Objects motion estimation via BSP tree modeling and Kalman filtering of stereo images

In this paper a computationally efficient algorithm for real time estimation of the position and orientation of moving objects from visual measurements of a system of fixed cameras is proposed. The algorithm is based on extended Kalman filtering of the measurements of the position of suitable feature points selected on the target objects. The effectiveness of the algorithm is improved by using a pre-selection method of the feature points which takes advantage of the Kalman filter prediction capability combined with a BSP tree modeling technique of the objects geometry. Computer simulations are presented to test the performance of the estimation process in the presence of noise, different types of lens geometric distortion, quantization and calibration errors.