3D Lidar Coordinate Error Correction via Segmented Geometric Modeling
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Solution Overview
Problem
Existing methods for error correction of three-dimensional (3D) lidar scanners fail to account for geometric structure coordinate errors, affecting the accuracy of spatial measurements.
Innovation Solution
A method is proposed that builds an error model to identify and correct spatial geometric errors in 3D lidar scanners by analyzing installation errors and using a calibration point group to generate a corrected coordinate calculation formula, improving measurement accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing error correction methods are used for 3D lidar scanners, then the measurement process can be completed, but the spatial geometric coordinate errors are not corrected, resulting in reduced measurement accuracy
Solution Approach 1:
The error correction method segments the overall error correction process into multiple independent correction modules: azimuth angle correction, pitch angle correction, and distance correction. Each module addresses specific error sources separately, allowing comprehensive correction of spatial geometric coordinate errors while maintaining measurement accuracy.
Solution Approach 2:
The method performs preliminary error modeling and calibration before actual measurement. By establishing an error model that includes multiple error factors and using calibration points to determine correction parameters in advance, the system prepares correction data that can be applied during measurement to achieve high accuracy without requiring complex real-time corrections.
2Measurement precision
If a comprehensive error model with multiple error factors is built, then measurement accuracy can be improved, but the device complexity and calculation burden increase
Solution Approach 1:
The comprehensive error model is segmented into multiple independent error factors (azimuth angle errors, pitch angle errors, distance errors, and their interactions). Each error factor is modeled separately with specific correction formulas, making the complex error correction process manageable and systematic rather than a single monolithic calculation.
Solution Approach 2:
The method transforms the error correction problem into parameter estimation through calibration. By using calibration points with known coordinates, the system solves for correction parameters (error factors) that can be stored and applied during measurement. This converts complex real-time error calculations into simpler parameter lookups and adjustments.
Data Source
AI summary
A method for coordinate error correction with a three-dimensional (3D) lidar scanner. The error source that affects the measurement accuracy of the three-dimensional coordinate is determined by building an error model, and then the error is modified to improve the measurement accuracy of the three-dimensional lidar scanner. The error correction method includes: building a theoretical calculation model, analyzing the source of measurement error, building an error model, solving the error model and implementing coordinate correction. During building the error model, 26 error factors are considered to obtain a calculation expression of the three-dimensional Cartesian coordinate. The calculation expression includes the amount of errors, the azimuth angle, the pitch angle and the distance.