Peter Krauthausen

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QR factorization is most often used as a " black box " algorithm , but is in fact an elegant computation on a factor graph. By computing a rooted clique tree on this graph, the computation can be parallelized across subtrees, which forms the basis of so-called multifrontal QR methods. By judiciously choosing the order in which variables are eliminated in(More)
– In this paper, a novel probabilistic approach to intention recognition for partial-order plans is proposed. The key idea is to exploit independences between subplans to substantially reduce the state space sizes in the compiled Dynamic Bayesian Networks. This makes inference more efficient. The main contributions are the computationally exploitable(More)
Fig. 1. Example of a Markov graph corresponding to a full SLAM problem where the robot moved four steps and made measurements on three landmarks. The vertices correspond to the robot poses (circles) and landmarks (squares). Abstract— The computational complexity of SLAM is dominated by the cost of factorizing a matrix derived from the measurements into a(More)
— In this paper, a multi-level approach to intention, activity, and motion recognition for a humanoid robot is proposed. Our system processes images from a monocular camera and combines this information with domain knowledge. The recognition works on-line and in real-time, it is independent of the test person, but limited to predefined viewpoints. Main(More)
—Since the advent of Monte-Carlo particle filtering, particle representations of densities have become increasingly popular due to their flexibility and implicit adaptive resolution. In this paper, an algorithm for the multiplication of a systematic Dirac mixture (DM) approximation with a continuous likelihood function is presented, which applies a(More)
– A non-parametric conditional density estimation algorithm for nonlinear stochastic dynamic systems is proposed. The contributions are a novel support vector regression for estimating conditional densities , modeled by Gaussian mixture densities, and an algorithm based on cross-validation for automatically determining hyper-parameters for the regression.(More)
Recognizing human intentions is part of the decision process in many technical devices. In order to achieve natural interaction, the required estimation quality and the used computation time need to be balanced. This becomes challenging , if the number of sensors is high and measurement systems are complex. In this paper, a model predictive approach to this(More)
— In this paper, a distance-based method for both multivariate non-parametric density and conditional density estimation is proposed. The contributions are the formulation of both density estimation problems as weight optimization problems for Gaussian mixtures centered about samples with identical parameters. Furthermore, the minimization is based on the(More)
— Estimating a user's intention is central to close human-robot cooperation. In this paper, the problem of performing intention recognition with tree-structured Dynamic Bayesian Networks for large environments with many features is addressed. The proposed approach reduces the computational complexity of inference O(b s) for tree-structured measurement(More)