Automatic three-dimensional measurement of large-scale structure based on vision metrology

Area of Science:

• Photogrammetric vision metrology
• 3D measurement technologies
• Optical metrology

Background:

• Fully automatic 3D measurement of large-scale structures is challenging.
• Existing methods often require manual intervention for target recognition and network orientation.

Purpose of the Study:

• To develop and verify key techniques for fully automatic 3D measurement of large-scale structures.
• To enhance automation in photogrammetric vision metrology.

Main Methods:

• Design of novel coded targets using circular retroreflective discs.
• Development of blob detection and clustering algorithms for target recognition.
• A three-stage strategy for automatic network orientation using view clustering.
• Introduction of the 'matching path' concept and a voting strategy for non-coded target matching.

Main Results:

• Effective detection and recognition of coded targets.
• Successful automatic network orientation through a three-stage strategy.
• Accurate matching of non-coded targets using the proposed matching path and voting strategy.
• Experimental validation on an airship keel demonstrating effectiveness and accuracy.

Conclusions:

• The proposed methods significantly advance automatic 3D measurement capabilities in photogrammetry.
• The developed techniques are effective for large-scale structure measurement with high accuracy.
• This work contributes to the field of automated optical metrology.