On the detection of systematic errors in terrestrial laser scanning data

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.