Abstract
In this paper, we focus on the use of Kalman filter approach to lane extraction and following. We assume a structured environment where a mobile vehicle equipped with a camera sensor, situated at a fixed distance from the vehicle frame, is navigating. A quadratic model of the road is considered. This enables the state vector of the filter to be coincided with the three parameters pertaining to a second-order polynom. The determination of the state model is carried out considering either a pure translation of the (i+1)th frame with respect to the ith frame attached respectively to the images at time t i and t i+1, or a combination of both translation and rotation. While the measurement model comes down to the camera images obtained using the inverse perspective transformation, which due to the road model, permits a straightforward link between the x–y co-ordinates of images and the state vector parameters of the road. The performance of these two state models are compared with a purely measurement approach where least squares methodology is performed regardless the state model.
The estimates of the filter are used by the vehicle in order to update its own knowledge about the environment and to accomplish the task of “road following”.
Furthermore, this permits the vision system to encompass into a more general structure of Integrated Supervisory Control Systems (ISCS), where multiple functionalities are allowed.
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References
Ayache, N. and Faugeras, O. D.: Maintaining representation of environment of a mobile robot, IEEE Trans. on Robotics and Automation 5(6) (1989), 804–819.
Bar Shalom, Y. and Rong Li, X.: Estimation and Tracking: Principles, Techniques and Software, Artech House, 1993.
Dickmanns, E. D. and Zapp, A.: A curvature-based scheme for improving road vehicle guidance by computer vision, SPIE, Mobile Robots 727 (1986), 161–168.
Harris, J. and Stocker, H.: Handbook of Mathematics and Computation Science, Springer, 2000.
Kanatani, K. and Watanabe, K.: Reconstruction of 3D road geometry from images for autonomous land vehicles, IEEE Trans. on Robotics and Automation 6(1) (1990), 127–132.
Sahli, H., De Muynck, P. and Cornelis, J.: A Kalman filter-based update scheme for road following, in: Proc. of MVA'96, IAPR Workshop on Machine Vision Applications, Tokyo, Japan, 1996, pp. 5–9.
Sheridan, T. B.: Telerobotics, Automation, and Human Supervisory Control, MIT Press, Cambridge, MA, 1992.
Soatto, S., Frezza, R. and Perona, P.: Motion estimation via Dynamic, IEEE Trans. on Automatic Control 41(3) (1996), 393–413.
Sorenson, H. W.: Kalman Filtering: Theory and Applications, IEEE Press, New York, 1985.
Tsai, Y.: An efficient and accurate camera calibration technique for 3D machine vision, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, 1986, pp. 364–374.
Tuytelaars, T., Zaatri, A., Van Brussel, H. and Van Gool, L.: An object recognition as a part of a supervisory control system, in: ICRA'2000, San Francisco, USA, 2000.
Zaatri, A.: Investigation into Integrated Supervisory Control Systems, Ph.D. Thesis, K.U. Leuven, 2000.
Zaatri, A. and Van Brussel, H.: Investigations in telerobotics using co-operative supervisory modes of control, in: Proc. of the SPIE Conf. the Int. Society for Optical Engineering, Vol. 3206, Pittsburg, PA, Oct. 1997, pp. 41–52.
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Oussalah, M., Zaatri, A. & Van Brussel, H. Kalman Filter Approach for Lane Extraction and Following. Journal of Intelligent and Robotic Systems 34, 195–218 (2002). https://doi.org/10.1023/A:1015694125384
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DOI: https://doi.org/10.1023/A:1015694125384