One of the core problems in mobile robotics is the localisation of the mobile robot, as the success rate of achieving localisation depends on many different factors. The core idea with my chosen topic is represented by probability, which advanced into its own field of robotics – probabilistic robotics, of which theories are incredibly widespread and popular in practice. Their advantage lies mainly in acknowledging uncertainties; in calculations, system planning and in presenting the state of a certain system through probabilistic distribution
The focus of my thesis is in the general problem of global localisation, which represents the problem of the known map and unknown starting location of the robot. In order to solve the aforementioned problem it is important to have knowledge of the localisation algorithms, which calculate the probabilistic distribution in a manner that leads to the successful localisation of a robot.
In support of my thesis I engineered two projects, which used two probabilistic algorithms: a histogram filter and a particle filter. Both descend from the Bayes filter, which represents the foundation for all probabilistic algorithms. The former algorithm uses, for the purposes of probabilistic distribution, a discreet grid localisation (in a defined grid cell) while the latter uses the Monte Carlo localisation method.