Calibration Methods for Large-Scale and High-Precision Globalization of Local Point Cloud Data Based on iGPS.

Area of Science:

• Metrology
• Geomatics Engineering
• Computer Vision

Background:

• Point cloud data offers high accuracy and density but is limited in spatial extent.
• Globalizing local point cloud data is crucial for measuring large components accurately.

Purpose of the Study:

• To propose a novel method for high-accuracy globalization of local point cloud data.
• To introduce calibration models for different application scenarios in point cloud globalization.

Main Methods:

• Utilizing an indoor Global Positioning System (iGPS) as an external measurement device.
• Developing and applying two distinct calibration models tailored to specific use cases.
• Conducting verification experiments to assess the accuracy and precision of the proposed method.

Main Results:

• Achieved average calibration errors of 0.12 mm and 0.17 mm for the two proposed calibration models.
• Demonstrated sustained calibration precision across a large spatial range (approximately 10 m × 10 m × 5 m).

Conclusions:

• The proposed iGPS-based method effectively achieves high-accuracy globalization of local point cloud data.
• The method maintains precision over large volumes, offering significant value for engineering applications requiring precise large-scale measurements.