Computational Imaging in Robotic Vision
Dr. Donald G. Dansereau
A postdoctoral scholar at the Stanford Computational Imaging Lab.
We are on the cusp of a robotics revolution that will transform how we work and live. Manufacturing, health, and service robots including autonomous cars and drones are set to profoundly impact our lives, and visual sensing will play a pivotal role in this transformation. However, there are deep challenges in how to best endow robotic autonomy with reliable visual perception.
This talk explores the tools of computational imaging as a means of meeting the requirements of this next generation of seeing robots. As in nature specialised embodiments benefit from specialised sensing, and I will explore how novel cameras can reduce computational burden while delivering more robustness. This approach yields more effective robots that can operate over a broader range of conditions and with greater autonomy. The talk concludes by highlighting key challenges and opportunities at the intersection of optics, algorithms, and robotic embodiment.
Dr. Donald G. Dansereau is a postdoctoral scholar at the Stanford Computational Imaging Lab. His research is focused on computational imaging for robotic vision, and he is the author of the Light Field Toolbox for Matlab. In 2004 he completed an MSc at the University Calgary, receiving the Governor General’s Gold Medal for his pioneering work in light field processing. In 2014 he completed a PhD on underwater robotic vision at the Australian Centre for Field Robotics, University of Sydney. Donald's industry experience includes physics engines for video games, computer vision for microchip packaging, and FPGA design for automatic test equipment. His field work includes marine archaeology on a Bronze Age city in Greece, hydrothermal vent mapping in the Sea of Crete, habitat monitoring off the coast of Tasmania, and wreck exploration in Lake Geneva.
For more information, please contact Prof Chun Wang