Common-Coordinate Device Localization Using Shared Relative Ranges
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Solution Overview
Problem
Ranging measurements for device localization are prone to uncertainty, making it challenging to determine a device's location accurately, especially in environments with ambiguous spatial coordinates.
Innovation Solution
Utilizing visual-inertial odometry (VIO) techniques in conjunction with simultaneous localization and mapping (SLAM) to generate a map of the environment, and creating a device constellation of relative signal source positions to improve localization accuracy, which can be shared among devices without revealing sensitive image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If typical ranging techniques are used for device localization, then the process is simple and computationally efficient, but the localization accuracy is poor due to uncertain spatial coordinates
Solution Approach 1:
The patent introduces an intermediary coordinate transformation system that maps ranging measurements from different device coordinate systems to a common reference frame. This intermediary layer resolves the ambiguity of spatial coordinates by establishing relational transformations between devices, enabling accurate localization without requiring each device to independently perform complex VIO/SLAM computations.
Solution Approach 2:
The system segments the localization problem into two distinct parts: (1) each device performs simple ranging measurements to determine relative distances to other devices, and (2) a coordinate transformation module separately computes the spatial relationships by transforming these measurements into a common coordinate system. This segmentation allows accurate localization while keeping individual device computations simple.
2Measurement precision
If VIO and SLAM techniques are used to generate environment maps, then localization accuracy is improved, but computational demands and processing time increase significantly
Solution Approach 1:
Instead of each device creating and storing full environment maps through computationally intensive VIO/SLAM, the system uses copied relative distance information from ranging measurements. Each device simply copies its ranging measurements to other devices, and the coordinate transformation system reconstructs spatial relationships from these copied data points, achieving accurate localization with minimal computational energy.
Solution Approach 2:
The patent extracts only the essential localization information (relative distances from ranging measurements) from the complex VIO/SLAM process. By taking out just the distance data and using coordinate transformations to reconstruct spatial relationships, the system achieves accurate localization without the heavy computational burden of full environment mapping.
3Measurement precision
If devices share full environment maps for localization, then other devices can achieve accurate positioning, but sensitive image data and privacy information are revealed
Solution Approach 1:
The system extracts only the necessary localization data (relative distances between devices) from the environment and excludes all sensitive image and privacy information. By sharing only these extracted distance measurements rather than full environment maps, the system enables accurate localization while preserving user privacy and preventing disclosure of sensitive visual data.
Solution Approach 2:
Instead of sharing permanent, detailed environment maps that contain sensitive information, the system uses disposable, ephemeral ranging measurements that are cheap to generate and transmit. These temporary distance data points are sufficient for localization purposes but contain no recoverable sensitive information, effectively replacing the need to share expensive, information-rich environment maps.
Data Source
AI summary
Techniques may include capturing a sequence of images of an environment using a camera of the mobile device. Techniques may also include generating a map of an environment using the sequence of images, the map including one or more walls and one or more signal sources. Techniques may furthermore include receiving one or more proximity messages from the one or more signal sources. Techniques may in addition include determining a position for the mobile device using the map of the environment and the one or more proximity messages. Other embodiments of this aspect include corresponding methods, computer systems, apparatus, and computer programs recorded on one or more computer storage devices, memories, or non-transitory computer readable media each configured to perform the actions of the techniques.