Metric sensing and control of a quadrotor using a homography-based visual inertial fusion method

Abstract

The combination of a camera and an Inertial Measurement Unit (IMU) has received much attention for state estimation of Micro Aerial Vehicles (MAVs). In contrast to many map based solutions, this paper focuses on optic flow (OF) based approaches which are much more computationally efficient. The robustness of a popular OF algorithm is improved using a transformed binary image from the intensity image. Aided by the on-board IMU, a homography model is developed in which it is proposed to directly obtain the speed up to an unknown scale factor (the ratio of speed to distance) from the homography matrix without performing Singular Value Decomposition (SVD) afterwards. The RANSAC algorithm is employed for outlier detection. Real images and IMU data recorded from our quadrotor platform show the superiority of the proposed method over traditional approaches that decompose the homography matrix for motion estimation, especially over poorly-textured scenes. Visual outputs are then fused with the inertial measurements using an Extended Kalman Filter (EKF) to estimate metric speed, distance to the scene and also acceleration biases. Flight experiments prove the visual inertial fusion approach is adequate for the closed-loop control of a MAV.