Autonomous System for a Racing Quadcopter In this paper, we present a methodology to make an autonomous drone fly through a sequence of gates only with onboard sensors. Our work is a solution to the AlphaPilot Challenge, proposed by the Lookheed Martin Company and the Drone Racing League. First, we propose a strategy to generate a smooth trajectory that passes through the gates. Then, we develop a localization system, which merges image data from an onboard camera with IMU data. Finally, we present an artificial vector field based strategy used to control the quadcopter. Our results are validated with simulations in the official simulator of the competition and with preliminary experiments with a real drone.

An Autonomous Quadrotor System for Robust HighSpeed Flight Through Cluttered Environments Without GPS Robust autonomous flight without GPS is key to many emerging drone applications, such as delivery, search and rescue, and warehouse inspection. These and other applications require accurate trajectory tracking through cluttered static environments, where GPS can be unreliable, while highspeed, agile, flight can increase efficiency. We describe the hardware and software of a quadrotor system that meets these requirements with onboard processing: a custom 300 mm wide quadrotor that uses two widefieldofview cameras for visualinertial motion tracking and relocalization to a prior map. Collisionfree trajectories are planned offline and tracked online with a custom tracking controller. This controller includes compensation for drag and variability in propeller performance, enabling accurate trajectory tracking, even at high speeds where aerodynamic effects are significant. We describe a system identification approach that identifies quadrotorspecific parameters via maximum likelihood estimation from flight data. Results from flight experiments are presented, which 1) validate the system identification method, 2) show that our controller with aerodynamic compensation reduces tracking error by more than 50% in both horizontal flights at up to 8.5 ms and vertical flights at up to 3.1 ms compared to the stateoftheart, and 3) demonstrate our system tracking complex, aggressive, trajectories.