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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We study a problem in vision-aided navigation in which an autonomous agent has to traverse a specified path in minimal time while ensuring extraction of a steady stream of visual percepts with low latency. Vision-aided robots extract motion estimates from the sequence of images of their on-board cameras by registering the change in bearing to landmarks in their environment. The computational burden of the latter procedure grows with the range of apparent motion undertaken by the projections of the landmarks, incurring a lag in pose estimates that should be minimized while navigating at high speeds. This paper addresses the problem of selecting a desired number of landmarks in the environment, together with the time parametrization of the path, to allow the agent execute it in minimal time while both (i) ensuring the computational burden of extracting motion estimates stays below a set threshold and (ii) respecting the actuation constraints of the agent. We provide two efficient approximation algorithms for addressing the aforementioned problem. Also, we show how it can be reduced to a mixed integer linear program for which there exist well-developed optimization packages. Ultimately, we illustrate the performance of our algorithms in experiments using a quadrotor.