Nodal Camera Calibration Using LiDAR Edge Correlation
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Solution Overview
Problem
Existing camera calibration methods for nodal camera systems are inadequate as they fail to account for small changes in calibration parameters due to mechanical deformation, thermal drifts, or inaccurate reassembly, leading to inaccurate registration and combination of data from different sensors.
Innovation Solution
A method for recalibrating a camera in a nodal camera system by acquiring data from multiple sensors, identifying edges in the data, splitting the data into grid cells, calculating offset vectors for maximum edge correlation, and using these vectors to calculate new calibration parameters that minimize offset magnitudes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If factory calibration parameters are used for nodal camera systems, then initial registration can be established, but the calibration parameters become unstable and inaccurate due to mechanical deformation, thermal drifts, or inaccurate reassembly
Solution Approach 1:
The system performs preliminary edge detection and feature identification in both color images and intensity images before calculating calibration parameters. This preliminary processing ensures that the calibration is based on robust, pre-identified features that are less susceptible to degradation from mechanical deformation or thermal drifts, thereby maintaining both reliability and precision.
Solution Approach 2:
The system uses feedback from comparing edges in color images with edges in intensity images to iteratively refine calibration parameters. By continuously monitoring the correlation between edges from different sensor modalities and adjusting parameters accordingly, the system compensates for instability and maintains high registration accuracy despite mechanical or thermal changes.
2Device complexity
If traditional calibration methods are used without edge correlation, then the calibration process is simpler, but the alignment accuracy between color images and point cloud data deteriorates
Solution Approach 1:
The calibration process is segmented into distinct stages: edge detection in color images, edge detection in intensity images, correlation analysis between edges, and calibration parameter calculation. This segmentation allows each stage to be optimized independently, maintaining overall simplicity while improving precision through systematic processing of edge information from both sensor types.
Solution Approach 2:
Edge correlation acts as an intermediary mechanism that bridges color image data and intensity image data. By establishing correlations between edges from both modalities, the system creates a robust intermediary step that enhances the accuracy of pixel-to-point correspondence without requiring direct complex interactions between all calibration parameters.
Data Source
AI summary
Systems and methods are provided to generate on-site calibration parameters for improved calibration of a LIDAR/camera system. Data is acquired from two or more sensors (for example, a camera and a LIDAR scanner) used with a nodal camera rig or aligned in another way. Edges in the two sets of data are identified. The data is partitioned for example by using a grid. Offset vectors between the edges in the two sets of data are identified. Calibration parameters are generated based on the offset vectors.