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Sensors 2014, 14(1), 995-1009; doi:10.3390/s140100995

Multiple Vehicle Cooperative Localization with Spatial Registration Based on a Probability Hypothesis Density Filter

1
Robotics and Embedded Systems, Technische Universität München, Garching bei München, Germany
2
Fortiss GmbH, Guerickestr. 25, München 80805, Germany
*
Author to whom correspondence should be addressed.
Received: 13 November 2013 / Revised: 22 December 2013 / Accepted: 26 December 2013 / Published: 8 January 2014
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [481 KB, 21 June 2014; original version 21 June 2014]   |  

Abstract

This paper studies the problem of multiple vehicle cooperative localization with spatial registration in the formulation of the probability hypothesis density (PHD) filter. Assuming vehicles are equipped with proprioceptive and exteroceptive sensors (with biases) to cooperatively localize positions, a simultaneous solution for joint spatial registration and state estimation is proposed. For this, we rely on the sequential Monte Carlo implementation of the PHD filtering. Compared to other methods, the concept of multiple vehicle cooperative localization with spatial registration is first proposed under Random Finite Set Theory. In addition, the proposed solution also addresses the challenges for multiple vehicle cooperative localization, e.g., the communication bandwidth issue and data association uncertainty. The simulation result demonstrates its reliability and feasibility in large-scale environments. View Full-Text
Keywords: random finite set; PHD filter; spatial registration random finite set; PHD filter; spatial registration
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Zhang, F.; Buckl, C.; Knoll, A. Multiple Vehicle Cooperative Localization with Spatial Registration Based on a Probability Hypothesis Density Filter. Sensors 2014, 14, 995-1009.

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