The topic of this thesis is occupancy mapping for mobile robots, with an emphasis on a novel method for continuous occupancy mapping using Gaussian processes. In the new method, spatial correlation is accounted for in a natural way, and an a priori discretization of the area to be mapped is not necessary as within most other common methods.

The main contribution of this thesis is the construction of a Gaussian process library for C++, and the use of this library to implement the continuous occupancy mapping algorithm. The continuous occupancy mapping is evaluated using both simulated and real world experimental data. The main result is that the method, in its current form, is not fit for online operations due to its computational complexity. By using approximations and ad hoc solutions, the method can be run in real time on a mobile robot, though not without losing many of its benefits.