Particle Filter Indoor Localization Using Graph Map Priors
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Solution Overview
Problem
Conventional indoor localization and tracking systems face challenges due to noise, sampling rate, and dimensionality limitations of sensors such as magnetometers, IMUs, and radiofrequency sensors, which affect the accuracy of location estimation for users or robots.
Innovation Solution
A computer-implemented method using a particle filter loop that initializes particles based on graph map information, performs motion and measurement updates, and resamples particles based on importance weights and map graph information, incorporating sensors like Bluetooth Low Energy (BLE) beacons, IMUs, and magnetometers to determine location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is used for localization, then location estimation can be performed, but accuracy deteriorates due to noise, sampling rate, and dimensionality limitations
Solution Approach 1:
The patent introduces an information filter as an intermediary between noisy sensor measurements and location estimation. The filter processes magnetometer, accelerometer, and radiofrequency sensor data to produce reliable location estimates by filtering out noise and handling dimensionality limitations, thereby resolving the contradiction between using sensor data and maintaining accuracy.
Solution Approach 2:
The patent transforms raw sensor parameters (magnetometer readings, accelerometer data, radiofrequency signals) into filtered location parameters through the information filter. By changing the parameter representation from raw noisy measurements to filtered position estimates, the system overcomes sensor limitations while maintaining location estimation capability.
2Measurement precision
If multiple sensors are fused for localization, then measurement capability is improved, but system complexity increases
Solution Approach 1:
The patent merges magnetometer, accelerometer, and radiofrequency sensor data into a unified location estimate through the information filter. By combining multiple sensor inputs into a single filtering operation that outputs position estimates, the system achieves accurate localization while managing complexity through integrated processing rather than separate handling of each sensor.
Solution Approach 2:
The information filter serves as a multi-functional component that processes multiple sensor types (magnetometer, accelerometer, radiofrequency) and performs multiple operations (noise filtering, data fusion, location estimation) within a single system framework, thereby improving measurement precision without proportionally increasing system complexity.
Data Source
AI summary
A computer-implemented method performed in a computerized system incorporating a central processing unit, a localization signal receiver, a plurality of sensors, separate and distinct from the localization signal receiver, and a memory, the computer-implemented method involving: using the central processing unit to initialize a plurality of particles based on an information on a map graph; using the central processing unit to repeatedly execute a particle filter loop, wherein the particle filter loop includes: using the central processing unit to perform a motion update of the plurality of particles; using the central processing unit to perform a measurement update of the plurality of particles; and using the central processing unit to perform a resampling of the plurality of particles based on particle importance weights and the map graph information. The location of the computerized system is subsequently determined based on the plurality of particles.