Perception-aware trajectory generation for aggressive quadrotor flight using differential flatness

Abstract

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 visualinertial 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%.

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FlightGoggles: Photorealistic Sensor Simulation for Perception-driven Robotics using Photogrammetry and Virtual Reality

“…In this section, we discuss application that FlightGoggles has been used for and potential future applications. Potential applications of FlightGoggles include: human-vehicle interaction, active sensor selection, multi-agent systems, and visual inertial navigation research for fast and agile vehicles [15], [21], [22]. The FlightGoggles simulator was used for the simulation part of the AlphaPilot challenge [23].…”

“…The experiments show that significant gains in estimation performance can be achieved by using the proposed vision aware planning algorithm as the speed of the quadcopter is increased. For details, we refer the reader to [22].…”

Aggressive Perception-Aware Navigation Using Deep Optical Flow Dynamics and PixelMPC

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