IMU Calibration Using Neural Networks and Environmental Compensation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional IMU sensor calibration techniques are complex, requiring extensive manual intervention and specialized expertise, prone to significant error margins, and are limited by intricate environmental factors, necessitating a more advanced and intelligent approach.
Innovation Solution
Utilizing deep neural networks to process raw sensor data directly, incorporating environmental sensors for compensation, and forming a virtual model that self-calibrates and adapts to conditions, reducing computational overhead and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional least squares or polynomial fitting methods are used for IMU calibration, then calibration can be performed with basic mathematical tools, but the calibration accuracy is limited and significant error margins remain
Solution Approach 1:
The patent replaces traditional mathematical calibration methods (least squares, polynomial fitting) with a neural network-based system. The neural network learns complex calibration transformations from training data, substituting conventional mathematical approaches with an intelligent system that achieves higher accuracy without requiring users to understand sensor physics or environmental factors.
Solution Approach 2:
The calibration system performs self-calibration using the neural network model. The system automatically processes sensor data through the trained neural network to generate calibrated output without requiring manual intervention, specialized expertise, or complex user operations. The neural network encapsulates all calibration knowledge and applies it autonomously.
2Measurement precision
If the TCAL parametric model is used for calibration, then sensor imperfections like misalignment and scale factor variations can be accounted for, but extensive manual calibration and specialized expertise are required
Solution Approach 1:
The patent replaces the complex TCAL parametric model with a neural network that automatically learns calibration parameters. Instead of requiring users to understand and apply complex mathematical transformations, the neural network internally processes raw sensor data and outputs calibrated results, eliminating the need for manual calibration operations and specialized expertise.
Solution Approach 2:
The neural network acts as an intermediary between raw sensor data and calibrated output. It absorbs the complexity of calibration transformations within its architecture, serving as a black-box mediator that translates uncalibrated sensor readings into accurate measurements without exposing users to the underlying complexity.
3Measurement precision
If deep neural networks are used to process raw sensor data, then calibration accuracy and adaptability are significantly improved, but computational overhead increases
Solution Approach 1:
The patent performs the computationally intensive neural network training and model optimization in advance, before deployment. The neural network is pre-trained on comprehensive calibration data and optimized for efficient inference. This preliminary action transfers computational burden from the operational phase to the setup phase, enabling accurate real-time calibration with minimal ongoing computational resources.
4Ease of operation
If conventional calibration methods are used, then the calibration process can be understood with basic sensor physics knowledge, but the methods are prone to significant error margins and require extensive manual intervention
Solution Approach 1:
The patent replaces conventional calibration methodologies with a neural network-based system that eliminates the need for users to understand sensor physics or environmental factors. The neural network internally handles all calibration complexity, providing reliable and consistent results without requiring manual intervention or specialized knowledge, thus improving both accessibility and reliability simultaneously.
Data Source
AI summary
Provided herein are methods for Inertial Measurement Unit (IMU) sensor compensation utilizing deep neural network (DNN) technology. In some embodiments, the methods involve receiving sensor values from gyroscopes, accelerometers, and non-motion sensors, then loading these values into a deep learning algorithm alongside true sensor values. Unlike traditional calibration techniques that rely on complex parametric models, this approach may utilize a neural network to directly process raw sensor data and enhance output signal accuracy. In some embodiments, the training process employs ground truth data for approximately 70% of inputs, with the remaining 30% dedicated to sensor compensation. The technique may be implemented in standalone IMUs, augmented IMUs, and navigation systems, offering improved accuracy and reduced computational overhead across various technological applications.