Sequential Monte Carlo Inverse Kinematics

In this paper we propose a new and original approach to solve the Inverse Kinematics problem. Our approach has the advantages to avoid the classical pitfalls of numerical inversion methods such as singularities, accept arbitrary types of constraints and exhibit a linear complexity with respect to degrees of freedom which makes it far more efficient for articulated figures with a high number of degrees of freedom. Our framework is based on Sequential Monte Carlo Methods that were initially designed to filter highly non-linear, non-Gaussian dynamic systems. They are used here in an online motion control algorithm that allows to integrate motion priors. Along with practical results that show the effectiveness and convenience of our method, we also describe potential follow-ups for our work.

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