Electromagnetic Tracking Self-Learning Distortion Correction
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Solution Overview
Problem
Electromagnetic tracking systems face reduced accuracy due to magnetic field distortions caused by ferrous or conductive objects, which can change frequently in environments, making continuous calibration impractical and undetectable.
Innovation Solution
A hybrid electromagnetic tracking system that combines magnetic and non-magnetic tracking subsystems to continuously update distortion correction data while in use, allowing the system to learn and adapt to changes in the magnetic environment, ensuring accurate and reliable position and orientation tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If electromagnetic tracking systems use calibration data to compensate for magnetic field distortions, then measurement precision is improved, but reliability deteriorates when ferrous or conductive objects are introduced or removed from the environment
Solution Approach 1:
The system transitions from static calibration data to dynamic self-learning correction. The electromagnetic tracking system continuously updates its distortion correction model during operation by comparing magnetic subsystem measurements with non-magnetic subsystem measurements, allowing the correction data to adapt when ferrous or conductive objects are introduced or removed from the environment.
Solution Approach 2:
The system implements a feedback mechanism where the non-magnetic tracking subsystem provides reference measurements that are compared against magnetic subsystem measurements. This feedback loop enables the system to detect when distortion patterns change due to introduced objects and automatically update correction data to maintain reliability.
2Reliability
If the calibration process is repeated frequently to account for changing magnetic distortion environments, then reliability is improved, but loss of time increases due to impractical recalibration requirements
Solution Approach 1:
The system performs continuous distortion correction learning during normal operation rather than requiring discrete recalibration events. The self-learning process runs continuously in the background, constantly updating correction data based on real-time comparisons between magnetic and non-magnetic subsystem measurements, eliminating the need for time-consuming recalibration procedures.
Solution Approach 2:
The electromagnetic tracking system automatically detects and adapts to changes in the magnetic distortion environment without external intervention. The system self-corrects by using the non-magnetic subsystem as a reference and autonomously updates its correction model, eliminating the need for operators to perform manual recalibration when objects are introduced or removed.
3Device complexity
If a single magnetic tracking subsystem is used, then device complexity is reduced, but measurement precision deteriorates in environments with magnetic distortions
Solution Approach 1:
The system merges magnetic and non-magnetic tracking subsystems into a hybrid architecture. The magnetic subsystem provides high-precision electromagnetic tracking while the non-magnetic subsystem provides distortion-free reference measurements. By combining these subsystems and using the non-magnetic data to correct magnetic measurements, the system achieves high precision in magnetically distorted environments without significantly increasing overall complexity.
Solution Approach 2:
The non-magnetic tracking subsystem serves as an intermediary that mediates the effect of magnetic distortions on the magnetic tracking subsystem. By comparing magnetic measurements with non-magnetic reference measurements, the system can identify and correct distortion-induced errors, maintaining measurement precision without requiring the magnetic subsystem to operate in isolation.
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
The system maintains high accuracy and reliability by continuously updating correction data to compensate for changes in magnetic distortions, even when ferrous or conductive objects are introduced or removed, without requiring frequent recalibration.
Implementation Method 1
Electromagnetic tracking systems use electromagnetic field emitters and sensors to track the position and/or orientation (PnO) of an object
Implementation Method 2
if a distortive element is present in an area close to an emitter or receiver, or at some location between an emitter-receiver pair, then eddy currents generated by the distortive element can cause errors to manifest in the sensor data collected by the tracking system
Data Source
AI summary
A system and method for collecting distortion compensation data to continually update and improve a distortion compensation algorithm for correcting position and orientation (PnO) information regarding an object tracked. The method may be performed while the object is freely in motion. Collection of the distortion compensation data is based on a first PnO solution based on the first PnO measurement data and irrespective of the second PnO measurement data, and a second PnO solution based on a correction algorithm that includes the first PnO measurement data, the second PnO measurement data, and PnO correction data. The first and second PnO solutions may be added to the PnO correction data to continually update and improve the distortion compensation algorithm.