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系統識別號 U0026-2207201109261000
論文名稱(中文) 光達點雲區塊化
論文名稱(英文) Segmentation of LiDAR Point Clouds
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系碩博士班
系所名稱(英) Department of Geomatics
學年度 99
學期 2
出版年 100
研究生(中文) 王淼
研究生(英文) Miao Wang
電子信箱 miaowang@geomatics.ncku.edu.tw
學號 p6891101
學位類別 博士
語文別 英文
論文頁數 137頁
口試委員 指導教授-曾義星
召集委員-陳良健
口試委員-史天元
口試委員-趙鍵哲
口試委員-邱式鴻
口試委員-林昭宏
中文關鍵字 光達  點雲  區塊化 
英文關鍵字 Segmentation  LiDAR  Point Cloud 
學科別分類
中文摘要 以空載或地面光達(Light Detection and Ranging, LiDAR)掃瞄得到的點雲資料是不規則分佈於被掃瞄物體表面的點觀測量,其中隱含豐富的空間資訊。欲判讀點雲所呈現的場景或物體,必須從中萃取出被掃瞄物的空間特徵,並用以進行三維物體模型重建。由於光達儀器之特殊掃瞄機制,光達點雲主要呈現物體的三維面特徵,無法精確地偵測物體的結構線和角點。而在實際的環境中,大多數的人造物體如建築、橋樑和道路,其表面則由平面所構成,經由相鄰平面的交會,可得到物體的結構線和角點。此外,曲面可經由結合相鄰的平面而得。因此,平面特徵是光達點雲中最重要的空間特徵。
區塊化(Segmentation)是最常用來從光達點雲中萃取面特徵的方法,點雲分佈的同質性(Coherence)和相鄰性(Proximity)是點雲區塊化演算法中主要考慮的二個條件,當點雲中的點被區分為同質且相鄰的點群時,即可萃取出其代表的空間特徵,例如:使用共平面條件可萃取出平面特徵物。除此之外,因為點雲分佈通常呈不規則狀,必須使用適當的資料結構重新組織點雲,以決定點分佈的相鄰關係。
本研究的目的是發展可通用於空載和地面光達資料的共面點區塊化方法。首先,本研究使用八分樹結構化體元空間(octree-structured voxel space)將點雲重新組織以建立點的相鄰關係及空間索引,以此資料結構為基礎,使用三維的connected-component labeling (CCL)演算法,以體元及點的相鄰關係為條件,可將點雲區分為相鄰的點群。此外,並可利用八分樹的階層式(hierarchical)關係建立out-of-core機制,以多解析度展示大量點雲。其次,本研究提出一種基於八分樹之分割-合併演算法,同時考量點的同質性及相鄰性,進行共平面點區塊化以萃取出點雲中的平面特徵。從這些共平面點和萃取出的平面特徵可推導出多種幾何屬性,包括:擬合平面的內在屬性、平面交會屬性及點雲分佈於平面的屬性等,作為點雲分類的基礎資料。
光達點雲中通常包含多種空間特徵,無法僅用一種演算法或一種條件完成所有特徵物的萃取,因此,需要綜合使用多個演算法及多種條件漸次地萃取出點雲中的空間資訊。本研究提出一個四步驟漸增式區塊化策略,分別使用八分樹結構化體元空間、CCL演算法、基於八分樹之分割-合併演算法和區域成長法,一步一步地將點雲區分為組織化的點群、相鄰點群、共平面點群及共曲面點群。本研究提出的方法均使用多個實際的空載或地面光達進行驗證其萃取空間特徵物的可行性。
英文摘要 LiDAR (Light Detection and Ranging) point clouds, which contain rich spatial information, are irregularly distributed point measurements of scanned object surfaces obtained from airborne or terrestrial LiDAR systems. To interpret the contents of LiDAR data, the implied spatial information must be extracted. The extracted spatial features can be used for reconstructing three-dimensional (3D) object models. Due to the scanning mechanism, surfaces are the dominant spatial features implied in LiDAR data. Plane features are the most important surfaces; they frequently correspond to man-made objects like buildings, bridges, and roads. Line and point features can be obtained from the intersection of planes and curved surfaces can be obtained from the union of planes.
Segmentation is often used to extract surface features from LiDAR data. The coherence and proximity of points are the two major criteria used by LiDAR data segmentation algorithms. Spatial features are extracted by segmenting point clouds into coherent and neighboring point groups. For example, the extraction of plane features uses planarity (points distributed on a common plane) as the coherence criterion. In order to handle the proximity of irregularly distributed LiDAR points, a suitable data structure is usually required.
This study focuses on the development of a general method for segmenting points distributed on a common surface, termed co-surface points hereafter, from both airborne and terrestrial LiDAR data. First, an octree-structured voxel space for handling the proximity of points is used to organize LiDAR data and establish neighborhood and spatial indices for points. Based on the obtained organization, a connected-component labeling algorithm for voxels is used for segmenting neighboring points. An out-of-core scheme that stores the constructed octree-structured voxel space enables multi-resolution rendering for a huge number of points. Second, an octree-based split-and-merge algorithm is proposed for segmenting coplanar points and extracting plane features from LiDAR data. Several inherent and derived geometric properties of the extracted plane features which include the inherent properties of a fitting plane, intersections of fitting planes, and properties of point distribution on a fitting plane, can be used for classifying LiDAR point clouds.
Common LiDAR data contain points of various spatial features that cannot be well segmented using only one algorithm and one criterion. A segmentation scheme that comprises several algorithms and employs various criteria is required to gradually segment LiDAR point clouds into several levels. A four-step incremental segmentation strategy composed of octree-structured voxel space organization, connected component labeling, octree-based split-and-merge, and a simple region growing, is thus proposed for segmenting LiDAR point clouds into organized points, neighboring point groups, coplanar point groups, and co-surface point groups step by step.
Several experiments were conducted for each of the proposed methods using both airborne and terrestrial point clouds. Results show that the proposed methods are promising and feasible for the extraction of surface features from LiDAR data.
論文目次 中文摘要 I
ABSTRACT III
誌 謝 V
CONTENTS VII
LIST OF TABLES IX
LIST OF FIGURES X
CHAPTER 1 INTRODUCTION 1
1.1 BACKGROUND AND RESEARCH MOTIVATION 1
1.2 RESEARCH OBJECTIVE AND SCOPE 4
1.3 CONTRIBUTION TO KNOWLEDGE 6
1.4 THESIS ORGANIZATION 7
CHAPTER 2 REVIEW OF SEGMENTATION THEORY OF LIDAR POINT CLOUDS 9
2.1 PROPERTIES OF LIDAR POINT CLOUDS 10
2.2 COHERENCE AND PROXIMITY OF POINT CLOUDS 12
2.3 POINT CLOUD SEGMENTATION ALGORITHMS 16
CHAPTER 3 ORGANIZAING POINT CLOUDS WITH OCTREE-STRUCTURED VOXEL SPACE 21
3.1 INTRODUCTION 21
3.2 CONSTRUCTION OF OCTREE-STRUCTURED VOXEL SPACE FOR POINT CLOUDS 23
3.3 NEIGHBORHOOD OF POINTS AND ADJACENCY OF VOXELS 30
3.3.1 Neighboring Points, Point Density, and Point Spacing 30
3.3.2 Neighborhoods of Points Based on Adjacency of Voxels 32
3.4 USAGES OF OCTREE-STRUCTURED VOXEL SPACE 34
3.4.1 Grouping Neighboring Points and Neighboring Test of Points 34
3.4.2 Multi-resolution Rendering of Point Clouds 38
3.5 EXPERIMENTS 38
3.5.1 Dataset I: Airborne LiDAR Data of a Gable Roof Building 39
3.5.2 Dataset II: Airborne LiDAR Data of a Flat Region 45
3.5.3 Dataset III: Airborne LiDAR Data of a Mountainside Village 50
3.5.4 Dataset IV: Registered Terrestrial LiDAR Data of a Landscape 54
3.6 SUMMARY 58
CHAPTER 4 COPLANAR POINT SEGMENTATION USING AN OCTREE-BASED SPLIT-AND-MERGE ALGORITHM 61
4.1 INTRODUCTION 61
4.2 PLANE FITTING AND COPLANAR TEST FOR POINTS 63
4.3 OCTREE-BASED SPLIT-AND-MERGE ALGORITHM 67
4.4 GEOMETRIC PROPERTIES OF SEGMENTED COPLANAR POINT GROUPS 73
4.5 EXPERIMENTS 75
4.5.1 Dataset I: Airborne LiDAR point cloud of a gable roof building 75
4.5.2 Dataset II: Airborne LiDAR point cloud of a curved-roof building 84
4.5.3 Dataset III: Terrestrial LiDAR data 87
4.5.4 Dataset IV: Synthetic Point Cloud 91
4.6 SUMMARY 96
CHAPTER 5 INCREMENTAL SEGMENTATION STRATEGY 99
5.1 INTRODUCTION 99
5.2 FOUR-STEP INCREMENTAL SEGMENTATION FOR CO-SURFACE POINTS 100
5.3 EXPERIMENTS 104
5.3.1 Dataset I: Airborne LiDAR point cloud 104
5.3.2 Dataset II: Terrestrial LiDAR point cloud 112
5.3.3 Dataset III: Synthetics Point Cloud 114
CHAPTER 6 CONCLUSION AND FUTURE WORK 119
6.1 CONCLUSION 119
6.2 FUTURE WORK 121
BIBLIOGRAPHY 123
Appendix A INTERSECTIONS OF PLANES 129
Appendix B PROPERTIES OF POINT DISTRIBUTION ON A PLANE 133
CURRICULUM VITAE 137
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