Fiducial-Based UAV Navigation for GPS-Denied Positioning
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Solution Overview
Problem
Unmanned vehicles, such as UAVs, face navigation challenges due to unreliable GPS data in spotty coverage areas or under environmental conditions, leading to potential incorrect location navigation and obstacle collisions.
Innovation Solution
The implementation of fiducials along navigation routes, which provide ground truth data through image analysis, allowing UAVs to correct and compensate for navigation errors using geometric reconstruction, enabling autonomous operation independent of GPS data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GPS data is used for navigation, then autonomous navigation can be achieved, but navigation reliability deteriorates in spotty coverage areas
Solution Approach 1:
The patent introduces fiducial markers as intermediary objects placed at known locations along the navigation route. The unmanned vehicle captures images of these fiducial markers, and the system performs geometric reconstruction to calculate the vehicle's position relative to the fiducial's known location. This intermediary fiducial-based positioning system bridges the gap when GPS data becomes unreliable, providing accurate position information without direct dependence on GPS satellite signals.
2Measurement precision
If fiducial-based navigation is implemented, then navigation accuracy improves, but device complexity increases
Solution Approach 1:
The patent uses visual copying of fiducial marker images captured by the vehicle's camera to determine position. Instead of requiring complex direct measurement instruments, the system captures an image (a visual copy) of the fiducial marker, processes this image to extract geometric information, and reconstructs the vehicle's position from this copied visual data. This approach achieves high measurement precision using relatively simple imaging equipment rather than complex measurement devices.
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
Ensures accurate and reliable autonomous navigation by providing ground truth and velocity data, even in conditions where GPS data is unreliable, thereby preventing navigation errors and collisions.
Implementation Method 1
an image of the fiducial may be accessed. A location and a dimension of the fiducial may be determined from the image. Geometric reconstruction may be performed to determine a location of the unmanned vehicle
Data Source
AI summary
Techniques for facilitating an autonomous operation, such as an autonomous navigation, of an unmanned vehicle based on one or more fiducials. For example, image data of a fiducial may be generated with an optical sensor of the unmanned vehicle. The image data may be analyzed to determine a location of the fiducial. A location of the unmanned vehicle may be estimated from the location of the fiducial and the image. The autonomous navigation of the unmanned vehicle may be directed based on the estimated location.