光達資料八分樹結構化於平面特徵萃取

LIDAR Data Octree Structurizing for Plane Extraction

王淼(Miao Wang) ； 湯凱珮(Kai-Pei Tang) ； 曾義星(Yi-Hsing Tseng)

《航測及遙測學刊》 10卷1期 (2005/03) Pp. 59-70

https://doi.org/10.6574/JPRS.2005.10(1).5

摘要

光達可快速獲取被掃瞄物表面高精度及高密度的三維點雲資料。由於資料量非常龐大，其中包含豐富的空間資訊，為了有效率地處理光達資料，必須先將點雲組織成有利於後續處理的結構，並且保留其原有的資料精度。本文提出以八分樹結構組織光達點雲資料的方法，可將點雲資料組織成三維網格的型式，使用三維指標方式快速搜尋點雲資料，並從光達資料中萃取出隱含的三維平面資訊。本文提出的方法可以適用於空載及地面光達資料，對於具有多重反射或由多掃瞄站合併的點雲資料亦可適用。

關鍵字

光達 ； 八分樹 ； 三維網格 ； 平面萃取

並列摘要

Lidar point cloud is a collection of a large amount of accurate 3D point data densely distributed on the scanned object surface. It implicitly contains abundant spatial information. To explore valuable spatial information from Lidar data becomes an active research topic. However, the processing of the huge amount of point data needs unusually large CPU time and storage volume. A well organized data structure is needed for the analysis or extraction of spatial features from point cloud in an efficient way. This paper proposes a novel method using an octree structure to organize point cloud in a 3D regular grid. The nodes of octree structure represent the cells of the 3D grids, which can be accessed easily and quickly based on a designed 3D array index.
Two 3D plane extraction methods, named split & merge and 3D region growing, are proposed. These two methods fully explore the designed octree structure of point cloud. The plane extraction methods are practicable to both airborne and ground-based Lidar data. Test data include an airborne Lidar data set containing points of multiple echoes and a combined point cloud data set from several scan stations of ground-based Lidar were processed. The test results show meaningful feature extraction from lidar data. This study provides a stepping stone for the extraction of spatial information from lidar data.

並列關鍵字

Lidar ； Laser Scanning ； Octree ； 3D Grid ； Plane extraction