UAV Scan Target Framing for Autonomous 3D Reconstruction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current unmanned aerial vehicle (UAV) systems lack the capability for autonomous and efficient scanning of complex three-dimensional targets with concavities, irregular surfaces, and oblique geometries, requiring manual operation and extensive human intervention for data review and additional scanning.
Innovation Solution
The implementation of a UAV system that autonomously scans three-dimensional targets by generating a lower-resolution 3D model, dynamically updating the scan plan, and improving resolution in real-time using onboard processors and image sensors, enabling reliable framing, robust feature detection, and reduced operator attention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual operation is used for UAV scanning, then operator control flexibility is maintained, but scanning efficiency and productivity are reduced
Solution Approach 1:
The UAV system performs autonomous scanning by automatically generating scan plans, navigating to scan positions, capturing images, and processing data without continuous human intervention. The system serves itself by using onboard processors to generate lower-resolution 3D models and update scan plans dynamically, enabling self-directed operation that significantly improves scanning productivity
Solution Approach 2:
The system generates a preliminary lower-resolution 3D model before high-resolution scanning to pre-plan the scan path and identify concavities and irregular surfaces. This preliminary action allows the UAV to optimize its scanning trajectory in advance, improving efficiency by avoiding redundant flights and ensuring complete coverage of complex geometries
2Measurement precision
If comprehensive scanning of complex targets is performed, then measurement completeness is improved, but scanning time and duration are increased
Solution Approach 1:
The system performs a partial scan initially to generate a lower-resolution 3D model, then uses this model to guide a targeted second scan for high-resolution detail. This partial action approach ensures complete coverage of complex geometries without requiring exhaustive scanning from all angles, reducing total scanning time while maintaining measurement completeness
Solution Approach 2:
The scan plan is dynamically updated during flight based on the generated lower-resolution 3D model. The system adapts the scanning trajectory in real-time to optimize coverage of concavities and irregular surfaces, adjusting the flight path to minimize scanning duration while ensuring comprehensive measurement of all target features
3Measurement precision
If high-resolution scanning is performed on complex targets, then data accuracy is improved, but the need for additional scanning and human intervention increases
Solution Approach 1:
The system uses feedback from the lower-resolution 3D model to continuously update and refine the high-resolution scan plan. By processing captured images in real-time and comparing them against the preliminary model, the system identifies areas requiring additional scanning and automatically adjusts the flight path, ensuring data accuracy while minimizing the need for manual intervention and reducing operational complexity
Solution Approach 2:
The patent replaces manual mechanical operation with automated computational processes. Onboard processors generate 3D models and update scan plans through algorithmic decision-making rather than human operators, substituting mechanical control with electronic automation. This reduces operational complexity by eliminating the need for human judgment and manual adjustments during scanning
4Speed
If real-time 3D reconstruction is implemented, then data processing speed is improved, but computational resource requirements are increased
Solution Approach 1:
The system segments the 3D reconstruction process into two distinct stages: first generating a lower-resolution 3D model for rapid overview and scan planning, then performing high-resolution reconstruction only on identified areas of interest. This segmentation allows real-time processing to occur at lower computational intensity during the initial phase, reducing energy consumption while maintaining fast data processing speed
Solution Approach 2:
The system applies different quality levels to different regions of the scan target. The lower-resolution model provides sufficient quality for navigation and planning, while high-resolution reconstruction is applied locally only to areas requiring detailed examination. This local quality approach reduces overall computational energy requirements while maintaining processing speed for critical areas
Data Source
AI summary
In some examples, an image of a scan target is presented in a user interface on a display associated with a computing device. The user interface receives at least one user input indicating at least one point in a perimeter or edge of a volume for encompassing the scan target presented in the image of the scan target. A graphical representation of the volume in relation to the image of the scan target is generated in the user interface. Information for defining a location of at least a portion of the volume in three-dimensional space is sent to an unmanned aerial vehicle (UAV) to cause, at least in part, the UAV to scan at least a portion of the scan target corresponding to the volume.