journal article·2023Open access

Relative-Position Estimation Based on Loosely Coupled UWB–IMU Fusion for Wearable IoT Devices

A. S. M. Sharifuzzaman Sagar, Taein Kim, Soyoung Park, Hee Seh Lee, Hyung Seok Kim

Computers, Materials & Continua· Volume 75· Issue 1

Abstract

Relative positioning is one of the important techniques in collaborative robotics, autonomous vehicles, and virtual/augmented reality (VR/AR) applications. Recently, ultra-wideband (UWB) has been utilized to calculate relative position as it does not require a line of sight compared to a camera to calculate the range between two objects with centimeter-level accuracy. However, the single UWB range measurement cannot provide the relative position and attitude of any device in three dimensions (3D) because of lacking bearing information. In this paper, we have proposed a UWB-IMU fusion-based relative position system to provide accurate relative position and attitude between wearable Internet of Things (IoT) devices in 3D. We introduce a distributed Euler angle antenna orientation which can be equipped with the mobile structure to enable relative positioning. Moving average and min-max removing preprocessing filters are introduced to reduce the standard deviation. The standard multilateration method is modified to calculate the relative position between mobile structures. We combine UWB and IMU measurements in a probabilistic framework that enables users to calculate the relative position between two nodes with less error. We have carried out different experiments to illustrate the advantages of fusing IMU and UWB ranges for relative positioning systems. We have achieved a mean accuracy of 0.31 m for 3D relative positioning in indoor line of sight conditions.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Indoor and Outdoor Localization Technologies · Robotics and Sensor-Based Localization · Target Tracking and Data Fusion in Sensor Networks

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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