On the detection of systematic errors in terrestrial laser scanning data

Abstract

Abstract. Quality descriptions are parts of the key tasks of geodetic data processing. Systematic errors should be detected and avoided in order to insure the high quality standards required by structural monitoring. In this study, the iterative closest point (ICP) method was invested to detect systematic errors in two overlapping data sets. There are three steps to process the systematic errors: firstly, one of the data sets was transformed to a reference system by the introduction of the Gauss-Helmert (GH) model. Secondly, quadratic form estimation and segmentation methods are proposed to guarantee the overlapping data sets. Thirdly, the ICP method was employed for a finer registration and detecting the systematic errors. A case study was casted in which a dam surface in Germany was scanned by terrestrial laser scanning (TLS) technology. The results indicated that with the conjugation of ICP algorithm the accuracy of the data sets was improved approximately by 1.6 mm.

Biased and Unbiased Estimates Based on Laser Scans of Surfaces with Unknown Deformations

“…This is an unusual situation in typical geodetic approximation problems because systematic errors are considered most times only regarding the observations, not regarding the parameterization of the surface (e.g., [16,38]). However, the strategies for dealing with systematic errors in the observations can be tried to transfer to dealing with systematic errors in the functional model aiming at increasing reproducibility and unbiasedness.…”

Block-to-Point Fine Registration in Terrestrial Laser Scanning

“…These researches mean that firstly processing the point clouds and then using registration methods may be a reasonable way to get more accurate registration results. An attempt was performed earlier in [10], where a fine registration was estimated with the help of the ICP method. However, there are still some errors existing in the datasets.…”