Tim Bailey, Mitch Bryson, Hua Mu, John Vial, Lachlan McCalman and Hugh Durrant-Whyte

Decentralised Cooperative Localisation for Heterogeneous Teams of Mobile Robots

IEEE International Conference on Robotics and Automation, 2011


 

Description

A team of mobile robots develop correlated location estimates if they make "inter-robot" measurements, eg., range/bearing from one robot to another. This paper presents a distributed algorithm for efficiently combining available information to obtain optimal pose estimates of all robots. It makes use of the sparse information-form Gaussian representation. The essential algorithm requires a central server for part of the data fusion process, but the server is easily and efficiently duplicated, by self-replication or data forwarding, and so the algorithm is considered decentralised.
 

Abstract

This paper presents a distributed 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. Inter-robot 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 (e.g., odometry, IMU, GPS) is fused locally on each platform. This is the distributed part 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 is easily duplicated for full 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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