Pole tower point cloud and model multi-scale registration method and system based on prior constraint

By constructing a prior knowledge base and combining local and global descriptors, the accuracy and stability issues in tower 3D modeling and point cloud registration are solved, achieving high-precision, automated, and robust multi-scale registration of tower point clouds and models. This system is suitable for tower 3D modeling and multi-scale registration of point clouds and models in transmission line construction and operation and maintenance.

CN121120712AActive Publication Date: 2025-12-12CHANGSHA NENGCHUAN INFORMATION TECH CO LTD
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Patent Information

Application Number
CN202511669503.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-14
Publication Date
2025-12-12
Estimated Expiration
2045-11-14

AI Technical Summary

Technical Problem

Existing technologies for 3D modeling and point cloud registration of power transmission towers suffer from insufficient accuracy, low efficiency, poor compatibility with multi-source data, unstable matching of key structures, and insufficient adaptability, making it difficult to meet the high-precision requirements of power transmission line construction and operation.

Method used

A multi-scale registration method for tower point clouds and models based on prior constraints is adopted. By constructing a prior knowledge base, geometric element information, preset key points and semantic attributes are extracted. Combined with local and global descriptors, multi-scale feature matching and optimization objective function solution are performed to achieve high-precision, automated and robust registration.

Benefits of technology

It improves the matching accuracy of key structures, enhances the robustness and automation of the registration process, improves the adaptability of the system and the reliability of the matching results, and is suitable for high-precision registration in complex environments.

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Abstract

The invention relates to the technical field of three-dimensional image data processing, in particular to a priori constraint-based tower point cloud and model multi-scale registration method and system. The method comprises the following steps: S1, performing standardization processing on multi-source data to enable to-be-registered point cloud data to be matched with a point cloud representation standard of a prior knowledge base of a candidate model; s2, preliminary screening; s3, performing coarse registration, and performing candidate model rapid screening and key point priority sampling strategies; s4, fine registration is carried out, soft constraint optimization is adopted, priori knowledge is quantized into weights, and weighted ICP and nonlinear least square are combined for solving; and S5, performing matching scoring according to the matching parameters, and obtaining an optimal matching result of the to-be-registered point cloud data and the candidate model of coarse registration. A self-learning priori knowledge base is constructed, a high-confidence matching result is fed back to update the priori base, a dynamic weight adjustment strategy and a candidate screening rule are realized, and the adaptability of the system to different pole tower models is improved.
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