IMU Drift Correction via Peer-to-Peer Sensor Fusion
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Solution Overview
Problem
Existing motion tracking systems suffer from cumulative error known as 'drift' due to the integration of acceleration and gyroscopic data, making them unreliable for long-term position and orientation tracking without external reference markers.
Innovation Solution
The implementation of a new paradigm of IMU sensors that correct for drift without external signals, using a method that involves intrinsic location measurement and a peer-to-peer network to validate and enhance location data accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If IMU sensors are used for motion tracking, then position and orientation data can be obtained, but cumulative error (drift) accumulates over time making the data unreliable
Solution Approach 1:
The patent implements a feedback mechanism where multiple IMU sensors continuously monitor and compare their measurements. The system uses statistical calculations to detect drift patterns and applies corrections based on the ensemble behavior of multiple sensors, creating a self-correcting system that maintains long-term reliability without external references
Solution Approach 2:
The patent combines multiple IMU sensors into an array, fusing their measurements through statistical methods. By merging data from multiple independent sensors and applying weighted averaging with Kalman filtering, the system reduces individual sensor drift and achieves more reliable long-term tracking than single sensors could provide
2Measurement precision
If multiple IMU sensors are used to reduce drift through statistical calculations, then orientation tracking accuracy improves, but system cost, power consumption, and processing latency increase
Solution Approach 1:
The patent applies partial action by using a moderate number of IMU sensors (not requiring very large numbers) combined with optimized statistical processing. The system achieves significant drift reduction with a practical sensor count by using efficient Kalman filtering and weighted averaging algorithms that maximize accuracy while minimizing computational overhead and latency
3Reliability
If GPS data is fused with IMU data to reset drift, then position accuracy can be maintained, but error margin increases to several meters
Solution Approach 1:
The patent introduces an intermediary approach by using multiple IMU sensors as a bridge between absolute position references and continuous tracking. The ensemble of IMU sensors provides a more precise intermediate reference frame that reduces drift without the large errors inherent in direct GPS fusion, maintaining both reliability and precision
Data Source
AI summary
A method of determining locations of devices in a peer-to-peer network includes, at a first device, performing an intrinsic location measurement using a plurality of inertial measurement units of the first device and broadcasting to other devices first location information of the first device based on the intrinsic location measurement. The method further includes, at a second device, receiving the first location measurement information of the first device from the first device, receiving second location measurement information of the first device from a third device, and comparing the first and second location information of the first device. Based on whether the first and second location information match, the method includes, at the second device, adjusting a trust factor for the first device and broadcasting the adjusted trust factor and consensus location information of the first device.