UAV Scan Planning for Complex 3D Structure Reconstruction
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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 a scan plan, and iteratively refining the model in real-time, using onboard processors for navigation and image capture, allowing for consistent framing and robust feature detection without manual control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual operation is used to scan complex three-dimensional targets, then human intervention can review data and adjust scanning, but scanning speed and efficiency deteriorate due to extensive manual control requirements
Solution Approach 1:
The UAV system performs autonomous scanning operations by automatically navigating around the target structure, capturing images, and processing data without continuous manual intervention. The system serves itself by making independent decisions about flight paths, image capture timing, and real-time 3D model generation, thereby improving scanning speed while maintaining accuracy through automated control algorithms.
Solution Approach 2:
The system performs preliminary actions by pre-planning the scanning mission, pre-processing captured images to detect features, and pre-generating 3D models before completing the full scanning operation. This allows the UAV to anticipate required movements and adjust its flight path proactively, improving efficiency while ensuring comprehensive coverage of complex geometries.
2Reliability
If traditional scanning methods are used for complex geometries with concavities and irregular surfaces, then comprehensive data collection is possible, but the system complexity increases requiring extensive human intervention
Solution Approach 1:
The UAV system implements continuous feedback loops where captured images are processed in real-time to detect features and update the 3D model. The system uses this feedback to automatically adjust its flight path, ensuring comprehensive coverage of concavities and irregular surfaces. This automated feedback mechanism improves data completeness while reducing system complexity by eliminating the need for manual data review and intervention.
Solution Approach 2:
The scanning system dynamically adapts its operation by continuously updating the 3D model during flight and adjusting the flight path in real-time based on detected features. This dynamic approach allows the UAV to automatically navigate complex geometries with concavities and irregular surfaces, maintaining data completeness while reducing the need for complex pre-planning and manual intervention.
3Measurement precision
If real-time 3D reconstruction is implemented, then scanning accuracy and feature detection improve, but computational requirements and processing complexity increase
Solution Approach 1:
The system segments the 3D reconstruction process into discrete stages: image capture, feature detection, model generation, and validation. By processing images and detecting features in sequential segments rather than all at once, the computational complexity is managed more effectively while maintaining high feature detection accuracy through focused, stage-specific processing algorithms.
Solution Approach 2:
The system performs partial 3D reconstruction actions by generating and validating the 3D model incrementally during the scanning flight rather than completing the entire reconstruction after data collection. This partial action approach allows real-time accuracy improvement while managing processing complexity through distributed computation across multiple flight segments.
Data Source
AI summary
In some examples, an unmanned aerial vehicle (UAV) may determine, based on a three-dimensional (3D) model including a plurality of points corresponding to a scan target, a scan plan for scanning at least a portion of the scan target. For instance, the scan plan may include a plurality of poses for the UAV to assume to capture images of the scan target. The UAV may capture with one or more image sensors, one or more images of the scan target from one or more poses of the plurality of poses. Further, the UAV may determine an update to the 3D model based at least in part on the one or more images. Additionally, the UAV may update the scan plan based at least in part on the update to the 3D model.