Author name cluster

Rosario Aragues

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5 papers

1 author row

ICRA Conference 2013 Conference Paper

Feature-based map merging with dynamic consensus on information increments

Rosario Aragues
Carlos Sagüés
Youcef Mezouar

We study the feature-based map merging problem in robot networks. Each robot observes the environment and builds a local map. Simultaneously, robots communicate and compute the global map of the environment; this communication is range-limited. We propose a dynamic strategy based on consensus algorithms that is fully distributed and does not rely on any particular communication topology. Robots reach consensus on the latest global map, using the increments between their previous and current local maps. Under mild connectivity conditions, our merging algorithm asymptotically converges to the global map. We give proofs of unbiasedness of this global map, at each step and robot. Our approach has been validated using real RGB-D images.

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ICRA Conference 2011 Conference Paper

A first-order solution to simultaneous localization and mapping with graphical models

Luca Carlone
Rosario Aragues
José A. Castellanos 0001
Basilio Bona

In this work we investigate the problem of Simultaneous Localization And Mapping (SLAM) for the case in which the information acquired by the robot is modeled as a network of constraints in a graphical model. Analyzing the resulting formulation we propose a closed-form approach to tackle the problem, which is proved to retrieve a first-order approximation of the actual nonlinear solution, under mild assumptions on the structure of the involved covariance matrices. The outcome of the analysis reveals several desirable properties of the proposed approach: no initial guess for optimization is needed and the technique is able to correctly estimate robot posterior also in presence of arbitrarily long loops. The approach is further validated by means of extensive simulations and real tests, and the consistency of the estimation process is also evaluated. We remark that this work is not intended to extend the already crowded literature on SLAM but is aimed at providing a consistent analytical insight, useful for efficiently attacking several open research issues, like active SLAM and exploration, for which the computational cost of simulating SLAM posterior still constitutes a troublesome bottleneck.

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ICRA Conference 2011 Conference Paper

Multi-agent localization from noisy relative pose measurements

Rosario Aragues
Luca Carlone
Giuseppe Carlo Calafiore
Carlos Sagüés

In this paper we address the problem of estimating the poses of a team of agents when they do not share any common reference frame. Each agent is capable of measuring the relative position and orientation of its neighboring agents, however these measurements are not exact but they are corrupted with noises. The goal is to compute the pose of each agent relative to an anchor node. We present a strategy where, first of all, the agents compute their orientations relative to the anchor. After that, they update the relative position measurements according to these orientations, to finally compute their positions. As contribution we discuss the proposed strategy, that has the interesting property that can be executed in a distributed fashion. The distributed implementation allows each agent to recover its pose using exclusively local information and local interactions with its neighbors. This algorithm has a low memory load, since it only requires each node to maintain an estimate of its own orientation and position.

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ICRA Conference 2010 Conference Paper

Dynamic consensus for merging visual maps under limited communications

Rosario Aragues
Jorge Cortés 0001
Carlos Sagüés

In this paper we present an algorithm for merging visual maps in a robot network. Along the operation, each robot observes the environment and builds and maintains its local map. Simultaneously, the robots communicate and build a global map of the environment. The communication between the robots is limited, and, at every time instant, each robot can only exchange data with its neighboring robots. We provide a distributed solution to the problem which does not rely on any particular communication topology and is robust to changes in the topology. Each robot computes and tracks the global map based on local interactions with its neighbors. Our contribution is the extension of distributed sensor fusion ideas to the problem of dynamic map merging. Under mild connectivity conditions on the communication graph, this algorithm asymptotically converges to the global map. The real experiments have been carried out with visual information, which is of special interest in robotics.

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IROS Conference 2009 Conference Paper

Motion control strategies for improved multi robot perception

Rosario Aragues
Jorge Cortés 0001
Carlos Sagüés

This paper describes a strategy to select optimal motions of multi robot systems equipped with cameras in such a way that they can successively improve the observation of the environment. We present a solution designed for omnidirectional cameras, although the results can be extended to conventional cameras. The key idea is the selection of a finite set of candidate next positions for every robot within their local landmark-based stochastic maps. In this way, the cost function measuring the perception improvement when a robot moves to a new position can be easily evaluated on the finite set of candidate positions. Then, the robots in the team can coordinate based on these small pieces of information. The proposed strategy is designed to be integrated with a map merging algorithm where robots fuse their maps to get a more precise knowledge of the environment. The interest of the proposed strategy for uncertainty reduction is that it is suitable for visual sensing, allows an efficient information exchange, presents a low computational cost and makes the robot coordination easier.

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