中文摘要
自走車可以提供多樣化的應用，如日常用品的搬運,屋內的打掃。它可成為個人的日常生活上的好幫手。為了要達到這個目的，必須為自走車設計導航系統，使得自走車能航行在未知的環境中。在本論文之中，我們透過電腦視覺的技術，設計了自走車導航系統的兩個重要的部分。首先，我們提出解決自走車偵測前方障礙物的問題。其次，我們要解決的問題是應用先前的結果，以達到即時視覺辨認前方的導引標誌。
首先，我們應用立體視覺系統以對自走車前方的障礙物即時且精確的被偵測與定位。在這裡所使用的技巧是利用兩隻攝影機之間平面衍生視差的觀念。這項觀念讓我們可以很清楚的從影像中找到障礙物。為了要能精確的測量自走車與障礙物之間的距離，我們使用了Shi 和Tomasi的方法做特徵點的擷取，最後對擷取出的特徵點做三維的重建，由此可以建立出精確的障礙物地圖。
其次，我們提出了快速的視覺辨認演算法。這個方法是基於測量Hausdorff distance來辨識出不同標誌。結合前面障礙物偵測與定位的結果，可以刪除標誌不可能存在的區域，以減少被比對的影像。如此可以大幅的減少搜尋所需要的時間。

Abstract

An Autonomous Mobile Robot (AMR) offers various applications such as commodities moving or household cleaning could be a good personal assistant in our daily life. To achieve the purpose, a navigation system must be designed for leading the AMR moving in an unknown environment. In this thesis, computer vision techniques were employed for designing the AMR navigation system via computer vision techniques .Firstly, a stereo vision system has been set-up for detecting and locating the obstacles accurately in real-time. It used the concept of planar parallax of two CCD cameras. The planar parallax characteristic can quickly be used to detect the obstacles in the image plane. The method proposed by Shi and Tomasi has been used to extract the feature points and the distance between AMR and obstacles can be measured. With these messages, a map with obstacles in front of the AMR can be created for referenced. Secondly, an algorithm based on the Hausdorff distance was proposed for detecting the signs marked in the environment for some indicating purpose. By applying the previous result, the searching time for matching a special sign can be reduced largely.

參考文獻

1. Y. Aoyagi and T. Asakura, "A study on traffic sign recognition in scene image using genetic algorithms and neural networks", Proceedings of IECON'96(Taipei, Taiwan) ,Vol. 3, 1996, pp. 1838 -1843

2. N. Ayache and F. Lustman, " Trinocular Stereo Vision for Robotics", IEEE Trans. on Pattern Analysis and Machine Intelligence Vol.13, No.1, 1991, pp.73-85.

3. M. Bertozzi and A. Broggi , "GOLD: A Parallel Real-Time Stereo Vision System for Generic Obstacle and Lane Detection," IEEE Trans. on Image Processing, Vol.7, No.1, 1998, pp.62-81

4. A. de la Escalera, L. Moreno, M. Salichs, and J. Armingol, " Road traffic sign detection and classification", IEEE Trans. on Industrial Electronics, Vol.44, No.6, 1997, pp.848-858

5. A. Elfes , "Using occupancy grids for mobile robot perception and navigation", Computer Magazine, June 1989, pp. 46-57.

6. E. Elkonyaly, F. Areed, Y. Enab, and F. Zada, "Range sensory-based navigation in unknown terrains", in Proc. SPIE, Vol.2591, 1995, pp.76-85

7. O. Faugeras and Q.-T. Luong, The Geometry of Multiple Images, MIT Press, Cambridge, MA, 2001.

8. J. Hancock , M. Hebert and C. Thorpe, "Laser intensity-based obstacle detection Intelligent Robots and Systems", 1998 IEEE/RSJ International Conference on Intelligent Robotic Systems, Vol. 3 , 1998, pp.1541-1546

9. C. Harris and M. Stephens , "A combined corner and edge detector", Proceedings of the 4th Alvey Vision Conference, 1988, pages 147-151,

10. R. Hartley and P. Sturm, "Triangulation", Computer Vision and Image Understanding, Vol.68 , No 2, 1997, pp. 146-157.

11. R. Hartley and A. Zisserman, Multiple View Geometry in computer vision, Cambridge University Press, Cambridge, 2000.

12. H. Hashimoto, T. Kubota, M. Sato and F. Harashima, "Visual control of robotic manipulator based on neural networks Industrial Electronics", IEEE Trans. on , Vol.39, No. 6, 1992 , pp.490-496

13. B. Heisele and W. Ritter, "Obstacle detection based on color blob flow", Proceedings Intelligent Vehicles Symposium '95, Detroit, 1995 , pp. 282-286

14. H. Ishiguro and S. Tsuji, "Active Vision By Multiple Visual Agents", Proceedings of the 1992 lEEE/RSJ International Conference on Intelligent Vehicles, Vol.3, 1992,pp. 2195 -2202

15. W. Kruger, W. Enkelmann, and S. Rossle, "Real-time estimation and tracking of optical flow vectors for obstacle detection", Proceedings of the Intelligent Vehicles Symposium, Detroit, 1995, pp.304-309

16. M.K. Leung, Liu Yuncai and T.S. Huang, "Estimating 3d vehicle motion in an outdoor scene from monocular and stereo image sequences", Proceedings of the IEEE Workshop on Visual Motion,1991, pp. 62-68

17. L.M. Lorigo, R.A. Brooks and W.E.L. Grimsou, "Visually-guided obstacle avoidance in unstructured environments", Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vol.1 , 1997, pp.373-379

18. M.I.A. Lourakis and S.C. Orphanoudakis, "Using planar parallax to estimate the time-to-contact", 1999 IEEE Computer Vision and Pattern Recognition Conference, Fort Collins, Colorado, USA, vol. 2,1999, pp. 640-645

19. Q.-T. Luong, J. Weber, D. Koller, and J. Malik, "An integrated stereo-based approach to automatic vehicle guidance", In 5th International Conference on Computer Vision, June 1995, pages 52-57.

20. G. Piccioli et. al. , "Robust method for road sign detection and recognition", Image and Vision Computing, Vol.14, 1996, pp.209-223.

21. L. Robert, M. Buffa , and M. Hebert., "Weakly-calibrated stereo perception for robot navigation", Proc. 5th International Conference on Computer Vision, Cambridge, MA , June 1995 , pp. 46-51.

22. W. J. Rucklidge. Efficient Visual Recognition Using the Hausdorff Distance, Springer-Verlag, Berlin, 1996.

23. P. Seitz, G. K. Lang, B. Gilliard, and J. C. Pandazis, "The robust recognition of traffic signs from a moving car", In Proc.13th DAGM Symposiun on pattern recognition, 1991,pages 287-294.

24. J. Shi and C. Tomasi, "Good features to track", In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 1994 , pp.593-600

25. M. Xie , L. Trassoudaine , J. Alizon, and Gallice , "Road obstacle detection and tracking by an active and intelligent sensing strategy", Machine Vision and Applications, Vol.7, 1994, pp.165-177.

26. http://www.cs.cornell.edu/Info/People/dph/hausdorff/hausdorff.html