AR Mapping System Using Event Graph Delta Updates
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Solution Overview
Problem
Current augmented reality (AR) mapping systems lack the ability to efficiently aggregate and update real-time data from multiple AR devices, leading to incomplete and outdated representations of real-world environments, which limits their ability to provide immersive and interactive experiences.
Innovation Solution
An AR mapping system that receives real-time data from multiple devices, determines current mapping and object data, calculates deltas between this data and baseline maps, and updates an event graph to reflect changes in the environment, enabling improved mapping and interaction tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time data is aggregated from multiple AR devices, then the completeness and accuracy of environment mapping is improved, but the system complexity and data processing load increase
Solution Approach 1:
The system segments the environment mapping task by dividing it into multiple local mapping operations performed by individual AR devices, then combines these segmented results into a comprehensive global map. Each device independently maps its local field of view, and the server aggregates these segments to build the complete environment model, resolving the contradiction between mapping accuracy and system complexity.
Solution Approach 2:
A centralized server acts as an intermediary between multiple AR devices and the global environment map. The server receives mapping data from various devices, processes and merges the information, manages the event graph, and distributes updated maps back to devices. This intermediary architecture simplifies the overall system complexity by centralizing coordination while maintaining high mapping accuracy through aggregated device data.
2Reliability
If baseline map data is continuously updated with current mapping data, then the real-time awareness of environment changes is improved, but the data processing time and computational resources increase
Solution Approach 1:
Instead of processing and updating the entire baseline map continuously, the system updates only the specific portions of the map that have changed, represented as deltas. The event graph tracks changes incrementally, allowing the system to process minimal necessary data rather than performing excessive full-map updates, thus reducing processing time while maintaining real-time awareness reliability.
Solution Approach 2:
The system implements feedback mechanisms where the event graph monitors environmental changes and triggers selective map updates only when relevant changes are detected. This feedback-driven approach ensures reliable real-time awareness by continuously monitoring for changes while minimizing unnecessary processing, updating the baseline map only when actual environmental modifications occur.
3Adaptability or versatility
If the event graph tracks detailed object interactions and state changes, then the interactive experience quality is improved, but the memory requirements and data storage needs increase
Solution Approach 1:
The system extracts only the essential interaction data and state changes needed for AR experiences from the complete environment model. The event graph stores selectively extracted information about object interactions and state transitions rather than maintaining complete detailed records of all environmental elements, reducing data storage requirements while preserving interactive experience quality.
Solution Approach 2:
The event graph organizes interaction data in a graph structure with nodes and edges, representing objects and their relationships in a dimensional framework optimized for tracking interactions. This dimensional organization allows efficient storage of interaction patterns and state changes without requiring proportional increases in raw data storage, as the graph structure compresses and relationships are encoded more compactly than exhaustive detailed records.
Data Source
AI summary
The disclosed computer-implemented method may include receiving, from devices in an environment, real-time data associated with the environment and determining, from the real-time data, current object data for the environment. The current object data may include both state data and relationship data for objects in the environment. The method may also include determining object deltas between the current object data and prior object data from an event graph. The prior object data may include prior state data and prior relationship data for the objects. The method may include detecting an unknown state for one of the objects, inferring a state for the object based on the event graph, and updating the event graph based on the object deltas and the inferred state. The method may further include sending updated event graph data to the devices. Various other methods, systems, and computer-readable media are also disclosed.