Pose-Georeferenced UAV Analytics Without Image Stitching
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Solution Overview
Problem
Current methods for capturing aerial images of agricultural fields using UAVs face challenges in maximizing detail while minimizing time and data volume, particularly when precise item locations are required, as they often involve high overlap stitching that increases time and data volume, or require multiple imaging systems.
Innovation Solution
A method and system that analyze drone images captured by a UAV to identify and transform pixel-space locations of items of interest to world-space locations without transforming the images, using a UAV with one or two imaging systems and applying transforms to determine world-space locations, which can generate information like spot spray prescriptions or crop counts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If stitching images together with high overlap is used to determine precise locations, then measurement precision is improved, but time to collect imagery increases and data volume increases
Solution Approach 1:
The patent extracts only the essential information needed for location determination (pixel-space locations of items of interest) from the images, rather than processing entire stitched image datasets. This allows precise location measurement without requiring time-consuming stitching operations with high overlap between images.
Solution Approach 2:
Instead of stitching images together first and then determining locations, the patent inverts the process by determining pixel-space locations directly from individual images and then transforming those locations to world-space coordinates. This avoids the time-consuming stitching step while maintaining location precision.
2Measurement precision
If stitching images together with high overlap is used to determine precise locations, then measurement precision is improved, but data volume increases
Solution Approach 1:
The patent extracts only the necessary pixel-space location data from individual images rather than storing and processing large volumes of stitched image data. This extraction approach maintains measurement precision while significantly reducing the quantity of data that must be stored and processed.
Solution Approach 2:
The patent inverts the conventional approach by working with location coordinates directly rather than with stitched image data. This allows precise location determination with minimal data volume, as only coordinate transformations are performed rather than full image stitching operations.
3Manufacturing precision
If two imaging systems are used, one optimized for detail and one optimized for stitching, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent makes a single imaging system perform multiple functions by using it both to capture detailed images and to determine precise locations through pixel-space coordinate extraction and transformation. This eliminates the need for separate imaging systems while maintaining both image quality and location precision.
Solution Approach 2:
Instead of using one imaging system for detail and another for location determination, the patent inverts the approach by using a single system for both purposes through coordinate transformation mathematics, thereby reducing device complexity while maintaining manufacturing precision.
Data Source
AI summary
Precision agriculture methods and systems where drone images of an agricultural field are captured by a UAV and analyzed to generate information regarding the agricultural field. Items of interest are identified in the images, the pixel-space locations of the items of interest are determined, and the world-space locations of the items of interest are then determined using the pixel-space locations. The transformation from pixel-space location to world-space location occurs without transforming the images or processing transformed images.