The goal of this study is to generate high- resolution seafloor maps using a Side-Scan Sonar (SSS) on board of an Autonomous Underwater Vehicle (AUV). This is achieved by explicitly taking into account the SSS operation as follows. First, the raw SSS data is corrected both in intensity, by removing the effects of the uneven ensonification pattern, and range, by projecting the data to the sea floor. Second, the AUV pose is estimated continuously. Third, a probabilistic SSS model is defined and used to estimate the probability of each seafloor region to be observed. This probabilistic information is then used to weight the contribution of each SSS measurement to the map. Because of these models, arbitrary map resolutions can be achieved, even beyond the sensor resolution.