完整后设资料纪录
DC 栏位语言
dc.contributor.author邱继珉en_US
dc.contributor.authorChiu, Chi-Minen_US
dc.contributor.author张智安en_US
dc.contributor.authorTeo, Tee-Annen_US
dc.date.accessioned2014-12-12T02:44:23Z-
dc.date.available2014-12-12T02:44:23Z-
dc.date.issued2013en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#GT070151279en_US
dc.identifier.urihttp://hdl.handle.net/11536/75898-
dc.description.abstract  近年来随着软硬体技术演进,各领域如数码城市、适地性服务(local-based services, LBS)、都市设施管理及智慧运输系统(Intelligence Transportation System, ITS)对于细致道路资讯需求增加,有效地获取细致道路资讯成为重要课题。车载光达系统(Mobile Lidar System, MLS)行驶在道路上以直接地理对位(Direct Georeference)方式进行雷射扫描,能够快速获取三维道路资讯以取代传统道路调查,然而光达点云庞大资料量及盲资料(blind data)特性,增加了车载光达应用之挑战。
  本研究目的为发展车载光达资料自动化道路类杆状物件重建方法,道路类杆状物件为道路之基础设施,包含路灯、交通号志、道路标志、行道树等。研究方法分为资料前处理、道路物件侦测、分类及重建。资料前处理部分,以载台轨迹为基础进行资料切割以减低运算负担,并滤除路廊两侧墙面;道路物件侦测部分,本文发展了一个多尺度运算架构,包含网格尺度、点尺度及重叠区处理三阶段流程;物件分类以知识库方法(knowledge-based classification)对侦测出的道路物件分类,并针对重叠区及非重叠区建立不同分类模式;最后以模型导向方式进行道路物件重建。
  本研究使用的资料是以Riegl VMX-250车载光达系统获取而得,测试地点为台北市民权东路,总路段约为3.4公里。在侦测阶段,侦测正确率约为95%,所提出之多尺度方法可较单一尺度方法提升20%以上效率,而道路物件分类部分,整体精度约为70%,主要误差来源为受到树冠完整覆盖之物件,道路物件重建部分,与外业测量获取之道路调查资料比较,位置精度约为5公分,研究成果显示所提出的方法可有效地以车载光达资料进行三维道路物件重建。
zh_TW
dc.description.abstractThe need of three-dimensional road modeling is gradually increasing due to the development of various applications such as cyber city, local-based services (LBS), urban infrastructure management, and intelligence transportation system (ITS). Mobile lidar system (MLS) acquires detailed and accurate 3D point clouds along road corridors. However, the blind characteristics and the huge amount of point clouds still make it difficult for application. Hence, the automatic recognition process for MLS data is needed to improve the computational time and cost for road modeling.

The objective of this research is to develop an automatic process for pole-like road objects reconstruction from MLS data. The major work includes four parts. First, the raw data is partitioned and the building façades on the roadside are removed through data pre-processing. Second, a multi-scale approach for pole detection is presented. Third, the verity of detected objects are classified through knowledge-based classification. Forth, a model-based approach is utilized for reconstruction.

The test data is acquired by Riegl VMX-250 mobile lidar system which is located at Minquan Eastern Road, Taipei, Taiwan. The length of the test area is about 3.4 kilometers. The experimental results indicates that the correctness of detection is about 95%. The overall accuracy of classification reaches 70% and the accuracy of reconstruction is about 5cm. The results indicated that the proposed method can detect and reconstruct pole-like road objects from MLS data effectively.
en_US
dc.language.isoen_USen_US
dc.subject车载光达系统zh_TW
dc.subject道路调查zh_TW
dc.subject区块化zh_TW
dc.subject分类zh_TW
dc.subject重建zh_TW
dc.subjectMobile Lidar Systemen_US
dc.subjectRoad Inventoryen_US
dc.subjectSegmentationen_US
dc.subjectClassificationen_US
dc.subjectReconstructionen_US
dc.title以多尺度萃取方法进行车载光达资料之类杆状道路物件重建zh_TW
dc.titlePole-like Road Object Extraction from Mobile Lidar System Based on Multi-scale Approachen_US
dc.typeThesisen_US
dc.contributor.department土木工程系所zh_TW
显示于类别:Thesis