Panoramic Disparity Image Stitching Using Pre-calculated Homography
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Solution Overview
Problem
Current disparity image stitching methods in driverless technology face challenges in achieving real-time performance and seamless stitching due to high calculation complexity and poor stitching effects, especially when using single pairs of binocular cameras with limited field angles.
Innovation Solution
A method that pre-calculates homography matrices using prior information of camera positions and improves the graph cut algorithm for efficient stitching of disparity images, fusing them with visible light images to enhance environmental depth observation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If feature point matching method is used to calculate external parameters and homography matrix, then stitching precision is improved, but calculation time increases significantly
Solution Approach 1:
The patent pre-calculates and stores the homography matrix H and external parameters (rotation matrix R and translation vector T) between camera coordinate systems before actual stitching operations. This preliminary computation allows the system to skip time-consuming feature matching during real-time operation, achieving both high precision and real-time performance by performing the complex calculation once and reusing the results repeatedly.
2Manufacturing precision
If traditional graph cut algorithm is used for stitching, then stitching quality is improved, but calculation complexity increases
Solution Approach 1:
The patent divides the image into multiple layers and processes each layer separately through the graph cut algorithm. This segmentation reduces the calculation complexity for each individual layer while maintaining overall stitching quality. By processing layers independently rather than the entire image at once, the algorithm becomes computationally more manageable while still achieving seamless stitching results.
3Device complexity
If single pair of binocular cameras is used, then system complexity is reduced, but field angle coverage is insufficient
Solution Approach 1:
The patent merges images from multiple pairs of binocular cameras by establishing camera coordinate transformations and performing stitching operations. Multiple camera systems are combined into a unified panoramic view, expanding the field angle coverage while maintaining manageable system complexity through standardized processing pipelines for coordinate transformation and image fusion.
Data Source
AI summary
The present invention discloses a disparity image stitching and visualization method based on multiple pairs of binocular cameras. A calibration algorithm is used to solve the positional relationship between binocular cameras, and the prior information is used to solve a homography matrix between images; internal parameters and external parameters of the cameras are used to perform camera coordinate system transformation of depth images; the graph cut algorithm has high time complexity and depends on the number of nodes in a graph; the present invention divides the images into layers, and solutions are obtained layer by layer and iterated; then the homography matrix is used to perform image coordinate system transformation of the depth images, and a stitching seam is synthesized to realize seamless panoramic depth image stitching; and finally, depth information of a disparity image is superimposed on a visible light image.