In this paper we consider the problem of creating a two dimensional spatial representation of gas distribution with a mobile robot. In contrast to previous approaches to the problem of gas distribution mapping (GDM) we do not assume that the robot has perfect knowledge about its position. Instead we develop a probabilistic framework for simultaneous localisation and occupancy and gas distribution mapping (GDM/SLAM) that allows to account for the uncertainty about the robot’s position when computing the gas distribution map. Considering the peculiarities of gas sensing in real-world environments, we show which dependencies in the posterior over occupancy and gas distribution maps can be neglected under certain practical assumptions. We develop a Rao-Blackwellised particle filter formulation of the GDM/SLAM problem that allows to plug in any algorithm to compute a gas distribution map from a sequence of gas sensor measurements and a known trajectory. In this paper we use the Kernel Based Gas Distribution Mapping (Kernel- GDM) method. As a first step towards outdoor gas distribution mapping we present results obtained in a large, uncontrolled, partly open indoor environment.