Camera-to-Ground Alignment Using Ground Plane Feature Selection
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Solution Overview
Problem
Determining camera to ground alignment for generating virtual surround view images in vehicles is computationally intensive, especially in real-time applications.
Innovation Solution
A method and system that involve receiving image data, determining feature points, selecting ground points, calculating a ground normal vector using a sliding window method, and generating camera to ground alignment values to optimize image data for a virtual bird's-eye view, considering vehicle velocity, lighting conditions, and vehicle dynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to determine camera to ground alignment, then alignment accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the image processing task by identifying and selecting only ground-related feature points (road surface, curbs, sidewalks) rather than processing all feature points in the image. This segmentation of the feature set reduces computational complexity while maintaining alignment accuracy for the relevant ground plane.
Solution Approach 2:
The patent extracts and isolates ground-specific features from the entire image scene by using dynamic 2D masks and homography constraints to identify only those feature points that lie on the ground plane. This extraction process eliminates unnecessary computational overhead from non-ground features while preserving the accuracy needed for camera-to-ground alignment.
2Productivity
If real-time processing is implemented, then processing speed is improved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining the ground plane model and its constraints (homography matrix, dynamic 2D masks) before actual image processing. This preliminary setup allows the system to quickly identify ground features in real-time without performing complex full-image analysis during critical processing moments, thereby achieving real-time performance.
Solution Approach 2:
The patent introduces dynamic 2D masks that adapt to changing scene conditions and vehicle motion. These dynamic masks are updated efficiently based on current image data and vehicle state, allowing the system to maintain real-time processing capability while adapting to varying environmental conditions without requiring computationally intensive reprocessing of the entire image.
3Measurement precision
If feature points are selected from the entire image, then measurement accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies local quality by focusing computational resources only on regions of the image that contain ground features. Using dynamic 2D masks, the system identifies and processes only the local regions containing road surfaces, curbs, and sidewalks, rather than analyzing the entire image. This localized approach maintains measurement accuracy for ground points while significantly reducing processing time.
Solution Approach 2:
The patent employs partial action by selecting and processing only the subset of feature points that are relevant to ground plane determination. Rather than processing all feature points in the image, the system identifies and processes only those feature points that satisfy ground-specific constraints, achieving sufficient measurement accuracy with reduced processing effort and time.
Data Source
AI summary
Methods and systems for a vehicle are provided. In one embodiment, the method includes: receiving image data defining a plurality of images associated with an environment of the vehicle; determining, by a processor, feature points within at least one image of the plurality of images; selecting, by the processor, a subset of the feature points as ground points; determining, by the processor, a ground plane based on the subset of feature points; determining, by the processor, a ground normal vector from the ground plane; determining, by the processor, the ground normal vector based on a sliding widow method; determining, by the processor, a camera to ground alignment value based on the ground normal vector; and generating, by the processor, second image data based on the camera to ground alignment value.