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—This article investigates the problem of Simultaneous Localization and Mapping (SLAM) from the perspective of linear estimation theory. The problem is first formulated in terms of graph embedding: a graph describing robot poses at subsequent instants of time needs be embedded in a three-dimensional space, assuring that the estimated configuration maximizes(More)
In this paper, we study the feature-based map merging problem in robot networks. While in operation, each robot observes the environment and builds and maintains a local map. Simultaneously, each robot communicates and computes the global map of the environment. Communication between robots is range-limited. We propose a dynamic strategy, based on consensus(More)
We propose an anchorless distributed technique for estimating the centroid of a network of agents from noisy relative measurements. The positions of the agents are then obtained relative to the estimated centroid. The usual approach to multi-agent localization assumes instead that one anchor agent exists in the network, and the other agents positions are(More)
— In several multi agent control problems, the convergence properties and the convergence speed of the system depend on the algebraic connectivity of the graph. We present a novel distributed algorithm where the agents estimate this algebraic connectivity, obtaining a more accurate estimate at each iteration. This algorithm relies on the distributed(More)
— 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(More)
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(More)
We address the data association problem of features that are observed by a robotic network. Every robot in the network has limited communication capabilities and can only exchange local matches with its neighbors. We propose a distributed algorithm that takes these local matches and, by their propagation in the network, computes global correspondences. When(More)
In this paper we present a solution for merging feature-based maps in a robotic network with limited communication. We consider a team of robots that explore an unknown environment and build local stochastic maps of the explored region. After the exploration has taken place, the robots communicate and build a global map of the environment. This problem has(More)
In this paper we discuss feature parameterization and initialization for bearing-only data obtained from vision sensors. The interest of this work refers to the comparison of the bearing-only data representation and ini-tialization techniques. The behavior of the algorithm is analyzed for different robot motions and depth of the features. The results are(More)