Model-Based Real-Time Motion Tracking using Dynamical Inverse Kinematics on SO(3)

TL;DR

This paper introduces a real-time motion tracking method for highly articulate systems using dynamical inverse kinematics on SO(3), validated on human and humanoid models for accuracy and efficiency.

Contribution

It presents a novel real-time inverse kinematics approach based on differential kinematics and Lyapunov analysis, suitable for time-critical applications.

Findings

Validated on human and humanoid models

Achieved real-time performance with high accuracy

Compared favorably to iterative optimization algorithms

Abstract

This paper contributes towards the development of motion tracking algorithms for time-critical applications, proposing an infrastructure for solving dynamically the inverse kinematics of highly articulate systems such as humans. We present a method based on the integration of differential kinematics using distance measurement on SO(3) for which the convergence is proved using Lyapunov analysis. An experimental scenario, where the motion of a human subject is tracked in static and dynamic configurations, is used to validate the inverse kinematics method performance on human and humanoid models. Moreover, the method is tested on a human-humanoid retargeting scenario, verifying the usability of the computed solution for real-time robotics applications. Our approach is evaluated both in terms of accuracy and computational load, and compared to iterative optimization algorithms.