Robotics and Perception at both departments of Informatics – University of Zurich; Neuroinformatics
Davide Scaramuzza is professor of robotics and perception at both departments of Neuroinformatics (University of Zurich & ETH Zurich) and Informatics (University of Zurich), where he does research at the intersection of robotics and computer vision. He did his PhD in robotics and computer vision at ETH Zurich (with Roland Siegwart) and a postdoc at the University of Pennsylvania (with Vijay Kumar and Kostas Daniilidis). From 2009 to 2012, he led the European project sFly, which introduced the PX4 autopilot and pioneered visual-SLAM–based autonomous navigation of micro drones. From 2015 to 2018 he was part of the DARPA FLA program. For his research contributions, he was awarded the prestigious IEEE Robotics and Automation Society Early Career Award, the Misha Mahowald Neuromorphic Engineering Award, the SNSF-ERC Starting Grant (equivalent to NSF Career Award), Google, Intel, Qualcomm, and KUKA awards, as well as several conference and journal paper awards (e.g., IEEE Trans. of Robotics Best Paper Award in 2018). He coauthored the book “Introduction to Autonomous Mobile Robots” (published by MIT Press) and more than 100 papers on robotics and computer vision. In 2015, he cofounded a venture, called Zurich-Eye, dedicated to visual-inertial navigation solutions for mobile robots, which today is Facebook-Oculus Zurich. He was also the strategic advisor of Dacuda, an ETH spinoff dedicated to inside-out VR solutions, which today is Magic Leap Zurich. Many aspects of his research have been prominently featured in the popular press, such as The New York Times, Discovery Channel, BBC, IEEE Spectrum, MIT Technology Review.
Learning Vision-based, Agile Drone Flight: from Frames to Event Cameras
Autonomous quadrotors will soon play a major role in search-and-rescue and remote-inspection missions, where a fast response is crucial. Quadrotors have the potential to navigate quickly through unstructured environments, enter and exit buildings through narrow gaps, and fly through collapsed buildings. However, their speed and maneuverability are still far from those of birds and human pilots. Human pilots take years to learn the skills to navigate drones. Autonomous, vision-based agile navigation through unknown, indoor environments poses a number of challenges for robotics research in terms of perception, state estimation, planning, and control. In this talk, I will show that how the combination of both model-based and machine learning methods united with the power of new, low-latency sensors, such as event-based cameras, allow drones to achieve unprecedented speed and robustness by relying solely on the use of passive cameras, inertial sensors, and onboard computing.