Jannik Steinbring

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An accurate Linear Regression Kalman Filter (LRKF) for nonlinear systems called Smart Sampling Kalman Filter (S2KF) is introduced. It is based on a new low-discrepancy Dirac Mixture approximation of Gaussian densities. The approximation comprises an arbitrary number of optimally and deterministically placed samples in the entire state space, so that the(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)
We consider the task of recursively estimating the pose and shape parameters of 3D objects based on noisy point cloud measurements from their surface. We focus on objects whose surface can be constructed by transforming a plane curve, such as a cylinder that is constructed by extruding a circle. However, designing estimators for such objects is challenging,(More)
Since the last years, Graphics Processing Units (GPUs) have massive parallel execution capabilities even for non-graphic related applications. The field of nonlinear state estimation is no exception here. Particle Filters have already been successfully ported to GPUs. In this paper, we propose a GPU-accelerated variant of the Progressive Gaussian Filter(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 (S2KF) with a new point symmetric Gaussian sampling scheme. This not only improves the S2KF’s estimation quality, but also reduces the time needed to(More)
Modeling 2D extended targets with star-convex Random Hypersurface Models (RHMs) allows for accurate object pose and shape estimation. A star-convex RHM models the shape of an object with the aid of a radial function that describes the distance from the object center to any point on its boundary. However, up to now only linear estimators, i.e., Kalman(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 by(More)
In this paper, we present a novel approach to optimally fuse estimates in distributed state estimation for linear and nonlinear systems. An optimal fusion requires the knowledge of the correct correlations between locally obtained estimates. The naive and intractable way of calculating the correct correlations would be to exchange information about every(More)