隨著科技的進步，對於室內空間資訊的需求越來越多，過去建立室內空間資訊的方法，時常受到造價昂貴、精準度不足和掃描速度緩慢等限制。在本論文中，我們基於三角測量法，分別以線型雷射掃描和結構投影掃描兩套系統建立空間點雲。兩套掃描系統皆採用主動式投射光源，適用於缺乏特徵點之環境，以建立完整之空間點雲。
　　在線型雷射掃描部分，我們對量測空間投射線型雷射光，接著從雷射光反射光的變形程度，從而推出深度距離的變化，並結合旋轉平台進行旋轉掃描，以非常低的成本建立出一套高精準度的室內掃描系統；結構投影系統部分，我們利用投影機投射特殊結構圖案，並以兩台相機同時拍攝影像，利用影像匹配找出對應座標點並算出空間座標資訊。由於結構投影掃描在單次拍攝即可產生大量點雲資料，因此可以用較少拍攝次數來完成待量測空間的掃描工作，此外我們優化其影像處理過程，大量減少了建立點雲資料所需要的影像處理時間。

　　As technology advances, one of the most demands is digitalized spatial information to overcome the problem by the old methods, which are expensive, imprecise and time consuming. In this study, we use a triangulation method to build two different systems, linear laser scan system and structural light scan system, to establish the point cloud. Because both scan systems use an active projection light source, they are suitable for environments that lack feature points, so that we can construct high-quality space point cloud under such environments.
　　For the linear laser scan system, we project linear laser light to the target space, and then obtain the depth information through the deformation of the laser line. Besides, a rotating platform is implemented to do scanning. Accordingly, we have successfully built an indoor scan system at low cost and high precision.
　　Secondly, we use a projector to project a specific structure pattern and use two cameras to capture images with the structure light. Then we apply image matching scheme to find the corresponding coordinates and get the space information. Since the system can produce a large number of point cloud in a single shot, only a few images are enough to complete the scan. Finally, we optimize the image processing to speed up the process in constructing the spatial point cloud.

1. C. H. Chen and K. T. Song, “Complete Coverage Motion Control of a Cleaning Robot Using Infrared Sensors,” IEEE International Conference on Mechatronics 2005, 543-548 (2005).
2. S. Kim and Y. Kim, “A mobile robot localization using ultrasonic sensors in indoor environment,” Robot and Human Communication, IEEE/RSJ international Conference on intelligent Robots and Systems 2003, 52-57 (2003).
3. Robot cleaner hits the shops.取自http://news.bbc.co.uk/2/hi/technology/3031219.stm。
4. E. Brassart, C. Pegard and M. Mouaddib, “Localization using infrared beacons,” Robotica 18(2), 153–161 (2000).
5. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” IJCV 60, 91-110 (2004).
6. H. Durrant-Whyte and T. Bailey, “Simultaneous Localization and Mapping (SLAM): Part I” IEEE Robotics & Automation Magazine 13(2), 99–110 (2006).
7. C. Tomasi and T. Kanade, “Shape and Motion from Image Streams Under Orthography: A Factorization Method,” Int. Journal of Computer Vision 9(2), 137-154 (1992).
8. J. Shan and C. K. Toth, Topographic Laser Ranging And Scanning, (CRC Press UK, 2008).
9. 林朝國，3D雷射掃描儀於現場測繪之應用，中央警察大學鑑識科學研究所碩士論文，中華民國一百零一年。
10. Kinect v1和Kinect v2的徹底比較。取自http://www.buildinsider.net/small/kinectv2cpp/01。
11. Kinect開發學習筆記之(一)kinect介紹與應用。取自http://blog.csdn.net/zouxy09/article/details/8145592。
12. 身體就是控制器，微軟Kinect是怎麼做到的？取自https://www.techbang.com/posts/2936-get-to-know-how-it-works-kinect。
13. J. W. Goodman, Introduction to Fourier optics (McGraw-Hill, 2002).
14. 陳俊君，立體視覺三維重建影像匹配方法的研究，國立高雄應用科技大學
土木工程與防災科技研究所碩士論文，中華民國一百零三年。
15. 魏守德，快速與穩健的樣型比對演算法及其應用，國立清華大學資訊工程學所博士論文，中華民國九十六年。
16. 邱胤華，林君明，「利用多組特徵樣板圖形進行IC晶片瑕疵定位及檢測之技術」，第13屆全國AOI論壇與展覽，新竹市 (2014)。
17. B. F. Alexander, “Elimination of systematic error in subpixel accuracy centroid
estimation,” Opt. Eng. 30(9), 1320-1331 (1991).
18. 吳季樺，光學質心法應用於光電量測系統之研究，國立中央大學光電科學與工程學系博士論文，中華民國九十三年。
19. T. C. Strand, “Optical three-dimensional sensing for machine vision,” Opt. Eng., 24(1), 33-40 (1985).
20. G. Berkovic and E. Shafir, “Optical methods for distance and displacement measurements,” ADV OPT PHOTONICS 4(4), 441-471 (2012).
21. 旋轉矩陣 (Rotation Matrix)。取自http://silverwind1982.pixnet.net/blog/post/165223625%E6%97%8B%E8%BD%89%E7%9F%A9%E9%99%A3-%28rotation-matrix%29。
22. M. Alexa, J. Behr, D. Cohen-Or, S. Fleishman, D. Levin and C.T. Silva, “Computing and rendering point set surfaces,” IEEE Transactions on Visualization and Computer Graphics 9(1), 3-15 (2003).
23. R. B. Rusu, N. Blodow, Z. C. Marton, M. Dolha and M. Beetz, “Towards 3D Point cloud based object maps for household environments,” ROBOT AUTON SYST 56(11), 927-941(2008).
24. K. Khoshelham and S. O Elberink, “ Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications” Sensors 12(2), 1437-1454 (2012)