Fiducial Pattern Localization via Barker Code Diffraction
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Solution Overview
Problem
Existing object localization systems struggle to accurately localize objects, especially blurred objects, at close distances to a camera lens, with limited precision and sensitivity to background noise.
Innovation Solution
The use of two-dimensional (2D) Barker codes, which exhibit sharp autocorrelation peaks when aligned and near-zero values for other shifts, along with sine-modulated 2D Barker codes and averaging techniques to improve signal-to-noise ratio, enables precise object localization within one pixel resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional object localization methods are used, then the system can operate at close distances, but the localization precision deteriorates for blurred objects
Solution Approach 1:
The patent applies optical modulation techniques where fiducial markers are modulated with specific optical patterns (such as sinusoidal modulation) that create distinctive diffraction patterns. This allows the markers to be distinguished from blurred background objects through frequency domain analysis, effectively using optical 'color' or pattern changes to maintain detectability under defocus conditions.
Solution Approach 2:
The patent transforms the localization problem from spatial domain to frequency domain by using Fourier optics and diffraction patterns. Instead of relying on direct spatial features that blur with defocus, the system encodes position information in the frequency domain through diffraction patterns, where the zero-order diffraction peak provides precise localization even when spatial details are blurred.
2Length of stationary object
If fiducial markers are placed close to the camera lens, then hyperlocal imaging is achieved, but background noise interference increases
Solution Approach 1:
The patent introduces sinusoidally modulated fiducial markers as intermediary elements that mediate between the object plane and the detector. These markers create characteristic diffraction patterns that serve as unique signatures, allowing the system to distinguish signal from noise through pattern recognition in the frequency domain, effectively filtering out random background interference.
Solution Approach 2:
The patent changes the optical parameters of the fiducial markers by applying sinusoidal modulation, which transforms their diffraction characteristics. This parameter change creates a distinctive frequency signature that can be easily separated from background noise through spectral analysis, improving the signal-to-noise ratio even at close distances where hyperlocal imaging is performed.
3Measurement precision
If standard diffraction patterns are used, then object location can be determined, but sensitivity to background features and reduced immunity to noise occurs
Solution Approach 1:
The patent uses optical modulation to create distinctive frequency 'colors' in the diffraction patterns of fiducial markers. By modulating the markers with specific spatial frequencies, their diffraction patterns exhibit unique spectral signatures that can be distinguished from the random frequency content of background features, enhancing reliability through frequency domain discrimination.
Solution Approach 2:
The patent employs sinusoidal modulation of fiducial markers, creating periodic structures that produce characteristic diffraction patterns with regular intensity distributions. This periodicity creates predictable, repeatable patterns that are easily identifiable and distinguishable from aperiodic background features, improving the system's ability to reliably detect markers amidst noise.
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
This approach allows for accurate localization of blurred objects at extremely close distances to the camera lens, enhancing precision and immunity to background image features, while reducing signal-to-noise ratio through averaging techniques.
Implementation Method 1
fiducial patterns that produce 2D Barker code-like diffraction patterns at a camera sensor are etched or otherwise provided on a cover glass (CG) in front of a camera
Data Source
AI summary
Fiducial patterns that produce 2D Barker code-like diffraction patterns at a camera sensor are etched or otherwise provided on a cover glass in front of a camera. 2D Barker code kernels, when cross-correlated with the diffraction patterns captured in images by the camera, provide sharp cross-correlation peaks. Misalignment of the cover glass with respect to the camera can be derived by detecting shifts in the location of the detected peaks with respect to calibrated locations. Devices that include multiple cameras behind a cover glass with one or more fiducials on the cover glass in front of each camera are also described. The diffraction patterns caused by the fiducials at the various cameras may be analyzed to detect movement or distortion of the cover glass in multiple degrees of freedom.