Human-Level Performance with Autonomous Vision-Based Drones

Autonomous drones play a crucial role in search-and-rescue, delivery, and inspection missions, and promise to increase productivity by a factor of 10. However, they're still far below human pilots in terms of 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 in perception, learning, planning, and control. I'll show how the combination of both 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.