DDF-SAM: Fully Distributed SLAM using Constrained Factor Graphs

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Title: DDF-SAM: Fully Distributed SLAM using Constrained Factor Graphs
Author: Cunningham, Alexander ; Paluri, Manohar ; Dellaert, Frank
Abstract: We address the problem of multi-robot distributed SLAM with an extended Smoothing and Mapping (SAM) approach to implement Decentralized Data Fusion (DDF). We present DDF-SAM, a novel method for efficiently and robustly distributing map information across a team of robots, to achieve scalability in computational cost and in communication bandwidth and robustness to node failure and to changes in network topology. DDF-SAM consists of three modules: (1) a local optimization module to execute single-robot SAM and condense the local graph; (2) a communication module to collect and propagate condensed local graphs to other robots, and (3) a neighborhood graph optimizer module to combine local graphs into maps describing the neighborhood of a robot.We demonstrate scalability and robustness through a simulated example, in which inference is consistently faster than a comparable naive approach.
Description: ©2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Presented at the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 18-22 October 2010, Taipei, Taiwan. DOI: 10.1109/IROS.2010.5652875
Type: Post-print
URI: http://hdl.handle.net/1853/38326
ISSN: 2153-0858
Citation: Cunningham, A., Paluri, M., & Dellaert, F. (2010). "DDF-SAM: Fully Distributed SLAM Using Constrained Factor Graphs". Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 18-22 October 2010, 3025-3030.
Date: 2010
Contributor: Georgia Institute of Technology. Center for Robotics and Intelligent Machines
Georgia Institute of Technology. College of Computing
Publisher: Georgia Institute of Technology
Institute of Electrical and Electronics Engineers
Subject: Decentralized data fusion
Network topology
Robot mapping
Smoothing and mapping

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