Monte Carlo Ground Control Point Quality Evaluation
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Solution Overview
Problem
There is a lack of effective quality evaluation methods for ground control points in photogrammetry, which can lead to errors in photogrammetric survey results due to survey errors or point-placing errors.
Innovation Solution
A photogrammetric ground control point quality evaluation method based on the Monte Carlo test, which involves selecting a portion of ground control points for bundle adjustment optimization, calculating errors, and determining a quality coefficient for each ground control point.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ground control points are used for photogrammetric modeling, then positioning accuracy can be improved, but survey errors and point-placing errors may introduce significant errors into the model
Solution Approach 1:
The patent performs quality evaluation of ground control points before they are used in photogrammetric modeling. By conducting bundle adjustment optimization and Monte Carlo tests in advance, the system identifies and eliminates low-quality control points before they can introduce errors into the final model, thus preventing reliability issues while maintaining positioning accuracy.
Solution Approach 2:
The patent implements a feedback mechanism where quality evaluation results from bundle adjustment and Monte Carlo tests are used to iteratively optimize the selection of ground control points. The system calculates quality coefficients and uses this feedback to eliminate poor-quality points, continuously improving model reliability while preserving positioning accuracy.
2Reliability
If quality evaluation of ground control points is conducted, then photogrammetric errors can be reduced, but additional time and computational resources are required
Solution Approach 1:
The patent performs bundle adjustment optimization and Monte Carlo tests on a selected subset of ground control points (40%-60% as control points, remainder as check points) rather than processing all points equally. This partial action approach reduces computational time and resource requirements while still achieving effective error reduction through targeted quality evaluation.
3Measurement precision
If bundle adjustment optimization is performed multiple times with different control point selections, then quality evaluation accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent divides the ground control points into two segments: control points (40%-60%) used for bundle adjustment optimization and check points (remainder) used for validation. This segmentation allows the system to perform multiple iterations with different combinations while managing computational complexity through structured division of labor between control and check point sets.
Data Source
AI summary
A photogrammetric ground control point quality evaluation method based on Monte Carlo test relates to the field of photogrammetry technology. Firstly, aerial photographs and ground control points from a survey area are obtained, the ground control points are numbered, the aerial photographs are performed with point-placing and aerial triangulation densification. Secondly, a Monte Carlo test experiment is designed, a certain number of the ground control points are selected as control points with the rest as check points, ensuring each ground control point as the control point a certain number of times, and average errors of the ground control points are calculated. Thirdly, average values of the average errors of the ground control points are calculated, standard deviations of the average errors of the ground control points are calculated. Finally, a quality coefficient Q of each ground control point is calculated and evaluated according to quality evaluation standards.