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An approach for considering the object surface properties in a TLS stochastic model.

Gabriel Kerekes1, Volker Schwieger1

  • 1University of Stuttgart, Institute of Engineering Geodesy, Geschwister-Scholl-Str. 24D, 70174 Stuttgart, Germany.

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Summary

This study models random errors in Terrestrial Laser Scanning (TLS) caused by surface properties like roughness and reflectance. Incorporating these into a synthetic variance-covariance matrix (SVCM) improves measurement quality for structural health monitoring.

Keywords:
TLSelementary errorreflectanceroughnessspatial correlationsstochastic modelling

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Area of Science:

  • Geomatics Engineering
  • Optical Metrology
  • Structural Health Monitoring

Background:

  • Terrestrial Laser Scanning (TLS) fundamental principle involves laser-object surface interaction.
  • Surface optical properties (roughness, reflectance) are key sources of systematic and random TLS errors.
  • These errors impact structural health monitoring applications using TLS data.

Purpose of the Study:

  • To develop an approach for quantifying random errors in TLS measurements caused by object surface properties.
  • To model the effects of surface roughness and reflectance on TLS data.
  • To enhance the stochastic model for TLS by introducing a new method for variance and covariance determination.

Main Methods:

  • Modeling surface properties' effects on TLS measurements using elementary error theory.
  • Developing a synthetic variance-covariance matrix (SVCM) to represent these errors.
  • Validating the approach with real-world measurements from cast stone façade elements.

Main Results:

  • The proposed method effectively models random errors stemming from surface roughness and reflectance.
  • The synthetic variance-covariance matrix (SVCM) provides a structured way to incorporate these error sources.
  • Validation confirmed that using an appropriate SVCM improves the quality of TLS-based estimations.

Conclusions:

  • Accounting for surface properties through an SVCM is crucial for accurate TLS measurements.
  • The developed approach offers a significant improvement for TLS stochastic modeling.
  • This methodology enhances the reliability of structural health monitoring using TLS data.