TY - GEN
T1 - Plane fitting methods of LiDAR point cloud
AU - Huang, Chien Ming
AU - Tseng, Yi-Hsing
PY - 2008/12/1
Y1 - 2008/12/1
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:84865619537
SN - 9781615676156
T3 - 29th Asian Conference on Remote Sensing 2008, ACRS 2008
SP - 1925
EP - 1930
BT - 29th Asian Conference on Remote Sensing 2008, ACRS 2008
T2 - 29th Asian Conference on Remote Sensing 2008, ACRS 2008
Y2 - 10 November 2008 through 14 November 2008
ER -