UWB-Inertial and Geomagnetic Sensor Fusion for Relative Pose Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for estimating the relative position and pose of external objects in augmented reality (AR) using WiFi, Bluetooth Low Energy (BLE), ultra-wide band (UWB), and inertial sensors suffer from inaccuracies due to noise and error accumulation, making precise measurement challenging.
Innovation Solution
An electronic device that combines ultra-wide band (UWB) frequency signals with inertial and geo-magnetic sensors to estimate relative position and pose, using a processor to fuse data through Extended Kalman Filters, applying time of arrival measurements and sensor values to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If WiFi or Bluetooth Low Energy (BLE) is used to estimate relative position and pose, then the system can operate in indoor circumstances, but the measurement precision is poor due to great error range
Solution Approach 1:
The patent combines multiple sensing systems (UWB, inertial sensors, geo-magnetic sensors) into a unified measurement system. The UWB provides accurate distance measurements, inertial sensors track orientation changes, and geo-magnetic sensors compensate for drift, together achieving precise indoor positioning that none of the individual systems can achieve alone.
Solution Approach 2:
The system uses a composite sensing architecture that integrates different types of sensors with complementary characteristics. Each sensor type contributes its strengths: UWB for ranging, inertial sensors for motion tracking, and geo-magnetic sensors for absolute orientation reference, creating a robust hybrid positioning system.
2Measurement precision
If time of arrival (TOA) with UWB signal is used to estimate relative position, then the measurement precision is improved, but the device complexity increases due to additional sensors and processing
Solution Approach 1:
The system design makes each sensor serve multiple functions: the inertial measurement unit (IMU) not only tracks orientation but also provides motion data for velocity estimation; the geo-magnetic sensor compensates for drift in the inertial system and provides absolute heading reference. This multi-functionality reduces the need for additional dedicated sensors.
Solution Approach 2:
The system implements feedback mechanisms where the estimated position and orientation from UWB and inertial sensors are continuously refined using geo-magnetic sensor data. The Kalman filter uses feedback from multiple sensor streams to correct errors and maintain accurate positioning, reducing the impact of individual sensor limitations.
3Measurement precision
If inertial sensor and geo-magnetic sensor are used to estimate relative pose, then the measurement precision is improved, but the reliability decreases due to great influence by external noise and rapid dispersion caused by accumulation of errors
Solution Approach 1:
The system employs feedback through the Kalman filter that continuously compares predicted states with actual sensor measurements. When drift or noise causes deviations, the filter uses feedback from UWB distance measurements and geo-magnetic orientation data to correct the inertial sensor estimates, preventing error accumulation and maintaining reliability over time.
Solution Approach 2:
The Kalman filter acts as an intermediary that reconciles conflicting information from different sensors. It weighs the reliability of each sensor input dynamically, using UWB data to correct inertial drift and geo-magnetic data to reset orientation, thereby mediating between sensors with different error characteristics to produce a reliable combined estimate.
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 solution enables precise estimation of relative position and pose by reducing errors, enhancing the realism of augmented reality experiences.
Implementation Method 1
an inertial sensor configured to measure an acceleration of the electronic device and an angular velocity of the electronic device
Implementation Method 2
a geo-magnetic sensor configured to measure a magnetic field around the electronic device
Implementation Method 3
estimate a first relative position of the external electronic device relative to the electronic device, based on a time of arrival at which a signal transmitted from the at least one anchor arrives at the external electronic device
Data Source
AI summary
An electronic device includes a first sensor including an anchor and a tag, and a second sensor including an inertial sensor and a geo-magnetic sensor, and a processor configured to estimate a first relative position of an external electronic device based on a time of arrival at which a signal from the anchor arrives at the external device, estimate a first relative pose of the external electronic device, based on measurements of the inertial sensor and the geo-magnetic sensor, convert an acceleration of the electronic device in a sensor frame into an acceleration in a navigation frame, based on the relative pose of the external electronic device, calculate a relative acceleration of the external device, based on the converted acceleration, and estimate a relative position and a relative pose of the external electronic device by applying the calculated relative position and the first relative position of the external electronic device to an Extended Kalman Filter.