This talk argues for combining the fields of robotic vision and computational imaging. Both consider the joint design of hardware and algorithms, but with dramatically different approaches and results. Roboticists seldom design their own cameras, and computational imaging seldom considers performance in terms of autonomous decision-making.The union of these fields considers whole-system design from optics to decisions. This yields impactful sensors offering greater autonomy and robustness, especially in challenging imaging conditions. Motivating examples are drawn from autonomous ground and underwater robotics, and the talk concludes with recent advances in the design and evaluation of novel cameras for robotics applications.
Donald G. Dansereau joined the Stanford Computational Imaging Lab as a postdoctoral scholar in September 2016. His research is focused on computational imaging for robotic vision, and he is the author of the open-source Light Field Toolbox for Matlab. Dr. Dansereau completed B.Sc. and M.Sc. degrees in electrical and computer engineering at the University of Calgary in 2001 and 2004, receiving the Governor General's Gold Medal for his work in light field processing. His industry experience includes physics engines for video games, computer vision for microchip packaging, and FPGA design for high-throughput automatic test equipment. In 2014 he completed a Ph.D. in plenoptic signal processing at the Australian Centre for Field Robotics, University of Sydney, and in 2015 joined on as a research fellow at the Australian Centre for Robotic Vision at the Queensland University of Technology, Brisbane. Donald's field work includes marine archaeology on a Bronze Age city in Greece, seamount and hydrothermal vent mapping in the Sea of Crete and Aeolian Arc, habitat monitoring off the coast of Tasmania, and hydrochemistry and wreck exploration in Lake Geneva.