  We present a new method of learning a continuous occupancy field for use in
robot navigation. Occupancy grid maps, or variants of, are possibly the most
widely used and accepted method of building a map of a robot's environment.
Various methods have been developed to learn continuous occupancy maps and have
successfully resolved many of the shortcomings of grid mapping, namely, priori
discretisation and spatial correlation. However, most methods for producing a
continuous occupancy field remain computationally expensive or heuristic in
nature. Our method explores a generalisation of the so-called Ising model as a
suitable candidate for modelling an occupancy field. We also present a unique
kernel for use within our method that models range measurements. The method is
quite attractive as it requires only a small number of hyperparameters to be
trained, and is computationally efficient. The small number of hyperparameters
can be quickly learned by maximising a pseudo likelihood. The technique is
demonstrated on both a small simulated indoor environment with known ground
truth as well as large indoor and outdoor areas, using two common real data
sets.
