Model-Based Real-Time Motion Tracking using Dynamical Inverse Kinematics on SO(3)
This addresses motion tracking for time-critical applications such as robotics, though it appears incremental as it builds on existing inverse kinematics methods.
The paper tackles real-time motion tracking for articulate systems like humans by proposing a dynamical inverse kinematics method on SO(3), achieving validation in human and humanoid models with evaluations of accuracy and computational load compared to iterative algorithms.
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.