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Past Research

Unsupervised Learning for Processing Underwater Imagery

Deep learning has demonstrated great success in modeling complex nonlinear systems but requires a large amount of training data, which is difficult to compile in subsea environments. Our prior work leverages physics-based models of underwater image formation to develop unsupervised learning approaches to advance perceptual capabilities of underwater robots. In particular, we have focused on unsupervised learning for color correction and depth estimation of monocular and stereo underwater imagery.


Perception for Autonomous Driving

We have also collaborated with the Ford Center for Autonomous Vehicles at University of Michigan to improve perception for autonomous vehicles in urban environments. The videos below show results from our work on transferring sensor-based effects from real data to simulated data to improve results of training on simulated data for the task of object detection.


Light Field Imaging in Underwater Environments

Light field cameras have a microlens array between the camera's main lens and image sensor, enabling recovery of a depth map and high resolution image from a single optical sensor. Our research has focused on leveraging light field cameras to improve underwater perception, with tasks including real-time 3D reconstruction and underwater image dehazing.

Underwater Bundle Adjustment

Our work developing underwater bundle adjustment integrates color correction into the structure recovery procedure for multi-view stereo reconstruction in underwater environments.

Robotic Survey of Sunken Pirate City

Our team conducted a robotic survey of the submerged city of Port Royal, Jamaica to create a 3D reconstruction of the marine archaeological site. [Read more]