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Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/81364
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| Title: | 車載光達點雲中直立圓桿之模塑 |
| Other Titles: | Modeling of Vertical Pole-Like Objects from Vehicle-Borne LiDAR Point Cloud |
| Authors: | 邱式鴻
Chio, Shih-Hong |
| Contributors: | 地政系 |
| Keywords: | 車載光達;直立圓桿;移動測繪
Mobile mapping;Vehicle-borne LiDAR;Vertical pole-like object |
| Date: | 2014-01 |
| Issue Date: | 2016-02-19 16:15:32 (UTC+8) |
| Abstract: | 隨著移動測繪系統（mobile mapping system，MMS）發展，車載光達系統可有效獲取詳細路廊（road corridor）的三維點雲資料。由於車載光達系統紀錄大量的點雲資料與複雜的路廊資訊，需經處理，才可模塑點雲中路廊的物件。其中，直立圓桿是路廊資訊中重要地物的基本元件，故本研究發展模塑車載點雲中直立圓桿的演算法。直立圓桿模塑的演算法必須面臨車載光達系統所蒐集的點雲無法完整涵蓋整個直立圓桿表面，以及所蒐集的點雲不僅包含直立圓桿上的點、亦可能包含附著於直立圓桿上的物件點（如廣告招牌等）等兩個重大的問題，意即這些點雲資料蒐集不完全且包含許多雜訊。因此，本研究所發展的演算法中先以物空間資訊將車載點雲中的地面點濾除並留下非地面點，其中地面點包含屬於路面與人行道上的點。接著，將非地面點透過八分樹體元結構化（octree-structured voxel space）後，並以其相鄰性加以群聚，進而組成非地面點之點群。由於直立圓桿上可能含有許多附著物（如廣告招牌等），因此本研究發展以RANSAC（RANdom SAmple Consensus）為基礎之演算法判斷經前述處理聚集後之非地面點群是否包含直立圓桿，並計算其圓面參數。實驗結果顯示在複雜的街景中，本研究所發展直立圓桿模塑之漏授率（Omission）為31.8%、誤授率（Commission）為60.5%；各直立圓桿求定之圓面參數與人工量測之結果比較在X坐標方向的RMSE為0.032 m，在Y坐標方向RMSE為0.046 m，而半徑的RMSE則為0.031 m。
With the development of mobile mapping system (MMS), vehicle-borne LiDAR system can obtain precise 3D point cloud of the detailed road corridor efficiently. Because the vehicle-borne LiDAR system records numerous points cloud and complicated information of road corridor, those point cloud can be used in reconstructing the objects in road corridor after data preprocessing. In the objects of road corridor, vertical pole-like objects is one of most important and basic objects. Therefore, this study focuses on modeling vertical pole-like objects from vehicle-borne LiDAR point cloud. In the vehicle-borne point cloud, the data might describe vertical pole-like objects incompletely and might be always with many irrelevant points from the attached objects. First of all, the ground points will be filtered through scene knowledge. Then, the non-ground points will be clustered through the octree-structured voxel space and connected-component labeling (CCL) algorithm. In the clustering LiDAR points, they cannot describe complete vertical pole-like objects and some points might belong to attached objects. Therefore, the automatic algorithm based on RANSAC (RANdom SAmple Consensus) is developed to extract and model vertical pole-like objects from those clustering LiDAR points in this study. The result shows the omission of vertical pole-like objects by the modeling approach developed by this study is 31.8% and the commission of vertical pole-like objects is 60.5% under the complicated street environment. Moreover, the circle parameters of vertical pole-like objects, i.e. the coordinate of circular center and the circular radius, are compared with those measured manually in this study. The RMSEs in X, Y coordinate components are 0.032 m and 0.046 m, respectively. The RMSE of circular radius is 0.031m. |
| Relation: | 國土測繪與空間資訊, 2(1), 23-41 |
| Data Type: | article |
| Appears in Collections: | [地政學系] 期刊論文
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| 2745.pdf | | 1369Kb | Adobe PDF2 | 446 | View/Open |
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