Aruco Code Extraction for GNSS-Denied UAS Landing
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Solution Overview
Problem
Uncrewed aircraft systems (UAS) face challenges in navigating to landing sites without Global Navigation Satellite System (GNSS) due to the absence of powered infrastructure, necessitating alternative sensor solutions for precise landing.
Innovation Solution
A method and system for extracting Aruco codes from vehicle camera images using multi-scale-binarization and adaptive thresholding to identify and verify landing surface markers, enhancing the robustness of marker extraction without relying on additional sensors like IMUs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GNSS ground stations and powered navigation features are installed at vertiports, then navigation reliability is improved, but infrastructure complexity and power requirements increase
Solution Approach 1:
The patent replaces complex powered navigation infrastructure (GNSS ground stations, active beacons) with passive optical markers (Aruco codes) that can be detected by the UAS onboard camera system. This substitution eliminates the need for powered infrastructure while maintaining navigation capability through image processing and computer vision algorithms.
2Device complexity
If passive optical markers are used instead of powered navigation infrastructure, then infrastructure complexity is reduced, but marker detection precision deteriorates
Solution Approach 1:
The patent applies preliminary image processing actions including color-to-grayscale conversion, multi-scale binarization, and adaptive thresholding to enhance the passive optical markers before detection. These preprocessing steps improve the contrast and detectability of the Aruco codes, ensuring precise marker identification even in varying lighting conditions.
Solution Approach 2:
The patent processes images at multiple scales through multi-scale binarization, analyzing the same image data at different resolution levels. This multi-dimensional approach enables robust detection of markers regardless of their size in the image, improving detection precision across varying distances and angles.
3Reliability
If multi-scale-binarization and adaptive thresholding are applied, then marker extraction robustness is improved, but processing time increases
Solution Approach 1:
The patent segments the image processing task into distinct stages: color-to-grayscale conversion, multi-scale binarization at different thresholds, edge detection, contour identification, and marker verification. This segmentation allows parallel processing of multiple scales and thresholds, improving robustness while managing computational complexity through organized processing pipelines.
Data Source
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AI summary
A method comprises capturing, with a vehicle vision sensor, a color image of a landing site including landing surface markers; converting the color image to a gray scale image; and performing multi-scale-binarization to detect multiple edges of the gray scale image and produce binary images. The method determines contours of edges of the binary images having closed shapes, detects closed shapes of contours of edges having four corners, and verifies whether four-sided candidate contours are valid as potential landing surface markers. If more than one contour is associated with a valid ID within a surface marker library, then the contour within the smallest window size is selected. If multiple contours with the same window size can be associated with a valid ID, then a mean of corresponding corners of multiple contours is computed. The method then performs corner refinement of valid four-sided candidate contours identified as potential landing surface markers.