Perception-Aware Trajectory Generation For Aggressive Quadrotor Flight Using Differential Flatness

Recent advances in visual-inertial state estimation have allowed quadrotor aircraft to autonomously navigate in unknown environments at operational speeds. In most cases, substantially higher speeds can be achieved by actively designing motion that reduces state estimation error. We are interested in autonomous vehicles running feature-based visual-inertial state estimation algorithms. In particular, we consider a trajectory optimization problem in which the goal is to maximize co-visibility of features, i.e. features are kept visible in the camera view from one keyframe to the next, increasing state estimation accuracy. Our algorithm is developed for autonomous quadrotor aircraft, for which position and yaw trajectories can be tracked separately. We assume that the desired positions of the vehicle are determined a priori, for instance, by a path planner that uses obstacles in the environment to generate a trajectory of positions with free yaw. This paper presents a novel algorithm that determines the yaw trajectory that jointly optimizes aggressiveness and feature co-visibility. The benefit of this algorithm was experimentally verified using a custom built quadrotor which uses visual inertial odometry for state estimation. The generated trajectories lead to better state estimation which contributes to improved trajectory tracking by a state-of-the-art controller under autonomous high-speed flight. Our results show that the root-mean-square error of the trajectory tracking is improved by almost 70%.