AR Calibration Using Distance Measurement for GNSS Alignment
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Solution Overview
Problem
Linking virtual reality (VR) and augmented reality (AR) devices to high-accuracy global navigation satellite system (GNSS) data has proven difficult.
Innovation Solution
A calibration method for AR devices using a camera, an antenna, and an electronic distance measurement (EDM) device, involving capturing distances and images to calculate 3D surface points and compute camera-to-antenna offsets, allowing precise alignment of AR models with real-world coordinates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If VR and AR devices are used to present digital image information, then immersive experience is improved, but accurate alignment with real-world coordinates becomes difficult
Solution Approach 1:
The patent replaces complex manual calibration procedures with an automated electronic calibration system. The computing device automatically captures images, extracts feature points, calculates device position and orientation, and determines calibration parameters without requiring manual measurement tools or complex mechanical adjustment mechanisms.
Solution Approach 2:
The calibration system is self-calibrating through automated image capture and processing. The device uses its own camera to capture calibration pattern images, and the computing device automatically processes these images to extract feature points and calculate calibration parameters, eliminating the need for external calibration equipment or manual intervention.
2Measurement precision
If manual calibration methods are used, then device setup is simple, but measurement precision and alignment accuracy deteriorate
Solution Approach 1:
The patent performs preliminary calibration before actual VR/AR operations. The calibration pattern is captured and processed in advance to determine the calibration parameters (device position, orientation, and scale factors) that are stored and used for subsequent operations, ensuring high precision without repeating the full calibration process each time.
Solution Approach 2:
Manual measurement tools and procedures are replaced with automated image capture and computer vision-based feature point extraction. The system uses the device's camera to capture calibration patterns and automatically calculates precise coordinates through image processing algorithms, achieving high measurement precision without manual intervention.
3Measurement precision
If automated calibration is implemented, then calibration accuracy is improved, but device complexity and processing requirements increase
Solution Approach 1:
A computing device serves as an intermediary between the VR/AR device and the calibration process. The computing device captures images through the VR/AR device's camera, processes the images to extract feature points, calculates the device's position and orientation, and determines calibration parameters, thereby managing the complexity externally rather than embedding it within the VR/AR device itself.
Solution Approach 2:
The system uses a digital copy of the calibration pattern captured through the camera instead of physical measurement tools. The calibration pattern is captured as an image, and feature points are extracted from this digital copy through image processing, replacing physical calibration artifacts and simplifying the overall system while maintaining high accuracy.
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 accurate superimposition of AR models onto real-world environments by determining precise positional relationships between camera and antenna, enhancing AR device accuracy and alignment with GNSS data.
Implementation Method 1
capturing, at a first time, a first distance to a first point using the EDM device
Data Source
AI summary
Techniques are described for calibrating an AR device having a camera, an antenna, an EDM device, and a display. First and second distances to first and second points are captured using the EDM device. First and second camera images containing the first and second points are captured using the camera. First and second 2D screen coordinates for the first and second points on the display are determined using the first and second camera images. First and second 3D surface points for the first and second points are computed based on the first and second 2D screen coordinates and the first and second distances. A position of the EDM device is computed using a 3D vector formed between the first and second 3D surface points. A camera-to-antenna offset is computed based on the position of the EDM device and a known antenna-to-EDM offset.