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| Title: | 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 |
| Authors: | 吳志文
Wu, Chih Wen |
| Contributors: | 邱式鴻
Chio, Shih Hong
吳志文
Wu, Chih Wen |
| Keywords: | 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 |
| Date: | 2013 |
| Issue Date: | 2015-06-01 12:19:18 (UTC+8) |
| Abstract: | 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. The results show the accuracy is enough for road inventory. |
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| Description: | 碩士
國立政治大學
地政研究所
101257027
102 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0101257027 |
| Data Type: | thesis |
| Appears in Collections: | [地政學系] 學位論文
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