Crowd-sourced Indoor Positioning Map Calibration
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Solution Overview
Problem
Current indoor navigation systems face challenges in achieving accurate and efficient calibration due to the labor-intensive and time-consuming fingerprint mapping process, which limits their deployment and scalability, especially in enclosed spaces where satellite-based navigation is unreliable.
Innovation Solution
A crowd-sourcing based approach utilizing mobile devices to collect and correlate wireless communication signal strength, magnetic field, and barometric pressure data from users within an area, allowing for the creation of a calibrated positioning map through the accumulation of user trajectories and averaging of data, thereby reducing the need for dedicated calibration and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fingerprint mapping process is used for calibration, then positioning accuracy can be achieved, but the process is labor-intensive and time-consuming
Solution Approach 1:
The system enables ordinary mobile device users to participate in the calibration process by having them naturally navigate through the area and collect data. Users are compensated with credits for their participation, transforming them from passive subjects to active contributors. This self-service approach eliminates the need for dedicated calibration personnel while maintaining data quality.
Solution Approach 2:
The calibration process operates periodically rather than continuously. The system collects data in discrete episodes when users naturally traverse through the area, triggering data collection at predetermined locations. This periodic data collection accumulates sufficient calibration data over time without requiring continuous dedicated effort.
2Measurement precision
If traditional fingerprint mapping process is used for calibration, then positioning accuracy can be achieved, but deployment and scalability are limited
Solution Approach 1:
The system leverages the universal mobile devices that users already carry for their daily activities. Instead of requiring specialized calibration equipment or dedicated calibration walks, any mobile device with appropriate sensors can contribute to calibration. This universality dramatically expands the pool of data collectors and accelerates deployment.
Solution Approach 2:
The system introduces a server as an intermediary that coordinates between calibration participants and the positioning system. The server manages data collection, processes contributions, and integrates calibration data into the positioning map. This intermediary infrastructure enables scalable deployment by centralizing coordination while distributing data collection across many users.
3Reliability
If dedicated calibration personnel are used, then calibration can be performed systematically, but costs increase
Solution Approach 1:
The system creates a virtual copy of the calibration process by aggregating data from multiple users' mobile devices. Instead of requiring physical presence of calibration personnel at every measurement point, the system uses copies of the calibration task performed by numerous users. This copying approach maintains calibration reliability through data aggregation while dramatically reducing costs.
Solution Approach 2:
The system changes the parameters of the calibration process by transitioning from a small-group expert approach to a large-scale citizen science approach. By changing the scale, distribution, and compensation mechanisms, the system maintains calibration quality while reducing costs. The parameter change involves shifting from dedicated personnel to compensated user participation.
Data Source
AI summary
A method and system for deploying a calibrated positioning map of an area. The method, executed in a processor of a server computing device, comprises generating, using the processor, a distribution of positioning data points based at least in part on a first set of fingerprint data, the positioning data points calibrated in accordance with respective positions within the area, receiving, at the memory, a second set of fingerprint data, processing, using the processor, the second set of fingerprint data and the positioning data points to generate an updated distribution of positioning data points, and when the updated distribution exceeds a threshold density of positioning data points, deploying the updated distribution as the positioning map of the area.