A Dynamic Vision Algorithm to Locate a Vehicle on a Nonstructured Road

Abstract

In this article, we present a method of nonmarked road following that is based on images coming from an onboard monochromatic camera. The principle is based first on a segmentation stage that makes it possible to locate the road area in the image, managing, if possible, the shadows on the roadway. The method is original since the algorithm must be running day as well as night (infrared camera) so it does not use color images. Furthermore, a single constant threshold is used whatever the analyzed sequence. Then, a localization stage estimates the vehicle’s location on the roadway. The estimate of the parameters L (road width) and α (camera inclination angle) (assumed known and constant for certain existing approaches) ensures a greater robustness of the other estimated parameters. Finally, a filtering stage is applied onto the previous data and predicts the position of the vehicle in the next image. Results are shown for each stage on both a normal nonmarked road and a forest lane sequence. The computational times are very low and will permit a real-time implementation on an experimental vehicle.

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References

Broggi, A. 1995. An image reorganization procedure for automotive road following systems. International Conference on Image Processing. Washington, DC: IEEE Computer Society .

Broggi, A., Bertozzi, M., Gregoretti, F., Passerone, F., Sansoè, C., and Reyneri, L. 1997. A dedicated image processor exploiting both spatial and instruction-level parallelism. CAMP’97 Computer Architectures for Machine Perception. Boston: IEEE Computer Society .

Chapuis, R., Potelle, A., Brame, J., and Chausse. 1995. Real time vehicle trajectory supervision on highway . International Journal of Robotics Research 14(6): 531–542 .

Denasi, S., Lanzone, C., Martinese, P., Pettiti, G., and Quagli, G. 1994. Real time system for road following and obstacle detection . Proceedings of SPIE. Vol. 2347 Machine Vision Applications, Architectures, and System Integration III, Boston, MA, pp. 70–79 .

Dickmanns, E. D., Behringer, R., Brudigam, C., Dickmanns, D., Thomanek, F., and Holt, V. 1993. An all-transputer visual autobahnautopilot/copilot. 4th ICCV Berlin Germany, pp. 608–615.

Dickmanns, E. D., and Mysliewetz, B. D. 1992. Recursive 3d road and relative ego-state recognition . Trans. on Pattern Analysis and Machine Intelligence 14(2): 199–213 .

Diebolt, F. 1996. Road markings recognition. ICIP96, Lausanne, Switzerland , p. 17P8-17P8.

Ekinci, M., and Thomas, B. 1996. Road junction recognition and turn-offs for autonomous road vehicle navigation. ICPR96, Vienne, Austria , p. C7A.2-C7A.2.

Ferruz, J., and Ollero, A. 1997. Autonomous mobile robot control in non-structured environments based on real-time video processing. IROS 97, Grenoble, France , pp. 725–731.

10.

Huang, C., and Gillies, D. 1995. Determination of an autonomous vehicle position by matching the road curvature. Proceedings of SPIE. Vol. 2591 Mobile Robot X, Philadelphia, PA , pp. 14–24.

11.

Kaske, A. 1997. Contribution à la Détection de Bords de Route imprécis. Implantation sur le Robot ROMANE. Ph.D. thesis, Institut National Polytechnique de Lorraine.

12.

Kaske, A., Wolf, D., and Husson, R. 1997. Lane boundary detection using statistical criteria . International Conference on Quality Control by Artificial Vision. QCAV’97, Le Creusot, France, pp. 324–329 .

13.

Kluge, K., and Thorpe, C. 1989. Explicit models for robot road following . IEEE International Conference on Robotics and Automation 2: 1148–1154 .

14.

Kluge, K., and Thorpe, C. 1995. The YARF system for vision-based road following . Mathematical and Computer Modeling 22(4-7): 213–233 .

15.

Kuan, D., and Sharma, K. 1987. Model based geometry reasoning for autonomous road following. Proc. on the 1987 IEEE International Conf. on Robotics and Automation, Raleigh, NC , pp. 416–423.

16.

Liu, J., Wu, Y., Liu, K., and Yang, J. 1994. Color road segmentation for ALV road following . Proceedings of SPIE. Vol. 2058. Mobile Robot VIII, Boston, MA, pp. 116–126 .

17.

Lowrie, J., Thomas, M., Gremban, K., and Turk, M. 1985. The autonomous land vehicle (ALV) preliminary road-following demonstration. Proc./SPIE. Vol. 579. Intelligent Robots and Computer Vision IV, Cambridge, MA , pp. 336–350.

18.

Rousseeuw, P. J., and Hubert, M. 1997. Recent developments in progress. In Statistical Procedures and Related Topics, vol. 31, pp. 201–214, Dodge Y. Institute of Mathematical Statistics Lecture Notes-Monograph Series, Hayward, CA .

19.

Spiegel, S., McIngvale, P., Olson, K., Scales, A., and Larsen, K. 1995. Autonomous navigation for unmaned ground vehicles . Proceedings of SPIE 2463: 206–216 .

20.

Thorpe, C., Hebert, M., Kanade, T., and Shafer, S. A. 1988. Vision and navigation for the Carnegie-Melon Navlab . IEEE Trans. on Pattern Analysis and Machine Intelligence 10(3): 362–373 .

21.

Turk, M. A., Morgenthaler, D. G., Gremban, K. D., and Marra, M. 1988. VITS-A vision system for autonomous land vehicle navigation . IEEE Trans. on Pattern Analysis and Machine Intelligence 10(3): 342–360 .