  We propose a low-cost laboratory platform for development and validation of
underwater mapping techniques, using the BlueROV2 Remotely Operated Vehicle
(ROV). Both the ROV and the objects to be mapped are placed in a pool that is
imaged via an overhead camera. In our prototype mapping application, the ROV's
pose is found using extended Kalman filtering on measurements from the overhead
camera, inertial, and pressure sensors; while objects are detected with a deep
neural network in the ROV camera stream. Validation experiments are performed
for pose estimation, detection, and mapping. The litter detection dataset and
code are made publicly available.
