Camera Self-Calibration via Hybrid Deep Learning and Geometric Optimization
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Solution Overview
Problem
Existing self-calibration methods for cameras, especially in safety-critical applications like automated driving, are not precise enough and require extensive computational resources, making them impractical for regular recalibration due to temperature changes and mechanical influences.
Innovation Solution
A hybrid method combining deep learning for feature extraction and correspondence searching with classic geometric optimization, allowing for the estimation of camera parameters from any image sequence, enabling online calibration and detection of parameter drifts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If classic calibration procedures are used, then measurement precision is improved, but device complexity and ease of operation deteriorate due to complex procedures requiring special equipment and expert operation
Solution Approach 1:
The system performs self-calibration automatically using images captured during normal operation. The calibration procedure executes itself without requiring external calibration equipment or expert intervention, transforming the calibration process from a manual expert task into an automated self-service operation that occurs in the background during regular camera use.
Solution Approach 2:
The invention extracts the calibration functionality from the complex traditional calibration procedure and special calibration equipment. By using only images captured during normal operation and performing correspondence searching and optimization algorithmically, the system removes the need for dedicated calibration devices and complex manual procedures, achieving calibration through extracted image data alone.
2Ease of operation
If deep learning methods are used for self-calibration, then ease of operation is improved, but measurement precision deteriorates due to dependence on training data and insufficient precision for safety-critical applications
Solution Approach 1:
The system merges the automatic operation capability of deep learning methods with the high precision of classic geometric optimization. The workflow combines automated feature extraction and correspondence searching (providing ease of operation) with precise optimization algorithms that refine camera parameters (ensuring measurement precision), achieving both automatic operation and high precision suitable for safety-critical applications.
Solution Approach 2:
The invention uses ascertained correspondences as an intermediary between the automated deep learning stage and the precision optimization stage. The correspondences extracted automatically serve as input for the optimization algorithm, bridging the gap between automated operation and precise parameter determination, enabling the system to achieve both ease of operation and measurement precision.
3Device complexity
If existing self-calibration algorithms are used, then device complexity is reduced, but measurement precision deteriorates due to insufficient precision and environmental requirements
Solution Approach 1:
The system dynamically adapts the calibration process by iteratively refining correspondences and re-optimizing parameters. Rather than using a static single-pass algorithm, the system performs multiple optimization iterations with updated correspondences, allowing the calibration process to dynamically improve precision while maintaining relative simplicity through automated execution.
Solution Approach 2:
The invention enables continuous calibration by performing optimization algorithms repeatedly with updated correspondences throughout the camera's operation. This continuous refinement of parameters maintains high precision without requiring complex interruptive calibration procedures, achieving both simplicity and precision through ongoing automated calibration actions.
4Ease of operation
If video-based self-calibration methods are used, then ease of operation is improved, but loss of time increases due to computational intensity requiring over 12 hours for training
Solution Approach 1:
The system performs preliminary feature extraction and correspondence searching using automated algorithms before the optimization stage. By preparing correspondences in advance during normal operation and storing them for later use, the system reduces the computational burden during actual calibration, enabling fast execution of the optimization algorithms without requiring extensive training time.
Solution Approach 2:
The invention replaces computationally intensive deep learning training with more efficient optimization algorithms that operate on pre-extracted correspondences. By substituting the mechanical training process with algebraic optimization methods that work on prepared data, the system achieves automatic calibration with dramatically reduced computational time, making real-time calibration practical.
Data Source
AI summary
A computer-implemented method for self-calibration of at least one camera. The method comprises: ascertaining at least two corresponding images from a sequence of recorded images, the recorded images resulting from recordings of the camera, the corresponding images having correspondences which are specific to at least one camera parameter, the camera parameter being specific to the geometric imaging behavior of the camera; ascertaining the respective correspondence by an application of an artificial neural network, the application being based on the corresponding images; determining at least the camera parameter based on the ascertained correspondences for the self-calibration of the camera.