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題名 以場景知識為基礎自動分類 車載光達點雲中之圓桿地物
Scene Knowledge-based Automatic Classification of Pole-like Objects from Vehicle-based LiDAR Point Cloud作者 吳志文
Wu, Chih Wen貢獻者 邱式鴻
Chio, Shih Hong
吳志文
Wu, Chih Wen關鍵詞 車載雷射掃描
點雲
圓桿地物
場景知識
地物分類
vehicle-based laser scanning
point cloud
pole-like object
scene knowledge
classification日期 2013 上傳時間 1-Jun-2015 12:19:18 (UTC+8) 摘要 車載雷射掃描(Vehicle-based Laser Scanning, VLS)系統可以直接獲取路廊的三維點雲(point cloud)資料，因此可用來獲取詳盡的路廊(road corridor)資訊，路廊資訊可進一步應用於噪音模擬、道路安全、道路及相關設施維護、定位服務、汽車和行人導航，甚至發展未來駕駛協助系統(future driver assistance system)，而過去許多研究中提到圓桿地物(如路樹、路燈、交通號誌桿、電力桿、電信桿等)為重要之地物，且已發展某些偵測與萃取方式。然而圓桿地物半徑不一致、傾斜、附著物干擾、地物緊鄰等問題導致其於萃取與偵測過程不易，因此本研究嘗試加入場景知識(scene knowledge)與運用RANSAC(RANdom SAmple Consensus)的概念協助偵測圓桿地物，並加以分類。首先，以車載雷射掃描系統所記錄之車行軌跡為基礎將點雲分離成地面點與非地面點，其後以八分樹(Octree)結構化與CCL演算法(connected-component labeling algorithm)將非地面點雲初步分割成數個點群。完成初步點雲分割後，則濾除鄰地地物點干擾、判斷點群中桿狀物數量以及再分割點群，以解決地物緊鄰之情形。最後，針對經分割後之點群，偵測與定位圓桿地物，並且加以細分成六種類型的圓桿地物。經兩個實驗區成果顯示，本研究藉由場景知識與RANSAC概念協助處理圓桿地物偵測與分類，其偵測之漏授率低於35%，且誤授率為29%，但受限於點雲密度、地物遮蔽、過多附著物、地物緊鄰等因素，使圓桿地物分類之整體精度仍低於45%。然而與人工數化成果比較，圓桿地物定位精度在x坐標方向RMSE約為0.040 m，在y坐標方向RMSE約為0.040 m，半徑r的RMSE約為0.020 m，已足夠公路設施基本資料清查規範之精度需求。
Vehicle-based laser scanning (VLS) system can be employed to directly collect huge 3D point clouds for the extraction of detailed road corridor information. The detailed road corridor information can be utilized for noise modeling, road safety, the maintenance of relevant road facilities, location-based services, navigation for cars and pedestrians, even for the development of future driver assistance system. Many past studies mentioned Pole-like Objects (PLOs) are important objects in road corridor information. However, many methods in past studies still cloud not overcome some problems, such as PLOs with different radius, tilt PLOs, attachments on the PLOs and PLOs near other objects. Therefore, this study will introduce scene knowledge and RANSAC method for PLOs detection and classification. First, point cloud will be segmented to ground points and non-ground points through the knowledge of the trajectory of vehicle traveling. Then, connected-component labeling (CCL) algorithm is used for point grouping by initial segmentation from the non-ground points in octree-structured voxel space. After initial segmentation, the near-ground points in each point group will be filtered and the numbers of candidate poles in each point group will be determined for further re-segmentation for extracting the PLOs. Finally, the PLOs will be extracted from the point groups and classified.The result shows the omission of PLOs detection is lower than 35% and the commission of PLOs detection is 29%. However, the overall accuracy of PLOs classification is 45% due to sparse point density, object occlusion, too many attached objects on the PLOs and adjacent objects. Moreover, the circular parameters of vertical PLOs, i.e. the coordinate of circular center and the circular radius, are checked with those measured manually in this study. The RMSEs in X, Y coordinate components are about 0.040m and 0.040m, respectively, and the RMSE of circular radius is about 0.020m. 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C., 1981, “Random Sample Consensus: a Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography”, Communications of the ACM, 24(6): pp. 381–395.Golovinskiy, A., Kim, V.G. and Funkhouser, T., 2009, “Shape-based Recognition of 3D Point Clouds in Urban Environments”, In Computer Vision, 2009 IEEE 12th International Conference on IEEE, Kyoto, September 29 - October 2.Hoover, A., Jean-Baptiste, G., Jiang, X., Flynn, P.J., Bunke, H., Goldgof, D.B., Bowyer, K., Eggert, D.W., Fitzgibbon, A., and Fisher, R.B., 1996, “An Experimental Comparison of Range Image Segmentation Algorithms”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(7): pp. 1-17.Ishikawa K., Tonomura , F., Amano, Y., Hashizume, T., 2013, “Recognition of Road Objects from 3D Mobile Mapping Data”, International Journal of CAD/CAM, 13(2): pp. 41~48.Kukko, A., Andrei, C.-O., Salminen, V.-M., Kaartinen, H., Chen, Y., Rönnholm, P., Hyyppä, H., Hyyppä, J., Chen, R., Haggrén, H., Kosonen, I. and Čapek, K., 2007, “Road Environment Mapping System of the Finnish Geodetic Institute -FGI Roamer-”, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 3(3/W52): pp. 241-247.Lehtomäki, M., Jaakkola, A., Hyyppä, J., Kukko, A. and Kaartinen, H., 2010, “Detection of Vertical Pole-Like Objects in a Road Environment Using Vehicle-Based Laser Scanning Data”, Remote Sensing, 2(3): pp. 641-664.Li, D., 2013, “Optimising Detection of Road Furniture (Pole-like Objects) in Mobile Laser Scanner Data”, Master Thesis, Faculty of Geo-Information Science and Earth Observation, University of Twente, Nederland. Luo, D. and Wang, Y., 2008, “Rapid Extracting Pillars by Slicing Point Clouds”, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B3b): pp. 215-218.Manandhar, D. and Shibasaki, R., 2001, “Feature Extraction from Range Data”, In Proceedings of the 22nd Asian Conference on Remote Sensing, Singapore, November 5 - November 9.Masuda H., Oguri S. and Jun H., 2013, “Shape Reconstruction of Poles and Plates from Vehicle-based Laser Scanning Data”, In Proceedings of The International Symposium on Mobile Mapping Technology, Tainan, May 1 - May 3.Mumtaz, S.A., 2007, “Extracting Physical and Environmental Information for Irish Roads Using Airborne Laser Scanning (ALS)”, MPhil Research Project, Dublin Institute of Technology.Petrie, G. and Toth, C.K., 2009, “Terrestrial Laser Scanners: In Topographic Laser Ranging and Scanning, Principles and Processing”, Boca Raton: CRC Press.Priestnall, G., Jaafar, J. and Duncan, A., 2000, “Extracting Urban Feature from LiDAR Digital Surface Models”, Computers, Environment and Urban Systems, 24(2): pp. 65-78.Pu, S., Rutzinger, M., Vosselman, G., and Elberink, S.O., 2011, “Recognizing Basic Structures from Mobile Laser Scanning Data for Road Inventory Studies”, ISPRS Journal of Photogrammetry and Remote Sensing, 66(6): pp. 28-39.Pu, S. and Vosselman, G., 2009a, “Knowledge Based Reconstruction of Building Models from Terrestrial Laser Scanning Data”, ISPRS Journal of Photogrammetry and Remote Sensing, 64 (6): pp. 575-584.Pu, S. and Vosselman, G., 2009b, “Building Facade Reconstruction by Fusing Terrestrial Laser Points and Images”, Sensors, 9 (6): pp. 4525–4542.Puente, I., González-jorge, H., Arias, P. and Armesto, J., 2012, “Land-based Mobile Laser Scanning Systems: a Review”, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(5-W12): pp. 163–168.Schwarz, K. P. and N. El-Sheimy, 2004, Mobile Mapping Systems – State of The Art and Future Trends”, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 35(B5): pp. 759-768.Sithole, G., 2001, “Filtering of Laser Altimetry Data Using a Slope Adaptive Filter”, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 34(3/W4), Annapolis.Tao, C.V., 2000, “Mobile Mapping Technology for Road Network Data Acquisition”, Journal of Geospatial Engineering, 2 (2): pp. 1–14.Vaaja, M., Hyyppä, J., Kukko, A., Kaartinen, H., Hyyppä, H. and Alho, P., 2011, “Mapping Topography Changes and Elevation Accuracies Using a Mobile Laser Scanner”, Remote Sensing, 3 (3): pp. 587–600.Vosselman, G., 2000, “Slope Based Filtering of Laser Altimetry Data”, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 33(B3/2): pp. 935-942.Vosselman, G., and Maas, H.-G., 2010, “Airborne and Terrestrial Laser Scanning”, GB: Whittles Publishing.Vosselman, G., Gorte, B.G.H., Sithole, G., and Rabbani, T., 2004, “Recognising Structure in Laser Scanner Point Clouds”, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(8-W2): pp. 33-38.Wang, C.K. and Hsu, P.H., 2007, “Building Detection and Structure Line Extraction from Airborne LiDAR Data”, Journal of Photogrammetry and Remote Sensing, 12(4): pp. 365-379.Wang, M. and Tseng, Y.H., 2010, “Automatic Segmentation of LiDAR Data into Coplanar Point Clusters Using an Octree-Based Split-and-Merge Algorithm”, Photogrammetric Engineering and Remote Sensing, 76(4): pp. 407-420.Yokoyama, H., Date, H,. Kanai, S. and Takeda, H., 2011, “Pole-like Objects Recognition from Mobile Laser Scanning Data Using Smoothing and Principal Component Analysis”, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(5/W12).Yokoyama, H., Date, H,. Kanai, S. and Takeda, H., 2013, “Detection and Classification of Pole-like Objects from Mobile Laser Scanning Data of Urban Environments”, International Journal of CAD/CAM, 13(2): pp. 31-40.Zhao, H. and Shibasaki, R., 2003, “Reconstructing a Textured CAD Model of an Urban Environment Using Vehicle-Borne Laser Range Scanners and Line Cameras”, Machine Vision and Applications, 14(1): pp. 35-41.三、 網頁參考文獻演算法筆記，2012，大量Point資料結構:k-Dimensional Tree， http://www.csie.ntnu.edu.tw/~u91029/Point.html。迅聯光電有限公司，2010，產品世界-移動載具型雷射掃描儀，取用日期：2010年12月28日，http://www.linkfast.com.tw/product_rieg_c.htm。 描述 碩士
國立政治大學
地政研究所
101257027
102資料來源 http://thesis.lib.nccu.edu.tw/record/#G0101257027 資料類型 thesis dc.contributor.advisor 邱式鴻 zh_TW dc.contributor.advisor Chio, Shih Hong en_US dc.contributor.author (Authors) 吳志文 zh_TW dc.contributor.author (Authors) Wu, Chih Wen en_US dc.creator (作者) 吳志文 zh_TW dc.creator (作者) Wu, Chih Wen en_US dc.date (日期) 2013 en_US dc.date.accessioned 1-Jun-2015 12:19:18 (UTC+8) - dc.date.available 1-Jun-2015 12:19:18 (UTC+8) - dc.date.issued (上傳時間) 1-Jun-2015 12:19:18 (UTC+8) - dc.identifier (Other Identifiers) G0101257027 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/75467 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 地政研究所 zh_TW dc.description (描述) 101257027 zh_TW dc.description (描述) 102 zh_TW dc.description.abstract (摘要) 車載雷射掃描(Vehicle-based Laser Scanning, VLS)系統可以直接獲取路廊的三維點雲(point cloud)資料，因此可用來獲取詳盡的路廊(road corridor)資訊，路廊資訊可進一步應用於噪音模擬、道路安全、道路及相關設施維護、定位服務、汽車和行人導航，甚至發展未來駕駛協助系統(future driver assistance system)，而過去許多研究中提到圓桿地物(如路樹、路燈、交通號誌桿、電力桿、電信桿等)為重要之地物，且已發展某些偵測與萃取方式。然而圓桿地物半徑不一致、傾斜、附著物干擾、地物緊鄰等問題導致其於萃取與偵測過程不易，因此本研究嘗試加入場景知識(scene knowledge)與運用RANSAC(RANdom SAmple Consensus)的概念協助偵測圓桿地物，並加以分類。首先，以車載雷射掃描系統所記錄之車行軌跡為基礎將點雲分離成地面點與非地面點，其後以八分樹(Octree)結構化與CCL演算法(connected-component labeling algorithm)將非地面點雲初步分割成數個點群。完成初步點雲分割後，則濾除鄰地地物點干擾、判斷點群中桿狀物數量以及再分割點群，以解決地物緊鄰之情形。最後，針對經分割後之點群，偵測與定位圓桿地物，並且加以細分成六種類型的圓桿地物。經兩個實驗區成果顯示，本研究藉由場景知識與RANSAC概念協助處理圓桿地物偵測與分類，其偵測之漏授率低於35%，且誤授率為29%，但受限於點雲密度、地物遮蔽、過多附著物、地物緊鄰等因素，使圓桿地物分類之整體精度仍低於45%。然而與人工數化成果比較，圓桿地物定位精度在x坐標方向RMSE約為0.040 m，在y坐標方向RMSE約為0.040 m，半徑r的RMSE約為0.020 m，已足夠公路設施基本資料清查規範之精度需求。 zh_TW dc.description.abstract (摘要) Vehicle-based laser scanning (VLS) system can be employed to directly collect huge 3D point clouds for the extraction of detailed road corridor information. The detailed road corridor information can be utilized for noise modeling, road safety, the maintenance of relevant road facilities, location-based services, navigation for cars and pedestrians, even for the development of future driver assistance system. Many past studies mentioned Pole-like Objects (PLOs) are important objects in road corridor information. However, many methods in past studies still cloud not overcome some problems, such as PLOs with different radius, tilt PLOs, attachments on the PLOs and PLOs near other objects. Therefore, this study will introduce scene knowledge and RANSAC method for PLOs detection and classification. First, point cloud will be segmented to ground points and non-ground points through the knowledge of the trajectory of vehicle traveling. Then, connected-component labeling (CCL) algorithm is used for point grouping by initial segmentation from the non-ground points in octree-structured voxel space. After initial segmentation, the near-ground points in each point group will be filtered and the numbers of candidate poles in each point group will be determined for further re-segmentation for extracting the PLOs. Finally, the PLOs will be extracted from the point groups and classified.The result shows the omission of PLOs detection is lower than 35% and the commission of PLOs detection is 29%. However, the overall accuracy of PLOs classification is 45% due to sparse point density, object occlusion, too many attached objects on the PLOs and adjacent objects. Moreover, the circular parameters of vertical PLOs, i.e. the coordinate of circular center and the circular radius, are checked with those measured manually in this study. The RMSEs in X, Y coordinate components are about 0.040m and 0.040m, respectively, and the RMSE of circular radius is about 0.020m. The results show the accuracy is enough for road inventory. en_US dc.description.tableofcontents 謝誌 I摘要 IIAbstract III目錄 IV圖目錄 VII表目錄 X第一章 緒論 1第一節 研究動機與目的 1第二節 論文架構 3第二章 文獻回顧 4第一節 雷射掃描原理 4一、 雷射掃描 4二、 車載雷射掃描 6第二節 點雲處理 7一、 點雲結構化 7二、 點雲濾除 9三、 點雲分割 9第三節 桿狀物偵測與分類 12一、 依據掃描資訊 13二、 利用桿狀物之點雲特徵 14三、 以機器學習方式 17四、 加入輔助資訊 19第四節 小結 19第三章 研究方法 21第一節 圓桿地物場景知識 22第二節 點雲結構化 28一、 八分樹 28二、 k維樹 29第三節 地面點與非地面點分離 30第四節 點雲初步分割 31第五節 桿狀物偵測與點群再分割 33一、 鄰地性判斷與非鄰地點分離 34二、 鄰地點中非桿狀特徵點濾除與桿狀物數量分析 35三、 點群再分割 37第六節 圓桿地物偵測、定位與分類 40一、 圓桿地物偵測 41二、 圓桿地物定位 47三、 圓桿地物分類 47第七節 精度評估 54一、 點群分類精度 54二、 圓桿地物定位精度之評估 55第四章 實驗成果與分析 56第一節 實驗區一成果與分析 57一、 實驗區與資料介紹 57二、 參數設定說明 60三、 非地面點分離 62四、 點雲初步分割之圓桿地物偵測 63五、 點雲再分割之圓桿地物偵測 65六、 圓桿地物分類 69七、 圓桿地物定位 70第二節 實驗區二成果與分析 72一、 實驗區與資料介紹 72二、 參數設定說明 75三、 非地面點分離 75四、 點雲初步分割之圓桿地物偵測 76五、 點雲再分割之圓桿地物偵測 77六、 圓桿地物分類 79七、 圓桿地物定位 81第三節 實驗區比較與分析 81一、 圓桿地物偵測 81二、 圓桿地物分類 82三、 圓桿地物定位 84第五章 結論與建議 85第一節 結論 85第二節 建議 86參考文獻 88 zh_TW dc.format.extent 8964766 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0101257027 en_US dc.subject (關鍵詞) 車載雷射掃描 zh_TW dc.subject (關鍵詞) 點雲 zh_TW dc.subject (關鍵詞) 圓桿地物 zh_TW dc.subject (關鍵詞) 場景知識 zh_TW dc.subject (關鍵詞) 地物分類 zh_TW dc.subject (關鍵詞) vehicle-based laser scanning en_US dc.subject (關鍵詞) point cloud en_US dc.subject (關鍵詞) pole-like object en_US dc.subject (關鍵詞) scene knowledge en_US dc.subject (關鍵詞) classification en_US dc.title (題名) 以場景知識為基礎自動分類 車載光達點雲中之圓桿地物 zh_TW dc.title (題名) Scene Knowledge-based Automatic Classification of Pole-like Objects from Vehicle-based LiDAR Point Cloud en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) 一、 中文參考文獻黃俊仁、鄭銘章、董基良、林志勇、黃維信、許峻嘉、曾志煌、陳茂南、邱雅莉，2007，公路設施基本資料清查規範，交通部運輸研究所。王淼，2011，「光達點雲區塊化」，國立成功大學測量及空間資訊學系博士論文：台南。林耿帆，2012，「以物件為基礎之光達點雲分類」，國立台灣大學土木工程學系碩士論文：台北市。賴泓瑞，2009，「以模型樣版為基礎之建物三維點雲建模演算法」，國立成功大學測量及空間資訊學系碩士論文：台南。王淼、湯凱佩、曾義星，2005，「光達資料八分樹結構化於平面特徵萃取」，『航測及遙測學刊』，10(1)：pp. 59-70。羅英哲、曾義星，2009，「光達點雲資料面特徵重建」，『航測及遙測學刊』，14(3)：pp. 171-184。二、 外文參考文獻Axelsson, P., 1999, “Processing of Laser Scanner Data—Algorithms and Applications”, ISPRS Journal of Photogrammetry and Remote Sensing, 54(2-3): pp. 138-147.Yang, B., and Dong, Z., 2013, “A Shape-based Segmentation Method for Mobile Laser Scanning Point Clouds”, ISPRS Journal of Photogrammetry and Remote Sensing, 81: pp. 19-30.Boulaassal, H., Landes, T., Grussenmeyer, P. and Tarsha-Kurdi, F., 2007, “Automatic Segmentation of Building Facades Using Terrestrial Laser Data”, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(3/W52): pp. 65-70.Brenner, C., 2009, “Extraction of Features from Mobile Laser Scanning Data for Future Driver Assistance Systems”, pp. 25-42 in Advances in GIScience: Lecture Notes in Geoinformation and Cartography, edited by Sester M. et al., Springer Berlin Heidelberg.Cabo, C., Ordoñez, C., García-Cortésa, S. and Martínez, J., 2014, “An Algorithm for Automatic Detection of Pole-like Street Furniture Objects from Mobile Laser Scanner Point Clouds”, ISPRS Journal of Photogrammetry and Remote Sensing, 87: pp. 47–56.Chen, Y.Z., Zhao, H.-J. and Shibasaki, R., 2007, “A Mobile System Combining Laser Scanners and Cameras for Urban Spatial Objects Extraction”, In Proceedings of the 6th International Conference on Machine Learning and Cybernetics: Hong Kong, China.Chiu, C.M., Teo, T.A., Lin, Y.T. and Li S.H., 2013, “Pole-like Roadside Objects Extraction from Mobile Lidar Point Clouds”, In Proceedings of the 34th Asian Conference on Remote Sensing, Singapore, October 20 – October 24.Dold, C., Brenner, C., 2006, “Registration of Terrestrial Laser Scanning Data Using Planar Patches and Image Data”, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(5): pp. 78–83.Douillard, B., Underwood, J., Kuntz, N., Vlaskine, V., Quadros, A., Morton, P. and Frenkel, A., 2011, “On the Segmentation of 3D LIDAR Point Clouds”, In Robotics and Automation (ICRA), 2011 IEEE International Conference on IEEE, pp. 2798-2805.El-Halawany, S.I., Lichti D.D., 2011, “Detection of Road Poles from Mobile Terrestrial Laser Scanner Point Cloud”, In Multi-Platform/Multi-Sensor Remote Sensing and Mapping (M2RSM), International Workshop on IEEE, pp. 1-6.Filin, S., 2004, “Surface Classification from Airborne Laser Scanning Data”, Computers & Geosciences, 30: pp. 1033-1041.Fischler, M. 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