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

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.