BROAD—A Benchmark for Robust Inertial Orientation Estimation

Daniel Laidig, Marco Caruso, Andrea Cereatti and Thomas Seel
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Daniel Laidig: Control Systems Group, Technische Universität Berlin, 10623 Berlin, Germany
Marco Caruso: Polito BIO Med Lab—Biomedical Engineering Lab, Politecnico di Torino, 10129 Torino, Italy
Andrea Cereatti: Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy
Thomas Seel: Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany

Data, 2021, vol. 6, issue 7, 1-20

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.

Keywords: inertial sensor; inertial measurement unit; orientation estimation; attitude estimation; magnetic disturbances; benchmark dataset (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2021
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