Human-level Performance with Autonomous Vision-based Drones
Autonomous drones play a crucial role in inspection, agriculture, logistics, and search-and-rescue missions and promise to increase productivity by a factor of 10. However, they still lag behind human pilots in speed, versatility, and robustness. What does it take to fly autonomous drones as agile as or even better than human pilots? Autonomous, agile navigation through unknown, GPS-denied environments poses several challenges for robotics research regarding perception, learning, planning, and control. In this talk, I will show how the combination of model-based and machine-learning methods, united with the power of new, low-latency sensors, such as event cameras, can allow drones to achieve unprecedented speed and robustness by relying solely on onboard computing. This can result in better productivity and safety of future autonomous aircraft.