A homography-based visual inertial fusion method for robust sensing of a Micro Aerial Vehicle

Abstract

The combination of a camera and an Inertial Measurement Unit (IMU) has received much attention for state estimation of Micro Aerial Vehicles. 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 an IMU, a homography model is developed and it is proposed to directly obtain speed (up to scale) from the homography matrix without performing Singular Value Decomposition (SVD) afterwards. The visual output is then fused with the inertial measurements using the Extended Kalman Filter (EKF) to estimate metric speed, distance to the scene and also acceleration bias. Real images and IMU data are collected from our quadrotor platform to evaluate the accuracy of the proposed approach.