Cross-Reality Map Merging with Wi-Fi/GPS-Localized Map Tiles
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Solution Overview
Problem
Existing cross reality (XR) systems face challenges in efficiently and accurately merging local environment maps with canonical maps, leading to increased computational requirements and latency, which affects the immersive experience of users.
Innovation Solution
A method involving a portable device that constructs a tracking map with location metadata, aligns features with a canonical map, and integrates the merged map to enhance localization and reduce computational load by selecting efficient sub-portions of the canonical map for merging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the entire canonical map is merged with the tracking map, then localization accuracy is improved, but computational requirements and processing time increase
Solution Approach 1:
The patent divides the canonical map into multiple sub-portions or tiles based on spatial location. Instead of processing the entire canonical map at once, the system identifies and merges only the relevant sub-portions that correspond to the device's current location and vicinity. This segmentation approach maintains localization accuracy by ensuring complete coverage of the relevant area while significantly reducing computational requirements by processing smaller, divided map sections.
Solution Approach 2:
The system extracts only the necessary sub-portion of the canonical map that is relevant to the device's current location and field of view. By using location metadata and spatial relationships, the patent identifies and extracts specifically the map tiles needed for accurate localization, discarding or deferring processing of irrelevant map areas. This extraction principle reduces processing load while preserving essential localization information.
2Area of stationary object
If the entire canonical map is merged with the tracking map, then complete environment coverage is improved, but latency increases
Solution Approach 1:
The canonical map is segmented into multiple spatial tiles that can be independently processed and merged. The system dynamically selects and merges only the tiles relevant to the current device location and movement, ensuring complete environment coverage over time while minimizing latency for each individual localization update by processing smaller, divided map sections sequentially or in parallel.
Solution Approach 2:
The system performs preliminary organization of the canonical map into pre-divided sub-portions with associated location metadata before the actual merging process. This preliminary structuring enables rapid identification and selection of relevant map tiles during runtime, reducing processing latency when the device needs to localize itself while still achieving complete environment coverage as the device moves through different areas.
3Productivity
If location metadata is used to select sub-portions of the canonical map, then processing efficiency is improved, but system complexity increases
Solution Approach 1:
The patent employs a universal location metadata structure that serves multiple functions: it identifies device position, determines relevant canonical map sub-portions, and facilitates the merging process. This multi-functional metadata approach improves processing efficiency by using a single data structure for multiple purposes while managing system complexity through standardized, reusable metadata schemas that can be applied consistently across different scenarios and device types.
Data Source
AI summary
A portable electronic system receives a set of one or more canonical maps and determines the sparse map based at least in part upon one or more anchors pertaining to the physical environment. The sparse map is localized to at least one canonical map in the set of one or more canonical maps, and a new canonical map is created at least by merging sparse map data of the sparse map into the at least one canonical map. The set of one or more canonical maps may be determined from a universe of canonical maps comprising a plurality of canonical maps by applying a hierarchical filtering scheme to the universe. The sparse map may be localized to the at least one canonical map at least by splitting the sparse map into a plurality of connected components and by one or more merger operations.