Indoor Positioning via Calendar Space Matching
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Solution Overview
Problem
Indoor positioning technologies face challenges in accurately determining locations without GNSS signals, as sensor-based solutions diverge in GNSS-denied areas, and obtaining reference locations indoors is difficult.
Innovation Solution
A method that involves obtaining calendar information to identify spaces, extracting space identifiers from indoor maps, and matching them with calendar data to determine reference location estimates, using a combination of calendar information and indoor maps to improve location accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor-based solutions are used for indoor positioning, then positioning can function in GNSS-denied areas, but the solutions quickly diverge and lose accuracy in the absence of GNSS fixes
Solution Approach 1:
The patent introduces calendar information as an intermediary data source to bridge the gap between coarse sensor-based positioning and accurate location determination. By matching calendar appointment locations with sensor-derived position data, the system obtains precise reference locations without requiring GNSS signals, thereby resolving the contradiction between reliability and precision in GNSS-denied environments.
Solution Approach 2:
The system uses the mobile device's own calendar data (appointment locations and times) to self-correct and refine its position estimates. The device leverages its stored calendar information to verify and improve the accuracy of sensor-based positioning, eliminating the need for external GNSS references and enabling self-sufficient accurate positioning indoors.
2Measurement precision
If GNSS-based positioning is used, then highly accurate position estimates can be obtained, but it does not work indoors and in urban canyons
Solution Approach 1:
The patent creates a universal positioning system that combines multiple positioning approaches (sensor-based positioning and calendar information matching) into a single framework that functions across diverse environments. The system automatically selects and integrates appropriate positioning methods based on availability, providing consistent accurate positioning whether the device is outdoors with GNSS, indoors without GNSS, or in urban canyons with partial signal coverage.
3Adaptability or versatility
If sensor data fusion with GNSS is used, then location estimation can be extended to GNSS-denied areas, but the solutions quickly diverge without GNSS fixes
Solution Approach 1:
The system implements feedback by continuously comparing sensor-derived position estimates with calendar appointment location data. When the device should be at a known appointment location (from calendar data), the system uses this expected position as feedback to correct and refine the sensor-based position estimates, preventing divergence and maintaining accuracy in GNSS-denied areas.
Data Source
AI summary
A method includes obtaining piece(s) of calendar information indicative of at least one appointment taking place in a space and extracting a set of space identifiers representing space(s) of the venue. The space identifier(s) for one or more spaces are extracted based on an indoor map of the venue. The method also includes determining or triggering determining whether at least a part of the set of space identifiers or one or more spaces of the set of space identifiers match(es) the at least one space as represented by the piece(s) of calendar information; and if a match is found: determining one or more reference location estimates. A respective reference location estimate is indicative of a location of the space that was determined to be a match. A corresponding apparatus, computer program product and system are also provided.