Position Estimation Model for Extended Reality Devices
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing position determining methods for extended reality devices, such as gamepads, face challenges in maintaining accurate positioning, especially when the device enters a shooting blind spot, leading to continuous displacement deviations due to accumulated errors from sensors like IMUs.
Innovation Solution
A method involving a position estimation model that takes a historical time queue and posture change information to predict an initial position, followed by multiple iterative stages to improve positioning accuracy, using error planes and probability distributions to refine the position prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor-based position tracking is used, then the device can operate in resource-constrained environments, but positioning accuracy deteriorates due to accumulated errors
Solution Approach 1:
The system pre-divides the positioning space into multiple error planes before the target object moves. These error planes are prepared in advance with different error expectations, allowing the system to quickly select and apply appropriate correction strategies without real-time complex calculations, thus improving positioning accuracy while maintaining resource efficiency
Solution Approach 2:
The positioning space is segmented into multiple discrete error planes, each representing a different error range. This segmentation transforms the continuous error correction problem into discrete plane selection and transition, simplifying the computational complexity while improving positioning accuracy through systematic error management
2Measurement precision
If iterative refinement stages are applied, then positioning accuracy is improved from coarse to fine, but computational time increases
Solution Approach 1:
The system employs periodic iterative refinement stages where error planes are processed in cycles. Each cycle refines the position estimate progressively, and the periodic structure allows the system to balance computational time investment with accuracy gains by stopping when sufficient precision is achieved or resource constraints are met
Solution Approach 2:
Error planes are pre-divided and error expectations are pre-calculated for each plane. This preliminary preparation eliminates the need for complex real-time calculations during iterative refinement, reducing computational time while maintaining the ability to achieve fine positioning accuracy through systematic progression through pre-prepared error levels
Data Source
AI summary
The present disclosure provides a position determining method, apparatus, electronic device and storage medium. The method includes: inputting a historical time queue and posture change information of a target object at a target point of time to a position estimation model to obtain an initial predicted position, wherein the historical time queue is used for storing historical position information of the target object at latest n historical points of time prior to the target point of time, and n is a preset positive integer not less than 2; and performing at least two iterative stages on the initial predicted position to obtain position information of the target object at the target point of time, wherein a positioning accuracy of any iterative stage is higher than a positioning accuracy of a previous iterative stage.