Vehicle Camera Alignment Using Bird's Eye View Feature Matching
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Solution Overview
Problem
Conventional camera-to-camera alignment techniques in vehicles rely on perspective views, leading to accuracy degradation due to road bank angle issues, long convergence times, and sensitivity to tuning variations, which affect perception and vehicle control operations.
Innovation Solution
A vehicle system utilizing a control module to create a Bird's Eye View (BEV) image from overlapping local regions of interest captured by multiple cameras, employing feature matching and pre-processing techniques to enhance alignment accuracy, including normalization and histogram equalization, and filtering to improve feature detection and alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional perspective view-based camera alignment is used, then the alignment process can be performed with simple image processing, but accuracy degrades due to road bank angle issues and convergence time increases
Solution Approach 1:
The patent transforms the alignment problem from 2D perspective views to a 3D Bird's Eye View representation. By projecting camera images onto a virtual BEV plane, the system achieves metrically accurate alignment that is invariant to camera perspectives and road bank angles, directly resolving the accuracy degradation issue while maintaining computational efficiency
2Adaptability or versatility
If perspective view-based feature matching is used, then the method is simpler to implement, but it becomes sensitive to tuning variations and road bank angle issues
Solution Approach 1:
The patent introduces a Bird's Eye View image as an intermediary representation between camera images and the physical world. This BEV intermediary transforms perspective-dependent feature matching into a metrically accurate 3D space representation, making the system robust to road bank angles while adding minimal complexity through standard image processing techniques
3Measurement precision
If multiple pre-processing techniques are applied to enhance feature detection, then alignment accuracy improves, but processing time increases
Solution Approach 1:
The patent applies pre-processing techniques such as histogram equalization and Gaussian blur in advance during the BEV image generation phase. By performing these operations beforehand rather than during iterative alignment optimization, the system enhances feature detection accuracy while minimizing impact on final alignment processing speed
Data Source
AI summary
A vehicle system includes a first camera configured to capture original images in a first perspective relative to a vehicle and a second camera configured to capture original images in a second perspective relative to the vehicle, and a control module configured to receive a first original image from the first camera and a second original image from the second camera, select an overlapping local region of interest from the first original image and the second original image for a birds eye view image, create the birds eye view image having the local region of interest, detect features in the birds eye view image, map detected features in the birds eye view image to the first original image and the second original image, and align at least the first camera and the second camera using the detected features. Other example vehicle systems and methods are also disclosed.