A 3D Motion and Structure Estimation Algorithm for Optical Head Tracker System

In this paper, a 3D motion and structure estimation algorithm using the extended Kalman filter (EKF) for the optical head tracker system is presented. The performance of this algorithm is evaluated by an experimental optical head tracker system. This system is composed of infrared LEDs and two infrared CCD cameras, which are used to filter out the interference of another light in a limited environment like the cockpit. Then, the feature detection algorithm, used to obtain the 2D position coordinates of the features (infrared LED) on the image plane, is implemented by using the thresholding and the masking techniques. The state vector of the 3D motion and structure estimation algorithm consists of nine variables such as the positions, the velocities and the angular rates of the head frame with respect to the camera reference frame. The measurement model of the EKF is obtained from the perspective camera model and the 3D motion model. Based on the derived system and measurement models, the angular rate of the pilot’s head is estimated by using the EKF since the rotation of the 3D motion model is expressed as the incremental rotation quaternion. To verify the experimental optical head tracker system, we used the rate table to compare the rotational performance of this tracker system with the inertial sensor.