Autonomous Underwater Vehicle Navigation and Mapping in Dynamic, Unstructured Environments

Clayton Kunz, Ph.D., 2012
Hanumant Singh, Advisor

This thesis presents a system for automatically building 3-D optical and bathymetric maps of underwater terrain using autonomous robots. The maps that are built improve the state of the art in resolution by an order of magnitude, while fusing bathymetric information from acoustic ranging sensors with visual texture captured by cameras.  As part of the mapping process, several internal relationships between sensors are automatically calibrated, including the roll and pitch offsets of the velocity sensor, the attitude offset of the multibeam acoustic ranging sensor, and the full six-degree of freedom offset of the camera. The system uses pose graph optimization to simultaneously solve for the robot's trajectory, the map, and the camera location in the robot's frame, and accounts for drifting and rotating terrain by estimating the orientation of the terrain being mapped at each time step in the robot's trajectory.  Relative pose constraints are introduced into the pose graph based on multibeam submap matching, while landmark-based constraints are used in the graph where visual features are available.  The two types of constraints work in concert in a single optimization, fusing information from both types of mapping sensors, yielding a texture-mapped 3-D mesh for visualization.