Decentralised cooperative localisation for heterogeneous teams of mobile robots

Decentralised cooperative localisation for heterogeneous teams of mobile robots,10.1109/ICRA.2011.5979850,Tim Bailey,Mitch Bryson,Hua Mu,John Vial,Lac

Decentralised cooperative localisation for heterogeneous teams of mobile robots  
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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 pro- vides experimental verification of the algorithms effectiveness in combining location information. I. INTRODUCTION Estimating the position and heading of each platform in a team of mobile robots is a fundamental capability for autonomous cooperation. However, a heterogeneous team may consist of some robots with high cost, high accuracy localisation sensors, and others with low cost sensors, and others perhaps with no form of exteroception at all. This paper presents an efficient means to compute a joint estimate of all robot poses. The team shares its information allowing more able robots to assist those with lower quality instru- mentation.
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