3D Map Generation Using Crowdsourced Sensor Clustering
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Solution Overview
Problem
Current methods for generating three-dimensional maps are costly and require dedicated devices, struggling to accurately represent indoor structures and surfaces blocked from satellite views, while also dealing with non-accurate altitude readings and incomplete sensor data.
Innovation Solution
The method employs crowd-sourced data from mobile devices, using sensor fusion and clustering to estimate accurate altitudes from diverse sensor readings, including GPS, barometers, accelerometers, and Wi-Fi signals, to create dynamic three-dimensional maps that update over time, reflecting population distribution and structural changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dedicated devices and costly methods are used for three-dimensional mapping, then map accuracy and completeness are improved, but cost and device complexity increase
Solution Approach 1:
The patent makes mobile devices perform multiple functions: they serve both as everyday communication tools and as three-dimensional mapping instruments. By utilizing existing sensors (GPS, barometers, accelerometers, Wi-Fi) already present in mobile devices, the system eliminates the need for dedicated mapping equipment while achieving comprehensive three-dimensional mapping coverage through crowd-sourced data collection.
Solution Approach 2:
The system enables mobile devices to automatically contribute to map generation without requiring specialized equipment. Users simply use their existing devices, and the system automatically processes sensor data from multiple devices to generate three-dimensional maps, making the mapping process self-service and eliminating the need for professional mapping teams with specialized equipment.
2Area of stationary object
If satellite imagery is used for mapping, then large area coverage is achieved, but indoor structures and blocked surfaces cannot be represented
Solution Approach 1:
The patent merges data from multiple sources including GPS coordinates, barometric pressure readings, Wi-Fi signal strengths, and accelerometer data from numerous mobile devices. By combining these diverse data sources, the system creates a comprehensive three-dimensional representation that captures both outdoor areas visible to satellites and indoor structures completely blocked from satellite view.
Solution Approach 2:
The system transitions from two-dimensional satellite imagery to three-dimensional mapping by incorporating altitude data from barometers and vertical position information from accelerometers. This dimensional enhancement allows the system to represent buildings, floors, and indoor structures with vertical depth, providing complete spatial information that satellite imagery alone cannot capture.
3Ease of operation
If non-accurate altitude readings are used, then data collection from mobile devices is simplified, but altitude estimation accuracy deteriorates
Solution Approach 1:
The system implements feedback mechanisms where altitude estimates from multiple devices are continuously refined. By aggregating data from numerous mobile devices passing through the same location and comparing their readings, the system identifies and corrects systematic errors in individual device measurements, progressively improving altitude estimation accuracy while maintaining simple data collection processes.
Solution Approach 2:
The patent combines altitude readings from multiple sensors (barometers, GPS, accelerometers) and multiple mobile devices to compensate for individual measurement inaccuracies. By merging these diverse data sources and using statistical methods to determine the most probable altitude values, the system achieves accurate altitude estimation without requiring each individual device to provide perfectly accurate readings.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables the generation of accurate and dynamic three-dimensional maps, effectively utilizing crowd-sourced data to improve altitude estimation and map updates, even in areas inaccessible to satellite imagery, while reducing costs and enhancing map accuracy and relevance.
Implementation Method 1
a barometer reading obtained by a barometer of the mobile device
Implementation Method 2
an absolute position and an absolute altitude determined by a Global Positioning System (GPS) sensor of the mobile device
Implementation Method 3
a set of accelerometer readings obtained from the mobile device... determining, based on the set of accelerometer readings, a change in altitude of the mobile device
Data Source
AI summary
A method, product and system for three dimensional map generation based on crowdsourced positioning readings. The method comprising obtaining a plurality of positioning readings of a plurality of mobile devices. Each reading of the plurality of positioning readings is indicative of an altitude, latitude and longitude of a mobile device, and is determined using one or more sensors of the mobile device. The method comprises clustering the plurality of positioning readings to determine clusters of positioning readings. for each cluster, an altitude value is computed, based on an altitude of each positioning reading in the cluster, whereby determining an estimated altitude based on non-accurate altitude readings. The three-dimensional map is generated based on the plurality of positioning readings and the altitude value of each cluster.