Abstract
Obstacle detection is an important component for many autonomous vehicle navigation systems. Several methods have been proposed using various active sensors such as radar, sonar and laser range finders. Vision based techniques have the advantage of relatively low cost and provide a large amount of information about the environment around an intelligent vehicle. This paper deals with the development of an accurate and efficient vision based obstacle detection method that relies on dense disparity estimation between a pair of stereo images. Firstly, the problem of disparity estimation is formulated as that of minimizing a quadratic objective function under various convex constraints arising from prior knowledge. Then, the resulting convex optimization problem is solved via a parallel block iterative algorithm which can be efficiently implemented on parallel computing architectures. Finally, we detect obstacles from the computed depth map by performing an object segmentation based on a surface orientation criterion.
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References
Labayrade, R., Aubert, D., Tarel, J.P.: Real time obstacle detection in stereovision on non flat road geometry through v-disparity representation. In: IEEE Intelligent Vehicle Symposium, vol. 2, pp. 646–651 (2002)
Bertozzi, M., Broggi, A., Fascioli, A., Lombardi, P.: Vision-based pedestrian detection: will ants help? IEEE Intelligent Vehicle Symposium 1, 17–21 (2002)
Yu, Q., Arajo, H., Wang, H.: Stereo-vision based real time obstacle detection for urban environments. In: ICAR 2003. 11th International Conference on Advanced Robotics, Coimbra, Portugal, vol. 3, pp. 1671–1676 (2003)
Demonceaux, C., Kachi-Akkouche, D.: Robust obstacle detection with monocular vision based on motion analysis. In: IEEE Intelligent Vehicles Symposium, Parme, Italie (June 2004)
Toulminet, G., Mousset, S., Bensrhair, A.: Fast And Accurate Stereo Vision-Based Estimation Of 3D Position And Axial Motion Of Road Obstacles. International Journal of Image Graphics 4(1), 99–126 (2004)
Labayrade, R., Royere, C., Gruyer, D., Aubert, D.: Cooperative fusion for multi-obstacles detection with use of stereovision and laser scanner. Autonomous Robots 19(2), 117–140 (2005)
Giachetti, A., Campani, M., Torre, V.: The use of optical flow for road navigation. IEEE Transactions on Robotics and Automation 14(1) (1998)
Hu, Z., Lamosa, F., Uchimura, K.: A complete U-V-Disparity study for stereovision based 3D driving environment analysis. In: Fifth International Conference on 3D Digital Imaging and Modeling, Ontario, Canada, pp. 204–211 (June 2005)
Parent, M., De La Fortelle, A.: Cybercars: Past, present and future of the technology. In: ITS World Congress 2005, San Francisco, USA (2005)
Fusiello, A., Trucco, E., Verri, A.: A compact algorithm for rectification of stereo pairs. Machine Vision and Applications 12(1), 16–22 (2000)
Miled, W., Pesquet, J.C., Parent, M.: Disparity map estimation using a total variation bound. In: CRV 2006. Proc. of the 3rd Canadian Conference on Computer and Robot Vision, Qubec, Canada, pp. 48–55 (June 7-9, 2006)
Gennert, M.A.: Brightness-based stereo matching. In: Proc. IEEE 2nd Int. Conf. Computer Vision, pp. 139–143. IEEE Computer Society Press, Los Alamitos (1988)
Combettes, P.L.: The foundations of set theoretic estimation. Proceedings of the IEEE 81(2), 182–208 (1993)
Combettes, P.L.: A block iterative surrogate constraint splitting method for quadratic signal recovery. IEEE Trans. on Signal Proc. 51, 1771–1782 (2003)
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60, 259–268 (1992)
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Miled, W., Pesquet, J.C., Parent, M. (2007). Robust Obstacle Detection Based on Dense Disparity Maps. In: Moreno Díaz, R., Pichler, F., Quesada Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2007. EUROCAST 2007. Lecture Notes in Computer Science, vol 4739. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75867-9_143
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DOI: https://doi.org/10.1007/978-3-540-75867-9_143
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