Rosario Aragues

TL;DR: This paper proposes a dynamic strategy, based on consensus algorithms, that is fully distributed and does not rely on any particular communication topology to merge feature-based map merging problem in robot networks.

TL;DR: The pose graph optimization problem is investigated, and an approximation of the maximum likelihood estimate, named LAGO (Linear Approximation for pose Graph Optimization), can be used as a stand-alone tool or can bootstrap state-of-the-art techniques, reducing the risk of being trapped in local minima.

TL;DR: A novel distributed algorithm for estimating the algebraic connectivity, that relies on the distributed computation of the powers of matrices, is presented, that provides proofs of convergence, convergence rate, and upper and lower bounds at each iteration of the estimatedgebraic connectivity.

TL;DR: A closed-form approximation to the full SLAM problem is proposed, under the assumption that the relative position and the relative orientation measurements are independent, and it is demonstrated that such refinement is often unnecessary, since the linear estimate is already accurate.

TL;DR: This paper presents a distributed algorithm for estimating the algebraic connectivity for undirected graphs with symmetric Laplacian matrices that relies on the distributed computation of the powers of the adjacency matrix and its main interest is that, at each iteration, agents obtain both upper and lower bounds for the truegebraic connectivity.