Extraction of Surface Features from LiDAR Point Clouds Using Incremental Segmentation Strategy

使用漸進式區塊化策略從光達點雲萃取面特徵

王淼(Miao Wang) ； 曾義星(Yi-Hsing Tseng)

《航測及遙測學刊》 19卷1期 (2014/11) Pp. 49-62

https://doi.org/10.6574/JPRS.2014.19(1).4

摘要

以空載或地面光達（Light Detection and Ranging, LiDAR）掃瞄得到的資料是不規則分佈於被掃瞄物體表面的點觀測量，特徵萃取是將光達資料轉換為空間資訊的關鍵程序，其中面特徵是光達資料中最主要的空間特徵。本文提出一個通用性的方法－漸增式區塊化策略－進行共面點雲區塊化，漸增式區塊化策略可依應用需求分為數個步驟，在每個步驟採用適當的演算法和運算條件。本研究提出的方法將面特徵萃取分為四個步驟：第一、使用八分樹結構化體元空間建立點雲間的相鄰關係；第二、使用相連成份標記（Connected Component Labeling）演算法將點雲區分為數個相鄰點群；第三、使用基於八分樹之分割－合併演算法從每個點群中萃取出平面特徵；最後，使用區域成長法將相鄰的平面特徵合併為曲面特徵。實際上，四個步驟分別將光達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。利用本研究提出的方法可採漸增方式進行大量點雲資料集之擷取及分析，實驗證明此方法可有效率地處理空載和地面光達點雲資料，而且，本方法的最終以及中間成果均可分別應用於不同目的的物體模塑。

關鍵字

光達點雲 ； 區塊化 ； 八分樹 ； 體元空間 ； 空間特徵 ； 漸增式

並列摘要

LiDAR (Light Detection and Ranging) point clouds are measurements of irregularly distributed points on scanned object surfaces acquired with airborne or terrestrial LiDAR systems. Feature extraction is the key to transform LiDAR data into spatial information. Surface features are dominant in most LiDAR data corresponding to scanned object surfaces. This paper proposes a general method to segment co-surface points. An incremental segmentation strategy is developed for the implementation, which comprises several algorithms and employs various criteria to gradually segment LiDAR point clouds into several levels. There are four operation steps. First, the proximity of point clouds is established as spatial indices defined in an octree-structured voxel space. Second, a connected-component labeling algorithm for voxels is applied for segmenting neighboring points. Third, coplanar points then can be segmented with the octree-based split-and-merge algorithm as plane features. Finally, combining neighboring plane features forms surface features. With respect to each step, processed LiDAR point clouds are segmented into organized points, neighboring point groups, coplanar point groups, and co-surface point groups. The proposed method enables an incremental retrieval and analysis of a large LiDAR dataset. Experiment results demonstrate the effectiveness of the segmentation algorithm in handling both airborne and terrestrial LiDAR data. The end results as well as the intermediate results of the segmentation may be useful for object modeling of different purposes using LiDAR data.

並列關鍵字

LiDAR Point Cloud ； Segmentation ； Octree ； Voxel Space ； Spatial Feature ； Incremental