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Parallel and Distributed Map Merging and LocalizationMap Merging

Parallel and Distributed Map Merging and Localization: Map Merging [This chapter presents a solutionMap merging for merging feature-based mapsFeature-based map in a robotic network with limited communicationLimited communication. We consider a team of robots exploring an unknown environment. Along its operation, each robot observes the environment and builds and maintains its local stochastic mapLocal map of the visited region. Simultaneously, the robots communicate and build a global mapGlobal merged map of the environment. The communication between the robots is limitedLimited communication and, at every time instant, each robot can only exchange data with its neighboring robotsNeighbor robot. This problem has been traditionally addressed using centralized schemesCentralized system or broadcasting methods. Instead, in this chapter we study a fully distributedDistributed system approach which is implementable in scenarios with limited communication. This solution does not rely on a particular communication topology and does not require any central node, making the system robust to individual failures. Each robot computes and tracks the global map based on local interactions with its neighborsNeighbor robot. Under mild connectivity conditions on the communication graph, the algorithm asymptotically converges to the global map. In addition, we analyze the convergence speed according to the information increase in the local maps. The results are validated through simulations.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Parallel and Distributed Map Merging and LocalizationMap Merging

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Publisher
Springer International Publishing
Copyright
© The Author(s) 2015
ISBN
978-3-319-25884-3
Pages
65 –87
DOI
10.1007/978-3-319-25886-7_4
Publisher site
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Abstract

[This chapter presents a solutionMap merging for merging feature-based mapsFeature-based map in a robotic network with limited communicationLimited communication. We consider a team of robots exploring an unknown environment. Along its operation, each robot observes the environment and builds and maintains its local stochastic mapLocal map of the visited region. Simultaneously, the robots communicate and build a global mapGlobal merged map of the environment. The communication between the robots is limitedLimited communication and, at every time instant, each robot can only exchange data with its neighboring robotsNeighbor robot. This problem has been traditionally addressed using centralized schemesCentralized system or broadcasting methods. Instead, in this chapter we study a fully distributedDistributed system approach which is implementable in scenarios with limited communication. This solution does not rely on a particular communication topology and does not require any central node, making the system robust to individual failures. Each robot computes and tracks the global map based on local interactions with its neighborsNeighbor robot. Under mild connectivity conditions on the communication graph, the algorithm asymptotically converges to the global map. In addition, we analyze the convergence speed according to the information increase in the local maps. The results are validated through simulations.]

Published: Oct 30, 2015

Keywords: Map merging; Map fusion; Limited communication; Distributed systems; Parallel computation

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