Camera to Ground Alignment via 2D Road Mask Filtering
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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 involves receiving image data, determining feature points using vehicle velocity and 3D projection methods, selecting ground points based on a 2D image road mask and 3D region, calculating a ground normal vector, and generating camera to ground alignment values to optimize image data for a virtual view.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional camera to ground alignment methods are used to generate virtual surround view images, then accurate alignment can be achieved, but computational intensity becomes excessive for real-time processing
Solution Approach 1:
The patent extracts only the essential ground-related features from images by using a ground mask to identify and select only ground points among all detected feature points. This extraction approach reduces the computational load by focusing processing only on relevant ground features rather than all features in the scene, thereby maintaining alignment accuracy while improving real-time processing efficiency
Solution Approach 2:
The patent segments the image processing task into distinct stages: feature point detection, ground mask application to filter ground points, ground plane fitting, and alignment calculation. This segmentation allows each stage to be optimized independently, with the ground mask acting as a computational filter that reduces the data volume for subsequent processing steps, resolving the contradiction between accuracy and processing speed
2Measurement precision
If feature points are determined using comprehensive 3D projection methods to ensure accuracy, then alignment precision improves, but processing time increases
Solution Approach 1:
The patent performs preliminary action by applying the ground mask before conducting full 3D projection and ground plane fitting operations. By pre-identifying ground regions and filtering ground points in advance, the system prepares the data in optimal form for subsequent alignment calculations, reducing the computational burden of time-consuming 3D operations while maintaining precision
Solution Approach 2:
The patent applies partial action by using the ground mask to select only the necessary subset of feature points that are relevant for ground alignment. Rather than processing all detected feature points through computationally intensive 3D projection methods, the system applies 3D processing only to the filtered ground points, achieving sufficient alignment precision with reduced processing time
Data Source
AI summary
Systems and methods for a vehicle are provided. In one embodiment, a 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 based on a fixed two dimensional image road mask and a three dimensional region; 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, 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.