Distance estimation algorithm for both long and short ranges based on stereo vision system

We present a distance measurement method based on stereo vision system while guaranteeing accuracy and reliability. It has been considered as difficult problem to measure both long and short distance with a stereo vision system accurately due to sampling error and camera sensor error. To resolve these problems of the stereo vision system, we utilize an algorithm which is consisted of a modified sub-pixel displacement method to enhance the accuracy of disparity and strong tracking Kalman filter (STKF) to reduce the camera sensor errors. Our displacement method and the usefulness of STKF are verified as compared to other displacement methods and conventional Kalman filter (CKF) through simulating on the several distance ranges. The Monte-Carlo simulation results show that our algorithm is capable of measuring up to hundreds of meters while root mean square error (RMSE) maintains about 0.04 at all ranges, even though the target vehicle maneuvers or moves nonlinearly.