UAV Parallax Imaging for Autonomous 3D Target Scanning
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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
1Ease of operation
If manual operation is used for scanning complex three-dimensional targets, then the operator can control the scanning process, but the productivity and efficiency are reduced due to extensive human intervention required
Solution Approach 1:
The system enables autonomous scanning by allowing the UAV to automatically generate scan plans, navigate to capture positions, and perform image capture without continuous human intervention. The onboard processor independently handles 3D model generation and scan plan updates, making the system self-sufficient in complex scanning operations
Solution Approach 2:
The system performs preliminary generation of lower-resolution 3D models to plan the scanning path before actual high-resolution data collection. This preliminary action allows the UAV to pre-determine capture positions and orientations, enabling more efficient execution of the scanning task
2Measurement precision
If comprehensive scanning of complex targets is performed to ensure complete data coverage, then the accuracy of the 3D model is improved, but the time required for data acquisition increases
Solution Approach 1:
The system generates a lower-resolution 3D model in advance to establish the scan plan and identify all necessary capture positions. This preliminary modeling ensures comprehensive coverage of complex geometries including concavities and oblique surfaces, while the pre-planned path optimizes the sequence to minimize total acquisition time
Solution Approach 2:
The scan plan is dynamically updated based on the generated 3D model, allowing the system to adapt the scanning path in real-time. This dynamic adjustment ensures complete coverage of complex target features while optimizing flight paths to reduce redundant movements and accelerate data collection
3Productivity
If real-time 3D reconstruction is implemented to reduce human intervention, then the productivity is improved, but the device complexity increases due to onboard processing requirements
Solution Approach 1:
The processing system is segmented into modular functional components: image capture sensors, onboard processors for generating lower-resolution 3D models, scan plan generation modules, and execution control systems. This segmentation allows real-time autonomous operation while managing complexity through functional decomposition
Solution Approach 2:
The lower-resolution 3D model serves as an intermediary representation that bridges the gap between raw image data and the final high-resolution output. This intermediate model enables the system to plan and execute comprehensive scanning paths without requiring immediate processing of all high-resolution data, reducing real-time computational burden
Data Source
AI summary
In some examples, an unmanned aerial vehicle (UAV) may identify a scan target. The UAV may navigate to two or more positions in relation to the scan target. The UAV may capture, using one or more image sensors of the UAV, two or more images of the scan target from different respective positions in relation to the scan target. For instance, the two or more respective positions may be selected by controlling a spacing between the two or more respective positions to enable determination of parallax disparity between a first image captured at a first position and a second image captured at a second position of the two or more positions. The UAV may determine a three-dimensional model corresponding to the scan target based in part on the determined parallax disparity of the two or more images including the first image and the second image.