ICRA 2011 Paper Abstract

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Paper WeP113.2

Leung, Keith Yu Kit (University of Toronto), Barfoot, Timothy (University of Toronto), LIU, Hugh H.T. (University of Toronto)

Distributed and Decentralized Cooperative Simultaneous Localization and Mapping for Dynamic and Sparse Robot Networks

Scheduled for presentation during the Regular Sessions "Networked Robots" (WeP113), Wednesday, May 11, 2011, 13:55−14:10, Room 5I

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on December 8, 2019

Keywords Networked Robots, Distributed Robot Systems, SLAM

Abstract

This paper presents a simultaneous localization and mapping (SLAM) algorithm that allows a recursive state estimation process to be both distributed and decentralized in a sparse robot network that is never guaranteed to be fully connected (communication-wise). In such a sparse network, a robot may not always have the latest odometry and measurements from other robots. Our approach allows robots to obtain a temporary (localization and map) estimate at the current timestep using information available locally, but we also ensure that the centralized-equivalent estimate can always be recovered by all robots at a later time; we do not require a robot to keep track of what other robots know when it applies the Markov property to discard past information. Our method is validated through a hardware SLAM experiment where we distribute data association hypotheses amongst a team of robots. Estimate errors are shown to validate the performance of our approach. We also discuss the trade-offs and show comparisons between our distributed approach versus a non-distributed one.

 

 

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