| dc.contributor | 地政系 | |
| dc.creator (作者) | Wu, Chih-Wen;Chio, Shih-Hong | |
| dc.creator (作者) | 吳志文;邱式鴻 | zh_TW |
| dc.date (日期) | 2012 | |
| dc.date.accessioned | 10-Apr-2015 15:25:05 (UTC+8) | - |
| dc.date.available | 10-Apr-2015 15:25:05 (UTC+8) | - |
| dc.date.issued (上傳時間) | 10-Apr-2015 15:25:05 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/74452 | - |
| dc.description.abstract (摘要) | With the promotion of the accuracy of the position and orientation system (POS), mobile mapping system (MMS) is developed quickly. Especially, vehicle-borne LiDAR system combines the technology of the POS and LiDAR (light detection and ranging) to collect precise 3D point cloud of the detailed road corridor efficiently. However, the processing of the plenty of those point could is time-consuming. Therefore, those point cloud have to be preprocessed for post-processing more efficiently. Since the road surface is the most important information in the road corridor, this study will focus on the road surface modeling. First of all, the collected 3D point cloud will be organized and projected onto vertical profiles which are defined by the moving trajectory of vehicle-borne LiDAR system. Then, 3D projected points in each vertical profile will be further segmented into line segments by Douglas-Peucker algorithm. Next, the scene knowledge will be employed to extract line segment point on road surface. Finally, the boundaries of the road surface will be fitted by roubust estimation from the points on the road boundary extracted from road line segment points. From the results, it shows the feasibility of the proposed algorithm. | |
| dc.format.extent | 176 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | 33rd Asian Conference on Remote Sensing 2012, ACRS 2012，Volume 2, 1031-1040 | |
| dc.subject (關鍵詞) | Douglas-peucker algorithm; Feature constraints; LIDAR (light detection and ranging); Lidar systems; Mobile mapping systems; Point cloud; Position and orientation system(POS); Road surface model; Algorithms; Optical radar; Remote sensing; Three dimensional; Vehicles; Roads and streets | |
| dc.title (題名) | Road surface modeling from vehicle-borne point cloud by profile analysis | |
| dc.type (資料類型) | conference | en |
