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Parallel and Distributed Map Merging and LocalizationDistributed Localization

Parallel and Distributed Map Merging and Localization: Distributed Localization [In this chapter we study the problem of distributed localization, which consists of establishing a common frame and computing the robots’ localization relative to this frame. Each robot is capable of measuring the relative pose of its neighboring robots. However, it does not know the poses of far robots, and it can only exchange data with neighbors using the range-limited communication network. The analyzed algorithms have the interesting property that can be executed in a distributed fashion. They allow each robot to recover localization using exclusively local information and local interactions with its neighbors. Besides, they only require each robot to maintain an estimate of its own pose. Thus, the memory load of the algorithm is low compared to methods where each robot must also estimate the poses of any other robot. We analyze two different scenarios and study distributed algorithms for them. In the first scenario each robot measures the noisy planar position and orientation of nearby robots to estimate its own full localization with respect to an anchor node. In the second case, robots take noisy measurements of the relative three-dimensional positions of their neighbors, which is used to estimate their three-dimensional positions with respect to the simultaneously computed centroid reference. When the centroid of the team is selected as common frame, the estimates are more precise than with any anchor selection.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Parallel and Distributed Map Merging and LocalizationDistributed Localization

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/lp/springer-journals/parallel-and-distributed-map-merging-and-localization-distributed-rC9optN6xs
Publisher
Springer International Publishing
Copyright
© The Author(s) 2015
ISBN
978-3-319-25884-3
Pages
37 –64
DOI
10.1007/978-3-319-25886-7_3
Publisher site
See Chapter on Publisher Site

Abstract

[In this chapter we study the problem of distributed localization, which consists of establishing a common frame and computing the robots’ localization relative to this frame. Each robot is capable of measuring the relative pose of its neighboring robots. However, it does not know the poses of far robots, and it can only exchange data with neighbors using the range-limited communication network. The analyzed algorithms have the interesting property that can be executed in a distributed fashion. They allow each robot to recover localization using exclusively local information and local interactions with its neighbors. Besides, they only require each robot to maintain an estimate of its own pose. Thus, the memory load of the algorithm is low compared to methods where each robot must also estimate the poses of any other robot. We analyze two different scenarios and study distributed algorithms for them. In the first scenario each robot measures the noisy planar position and orientation of nearby robots to estimate its own full localization with respect to an anchor node. In the second case, robots take noisy measurements of the relative three-dimensional positions of their neighbors, which is used to estimate their three-dimensional positions with respect to the simultaneously computed centroid reference. When the centroid of the team is selected as common frame, the estimates are more precise than with any anchor selection.]

Published: Oct 30, 2015

Keywords: Localization; Limited communication; Distributed systems; Parallel computation

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