MinJointTracker: Real-time inertial kinematic chain tracking with joint position estimation and minimal state size

2025-09-15

AI Summary

Existing inertial motion capture methods rely on offline calibration—such as segment lengths, IMU-to-segment orientations, and joint positions—resulting in cumbersome deployment. This paper proposes a real-time, calibration-free inertial chain tracking method based on recursive Bayesian filtering, which jointly estimates the global absolute orientation of each IMU and the constrained joint positions along the kinematic chain. By operating in a minimal state space, the approach simultaneously achieves drift-free global orientation estimation and precise joint localization. Crucially, only a single IMU is required to provide global heading reference, enabling full-chain motion reconstruction. To our knowledge, this is the first method achieving fully calibration-free, real-time, and drift-free inertial motion capture. Evaluated on simulated robotic arm and human lower-limb walking data, the algorithm demonstrates rapid convergence, strong robustness, and high accuracy in both relative and absolute orientation estimation.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Inertial motion capture is a promising approach for capturing motion outside the laboratory. However, as one major drawback, most of the current methods require different quantities to be calibrated or computed offline as part of the setup process, such as segment lengths, relative orientations between inertial measurement units (IMUs) and segment coordinate frames (IMU-to-segment calibrations) or the joint positions in the IMU frames. This renders the setup process inconvenient. This work contributes to real-time capable calibration-free inertial tracking of a kinematic chain, i.e. simultaneous recursive Bayesian estimation of global IMU angular kinematics and joint positions in the IMU frames, with a minimal state size. Experimental results on simulated IMU data from a three-link kinematic chain (manipulator study) as well as re-simulated IMU data from healthy humans walking (lower body study) show that the calibration-free and lightweight algorithm provides not only drift-free relative but also drift-free absolute orientation estimates with a global heading reference for only one IMU as well as robust and fast convergence of joint position estimates in the different movement scenarios.

Problem

• Real-time inertial motion capture without calibration requirements
• Simultaneous estimation of joint positions and IMU kinematics
• Minimal state size for efficient kinematic chain tracking

Innovation

• Real-time calibration-free inertial tracking
• Minimal state size estimation
• Drift-free orientation and joint estimation