ICRA 2011 Paper Abstract

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Paper WeA112.1

Bailey, Tim (University of Sydney), Bryson, Mitch (University of Sydney), Durrant-Whyte, Hugh (The University of Sydney), Mu, Hua (National University of Defense Technology), Vial, John (University of Sydney), McCalman, Lachlan (Australian Centre for Field Robotics, University of Sydney)

Decentralised Cooperative Localisation for Heterogeneous Teams of Mobile Robots

Scheduled for presentation during the Regular Sessions "Distributed Robot Systems II" (WeA112), Wednesday, May 11, 2011, 08:20−08:35, Room 5H

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 10, 2019

Keywords Localization, Distributed Robot Systems, Sensor Networks

Abstract

This paper presents a decentralised algorithm for performing joint localisation of a team of robots. The mobile robots have heterogeneous sensing capabilities, with some having high quality inertial and exteroceptive sensing, while others have only low quality sensing or none at all. By sharing information, a combined estimate of all robot poses is obtained. Interrobot range-bearing measurements provide the mechanism for transferring pose information from well-localised vehicles to those less capable.

In our proposed formulation, high frequency egocentric data (eg., odometry, IMU, GPS) is fused locally on each platform. This is the decentralised aspect of the algorithm. Inter-robot measurements, and accompanying state estimates, are communicated to a central server, which generates an optimal minimum mean-squared estimate of all robot poses. This server may be duplicated for redundant decentralisation. Communication and computation are efficient due to the sparseness properties of the information-form Gaussian representation. A team of three indoor mobile robots equipped with lasers, odometry and inertial sensing provides experimental verification of the algorithms effectiveness in combining location information.

 

 

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