Abstract:
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
takes into account the case where the terrain being mapped is drifting and rotating by
estimating the orientation of the terrain at each time step in the robot’s trajectory. Relative
pose constraints are introduced into the pose graph based on multibeam submap
matching using depth image correlation, 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
and yielding a texture-mapped 3-D mesh for visualization. The optimization framework
also allows for the straightforward introduction of constraints provided by the particular
suite of sensors available, so that the navigation and mapping system presented
works under a variety of deployment scenarios, including the potential incorporation
of external localization systems such as long-baseline acoustic networks. Results of using
the system to map the draft of rotating Antarctic ice floes are presented, as are
results fusing optical and range data of a coral reef.
