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.
Highlights
We further introduce error metrics that separately consider heading, inclination, and the total orientation error, specify well-defined benchmark metrics that can be used to assess and compare inertial orientation estimation (IOE) algorithm performance, and provide example code to calculate those metrics
When considering a set of trials, we report the mean of the root mean square error (RMSE) values obtained for each trial as a metric for the overall accuracy
In the following exemplary case study, we demonstrate the usefulness of the proposed benchmark and show how it can be employed to achieve an objective and broad assessment and comparison of IOE algorithms under different conditions by answering several exemplary research questions
Summary
Inertial measurement units (IMUs) have become small and lightweight and are used in an increasing number of application domains. They are integrated into various types of consumer electronics, used in autonomous drones and vehicles, and facilitate non-restrictive human motion tracking in various health care and sporting applications [1]. Examples of the latter include rehabilitation robotics [2], feedback-controlled neuroprostheses [3,4], and rehabilitation monitoring [5,6]
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