Paper Info
Title
A distributed maximum likelihood algorithm for multi-robot mapping.
Abstract
In the last decade, several algorithms, usually based on information filtering techniques, have been proposed to address multi-robot mapping problem. Less interest has been devoted to investigate a parallel or distributed organization of such algorithms in the perspective of multi-robot exploration. In this paper, we propose a distributed algorithm for map estimation based on Gauss-Seidel relaxation. The complete map is shared among independent tasks running on each robot, which integrate the independent robot measurements in local submaps, and a server, which stores contour nodes separating the submaps. Each task updates its local submap and periodically checks for inter-robot data associations. Gauss-Seidel relaxation is performed independently on each robot and afterwards on the contour nodes set on the server. Results illustrate the potential and flexibility of the new approach.
Year
DOI
Venue
2010
10.1109/IROS.2010.5652727
IROS
Keywords
Field
DocType
simultaneous localization and mapping,servers,maximum likelihood,mobile robots,parallel algorithm,clustering algorithms,maximum likelihood estimation,cartography,gauss seidel,robot kinematics,distributed algorithm,parallel algorithms
Data mining,Computer science,Server,Theoretical computer science,Artificial intelligence,Simultaneous localization and mapping,Cluster analysis,Computer vision,Parallel algorithm,Robot kinematics,Distributed algorithm,Robot,Mobile robot
Conference
ISSN
ISBN
Citations 
2153-0858
978-1-4244-6674-0
1
PageRank 
References 
Authors
0.37
12
2
Name
Order
Citations
PageRank
Dario Lodi Rizzini18312.58
Stefano Caselli231436.32