DDF-SAM: Fully distributed SLAM using Constrained Factor Graphs

• Published in: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems pp. 3025 - 3030
• Main Authors: Cunningham, A, Paluri, M, Dellaert, F
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2010
• Subjects: Equations; Mathematical model; Optimization; Robot kinematics; Simultaneous localization and mapping
• ISBN: 9781424466740; 1424466741
• ISSN: 2153-0858
• DOI: 10.1109/IROS.2010.5652875

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.