UAV 3D Reconstruction with In-Flight Model and Pose Updates
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 resulting in incomplete or inaccurate 3D reconstructions.
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 iteratively refining the model in real-time using onboard processors and image sensors, enabling consistent framing and robust feature detection, even in the presence of obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual operation is used to scan complex three-dimensional targets, then the operator can adjust to different geometries, but the scanning process becomes time-consuming and requires repeated site visits
Solution Approach 1:
The system enables autonomous scanning where the UAV automatically navigates, captures images, and reconstructs 3D models without continuous manual intervention. The automated flight control and image capture systems allow the UAV to independently complete scanning tasks, significantly improving productivity while maintaining adequate operational control.
Solution Approach 2:
The system performs preliminary actions by pre-planning scan paths and automatically positioning the UAV before actual scanning begins. The automated navigation system prepares flight trajectories in advance, allowing the UAV to efficiently approach and scan complex geometries without real-time manual guidance, thus speeding up the overall process.
2Reliability
If comprehensive scanning of complex geometries is performed manually, then complete coverage can be achieved, but the process requires multiple repeated visits to the site
Solution Approach 1:
The system continuously monitors scanning progress and automatically adjusts the flight path to ensure complete coverage of complex geometries. Real-time feedback from sensors and image processing allows the UAV to identify unscanned areas and return to capture them, guaranteeing comprehensive coverage in a single visit without requiring manual reassessment.
Solution Approach 2:
The scanning system dynamically adapts to complex geometries by automatically adjusting flight trajectories and capture parameters in real-time. The automated navigation system modifies the scan path on-the-fly to accommodate irregular surfaces and concavities, ensuring complete coverage without pre-programming for every possible geometry type.
3Measurement precision
If high-resolution 3D reconstruction is performed, then accuracy is improved, but the data processing time and computational requirements increase
Solution Approach 1:
The system segments the 3D reconstruction process into multiple stages, processing images in batches rather than all at once. By dividing the large dataset into manageable portions and processing them sequentially or in parallel, the system maintains high reconstruction accuracy while reducing the peak computational load and overall processing time.
Solution Approach 2:
The system initially performs a coarse scan to capture the overall geometry, then selectively refines specific areas requiring higher precision. This partial refinement approach achieves sufficient accuracy for most applications without processing every detail at maximum resolution, thereby reducing processing time while maintaining adequate measurement precision.
Data Source
AI summary
In some examples, an unmanned aerial vehicle (UAV) employs one or more image sensors to capture images of a scan target and may use distance information from the images for determining respective locations in three-dimensional (3D) space of a plurality of points of a 3D model representative of a surface of the scan target. The UAV may compare a first image with a second image to determine a difference between a current frame of reference position for the UAV and an estimate of an actual frame of reference position for the UAV. Further, based at least on the difference, the UAV may determine, while the UAV is in flight, an update to the 3D model including at least one of an updated location of at least one point in the 3D model, or a location of a new point in the 3D model.