Underwater localization and 3D mapping of submerged structures with a single-beam scanning sonar

We present a novel approach to perform underwater simultaneous localization and mapping (SLAM) using a small inspection-class remotely operated vehicle (ROV) equipped with a single-beam scanning sonar, amidst high levels of noise present in the sonar data, and in the absence of inertial/odometry measurements. Features are extracted from hierarchically grouped clusters of sonar returns, data association is performed via the iterative joint compatibility test, and the vehicle's trajectory and map are estimated using incremental smoothing and mapping (iSAM). The resulting point clouds derived from the ROV's sonar are used to produce Gaussian process occupancy maps, which interpolate among gaps in the acoustic range data to produce descriptive 3D maps of submerged structures. The proposed localization and mapping approach is demonstrated using data gathered in two harbor environments in close proximity to piers and seawalls.