Orientation Estimation Using Dual Inertial Sensors Without GPS
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Solution Overview
Problem
Existing methods for orientation estimation of objects using sensor units require additional signals like Wi-Fi or GPS, leading to increased power consumption and potential user errors due to specific calibration movements.
Innovation Solution
A system and method that utilize two sensor units on an object to estimate orientation relative to a sensor coordinate system using only measured angular and acceleration vectors, without requiring Wi-Fi or GPS signals, allowing for automatic and error-robust calibration by defining axes and transformation matrices based on these measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Wi-Fi or GPS signals are used for orientation estimation, then measurement precision is improved, but use of energy worsens
Solution Approach 1:
The patent extracts and removes the dependency on external Wi-Fi or GPS signals from the orientation estimation system. Instead, it uses only local inertial measurements from accelerometers and gyroscopes mounted on the object, eliminating the need for power-intensive wireless communication and GPS reception while maintaining orientation estimation capability through pure inertial sensor fusion
Solution Approach 2:
The system performs self-calibration and self-orientation estimation using only its own onboard sensors without requiring external infrastructure. The calibration process automatically determines the relationship between the object coordinate system and sensor coordinate systems using only local accelerometer and gyroscope measurements, making the system energy-independent from external signal sources
2Manufacturing precision
If specific calibration movements are required, then manufacturing precision is improved, but ease of operation worsens
Solution Approach 1:
The calibration process is fully automated and performs self-calibration without requiring user intervention or specific calibration movements. The system automatically processes the inertial measurements to determine the transformation matrix between coordinate systems, eliminating the need for users to perform precise manual calibration gestures while maintaining high calibration accuracy
Solution Approach 2:
The system performs calibration automatically during normal operation without requiring a separate calibration phase. The calibration calculations are continuously updated based on incoming sensor data, so the orientation estimation is always calibrated without requiring preliminary user actions or specific movement patterns
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
Enables accurate and power-efficient orientation estimation and gesture recognition in a Wi-Fi-free and GPS-free environment, reducing user intervention and improving calibration reliability.
Implementation Method 1
designed to measure a first angular velocity vector ω11 located in a first sensor coordinate system 20a of the first sensor unit 10a and a first acceleration vector a11 located in the first sensor coordinate system 20a of the first sensor unit 10a
Implementation Method 2
designed to measure a first angular velocity vector ω11 located in a first sensor coordinate system 20a of the first sensor unit 10a and a first acceleration vector a11 located in the first sensor coordinate system 20a of the first sensor unit 10a
Implementation Method 3
designed to measure a second angular velocity vector ω22 located in a second sensor coordinate system 20b of the second sensor unit 10b and a second acceleration vector a22 located in the second sensor coordinate system 20b of the second sensor unit 10b
Implementation Method 4
designed to measure a second angular velocity vector ω22 located in a second sensor coordinate system 20b of the second sensor unit 10b and a second acceleration vector a22 located in the second sensor coordinate system 20b of the second sensor unit 10b
Implementation Method 5
calculate a sensor transformation matrix Rsensor from the first sensor coordinate system 20a into the second sensor coordinate system 20b taking into account the first angular velocity vector ω11 and the second angular velocity vector ω22 such that the following applies: ω22=Rsensor*ω11
Implementation Method 6
transform the first acceleration vector a11 from the first sensor coordinate system 20a into the second sensor coordinate system 20b using the sensor transformation matrix Rsensor such that the following applies: a12=Rsensor*a11
Data Source
AI summary
An evaluation device and method for orientation estimation for two sensor units arranged on an object. The method including: ascertaining a sensor transformation matrix from a first sensor coordinate system fixed to a first sensor unit into a second sensor coordinate system fixed to a second sensor unit; transforming a first acceleration vector measured using the first sensor unit into the second sensor coordinate system; and defining a first axis {tilde over (x)} located in the second sensor coordinate system, corresponds to a first coordinate x of an object coordinate system extending through the first sensor unit and the second sensor unit, the object coordinate system being fixed to the object, based on the second angular velocity vector, its time derivative, and a difference vector between a second acceleration vector measured using the second sensor unit minus the first acceleration vector transferred into the second sensor coordinate system.