Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-12240
For citation please use:
Main Title: BROAD—A Benchmark for Robust Inertial Orientation Estimation
Author(s): Laidig, Daniel
Caruso, Marco
Cereatti, Andrea
Seel, Thomas
Type: Article
URI: https://depositonce.tu-berlin.de/handle/11303/13454
http://dx.doi.org/10.14279/depositonce-12240
License: https://creativecommons.org/licenses/by/4.0/
Abstract: Inertial measurement units (IMUs) enable orientation, velocity, and position estimation in several application domains ranging from robotics and autonomous vehicles to human motion capture and rehabilitation engineering. Errors in orientation estimation greatly affect any of those motion parameters. The present work explains the main challenges in inertial orientation estimation (IOE) and presents an extensive benchmark dataset that includes 3D inertial and magnetic data with synchronized optical marker-based ground truth measurements, the Berlin Robust Orientation Estimation Assessment Dataset (BROAD). The BROAD dataset consists of 39 trials that are conducted at different speeds and include various types of movement. Thereof, 23 trials are performed in an undisturbed indoor environment, and 16 trials are recorded with deliberate magnetometer and accelerometer disturbances. We furthermore propose error metrics that allow for IOE accuracy evaluation while separating the heading and inclination portions of the error and introduce well-defined benchmark metrics. Based on the proposed benchmark, we perform an exemplary case study on two widely used openly available IOE algorithms. Due to the broad range of motion and disturbance scenarios, the proposed benchmark is expected to provide valuable insight and useful tools for the assessment, selection, and further development of inertial sensor fusion methods and IMU-based application systems.
Subject(s): inertial sensor
inertial measurement unit
orientation estimation
attitude estimation
magnetic disturbances
benchmark dataset
Issue Date: 27-Jun-2021
Date Available: 26-Jul-2021
Language Code: en
DDC Class: 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Sponsor/Funder: DFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berlin
Journal Title: Data
Publisher: MDPI
Volume: 6
Issue: 7
Article Number: 72
Publisher DOI: 10.3390/data6070072
EISSN: 2306-5729
TU Affiliation(s): Fak. 4 Elektrotechnik und Informatik » Inst. Energie- und Automatisierungstechnik » FG Regelungssysteme
Appears in Collections:Technische Universität Berlin » Publications

Files in This Item:
data-06-00072-v2.pdf
Format: Adobe PDF | Size: 1.36 MB
DownloadShow Preview
Thumbnail

Item Export Bar

This item is licensed under a Creative Commons License Creative Commons