Title: Consistent data association in multi-robot systems with limited communications
Authors: Rosario Aragues,Eduardo Montijano, and Carlos Sagues
Abstract:
In this paper we address the data association
problem of features observed by a robot team with limited communications.
At every time instant, each robot can only exchange
data with a subset of the robots, its neighbors. Initially, each
robot solves a local data association with each of its neighbors.
After that, the robots execute the proposed algorithm to agree
on a data association between all their local observations which
is globally consistent. One inconsistency appears when chains of
local associations give rise to two features from one robot being
associated among them. The contribution of this work is the
decentralized detection and resolution of these inconsistencies.
We provide a fully decentralized solution to the problem. This
solution does not rely on any particular communication topology.
Every robot plays the same role, making the system robust to
individual failures. Information is exchanged exclusively between
neighbors. In a finite number of iterations, the algorithm finishes
with a data association which is free of inconsistent associations.
In the experiments, we show the performance of the algorithm
under two scenarios. In the first one, we apply the resolution
and detection algorithm for a set of stochastic visual maps. In
the second, we solve the feature matching between a set of images
taken by a robotic team.
[link]
No comments:
Post a Comment