Navigating and mapping with the SPARUS AUV in a natural and unstructured underwater environment

In spite of the recent advances in unmanned underwater vehicles (UUV) navigation techniques, robustly solving their localization in unstructured and unconstrained areas is still a challenging problem. In this paper, we propose a pose-based algorithm to solve the full Simultaneous Localization And Mapping (SLAM) problem for an Autonomous Underwater Vehicle (AUV), navigating in the unknown and unstructured environment. A probabilistic scan matching technique using range scans gathered from a Mechanical Scanning Imaging Sonar (MSIS) is used together with the robot dead-reckoning displacements. The raw data from the sensors are processed and fused in-line with an augmented state extended Kalman filter (EKF), that estimates and keeps the scans poses. The proposed SLAM method has been tested with a real world dataset acquired from the Sparus AUV, guided in a natural underwater environment.