Crowdsourced Indoor Navigation Map Correction
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Solution Overview
Problem
Mobile device sensors, such as those in cellular phones, face errors like inertial drift and magnetic interference, leading to poor location accuracy indoors due to uncorrected data, which degrades information over time.
Innovation Solution
The creation and use of navigation maps that correct indoor location and heading accuracy by detecting and sharing structural features through inertial tracking, Wi-Fi, magnetic, and acoustic signals, allowing for improved location and navigation services by correlating data from multiple devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are used for indoor location tracking, then location information can be obtained, but inertial drift and magnetic interference cause location accuracy to degrade over time
Solution Approach 1:
The system performs preliminary actions by detecting structural features (walls, floors, ceilings) and creating a navigation map before location degradation occurs. This pre-established map serves as a reference framework that corrects subsequent location data, preventing the accumulation of inertial drift errors rather than merely correcting them later.
Solution Approach 2:
The system implements feedback by continuously comparing sensor-derived location data against the pre-created navigation map. When discrepancies are detected (indicating inertial drift), the system uses the map as a reference to correct the location estimate, creating a closed-loop system that maintains accuracy over time.
2Loss of information
If multiple sensors are used for location tracking, then more data is available, but magnetic interference and other errors increase
Solution Approach 1:
The system extracts and removes the harmful magnetic interference component from the sensor data. By detecting structural features that are immune to magnetic interference (using accelerometer and barometer data), the system creates a reference framework that excludes the corrupted magnetic heading information, effectively taking out the harmful factor from the location calculation.
Solution Approach 2:
The navigation map acts as an intermediary between the corrupted sensor data and the final location estimate. Instead of directly using magnetic heading data that is susceptible to interference, the system uses the structural feature map as an intermediate reference to infer correct orientation and position, mediating the harmful effect of magnetic interference.
3Measurement precision
If structural features are detected and shared through crowdsourcing, then navigation accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex task of indoor navigation into distinct modules: structural feature detection, navigation map creation, feature matching, and location correction. Each module handles a specific aspect of the problem, making the overall system more manageable and maintainable despite the increased functionality.
Solution Approach 2:
The structural feature detection system serves multiple functions simultaneously: it creates the navigation map, provides reference points for location correction, enables route adherence monitoring, and supports crowdsourced map improvement. This multi-functionality reduces the need for separate systems for each task, managing complexity through consolidation.
Data Source
AI summary
A location and mapping service is described that creates a global database of indoor navigation maps through crowd-sourcing and data fusion technologies. The navigation maps consist of a database of geo-referenced, uniquely described features in the multi-dimensional sensor space (e.g., including structural, RF, magnetic, image, acoustic, or other data) that are collected automatically as a tracked mobile device is moved through a building (e.g. a person with a mobile phone or a robot). The feature information can be used to create building models as one or more tracked devices traverse a building.