14,655 research outputs found
Cooperative localization for mobile agents: a recursive decentralized algorithm based on Kalman filter decoupling
We consider cooperative localization technique for mobile agents with
communication and computation capabilities. We start by provide and overview of
different decentralization strategies in the literature, with special focus on
how these algorithms maintain an account of intrinsic correlations between
state estimate of team members. Then, we present a novel decentralized
cooperative localization algorithm that is a decentralized implementation of a
centralized Extended Kalman Filter for cooperative localization. In this
algorithm, instead of propagating cross-covariance terms, each agent propagates
new intermediate local variables that can be used in an update stage to create
the required propagated cross-covariance terms. Whenever there is a relative
measurement in the network, the algorithm declares the agent making this
measurement as the interim master. By acquiring information from the interim
landmark, the agent the relative measurement is taken from, the interim master
can calculate and broadcast a set of intermediate variables which each robot
can then use to update its estimates to match that of a centralized Extended
Kalman Filter for cooperative localization. Once an update is done, no further
communication is needed until the next relative measurement
Message Passing-Based 9-D Cooperative Localization and Navigation with Embedded Particle Flow
Cooperative localization (CL) is an important technology for innovative
services such as location-aware communication networks, modern convenience, and
public safety. We consider wireless networks with mobile agents that aim to
localize themselves by performing pairwise measurements amongst agents and
exchanging their location information. Belief propagation (BP) is a
state-of-the-art Bayesian method for CL. In CL, particle-based implementations
of BP often are employed that can cope with non-linear measurement models and
state dynamics. However, particle-based BP algorithms are known to suffer from
particle degeneracy in large and dense networks of mobile agents with
high-dimensional states.
This paper derives the messages of BP for CL by means of particle flow,
leading to the development of a distributed particle-based message-passing
algorithm which avoids particle degeneracy. Our combined particle flow-based BP
approach allows the calculation of highly accurate proposal distributions for
agent states with a minimal number of particles. It outperforms conventional
particle-based BP algorithms in terms of accuracy and runtime. Furthermore, we
compare the proposed method to a centralized particle flow-based
implementation, known as the exact Daum-Huang filter, and to sigma point BP in
terms of position accuracy, runtime, and memory requirement versus the network
size. We further contrast all methods to the theoretical performance limit
provided by the posterior Cram\'er-Rao lower bound (PCRLB). Based on three
different scenarios, we demonstrate the superiority of the proposed method.Comment: 14 pages (two column), 7 figure
Local Maps: New Insights into Mobile Agent Algorithms
In this paper, we study the complexity of computing with mobile agents having small local knowledge. In particular, we show that the number of mobile agents and the amount of local information given initially to agents can significantly influence the time complexity of resolving a distributed problem. Our results are based on a generic scheme allowing to transform a message passing algorithm, running on an -node graph , into a mobile agent one. By generic, we mean that the scheme is independent of both the message passing algorithm and the graph . Our scheme, coupled with a well-chosen clustered representation of the graph, induces \widetilde{O}(n)kkO(n/\sqrt{k})Gnn^{\epsilon}\widetilde{O}(D)D\epsilon\widetilde{O}(1)\widetilde{O}(n)\widetilde{O}(D)$ time algorithms
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