Nonparametric Belief Propagation for Distributed Tracking of Robot Networks With Noisy Inter-Distance Measurements

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Executive Summary

The authors consider the problem of tracking multiple moving robots using noisy sensing of inter-robot and inter-beacon distances. Sensing is local: there are three fixed beacons at known locations, so distance and position estimates propagate across multiple robots. They show that the technique of Non-parametric Belief Propagation (NBP), a graph-based generalization of particle filtering, can address this problem and model multi-modal and ring-shaped uncertainty distributions. NBP provides the basis for distributed algorithms in which messages are exchanged between local neighbors. Generalizing previous approaches to localization in static sensor networks, they improve efficiency and accuracy by using a dynamics model for temporal tracking.

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