Optic-Flow Based Control of a 46g Quadrotor

We aim at developing autonomous miniature hov- ering flying robots capable of navigating in unstructured GPS- denied environments. A major challenge is the miniaturization of the embedded sensors and processors allowing such platforms to fly autonomously. In this paper, we propose a novel ego-motion estimation algorithm for hovering robots equipped with inertial and optic-flow sensors that runs in real- time on a microcontroller. Unlike many vision-based methods, this algorithm does not rely on feature tracking, structure estimation, additional distance sensors or assumptions about the environment. Key to this method is the introduction of the translational optic-flow direction constraint (TOFDC), which does not use the optic-flow scale, but only its direction to correct for inertial sensor drift during changes of direction. This solution requires comparatively much simpler electronics and sensors and works in environments of any geometries. We demonstrate the implementation of this algorithm on a miniature 46g quadrotor for closed-loop position control

Estimating ego-motion in panoramic image sequences with inertial measurements

This paper considers the problem of estimating the focus of expansion of optical flow fields from panoramic image sequences due to ego-motion of the camera. The focus of expansion provides a measurement of the direction of motion of the vehicle that is

Estimating Ego-Motion in Panoramic Image Sequences with Inertial Measurements

This paper considers the problem of estimating the focus of expansion of optical flow fields from panoramic image sequences due to ego-motion of the camera. The focus of expansion provides a measurement of the direction of motion of the vehicle that is a key requirement for implementing obstacle avoidance algorithms. We propose a two stage approach to this problem. Firstly, external angular rotation measurements provided by an on-board inertial measurement unit are used to de-rotate the observed optic flow field. Then a robust statistical method is applied to provide an estimate of the focus of expansion as well as a selection of inlier data points associated with the hypothesis. This is followed by a least squares minimisation, utilising only the inlier data, that provides accurate estimates of residual angular rotation and focus of expansion of the flow. The least squares optimisation is solved using a geometric Newton algorithm. For the robust estimator we consider and compare RANSAC and a k-means algorithm. The approach in this paper does not require explicit features, and can be applied to patchy, noisy sparse optic flow fields. The approach is demonstrated in simulations and on video data obtained from an aerial robot equipped with panoramic cameras