I am a PhD Candidate in Robotics at the University of Michigan working in the Deep Robot Optical Perception (DROP) Lab. My research interests include perception for marine robotics, light field imaging, and unsupervised learning. I hold an M.S. in Robotics from University of Michigan and a B.S.E. in Mechanical and Aerospace Engineering with a Certificate in Applications of Computing from Princeton University.

[CV][Google Scholar]


Unsupervised Learning

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 deep sea environments. Using WaterGAN, we generate a large training dataset of paired imagery, both raw underwater and true color in-air, as well as depth data. This data serves as input to a novel end-to-end network for color correction of monocular underwater images. Due to the depth-dependent water column effects inherent to underwater environments, we show that our end-to-end network implicitly learns a coarse depth estimate of the underwater scene from monocular underwater images.

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. I am interested in using light field cameras for underwater perception.

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]


"WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images." (Jie Li, Katherine A. Skinner, and Matthew Johnson-Roberson), In IEEE Robotics and Automation - Letters, 2017. [BibTeX, PDF]

"Automatic Color Correction for 3D Reconstruction of Underwater Scenes ." (Katherine A. Skinner, Eduardo Iscar, and Matthew Johnson-Roberson), In IEEE International Conference on Robotics and Automation, 2017. [BibTeX]

"Towards Real-time Underwater 3D Reconstruction with Plenoptic Cameras." (Katherine A. Skinner and Matthew Johnson-Roberson), In IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016. [BibTeX][PDF]

"Detection and Segmentation of Underwater Archaeological Sites Surveyed with Stereo-Vision Platforms." (Katherine A. Skinner and Matthew Johnson-Roberson), In MTS/IEEE OCEANS, 2015. [BibTeX][PDF]

"Bathymetric factor graph SLAM with sparse point cloud alignment." (Vittorio Bichucher, Jeffrey M. Walls, Paul Ozog, Katherine A. Skinner, and Ryan M. Eustice), In MTS/IEEE OCEANS, 2015.[BibTeX, PDF]


EECS 442: Computer Vision -- Fall 2016, Graduate Student Instructor

This course is an introduction to 2D and 3D computer vision offered to upper class undergraduates and graduate students. Topics include camera models, multi-view geometry, stereo reconstruction, low-level image processing methods, segmentation, clustering, and high-level vision techniques such as object recognition.

ENG 100: Introduction to Engineering -- Fall 2016, Guest Lecturer

This course is an introduction to underwater vehicle design for freshmen undergraduates. [Lecture Slides]



Our lab coordinated with Wayne State University's Gaining Options - Girls Investigate Real Life (GO-GIRL) program to design a summer workshop for high school girls as an introduction to engineering. The workshop involved building a SeaPerch remotely operated vehicle (ROV) and testing it in a lab setting and in a local pond to gather data. [Tutorial]

More Involvement

Robotics Graduate Student Council
Undergraduate Research Opportunity Program
Mentor2Youth Program [Tutorial]
Discover Engineering Summer Program [Workshop Slides]
GradSWE Elementary School Science Program
Robotics Day Planning Committee
A World in Motion