基於水下載具的發展愈漸成熟，根據不同的環境與需求，可以安裝相對應的感測器來達成任務。因此本文研究主要目的在架設一個相機模組搭配線形雷射光作為輔助，利用即時影像處理技術來量測並記錄障礙物的深度資訊，同時將掃描範圍內的資訊進行分析與組合，建構出包含深度的3D資料。本研究的理論基礎是在單點雷射測距的理論上進行延伸，當待測物與相機模組的距離改變時，則雷射光在CCD上的成像亦會不同，利用此原理將單點雷射光更換為線形雷射光，則線形雷射光照射到的每一點都可被視為一筆距離資料，而資料的數據量取決於相機的解析度。將此相機模組與待測物以一定速度做相對運動並同時記錄下各個位置的數據，則經過分析與拼接後便能繪製出待測面的深度圖。在後續研究中，目標將此相機模組朝下安裝於一無人水下載具，透過移動載具來蒐集所經過範圍之地形資料，藉此達成建構水下地形之目標。

Based on the development of underwater vehicle is becoming more and more mature. According to different environments and demands, we can install the corresponding sensor to achieve the task. Therefore, the main purpose of this research is to set up a camera module with a linear laser instrument. Use real-time image processing technology to measure and record the depth information of obstacles, and at the same time, analyze and combine the numeric to construct 3D data. The theoretical basis of this research is to extend the theory of single-point laser ranging. When the distance between the object and the camera is changed, the image of the laser light on the CCD will also be different, using this principle to replace single-point laser light with line laser light. Every point projected by the linear laser light can be regarded as one of the distance data. Let the camera module and the object move relative to each other at a certain speed and record the data of each position at the same time. Then we can draw the depth map of obstacles by analyzing and combining the data. In the follow-up study, the goal is to install the camera module downwards on the unmanned underwater vehicle to collect terrain data, thereby achieving the goal of constructing underwater terrain.

1. Caarullo A., and Parvis M., “An ultrasonic sensor for distance measurement in automotive applications,” IEEE Sensors Journal, Vol. 1, No. 2, pp.143-147, 2001.
2. Chen H. H., and Wu C. M., “An Algorithm of Image Processing for Underwater Range Finding by Active Triangulation,” Ocean Engineering, Vol.31, Nos.8-9, pp.1037-1062, 2004.
3. Chen H. H., “Vision-based tracking with projective mapping for parameter identification of remotely operated vehicles,” Ocean Engineering Volume 35, Issue 10, July 2008, pp. 983-994.
4. Culshaw B., Pierce G., and Jun P., “Non-contact measurement of the mechanical properties of materials using an all-optical technique,” IEEE Sensor J., vol. 3, no.1, pp. 62-70, Feb. 2003.
5. Gonzalez R. C., and Woods R. E., Digital Image Processing, Addison Wesley Publishing Company, pp. 191-200, 1992.
6. Gulden P. G., Becker D., and Vossiek M., “Novel optical distance sensor based on MSM technology,” IEEE Sensors J., vol. 4, no. 5, pp. 612-618, Oct. 2004.
7. José L. L., Angel E. C., Pedro R. F., and Yamilet P., “Sensor for Distance Measurement Using Pixel Gray-Level Information,” Sensors 2009, 9, 8896-8906, doi:10.3390/s91108896.
8. Kim H. I., Kim D. E., Cha Y. S., Lee K. H., and Kuc T. Y., “An embodiment of stereo vision system for mobile robot for real-time measuring distance and object tracking,” Control, Automation and Systems, 2007.ICCAS’07. International Conference on, vol., no., pp.1029-1033, 17-20 Oct. 2007.
9. KlimKov Y. M., “A laser polarimetric sensor for measuring angular displacement of objects,” in Proc. Eur. Conf. Laser and Electro-Optics, pp.190-190.Sep,8-13, 1996.
10. Li L., Feng Z., and Feng Y., “Accurate Calibration of Stereo Cameras for Machine Vision,” JCS&T, Vol. 4, No. 3, Oct. 2004, pp. 147-150.
11. Liu Z., and Chen T., “Distance Measurement System Based on Binocular Stereo Vision,” Artificial Intelligence, 2009.JCAI’09. International Joint Conference on, Vol., no., pp.456-459, 25-26 April 2009.
12. Lu M. C., Wang W. Y., and Chu C. Y., “Image-based distance and area measuring system,” Sensor Journal, IEEE, vol.6, no.2, pp.495-503, April 2006.
13. Muljowidodo K., Rasyid M. A., SaptoAdi N., and Agus B., “Vision based distance Measurement system using single laser pointer Design for underwater vehicle”, Indian Journal of Marine Sciences Vol. 38(3), pp.324-331, September 2009.
14. Nan J., and Zhongding J., “Distance Measurement from Single Image Based on Circles,” Acoustics, Speech and Signal Processing. ICASSP 2007. IEEE International Conference on, vol.1, no., pp. I-809-I-812,2007.
15. Otsu N., “A Threshold Selection Method from Gray-Level Histograms,” IEEE Transactions on System, Man, and Cybernetics, Vol. 9, No. 1, pp.62-66, 1979.
16. Tsai R., “A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses,” Robotics and Automation, IEEE Journal of, vol. 3, no. 4, pp. 323-344, August 1987.
17. Woods R. E., and Gonzalez R. C., Digital Image Processing, 3^rded., Prentice Hall, 2008.
18. Wu J., Li Q., Fei W., “A novel location method based on 2D laser scanning sensor terrain matching for UAV autonomous flight,” IET International Conference on Information Science and Control Engineering, 2012 .
19. Yasuda A., Kuwashima S., and Kanai Y., “A shipborne- type wave-height meter for oceangoing vessels, using microwave Doppler radar,” IEEE J. Ocean. Eng., vol. OE-10, no.2, pp.138-143, Apr. 1985.
20. Zhengyou Z., “Flexible camera calibration by viewing a plane from unknown orientations,” Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, vol.1, no., pp.666-673 vol.1,1999.
21. 吳佳瑋, 影像處理技術運用於距離估測及影像亮度形心估算之研究國立成功大學系統及船舶機電工程學系碩士論文,2011.
22. 連國珍, 數位影像處理MATLAB, 儒林圖書有限公司, 2009.
23. 賴岱佑, 劉敏, 數位影像處理技術手冊,文魁資訊股份有限公司,2009.