An Average Error Ellipsoid Model for Evaluating Precision of Point Cloud from TLS
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SHORT NOTE
An Average Error Ellipsoid Model for Evaluating Precision of Point Cloud from TLS Xijiang Chen 1 & Guang Zhang 1 & Jianhua Zhang 1 & Hao Wu 1 & Tieding Lu 3 & Wei Xuan 2
Received: 25 February 2015 / Accepted: 27 January 2016 # Indian Society of Remote Sensing 2016
Abstract A terrestrial laser scanner (TLS) measures individual point with a precision in the order of millimeters. The evaluation of point cloud precision, although directly affecting the application of TLS like the precision of deformation extraction and digital elevation model (DEM) reconstruction, is still not well understood. Point cloud precision is different from individual point precision, which was influenced by positional error and the adjacent error spaces. This paper is focused on a new evaluation model of point cloud precision based on the point cloud error ellipsoid. The kernel of this model is the computation of the overlap of adjacent error ellipsoid and the determination of functional relationship between average error ellipsoid volume and point cloud accuracy. Furthermore, the effectiveness of the evaluation model of point cloud precision is discussed with a validation experiment. This paper briefly outlines the key advantage of the proposed evaluation model, such as the capability of providing local point cloud precision of building or other architecture approximately plane.
Keywords Point cloud precision . TLS . Point cloud . Spot . Incidence angle . Sampling intervals
* Xijiang Chen [email protected]
1
Wuhan University of Technology, 122 Luoshi Road, Wuhan, China
2
School of Geodesy & Geomatics, Wuhan University, 129 Luoyu Road, Wuhan, China
3
Faculty of Geomatics, East China Institute of Technology, JiangXi, China
Introduction In a last few years, the terrestrial laser scanner (TLS) is a wellknown device applied in various fields, such as geology (Armesto et al. 2009; Feng et al. 2012), as-built surveys (Tang et al. 2010), documentation (Grussenmeyer et al. 2011), deformation analyses (Bitelli et al. 2004), monitoring techniques (Golparvar-Fard et al. 2011) and dimensional control (Lam 2006). Its product and object extraction performance are intimately related to the point cloud precision. There is lack of appropriate evaluation model to determine the point cloud precision, so as to further improve the precision and efficiency of its object extraction. There are some methods proposed by some authors to evaluate the point cloud precision, like Lohmann and Koch (1999), perform point cloud precision studies by directly comparing scan feature data with reference data. The average of overlapping point cloud as the reference data, and the discrepancies between overlapping strips can be used to evaluate the point cloud precision (Kager and Kraus 2001). An analogous case is described in Crombaghs and Elberink (2002), which used the overlapping strips to evaluate the height precision of laser scanning data. The system errors of laser scanner often lead to the deformation of laser scanner data strips. To decrease the impac
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