Transformer IMU Calibrator: Dynamic On-body IMU Calibration for Inertial Motion Capture

TL;DR

This paper introduces a real-time, dynamic IMU calibration method using a Transformer model that relaxes static assumptions, enabling long-term, accurate motion capture with sparse IMUs in diverse scenarios.

Contribution

It presents the first implicit IMU calibration approach that operates in real-time during motion, expanding application scenarios beyond static conditions.

Findings

Achieved real-time estimation of calibration matrices during motion

Developed a Transformer-based model for calibration mapping

Enabled long-term, accurate motion capture with sparse IMUs

Abstract

In this paper, we propose a novel dynamic calibration method for sparse inertial motion capture systems, which is the first to break the restrictive absolute static assumption in IMU calibration, i.e., the coordinate drift RG'G and measurement offset RBS remain constant during the entire motion, thereby significantly expanding their application scenarios. Specifically, we achieve real-time estimation of RG'G and RBS under two relaxed assumptions: i) the matrices change negligibly in a short time window; ii) the human movements/IMU readings are diverse in such a time window. Intuitively, the first assumption reduces the number of candidate matrices, and the second assumption provides diverse constraints, which greatly reduces the solution space and allows for accurate estimation of RG'G and RBS from a short history of IMU readings in real time. To achieve this, we created synthetic datasets of paired RG'G, RBS matrices and IMU readings, and learned their mappings using a Transformer-based model. We also designed a calibration trigger based on the diversity of IMU readings to ensure that assumption ii) is met before applying our method. To our knowledge, we are the first to achieve implicit IMU calibration (i.e., seamlessly putting IMUs into use without the need for an explicit calibration process), as well as the first to enable long-term and accurate motion capture using sparse IMUs. The code and dataset are available at https://github.com/ZuoCX1996/TIC.