Towards Constant-Time SLAM on an Autonomous Underwater Vehicle Using Synthetic Aperture Sonar

This paper applies a new constant-time, consistent and convergent Simultaneous Localization and Mapping (SLAM) algorithm to an autonomous underwater vehicle (AUV). A constant-time SLAM algorithm offers computation independent of workspace size and is one key component in the development of truly autonomous agents. The real-time deployment of such a system would be a landmark achievement for the mobile robotics community. This paper describes progress towards this goal focusing on the sub-sea domain — an area set to benefit massively from the autonomy afforded by SLAM. The primary sensor used in this work is a sixteen element synthetic aperture sonar (SAS) carried on the nose of the AUV “Caribou”. Using a novel target detection strategy, data gathered from a 40 minute survey is processed by the new SLAM algorithm and the results compared to both a ground truth and the quadratic time “gold standard” full covariance SLAM algorithm.