Transformer IMU Calibrator: Dynamic On-body IMU Calibration for Inertial Motion Capture
Chengxu Zuo, Jiawei Huang, Xiao Jiang, Yuan Yao, Xiangren Shi, Rui Cao, Xinyu Yi, Feng Xu, Shihui Guo, Yipeng Qin

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…
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Taxonomy
TopicsHuman Motion and Animation · Inertial Sensor and Navigation · Balance, Gait, and Falls Prevention
