| Summary: | Data quality assurance (QA) is a critical process to maximize the accuracy of terrestrial laser scanner (TLS) deliverables. Numerous errors augmented in TLS measurements have made that calibration procedure is crucial. Due to the atmospheric disturbances (e.g. temperature, pressure, and humidity), a priori calibration parameters have demonstrated less significant to the TLS QA. Hence, on-site calibration has become an option to resolve this limitation. However, reliance on features existence or surfaces for targets distribution has limited the existing on-site TLS calibration approaches for indoor implementation. Thus, this study has investigated a method that is applicable for outdoor on-site data quality assurance. Through point-based self-calibration, this research has systematically reduced the dimension of surfaces required to distribute all targets. The results obtained have demonstrated that with the surface dimension of 3.6m (width) x 1.2m (height) for targets distribution, derived calibration parameters can improve the accuracy of raw TLS data up to 57 percent. However, statistical analysis and synthetic errors experiment have indicated disagreement with calibration parameters derived for angular measurement. Through a significant effect experiment, the outcomes demonstrated that the minimum configuration for TLS self-calibration only applicable to derive errors in range measurement. The findings from multi-experiments concluded that the study has significantly quantified the sensitivity of minimum network configuration in deriving the TLS systematic errors through point-based self-calibration. © 2023, Construction Research Institute of Malaysia. All rights reserved.
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