Vision-Aided Inertial Navigation Based on Ground Plane Feature Detection

In this paper, a motion estimation approach is introduced for a vision-aided inertial navigation system. The system consists of a ground-facing monocular camera mounted on an inertial measurement unit (IMU) to form an IMU-camera sensor fusion system. The motion estimation procedure fuses inertial data from the IMU and planar features on the ground captured by the camera. The main contribution of this paper is a novel closed-form measurement model based on the image data and IMU output signals. In contrast to existing methods, our algorithm is independent of the underlying vision algorithm for image motion estimation such as optical flow algorithms for camera motion estimation. The algorithm has been implemented using an unscented Kalman filter, which propagates the current and the last state of the system updated in the previous measurement instant. The validity of the proposed navigation method is evaluated both by simulation studies and by real experiments.