Analysis of the temporal correlations of TLS range observations from plane fitting residuals

authored by: Gaël Kermarrec, Michael Lösler, Jens Hartmann
Abstract: Terrestrial laser scanners (TLS) record a large number of points within a short time. Temporal correlations between observations are unavoidable but often neglected in stochastic modelling. The main consequences are an overestimated precision of the point clouds and potential wrong test decisions when used for deformation analysis with rigorous statistical procedures. Regarding physical considerations, a fractional Gaussian noise, defined by a so-called Hurst exponent, or a combination of fractional Gaussian noises could be used to model the noise of range measurements from a sensor perspective; Temporal correlations are expected to have a long-range dependency due to the high recording rate of the TLS. Scanning settings and configurations can affect the global correlation parameters. These effects can be quantified from the residuals of a least-squares surface approximation from the TLS point cloud. Based on simulation results, real data correlation analysis from indoor and outdoor experiments can be better understood which makes the identification of the dominant correlating noise source possible. Our methodology combines two Hurst-estimators: the Whittle maximum likelihood and the generalised Hurst estimator; It paves the way for a simple and global model for describing the temporal noise of TLS range correlations, usable in point clouds analysis independently of the object under consideration.
Organisation(s): Geodetic Institute
QuantumFrontiers
External Organisation(s): Frankfurt University of Applied Sciences
Type: Article
Journal: ISPRS Journal of Photogrammetry and Remote Sensing
Volume: 171
Pages: 119-132
No. of pages: 14
ISSN: 0924-2716
Publication date: 01.2021
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Atomic and Molecular Physics, and Optics, Engineering (miscellaneous), Computer Science Applications, Computers in Earth Sciences
Electronic version(s): https://doi.org/10.15488/11164 (Access: Open)
https://doi.org/10.1016/j.isprsjprs.2020.10.012 (Access: Closed)

BibTeX

@article{cc27aa1bb8ba4d2ab4165b449de340f6,
title = "Analysis of the temporal correlations of TLS range observations from plane fitting residuals",
abstract = "Terrestrial laser scanners (TLS) record a large number of points within a short time. Temporal correlations between observations are unavoidable but often neglected in stochastic modelling. The main consequences are an overestimated precision of the point clouds and potential wrong test decisions when used for deformation analysis with rigorous statistical procedures. Regarding physical considerations, a fractional Gaussian noise, defined by a so-called Hurst exponent, or a combination of fractional Gaussian noises could be used to model the noise of range measurements from a sensor perspective; Temporal correlations are expected to have a long-range dependency due to the high recording rate of the TLS. Scanning settings and configurations can affect the global correlation parameters. These effects can be quantified from the residuals of a least-squares surface approximation from the TLS point cloud. Based on simulation results, real data correlation analysis from indoor and outdoor experiments can be better understood which makes the identification of the dominant correlating noise source possible. Our methodology combines two Hurst-estimators: the Whittle maximum likelihood and the generalised Hurst estimator; It paves the way for a simple and global model for describing the temporal noise of TLS range correlations, usable in point clouds analysis independently of the object under consideration.",
keywords = "Correlations, Hurst parameter, Least-squares, Residuals, Terrestrial Laser Scanner",
author = "Ga{\"e}l Kermarrec and Michael L{\"o}sler and Jens Hartmann",
note = "Funding Information: This study is supported by the Deutsche Forschungsgemeinschaft under the project KE2453/2-1. The authors warmly thank Kamiel-Karl Heidberg and Jan J{\"u}ngerink for having performed the indoor and outdoor measurements.",
year = "2021",
month = jan,
doi = "10.15488/11164",
language = "English",
volume = "171",
pages = "119--132",
journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
issn = "0924-2716",
publisher = "Elsevier",
}