Sonar-based FastSLAM in an underwater environment using walls as features

A lot of research has been done in the area of Simultaneous Localization and Mapping (SLAM). It is particularly challenging in underwater environments where many of the self-localization methods used at land no longer work. This paper presents a method to use a rotating scanning sonar as the sole sensor to perform SLAM in harbors or natural waterways. We present a feature extraction process which is capable of extracting walls of arbitrary shape. These extracted wall features are then used to perform SLAM using the well-known FastSLAM algorithm. We show that SLAM is possible given this type of sensor and using our feature extraction process. The algorithm was validated on an open water test site and will be shown to provide localization accuracy generally within the error of the GPS ground truth.