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.

Maximum Likelihood Path Planning for Fast Aerial Maneuvers and Collision Avoidance We propose a planning method to enable fast autonomous flight in cluttered environments. Typically, autonomous navigation through a complex environment requires a continuous search on a graph generated by a kconnected grid or a probabilistic scheme. As the vehicle travels, updating the graph with data from onboard sensors is expensive as is the search on the graph especially if the paths must be kinodynamically feasible. We propose to avoid the online search to reduce the computational complexity. Our method models the environment differently in two separate regions. Obstacles are considered to be deterministically known within the sensor range and probabilistically known beyond the sensor range. Instead of searching for the path with the lowest cost (typically the shortest path), the method maximizes the likelihood to reach the goal in determining the immediate next step for navigation. With such a problem formulation, the online method realized by a trajectory library can determine a path within 0.20.3ms using a single CPU thread on a modem embedded computer. In experiments, it enables a lightweight UAV to fly at 10ms in a cluttered forest environment (see Fig. 1 as an example).