Deep-sea seafloor shape reconstruction from side-scan sonar data for AUV navigation

Dead-reckoning navigation in the deep sea is subject to errors due to accumulation of sensor inaccuracies. As no global referencing method exists for the deep sea like, e.g., GNSS (global navigation satellite system) for land or airborne vehicles other referencing solutions need to be employed. SLAM (Simultaneous Localization And Mapping) is a technique that exploits significant environmental features to reduce the positioning error of a vehicle and to simultaneously build a map of the mission environment. It is crucial for SLAM methods to recognize places that have been visited before. In many cases this is done by extracting salient features from the environment. Obtaining those landmarks from side-scan sonar data is a challenging task as the side-scan sonar data does not consist of spatial information but rather represents an echo amplitude over time. In Coiras et al. it is shown how a seafloor shape can be estimated from side-scan sonar data by inversion and regularization. In this paper their method is extended to allow arbitrary vehicle motion. The results of the work will be presented in examples of synthetic and real data.