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ICRA
2005
IEEE

Learning Sensor Network Topology through Monte Carlo Expectation Maximization

10 years 3 months ago
Learning Sensor Network Topology through Monte Carlo Expectation Maximization
— We consider the problem of inferring sensor positions and a topological (i.e. qualitative) map of an environment given a set of cameras with non-overlapping fields of view. In this way, without prior knowledge of the environment nor the exact position of sensors within the environment, one can infer the topology of the environment, and common traffic patterns within it. In particular, we consider sensors stationed at the junctions of the hallways of a large building. We infer the sensor connectivity graph and the travel times between sensors (and hence the hallway topology) from the sequence of events caused by unlabeled agents (i.e. people) passing within view of the different sensors. We do this based on a firstorder semi-Markov model of the agent’s behavior. The paper describes a problem formulation and proposes a stochastic algorithm for its solution. The result of the algorithm is a probabilistic model of the sensor network connectivity graph and the underlying traffic p...
Dimitri Marinakis, Gregory Dudek, David J. Fleet
Added 25 Jun 2010
Updated 25 Jun 2010
Type Conference
Year 2005
Where ICRA
Authors Dimitri Marinakis, Gregory Dudek, David J. Fleet
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