Simultaneous Localization and Mapping for Wireless Access Point Positioning
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Solution Overview
Problem
Existing location determination methods for mobile computing devices often rely on GPS or wireless access points, but they struggle when both the device's location and WiFi access point positions are initially unknown, leading to inaccuracies in localization and mapping.
Innovation Solution
The method employs simultaneous localization and mapping (SLAM) using received signal strength indication (RSSI) data from multiple wireless access points, combining it with GPS data and dead reckoning to estimate device and access point positions iteratively, refining estimates with each new data point and optimizing without keeping any constant.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS or wireless access point methods are used for location determination, then location can be determined, but inaccuracies occur when both device location and access point positions are initially unknown
Solution Approach 1:
The patent combines multiple location determination techniques (GPS, WiFi signal strength, cellular base station) into a unified SLAM framework. By merging these different data sources and treating them together in a simultaneous optimization process, the system overcomes the limitations of individual methods when initial positions are unknown, achieving both accurate device localization and access point positioning.
Solution Approach 2:
The patent implements an iterative dynamic optimization process where device positions and access point locations are continuously refined together. Rather than using static initial positions, the system dynamically adjusts both device trajectories and access point positions through repeated optimization cycles, allowing the solution to evolve and improve accuracy over time.
2Measurement precision
If simultaneous localization and mapping is performed to solve both device and access point positions, then accurate location estimates can be obtained, but computational complexity increases
Solution Approach 1:
The patent segments the simultaneous optimization problem into manageable components by separating device position estimation from access point position estimation in the iterative process. Each iteration focuses on refining one aspect while holding others relatively stable, then alternates between them, breaking down the complex joint optimization into sequential sub-problems that are computationally more tractable.
Solution Approach 2:
The patent performs preliminary actions by initializing device positions using available GPS data and access point positions using known databases or preliminary estimates before entering the iterative SLAM optimization. This preliminary setup provides reasonable starting points that guide the optimization process and reduce the computational burden of finding optimal solutions from scratch.
3Measurement precision
If iterative refinement of WiFi signal strength map is performed, then location accuracy improves, but processing time increases
Solution Approach 1:
The patent implements continuous iterative refinement where each iteration builds upon the previous results without discarding prior information. The WiFi signal strength map and position estimates are continuously updated and refined across multiple passes through the data, maintaining and improving accuracy progressively while avoiding redundant computations by carrying forward useful intermediate results.
Solution Approach 2:
The system incorporates feedback mechanisms where position estimates from previous iterations are used to guide subsequent optimization steps. The refined position estimates feed back into the signal strength modeling, which in turn improves future position estimates, creating a self-correcting iterative process that converges toward accurate solutions while monitoring progress to terminate when sufficient accuracy is achieved.
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 provides accurate and trustworthy location estimates for both devices and access points, even in environments where GPS data is limited, by iteratively refining the WiFi signal strength map and aligning relative paths with absolute positions.
Implementation Method 1
a mobile computing device may receive a signal from either a cellular base station or an 802.11 access point
Implementation Method 2
A respective trace of data includes received signal strength indication (RSSI) data for a plurality of wireless access points (AP)
Data Source
AI summary
Examples describe systems and methods for iteratively determining a signal strength map for a wireless access point (AP) aligned to position coordinates, positions of a device, and positions of the wireless APs. An example method includes selecting traces and a wireless AP among the traces for which data is indicative of a threshold amount of information to estimate a position of the device and a position of the wireless AP, selecting first characteristics from the traces to remain constant and second characteristics to be variable, and selecting a localization constraint that provides boundaries on the position of the device and the position of the wireless AP. The method also includes performing a simultaneous localization and mapping (SLAM) optimization of the position of the device and the position of the wireless AP based on the localization constraint with the first characteristics held constant and the second characteristics allowed to vary.