A Metrology-Driven Self-Calibration Framework for Terrestrial Laser Scanner Sensor Systems

Abstract

Terrestrial laser scanning (TLS), also referred to as terrestrial LiDAR, has become an essential close-range remote sensing technique for high-precision engineering surveying, deformation monitoring, industrial inspection, and cultural heritage documentation. The geometric reliability of TLS point clouds strongly depends on the effective compensation of instrumental systematic errors through in situ self-calibration. However, conventional target-based self-calibration often suffers from strong coupling between calibration parameters and exterior orientation parameters, whereas recently developed coplanarity-constrained formulations generally require highly redundant target networks, limiting their field efficiency. To address this limitation, this study proposes a variance inflation factor (VIF)-driven minimal network design strategy for efficient in situ geometric self-calibration of TLS systems. Unlike the commonly used geometric dilution of precision, VIF provides a dimensionless statistical alternative that effectively resolves the dimensional inconsistency inherent in traditional GDOP when handling mixed angular and distance parameters. A differential evolution algorithm is employed to search for hybrid calibration networks that minimize parameter coupling while preserving the physical interpretability of the National Institute of Standards and Technology (NIST) 10-parameter instrumental error model. Five digital twin simulation experiments and a physical validation experiment using a Faro Focus 350 scanner were conducted to evaluate the proposed method. The results show that the optimized network substantially reduces the number of required targets while maintaining high calibration accuracy. The final configuration, which combines VIF-optimized target placement with a dual-station height-difference constraint, reduces the condition number of the normal equations to below 60 and yields a mean system VIF close to 10. The maximum parameter correlation coefficient among the key calibration parameters is constrained to approximately 0.75, indicating near-optimal parameter decoupling under the limited field-of-view geometry of the instrument. These findings demonstrate that the proposed VIF-driven network design provides a highly effective strategy for field-efficient TLS self-calibration and improves the geometric reliability of terrestrial LiDAR point clouds in high-precision remote sensing applications.