Indoor Navigation Map Correction via Sensor Fusion
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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.
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, navigation, and routing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If mobile device sensors are used for indoor location tracking, then location services can be provided without external infrastructure, but sensor errors such as inertial drift and magnetic interference cause degraded location accuracy over time
Solution Approach 1:
The system continuously compares sensor-derived location estimates with actual locations obtained from Wi-Fi access points, magnetic field signatures, and acoustic signals. This feedback loop enables real-time correction of inertial drift and magnetic interference errors, maintaining location accuracy over extended periods without external infrastructure
Solution Approach 2:
The patent introduces intermediate reference points (Wi-Fi access points, magnetic field signatures, acoustic signals) that serve as mediators between the mobile device sensors and the ultimate location determination. These intermediaries provide correction data that compensates for sensor errors, resolving the contradiction between autonomous tracking and measurement precision
2Measurement precision
If multiple signal sources (Wi-Fi, magnetic, acoustic) are integrated for location correction, then location accuracy improves, but system complexity increases
Solution Approach 1:
The system employs a universal correction framework that can utilize multiple signal sources (Wi-Fi, magnetic field, acoustic signals) through a common processing architecture. This multi-functional approach allows the same core algorithms to handle different signal types, improving location accuracy while managing system complexity through standardized interfaces and unified data fusion procedures
3Manufacturing precision
If crowd-sourced feature mapping is implemented, then navigation map accuracy improves, but data processing and validation complexity increases
Solution Approach 1:
The crowd-sourced mapping system incorporates feedback mechanisms where multiple user contributions are continuously validated against existing maps and each other. Inconsistent or erroneous data is automatically detected and corrected through this feedback loop, enabling high-precision navigation maps to be built from crowd-sourced data while managing processing complexity through iterative validation
4Measurement precision
If sensor data is collected continuously for error correction, then location accuracy is maintained, but energy consumption increases
Solution Approach 1:
The system implements periodic correction cycles rather than continuous high-rate sampling. Sensor data is collected and processed at optimized intervals, performing error correction periodically using accumulated data and reference signals. This periodic approach maintains location accuracy while significantly reducing energy consumption compared to continuous real-time processing
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, to indicate signal strength throughout different parts of the building mode, and to illustrate a path of each tracked device associated with signal strength and other annotations.