Surround Camera Alignment Correction Under Vibration and Tolerance
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Solution Overview
Problem
Vehicle cameras become misaligned due to manufacturing tolerances and mechanical vibrations, leading to improperly oriented images that are not correctly aligned with the vehicle.
Innovation Solution
A method and system for aligning image data from multiple vehicle cameras by estimating incremental changes in camera poses using Least Mean Square (LMS) solutions and Random Sample Consensus (RANSAC) criterion estimation to correct alignment based on epipolar and closed-loop constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional camera alignment methods are used, then manufacturing simplicity is maintained, but alignment precision deteriorates due to manufacturing tolerances and mechanical vibrations
Solution Approach 1:
The system performs preliminary alignment calibration by capturing images at multiple known camera poses, establishing a mapping relationship between pose parameters and image features before actual alignment is needed. This pre-computed mapping enables rapid real-time alignment without complex iterative calculations during operation.
Solution Approach 2:
The patent replaces complex mechanical alignment adjustment mechanisms with a computational approach using image processing and pose estimation algorithms. Instead of physically adjusting camera mounts to achieve alignment, the system uses software-based correction through the established pose-image mapping relationship.
2Measurement precision
If real-time alignment correction is implemented, then alignment accuracy is improved, but computational processing time increases
Solution Approach 1:
The system pre-computes and stores the mapping between camera poses and corresponding image alignments during a calibration phase. During real-time operation, only simple lookup and interpolation operations are needed based on the current camera pose, avoiding complex optimization calculations and achieving both high accuracy and real-time performance.
3Area of stationary object
If multiple cameras are used for surround imaging, then coverage area is improved, but alignment difficulty increases due to cumulative errors
Solution Approach 1:
The system divides the surround camera system into multiple independent calibration units, each camera being calibrated separately against known pose references. By establishing individual pose-image mappings for each camera rather than attempting to align all cameras simultaneously, the system avoids cumulative alignment errors and enables independent optimization of each camera's alignment accuracy.
Data Source
AI summary
A method and system for correcting alignment of images received from cameras by estimating camera alignment parameters based on perturbative correction such that corrected alignment parameters satisfy pairwise epipolar constraints as well as global loop closure constraints. The corrected alignment parameters are estimated as a Least-Mean-Square solution to a system of linear equations and an estimation algorithm may be implemented as a part of a Random Sample Consensus criterion estimation for improved robustness. The method and system is fast and efficient and may be implemented in a real-time system.