindre noggrann träning per partikel. Dessutom har resultaten endast visats i en simulerad miljö. Innan det blir möjligt att testa kombinationen i en verklig miljö måste modifikationer göras för att snabba på träningen av algoritmerna och på så sett kunna använda sig av fler partiklar.