Stylus Pose Estimation Using Tip Camera and IMU Fusion
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Solution Overview
Problem
Existing 3D display systems struggle to accurately estimate the six-degree of freedom (6-DoF) pose of a stylus in interactive augmented reality (AR) and virtual reality (VR) experiences, limiting the precision and accuracy of user interactions.
Innovation Solution
Implementing a neural network model and/or a dataset-based model for 6-DoF pose estimation of a stylus, combined with an Inertial Measurement Unit (IMU) and integrated cameras, to determine the stylus's position and orientation in 3D space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional pose estimation methods are used, then the system complexity is low, but the measurement precision of stylus pose is insufficient
Solution Approach 1:
The patent combines multiple sensing technologies (IMU, cameras, neural networks, and Charuco codes) into a unified pose estimation system. The IMU provides inertial data, cameras capture visual information, neural networks process the data to estimate pose, and Charuco codes provide calibration references. This merging of multiple technologies achieves high measurement precision while managing system complexity through integrated processing.
Solution Approach 2:
The patent introduces an intermediary processing layer using neural networks and Charuco code recognition to bridge the raw sensor data (IMU and camera images) and the final pose estimation. This intermediary layer processes and fuses the multi-source data, transforming raw measurements into accurate 6-DoF pose information, thereby improving measurement precision without directly increasing the complexity of the overall system architecture.
2Measurement precision
If multiple sensors and models are integrated for pose estimation, then the measurement precision improves, but the computational resources and processing time increase
Solution Approach 1:
The patent segments the pose estimation process into distinct functional modules: IMU data processing, camera image processing, neural network inference, and Charuco code recognition. Each module handles specific computational tasks independently, allowing for optimized resource allocation. The segmentation enables the system to process data from multiple sensors through specialized algorithms, improving measurement precision while managing computational energy consumption by assigning appropriate processing power to each segment.
3Adaptability or versatility
If traditional 2D display systems are used, then the device complexity is low, but the adaptability for 3D interactive experiences is limited
Solution Approach 1:
The patent transitions from traditional 2D display capabilities to 3D interactive experiences by adding spatial dimensionality through stereoscopic display and 6-DoF pose tracking. The system enables users to interact with virtual objects in three-dimensional space, providing depth perception and spatial awareness. This dimensional enhancement significantly improves adaptability for 3D interactive applications while increasing device complexity through the addition of depth sensing, stereoscopic rendering, and six-degree-of-freedom tracking capabilities.
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
Enhances the precision and accuracy of user input by accurately estimating the 6-DoF pose of a stylus, enabling more precise interactions in AR and VR environments.
Implementation Method 1
the user input device may determine, via an inertial measurement unit (IMU), motion of the user input device in three-dimensional (3D) space
Implementation Method 2
a user input device may 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) 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 the user input device is directed and providing the images to a computer system. The user input device provides inertial measurement unit (IMU) data to the computer system as well. The computer system may then determine pose information associated with the user input device based on the images and IMU data 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.