Homography-based Vision-aided Inertial Navigation of UAVs in Unknown Environments

This paper investigates the navigation of small-scale unmanned aerial vehicles (UAVs) in unknown and GPS-denied environments. We consider a UAV equipped with a low-cost inertial measurement unit (IMU) and a monocular camera. The IMU can measure the specific acceleration and angular rate of the UAV. The IMU measurements are assumed to be corrupted by white noises and unknown constant biases. Hence the position, velocity and attitude of the UAV estimated by pure IMU dead reckoning will all drift over time. The monocular camera takes image sequences of the ground scene during flight. By assuming the ground scene is a level plane, the vision measurement, homography matrices, can be obtained from pairs of consecutive images. We propose a novel approach to fuse IMU and vision measurements by using an extended Kalman filter (EKF). Unlike conventional approaches, homography matrices are not required to be decomposed. Instead, they are converted to vectors and fed into the EKF directly. In the end, we analyze the observability of the proposed navigation system. We show that the velocity and attitude of the UAV and the unknown biases in IMU measurements are all observable when noisy yaw angle can be measured using a magnetometer. Numerical simulations verify our observability analysis and show that all UAV states except the position can be estimated without drift. The position drift is significantly reduced compared to the IMU dead reckoning. I