Plane fitting methods of LiDAR point cloud

Chien Ming Huang, Yi-Hsing Tseng

研究成果: Conference contribution

4 引文 斯高帕斯(Scopus)

摘要

LiDAR (Light detection and ranging) point-cloud data contain abundant three-dimensional (3D) spatial information. Dense distribution of scanned points on object surfaces prominently implies surface features. Particularly, plane features commonly appear in a typical LiDAR dataset of artificial structures. Plane fitting is the key process for extracting plane features from LiDAR data. This paper investigates the two mostly applied plane fitting methods: Least Squares Fitting (LSF) and Principal Component Analysis (PCA). LSF is an iterative algorithm of finding the best-fit plane with the least-squares constraint of the distances from the scanned points to the plane. The PCA method calculates the eigenvector of point cloud as the normal for finding out the parameters of the optimal plane. The goal of this paper is to compare these two methods in terms of algorithm implementation, computation time, and robust estimation. Experimental results of computations with simulated data are shown for analysis.

原文English
主出版物標題29th Asian Conference on Remote Sensing 2008, ACRS 2008
頁面1925-1930
頁數6
出版狀態Published - 2008 十二月 1
事件29th Asian Conference on Remote Sensing 2008, ACRS 2008 - Colombo, Sri Lanka
持續時間: 2008 十一月 102008 十一月 14

出版系列

名字29th Asian Conference on Remote Sensing 2008, ACRS 2008
3

Other

Other29th Asian Conference on Remote Sensing 2008, ACRS 2008
國家Sri Lanka
城市Colombo
期間08-11-1008-11-14

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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