Cross-Reality Localization With Prioritized Geolocation Metadata
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Solution Overview
Problem
Existing cross reality (XR) systems face challenges in reducing latency and improving computational efficiency for localization and rendering of virtual content relative to the physical environment, especially when multiple users share the same XR experience.
Innovation Solution
The XR system employs a method to create and maintain persistent spatial information through a persistent map, which can be accessed by multiple users, and utilizes components that transform and localize data across different reference frames, including a map merge component and a localization service, to efficiently select and prioritize location metadata for rapid and accurate localization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the XR system processes all location metadata from multiple sensors to improve localization accuracy, then measurement precision improves, but computational requirements and latency increase
Solution Approach 1:
The patent segments location metadata into hierarchical levels (e.g., coarse-grained geographic location from GPS vs. fine-grained indoor positioning from Wi-Fi or Bluetooth). The system first processes coarse-grained data to determine general location, then selectively processes fine-grained data only when needed, reducing overall computational load and latency while maintaining accuracy where required.
Solution Approach 2:
The system performs preliminary processing of location metadata by pre-filtering and prioritizing data sources based on current context (e.g., outdoor vs. indoor environment, movement state). This preliminary action identifies which sensors are most likely to provide useful localization data, allowing the system to focus computational resources on the most relevant inputs and avoid processing unnecessary data, thereby reducing latency.
2Measurement precision
If the XR system uses multiple sensors and data sources for localization, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent implements a universal localization service that can process multiple types of location metadata from diverse sensors (GPS, Wi-Fi, Bluetooth, cellular towers, inertial sensors) through a unified framework. This multi-functional service adapts to different environments and scenarios, managing multiple data sources through a single coordinated system rather than requiring separate processing pipelines for each sensor type, thereby reducing overall system complexity.
Solution Approach 2:
The system introduces an intermediary localization service layer that sits between the multiple sensors and the XR application. This intermediary service consolidates and standardizes inputs from various sensors, performing filtering, fusion, and coordinate transformation in a centralized manner. This mediator simplifies the architecture by providing a single point of integration rather than requiring direct connections and coordination between multiple sensor systems and the application.
3Speed
If the system processes location metadata in real-time to reduce latency, then speed improves, but use of energy increases
Solution Approach 1:
The patent implements periodic updates of localization data at different frequencies based on context. Instead of continuously processing all sensor data at maximum rate, the system adjusts the update frequency dynamically - using higher frequencies when the user is stationary or in stable environments, and lower frequencies during rapid movement or when transitioning between environments. This periodic action with variable frequency reduces energy consumption while maintaining localization speed and accuracy when needed.
Solution Approach 2:
The system changes processing parameters dynamically based on operational context. It adjusts the level of processing intensity, data sampling rates, and computational algorithms used based on factors such as user movement state, environmental conditions, and current localization accuracy. This parameter adaptation allows the system to operate at high speed when necessary while conserving energy during stable periods, optimizing the trade-off between localization speed and power consumption.
Data Source
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AI summary
A cross reality system enables any of multiple devices to efficiently access previously stored maps. Both stored maps and tracking maps used by portable devices may have any of multiple types of location metadata associated with them. The location metadata may be used to select a set of candidate maps for operations, such as localization or map merge, that involve finding a match between a location defined by location information from a portable device and any of a number of previously stored maps. The types of location metadata may prioritized for use in selecting the subset. To aid in selection of candidate maps, a universe of stored maps may be indexed based on geo-location information. A cross reality platform may update that index as it interacts with devices that supply geo-location information in connection with location information and may propagate that geo-location information to devices that do not supply it.