Vehicle Camera Intrinsic Calibration Using Online Bundle Adjustment
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Solution Overview
Problem
Existing systems lack efficient methods for online intrinsic calibration of cameras in vehicles, particularly due to variations in intrinsic parameters caused by ambient temperature and aging, which affect focal length and principal point, necessitating a solution for accurate sensor data processing in autonomous systems.
Innovation Solution
Implementing a bundle adjustment algorithm for online intrinsic calibration, utilizing a processor to obtain multiple image frames, determine inlier points, and refine camera parameters, compensating for windshield and rolling shutter biases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple feedforward compensation is used to correct camera parameters, then the system complexity is reduced, but the calibration accuracy deteriorates due to inability to compensate for temperature and aging variations
Solution Approach 1:
The patent replaces complex mechanical calibration systems with a computational bundle adjustment algorithm that processes image data to determine camera parameters. This substitution achieves high calibration accuracy through software-based parameter refinement rather than hardware-based mechanical adjustment, resolving the contradiction between system complexity and measurement precision.
Solution Approach 2:
The system performs self-calibration by using its own captured image data to automatically refine camera parameters through bundle adjustment. This self-service mechanism eliminates the need for external calibration equipment or complex manual adjustment procedures, maintaining low system complexity while achieving high calibration accuracy through autonomous parameter optimization.
2Measurement precision
If offline calibration methods are used, then calibration thoroughness is improved, but the adaptability to dynamic environments deteriorates due to inability to update parameters during operation
Solution Approach 1:
The patent implements dynamic calibration by enabling the bundle adjustment algorithm to process image data and update camera parameters continuously during vehicle operation. This transforms the static offline calibration process into a dynamic system that adapts to changing environmental conditions, temperature variations, and aging effects in real-time, resolving the contradiction between calibration thoroughness and adaptability to dynamic environments.
Solution Approach 2:
The system maintains continuous calibration by repeatedly applying the bundle adjustment algorithm to incoming image data streams during normal vehicle operation. This continuous parameter refinement ensures the camera system remains accurately calibrated throughout its operational life, adapting to gradual changes from temperature fluctuations and component aging without requiring periodic shutdowns for recalibration.
3Measurement precision
If extensive bundle adjustment iterations are performed, then parameter accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies partial bundle adjustment by selectively refining only the most critical camera parameters (focal length, principal point) using a simplified iteration process. Rather than performing exhaustive adjustments on all possible parameters, the system focuses computational effort on the parameters that most significantly impact calibration accuracy, achieving sufficient precision with reduced processing time and resolving the contradiction between parameter accuracy and processing time.
Data Source
AI summary
Systems and techniques are provided for image processing for calibration. For example, a computing device can obtain image frames of a three-dimensional (3D) scene, each image frame including a plurality of two-dimensional (2D) points. Each 2D point corresponds to a 3D point. The computing device can determine a subset of 3D points by applying bundle adjustment on a set of 3D points (distributed over a field of view of the camera) and on fixed parameters of a camera. The computing device can determine inlier points from the subset of 3D points with a reprojection error less than a threshold value. The computing device can determine a final 3D points and final parameters of the camera by applying the bundle adjustment on the inlier points and on a prior set of camera parameters. The computing device can apply, to the camera, final intrinsic parameters of the final parameters of the camera.