Vehicle Camera Alignment Using Multi-View Synthetic Feature Matching
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Solution Overview
Problem
Current camera alignment methods for vehicles, particularly in autonomous and semi-autonomous systems, suffer from accuracy degradation and convergence time issues, leading to poor feature matching and reduced reliability in vehicle control operations.
Innovation Solution
A vehicle system employs online camera alignment using ensembled features from multiple views, involving the creation of synthetic local images based on local regions of interest and view settings, with automatic tuning techniques to adjust parameters, enhancing accuracy and robustness through feature detection and alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current camera alignment methods are used, then the alignment process can be completed, but accuracy degradation and convergence time issues occur
Solution Approach 1:
The patent segments the alignment process by identifying and matching specific feature points between camera views rather than processing entire images. The system divides the feature matching task into discrete keypoint detection and matching operations, which accelerates convergence while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary feature point detection and descriptor computation before the actual alignment process. By pre-processing and identifying candidate feature points in advance, the system reduces the computational burden during convergence, thereby reducing convergence time without sacrificing alignment accuracy.
2Reliability
If current camera alignment methods are used, then the alignment can be performed, but poor feature matching results occur
Solution Approach 1:
The patent implements feedback mechanisms where feature matching results are continuously evaluated and used to refine alignment parameters. The system uses matched feature points to compute alignment transformations, which are then applied and re-evaluated in an iterative process that improves both reliability and precision.
Solution Approach 2:
The patent adjusts matching parameters such as feature point selection criteria, descriptor thresholds, and transformation model parameters to optimize both feature matching reliability and alignment accuracy. By dynamically tuning these parameters based on scene conditions, the system achieves better overall performance.
Data Source
AI summary
A vehicle system includes one or more cameras configured to capture original images relative to a vehicle, and a control module configured to receive at least two original images from the one or more cameras of the vehicle, identify at least one target feature in the original images, select local regions of interest for the identified target feature in the original images, load view settings for each local region of interest, create one or more synthetic local images for each original image based on the local regions of interest and the loaded view settings, detect at least one feature in the one or more synthetic local images, and align the camera with an object associated with the vehicle using the detected feature. Other example vehicle systems and methods are also disclosed.