Stylus Pose Estimation Using Camera and IMU Fusion
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Solution Overview
Problem
Existing 3D display systems struggle with accurately estimating the six-degree of freedom (6-DoF) pose of a stylus in interactive augmented reality (AR) and virtual reality (VR) environments, limiting precise interaction with virtual objects.
Innovation Solution
Employing a neural network model and/or a dataset-based model, combined with an Inertial Measurement Unit (IMU) and integrated cameras, to determine the 6-DoF pose of a stylus by capturing images and motion data, enabling precise interaction with virtual objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 3D display systems are used for stylus tracking, then the system structure is simple, but the pose estimation accuracy is insufficient for precise AR/VR interaction
Solution Approach 1:
The patent combines multiple sensing technologies (cameras for visual tracking, IMU for inertial measurement, magnetic field sensors for position detection) into an integrated stylus tracking system. This merging of multiple subsystems enables accurate 6-DoF pose estimation while managing system complexity through unified architecture
Solution Approach 2:
The tracking system is designed to perform multiple functions simultaneously: visual tracking of stylus position, orientation detection via IMU, magnetic field sensing for spatial localization, and haptic feedback provision. This multi-functionality achieves comprehensive pose estimation without requiring separate dedicated systems for each function
2Measurement precision
If multiple sensors (cameras, IMU) are integrated in the stylus, then the pose estimation accuracy improves, but the device complexity and computational requirements increase
Solution Approach 1:
The patent segments the pose estimation problem into multiple independent measurement components: visual position tracking via cameras, orientation measurement via IMU, and spatial localization via magnetic sensors. Each sensor type handles a specific aspect of pose estimation, reducing the complexity of any single sensing subsystem while achieving comprehensive 6-DoF accuracy through integration
3Measurement precision
If neural network models and dataset-based models are used for pose estimation, then the interaction precision with virtual objects improves, but the processing time and computational energy consumption increase
Solution Approach 1:
The patent employs pre-trained neural network models and dataset-based models that have been trained offline on large datasets. This preliminary training allows the system to perform rapid pose estimation during actual AR/VR interaction without requiring real-time heavy computation, thus reducing processing time while maintaining high precision
Solution Approach 2:
The patent replaces traditional computational geometry-based pose estimation methods with machine learning-based approaches (neural networks and dataset-based models). This substitution enables more accurate and robust pose estimation that can handle complex scenarios while optimizing processing efficiency through learned patterns
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 and precise interaction with virtual objects in 3D environments by determining the stylus's 6-DoF pose, enhancing user experience in AR and VR applications.
Implementation Method 1
determine, via an inertial measurement unit (IMU), motion of the user input device in three-dimensional (3D) space
Implementation Method 2
capture, e.g., via at least one camera of the user input device, images in a direction that the user input device is directed
Data Source
AI summary
Systems and methods for six-degree of freedom (6-DoF) pose estimation of a user input device, e.g., in a three-dimensional (3D) display system rendering interactive augmented reality (AR) and/or virtual reality (VR) experiences include the user input device capturing, via a camera disposed at a forward-facing tip of the user input device, images in a direction that the user input device is directed and determining, via an inertial measurement unit (IMU), motion of the user input device in three-dimensional (3D) space. The user input device may then determine pose information associated with the user input device based on the images and motion of the user input device. The determination of the pose information may be via usage of at least one of a neural network model, estimation model trained on a set of unique and identifiable patterns, and/or an estimation model trained on a dataset of images.