Abstract
This paper presents an approach to free space and obstacle detection based on stereo disparity, with applications in mobile robotics. Two maps of an area of interest on the ground plane are computed from the left and right views. Obstacles rising up from the ground occlude different areas in both maps. An obstacle map is computed as the binarized difference between the two maps. Morphological filtering is used to detect the free space from the obstacle map, while obstacles are detected by looking for instances of a suitable obstacle model. Results of the approach in road scenes are presented. Stereo images are taken from a car, with the objective of detecting other cars and free space in cluttered scenes for driving assistance.
Work partially supported by Fundació Caixa Castellö and Generalitat Valenciana (project CV97-TI-05-26)
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F. Thomanek, E. D. Dickmanns, and D. Dickmanns. Multiple object recognition and scene interpretation for autonomous road vehicle guidance. In IEEE Intelligent Vehicles'94, pages 231–236, 1994.
T. Zielke, M. Brauckmann, and W. von Seelen. Intensity and edge-based symmetry detection with an application to car-following. CVGIP: Image Understanding, 58:177–190, 1993.
W. Enkelmann. Obstacle detection by evaluation of optical flow field from image sequences, volume 427, pages 134–138. Springer Verlag, LNCS, 1990.
W. Kruger, W. Enkelmann, and S. Rossle. Real-time estimation and tracking of optical flow vectors for obstacle detection. In IEEE Intelligent Vehicles'95, pages 304–309, 1995.
Y. Zheng, D. G. Jones, S. A. Billings, J. E. W. Mayhew, and J. P. Frisby. Switcher: a stereo algorithm for ground plane obstacle detection. Image and Vision Computing, 8:57–62, 1990.
L. Matthies. Stereo vision for planetary rovers: stochastic modeling for near realtime implementation. International Journal of Computer Vision, 8:71–91, 1992.
J. M. Sanchiz. Calibration of a stereo rig based on cost function minimization. Technical Report DI 03-10/97, Universitat Jaume I, Castelló, Spain, 1997.
M. Bertozzi and A. Broggi. Vision-based vehicle guidance. Computer, July:49–55, 1997.
J. Serra. Image Analysis and Mathematical Morphology. Academic Press. London, 1982.
E. D. Dickmanns and B. D. Myslivwetz. Recursive 3-d road and relative ego-state recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14:199–213, 1992.
M. Schwartzinger, T. Zielke, D. Noll, M. Brauckmann, and W. von Seelen. Vision-based car following: detection, tracking, and identification. In IEEE Intelligent Vehicles'92, pages 24–29, 1992.
B. Ullmer. Vita–an autonomous road vehicle for collition avoidance in traffic. In IEEE Intelligent Vehicles'92, pages 36–41, 1992.
C. Thorpe. Vision and navigation: the Carnegie-Mellon Navlab. Kluwer Academic Publishers, 1990.
M. A. Turk, D. G. Morgenthaler, K. D. Gremban, and M. Marra. Vits — a vision system for autonomous land vehicle navigation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10:432–361, 1988.
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© 1998 Springer-Verlag Berlin Heidelberg
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Sanchiz, J.M., Broggi, A., Pla, F. (1998). Stereo vision-based obstacle and free space detection in mobile robotics. In: Pasqual del Pobil, A., Mira, J., Ali, M. (eds) Tasks and Methods in Applied Artificial Intelligence. IEA/AIE 1998. Lecture Notes in Computer Science, vol 1416. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64574-8_414
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DOI: https://doi.org/10.1007/3-540-64574-8_414
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