UAV Aerial Mapping With Reduced Frame Overlap and Fast Stitching
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Solution Overview
Problem
Current aerial mapping technologies with unmanned aerial vehicles (UAVs) require significant frame overlap for accurate image stitching and point cloud generation, leading to high computational demands and limited real-time processing capabilities, which restricts user feedback and adaptability during flights.
Innovation Solution
A method for improved aerial mapping that generates orthomosaic and point cloud data using reduced frame overlap, leveraging feature matching between consecutive frames and telemetry data, and implementing optimizations such as restricted bundle adjustments and compact data representations to enable rapid, local processing and user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If significant frame overlap is used for accurate image stitching and point cloud generation, then mapping accuracy is improved, but computational demands increase and real-time processing capability deteriorates
Solution Approach 1:
The patent segments the image processing task by using only consecutive frames for feature matching and point cloud generation, rather than processing all overlapping frames. This segmentation reduces the computational workload while maintaining mapping accuracy through the use of telemetry data to compensate for the reduced frame overlap.
Solution Approach 2:
The patent applies preliminary action by using telemetry data (GPS, IMU, barometer) to pre-compensate for camera motion and position between frames. This allows the system to achieve accurate mapping with reduced frame overlap, as the telemetry data provides prior information about camera movement that would otherwise require extensive frame overlap to determine.
2Measurement precision
If significant frame overlap is used for accurate image stitching, then stitching accuracy is improved, but computational processing time increases
Solution Approach 1:
The patent extracts and uses only the essential information from frames (consecutive frame features and telemetry data) for stitching, rather than processing all overlapping frame data. This extraction approach maintains stitching accuracy while reducing computational processing time by eliminating redundant calculations from excessive frame overlap.
Solution Approach 2:
The patent changes the parameters used for stitching from relying solely on visual feature matching across multiple overlapping frames to combining feature matching with telemetry data parameters (camera position, orientation, velocity). This parameter change allows accurate stitching with reduced frame overlap, thereby reducing processing time.
3Measurement precision
If extensive frame processing is performed for point cloud generation, then point cloud accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The patent segments the point cloud generation process to use only consecutive frames and their associated telemetry data, rather than processing all frames with overlap. This segmentation maintains point cloud accuracy by focusing computational resources on essential frame pairs while reducing overall energy consumption.
Solution Approach 2:
The patent introduces telemetry data as an intermediary that mediates between frames for point cloud generation. The telemetry data provides camera motion information that bridges gaps between consecutive frames, allowing accurate point cloud generation with reduced frame overlap and lower computational resource consumption.
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.