UAV Aerial Mapping Using Camera Pose Estimation With Lower Overlap
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Solution Overview
Problem
Current aerial mapping technologies with unmanned aerial vehicles (UAVs) require significant frame overlap for accurate mapping, leading to high computational processing demands and limited real-time processing capabilities, which restricts rapid user feedback and adaptability during flights.
Innovation Solution
A method for improved aerial mapping that generates orthomosaic and point cloud data with reduced frame overlap by robustly determining camera pose changes through feature matching and telemetry data, enabling near real-time processing and local generation of aerial maps, including orthorectified representations and projected maps, using a platform interfaced with UAVs for mission planning and execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If significant frame overlap is used for accurate mapping, then mapping accuracy is improved, but computational processing demands increase
Solution Approach 1:
The patent segments the mapping process into distinct phases: capture phase with moderate overlap, and processing phase using structure-from-motion algorithms that handle the segmented data efficiently. This segmentation allows using less overlap than traditional methods while maintaining accuracy through sophisticated post-processing of divided image sets.
Solution Approach 2:
The patent changes the parameter of frame overlap from the traditional high overlap (60-80%) to a reduced overlap (30-50%), and compensates by adjusting other parameters such as using feature-based matching algorithms and iterative optimization methods to maintain mapping precision with fewer overlapping frames.
2Measurement precision
If significant frame overlap is used for accurate mapping, then mapping accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by capturing images with sufficient but reduced overlap during the flight, then uses efficient feature detection and matching algorithms to pre-process the data into structured formats that accelerate the final mapping computation, reducing overall processing time while maintaining accuracy.
Solution Approach 2:
The patent replaces traditional mechanical/brute-force image matching methods with computational approaches using feature-based algorithms (such as SIFT, SURF, or ORB features) and structure-from-motion techniques, which significantly reduce processing time while maintaining or improving mapping accuracy.
3Measurement precision
If significant frame overlap is used, then accurate mapping is achieved, but real-time processing capability is limited
Solution Approach 1:
The patent segments the image processing into streaming operations where images are processed in sequences or batches as they are captured, enabling incremental construction of the map during flight. This segmentation allows real-time processing by handling data in manageable portions rather than waiting for complete data sets.
Solution Approach 2:
The patent maintains continuous useful action by processing images continuously during the flight mission rather than batch-processing after completion. The system continuously captures, processes, and updates the map in real-time, enabling rapid user feedback and adaptability during the flight while achieving accurate mapping through sustained processing.
Data Source
AI summary
A method for image generation, preferably including: generating a set of mission parameters for a UAV mission of the UAV associated with aerial scanning of a region of interest; controlling the UAV to perform the mission; generating an image subassembly corresponding to the mission; and/or rendering the image subassembly at a display.