Jannik Steinbring

Learn More
—In this paper, we introduce a new sample-based Gaussian filter. In contrast to the popular Nonlinear Kalman Filters, e.g., the UKF, we do not rely on linearizing the measurement model. Instead, we take up the Gaussian progressive filtering approach introduced by the PGF 42 but explicitly rely on likelihood functions. Progression means, we incorporate the(More)
—In this paper, we propose a progressive Gaussian filter, where the measurement information is continuously included into the given prior estimate (although we perform observations at discrete time steps). The key idea is to derive a system of ordinary first-order differential equations (ODE) that is used for continuously tracking the true non-Gaussian(More)
—In this work, we propose a novel way to approximating mixtures of Gaussian distributions by a set of deter-ministically chosen Dirac delta components. This approximation is performed by adapting a method for approximating single Gaussian distributions to the considered case. The proposed method turns the approximation problem into an optimization problem(More)
Nonlinear Kalman Filters are powerful and widely-used techniques when trying to estimate the hidden state of a stochastic nonlinear dynamic system. In this paper, we extend the Smart Sampling Kalman Filter (S 2 KF) with a new point symmetric Gaussian sampling scheme. This not only improves the S 2 KF's estimation quality, but also reduces the time needed to(More)
We present a new online approach to track human whole-body motion from motion capture data, i.e., positions of labeled markers attached to the human body. Tracking in noisy data can be effectively performed with the aid of well-established recursive state estimation techniques. This allows us to systematically take noise of the marker measurements into(More)