Florian Meyer

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The sigma point (SP) filter, also known as unscented Kalman filter, is an attractive alternative to the extended Kalman filter and the particle filter. Here, we extend the SP filter to nonsequential Bayesian inference corresponding to loopy factor graphs. We propose sigma point belief propagation (SPBP) as a low-complexity approximation of the belief(More)
This work is concerned with the development of a novel approach to language grounding in the context of autonomous robotics. A probabilistic first order knowledge base is used to build a database for action specific background knowledge for everyday manipulation tasks. It is shown that such a knowledge base can be used to find items in the robot’s current(More)
Secondary metabolites have a great potential as pharmaceuticals, but there are only a few examples where regulation of gene cluster expression has been correlated with ecological and physiological relevance for the producer. Here, signals, mediators, and biological effects of terrein production were studied in the fungus Aspergillus terreus to elucidate the(More)
We introduce the framework of cooperative simultaneous localization and tracking (CoSLAT), which provides a consistent combination of cooperative self-localization (CSL) and distributed target tracking (DTT) in sensor networks without a fusion center. CoSLAT extends simultaneous localization and tracking (SLAT) in that it uses also intersensor measurements.(More)
We propose low-complexity detectors for large MIMO systems with BPSK or QAM constellations. These detectors work at the bit level and consist of three stages. In the first stage, maximum likelihood decisions on certain bits are made in an efficient way. In the second stage, soft values for the remaining bits are calculated. In the third stage, these(More)
We present a factor graph framework and a particle-based belief propagation algorithm for distributed cooperative simultaneous localization and synchronization (CoSLAS) in decentralized sensor networks. The proposed algorithm jointly estimates the locations and clock parameters of the network nodes in a fully decentralized fashion. This estimation is based(More)
Localization and synchronization in wireless networks are strongly related when they are based on internode time measurements. We leverage this relation by presenting a message passing algorithm for cooperative simultaneous localization and synchronization (CoSLAS). The proposed algorithm jointly estimates the locations and clock parameters of the network(More)
We propose a Bayesian method for distributed sequential localization of mobile networks composed of both cooperative agents and noncooperative objects. Our method provides a consistent combination of cooperative self-localization (CS) and distributed tracking (DT). Multiple mobile agents and objects are localized and tracked using measurements between(More)
We introduce a distributed, cooperative framework and method for Bayesian estimation and control in decentralized agent networks. Our framework combines joint estimation of time-varying global and local states with information-seeking control optimizing the behavior of the agents. It is suited to nonlinear and non-Gaussian problems and, in particular, to(More)
We propose an algorithm for tracking an unknown number of targets based on measurements provided by multiple sensors. Our algorithm achieves low computational complexity and excellent scalability by running belief propagation on a suitably devised factor graph. A redundant formulation of data association uncertainty and the use of “augmented target(More)