Fully Distributed Scalable Smoothing and Mapping with Robust Multi-robot Data Association
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Abstract
In this paper we focus on the multi-robot perception
problem, and present an experimentally validated endto-
end multi-robot mapping framework, enabling individual
robots in a team to see beyond their individual sensor horizons.
The inference part of our system is the DDF-SAM algorithm [1],
which provides a decentralized communication and inference
scheme, but did not address the crucial issue of data association.
One key contribution is a novel, RANSAC-based, approach for
performing the between-robot data associations and initialization
of relative frames of reference. We demonstrate this system
with both data collected from real robot experiments, as well
as in a large scale simulated experiment demonstrating the
scalability of the proposed approach.
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2012-05
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