以多尺度萃取方法進行車載光達資料之類桿狀道路物件重建

車載光達系統（Mobile Lidar System, MLS）行駛在道路上以直接地理對位（Direct Georeferencing）方式進行雷射掃描，能夠快速獲取三維道路資訊進行道路調查，然而車載光達系統因資料量龐大及盲資料（Blind data）的特性，在應用上有許多挑戰。本研究發展車載光達資料自動化道路類桿狀物件重建方法，道路類桿狀物件包含路燈、交通號誌、及行道樹等。本研究採取模型導向重建策略，主要工作包含：(1)資料前處理、(2)類桿狀道路物件點雲偵測、及(3)物件點雲分類。資料前處理部分，對原始點雲進行分段分割並濾除路廊兩側牆面點雲；類桿狀道路物件點雲偵測部分，本文提出一個多尺度分割方法（Multi-scale Segmentation），包含體元（Voxel）尺度分割、點（Point）尺度分割及混合物分割三階段；物件點雲分類採取知識庫分類方法（Knowledge-based Classification）辨識出物件點雲所屬類別。研究成果顯示，在偵測階段成功率為95%，道路物件分類整體精度為72%，重建桿狀物件中心之位置精度在10公分以內，因此，所提出的方法可有效應用於車載光達之類桿狀道路物件重建。

關鍵字

車載光達系統；道路調查；分類；重建

並列摘要

Mobile lidar system (MLS) which acquires detailed and accurate 3D point clouds along road corridors can assist traditional road inventory work. However, the blind characteristics and the huge amount of point clouds still make it difficult for applications. Therefore, an automatic process for MLS is required. The objective of this study is to develop a novel approach for pole-like road objects extraction from MLS data. The pole-like road objects consist of street light, traffic light, trees, etc. The proposed framework is a model-driven approach. The major work contains three parts: (1) data pre-processing, (2) pole-like object detection, and (3) object classification. In data pre-processing, the raw data are partitioned into several road parts and the building façade aside road are removed to avoid mis-classification. In object detection, a multi-scale segmentation is presented to detect pole-like road objects. Finally, the knowledge-based approach is used to classify pole-like road objects. The experiments demonstrate that the correctness of detection is about 95% and the overall accuracy of classification reaches 72%. The positional accuracy of pole-like object reconstruction is better than 0.1 meters. The results indicate that the method can extract pole-like road objects from MLS data effectively.

並列關鍵字

mobile LiDAR system ； road inventory ； classification ； reconstruction

國際替代計量

以多尺度萃取方法進行車載光達資料之類桿狀道路物件重建

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