UAV Ground Control Point Selection Using Landmark-Based Photogrammetry
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Solution Overview
Problem
Conventional methods for establishing ground control points (GCPs) in photogrammetry using UAVs are tedious, expensive, and often impractical due to the need for manual placement and measurement, especially in inaccessible or unsafe areas, and rely on consumer-grade GPS with low accuracy, leading to erroneous 3D model orientation and scale.
Innovation Solution
Automated systems and processes using high precision satellite navigation receivers mounted on unmanned vehicles to identify and capture location information for preexisting objects as control points, eliminating the need for manual target placement and utilizing a combination of high and low accuracy GPS receivers to enhance accuracy and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual placement and measurement of GCPs is performed, then location accuracy can be improved, but the process becomes tedious and time-consuming
Solution Approach 1:
The system uses the UAV itself to automatically identify and capture images of preexisting objects that serve as GCPs, eliminating the need for manual intervention. The UAV flies over the area, captures images, and the system automatically processes these images to identify suitable GCPs and extract their coordinates, making the entire process self-service and highly efficient
Solution Approach 2:
The system pre-identifies potential GCPs by analyzing UAV-captured images before final measurement. By examining images taken during regular UAV flights, the system can preliminarily select suitable preexisting objects as GCPs, prepare their locations, and then perform precise coordinate capture, streamlining the overall process
2Measurement precision
If manual placement of GCP targets is performed, then measurement accuracy can be improved, but safety risks increase for inaccessible areas
Solution Approach 1:
The UAV autonomously captures images and the system automatically identifies GCPs without requiring human operators to physically access dangerous or inaccessible locations. The automated image processing and GCP selection eliminate the need for manual target placement in hazardous areas, ensuring operator safety while maintaining measurement capability
Solution Approach 2:
The system replaces the mechanical process of manual target placement and physical GPS measurement with an automated optical and computational approach. By using UAV-captured images and automated feature recognition algorithms, the system eliminates the need for physical intervention in dangerous areas
3Ease of manufacture
If consumer-grade GPS units are used on UAVs, then cost is reduced, but position accuracy deteriorates
Solution Approach 1:
The system introduces preexisting objects (natural or man-made features) as intermediary reference points. Instead of relying solely on direct GPS measurement of arbitrary points, the system uses these stable, identifiable features as mediators to establish accurate spatial relationships. The GPS coordinates of these preexisting objects serve as reliable reference data that compensates for consumer-grade GPS limitations
Solution Approach 2:
The system creates a photogrammetric model that copies and represents the real-world geometry and spatial relationships. By capturing images from multiple angles and using photogrammetry to reconstruct the 3D positions of preexisting objects, the system achieves accurate positioning that compensates for lower GPS precision, effectively copying the true spatial structure
4Manufacturing precision
If multiple GCPs are established to improve 3D model accuracy, then model precision is improved, but the complexity of the process increases
Solution Approach 1:
The automated system independently identifies multiple suitable GCPs, captures their coordinates, and integrates them into the photogrammetry workflow without requiring manual coordination. The system automatically manages the entire process of selecting, measuring, and utilizing multiple GCPs, reducing process complexity despite the increased number of control points
Solution Approach 2:
The same UAV and automated system handle multiple functions: capturing images for photogrammetry, identifying GCPs, extracting coordinates, and integrating reference data. This multi-functional approach allows the system to efficiently manage multiple GCPs without proportionally increasing complexity, as the same apparatus performs diverse tasks
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables quick and accurate identification and logging of control points, improving the precision and reliability of 3D model generation by providing precise position, scale, and orientation information, reducing the reliance on manual processes and low-accuracy GPS data.
Implementation Method 1
capturing location information of the first unmanned vehicle while being navigated within the first area, using a high precision, high accuracy satellite navigation receiver
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for an unmanned aerial vehicle control point selection system. A first unmanned aerial vehicle can navigate about a geographic area while a second unmanned aerial vehicle can capture images of the first unmanned aerial vehicle. Location information of the first unmanned aerial vehicle can be recorded, and a system can identify the first unmanned aerial vehicle in the captured images. Utilizing the recorded location information, the system can identify landmarks proximate to the first unmanned aerial vehicle identified in the images, and determine location information of the landmarks. The landmarks can be assigned as ground control points for subsequent flight plans.