Self-calibration is a fundamental technology used to estimate the relative posture of the cameras for environment recognition in unmanned system. We focused on the issue of recognition accuracy decrease caused by the vibration of platform and conducted this research to achieve on-line self-calibration using feature point's registration and robust estimation of fundamental matrix. Three key factors in this respect are needed to be improved. Firstly, the feature mismatching exists resulting in the decrease of estimation accuracy of relative posture. The second, the conventional estimation method cannot satisfy both the estimation speed and calibration accuracy at the same tame. The third, some system intrinsic noises also lead greatly to the deviation of estimation results. In order to improve the calibration accuracy, estimation speed and system robustness for the practical implementation, we discuss and analyze the algorithms to make improvements on the stereo camera system to achieve on-line self-calibration. Based on the epipolar geometry and 3D images parallax, two geometry constraints are proposed to make the corresponding feature points search performed in a small search-range resulting in the improvement of matching accuracy and searching speed. Then, two conventional estimation algorithms are analyzed and evaluated for estimation accuracy and robustness. The third, Rigorous posture calculation method is proposed with consideration of the relative posture deviation of each separated parts in the stereo camera system. Validation experiments were performed with the stereo camera mounted on the Pen-Tilt Unit for accurate rotation control and the evaluation shows that our proposed method is fast and of high accuracy with high robustness for on-line self-calibration algorithm. Thus, as the main contribution, we proposed methods to solve the on-line self-calibration fast and accurately, envision the possibility for practical implementation on unmanned system as well as other environment recognition systems.