Mobile Crowdsourcing Error Correction for Indoor Geolocation
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Solution Overview
Problem
Existing geolocation systems struggle to accurately determine indoor user locations due to signal blocking by physical barriers, requiring hardware installations and known layouts, which limits their effectiveness in indoor environments.
Innovation Solution
A mobile-based crowdsourcing platform that utilizes user data from sensors and direct input to map and refine floor plans, correcting errors through user verification and self-learning algorithms, eliminating the need for hardware devices and known layouts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS signals are used for geolocation, then outdoor location accuracy is improved, but indoor location determination fails due to signal blocking by physical barriers
Solution Approach 1:
The patent introduces wireless signals (WiFi, Bluetooth, cellular) as intermediary carriers to replace GPS for indoor location determination. These signals can penetrate physical barriers and provide location data indoors through triangulation and signal strength analysis, thus resolving the contradiction between outdoor GPS accuracy and indoor reliability
Solution Approach 2:
The system changes the measurement parameters from GPS satellite-based distance estimation to wireless signal-based methods including signal strength (RSSI), time of flight, and triangulation. This parameter transformation enables location determination to work effectively in indoor environments where GPS signals are blocked
2Measurement precision
If hardware devices are installed for indoor geolocation, then indoor location accuracy is improved, but device complexity and installation requirements increase
Solution Approach 1:
The patent makes mobile devices themselves serve dual purposes: they are both the location determination target and the measurement instrument. The device's own sensors, combined with environmental wireless signals and crowd-sourced data from other devices, enable location determination without requiring dedicated hardware installations
Solution Approach 2:
The system uses crowd-sourced location data from multiple mobile devices to collectively build and refine floor plans and location models. Each device contributes its location data to improve the overall system accuracy, eliminating the need for external hardware while achieving high precision through cooperative self-service
3Reliability
If known floor plans are required for indoor geolocation, then location determination reliability is improved, but adaptability to new environments decreases
Solution Approach 1:
The system performs preliminary crowd-sourced data collection to automatically generate floor plans before location determination begins. Mobile devices collect wireless signal characteristics and spatial relationships to build environmental models in advance, enabling reliable location determination in previously unmapped environments
Solution Approach 2:
The system continuously refines floor plans and location models using feedback from crowd-sourced location data. As more devices provide location information, the system validates and updates the environmental model, improving both reliability and adaptability through iterative optimization
Data Source
AI summary
The present invention provides systems and methods for providing error correction and management in a mobile-based crowdsourcing platform, specifically a platform providing geolocation services. More specifically, the system of the present invention includes a plurality of remote mobile devices configured to communicate and exchange data with a cloud-based service, such as a crowdsourcing platform. The crowdsourcing platform generally provides a geolocation service based on the crowdsourcing, or polling, of users of the mobile devices so as to determine location and movement of the users within a specific environment. The system is further configured to automatically render a floor plan or layout of a location based on the user data. The system is further configured to recognize and recalibrate data errors during the collection and aggregation of the crowd-sourced data.