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

PubDate: June 2025

Teams:Xiamen University；Bournemouth University，Tsinghua University；Cardiff University

Writers:CHENGXU ZUO, JIAWEI HUANG, XIAO JIANG, YUAN YAO, XIANGREN SHI, RUI CAO, XINYU YI, FENG XU, SHIHUI GUO, YIPENG QIN,

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

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.