Delayed-State Information Filter for Cooperative Decentralized Tracking

J. Capitan, L. Merino, F. Caballero, and A. Ollero. Delayed-State Information Filter for Cooperative Decentralized Tracking. In Proceedings of the IEEE International Conference on Robotics and Automation, ICRA, 2009.

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Abstract

This paper presents a decentralized data fusionapproach to perform cooperative perception with data gatheredfrom heterogeneous sensors, which can be static or carried byrobots. Particularly, a Decentralized Delayed-State ExtendedInformation Filter (DDSEIF) is described, where full statetrajectories are considered to fuse the information. This permitsto obtain an estimation equal to that obtained by a centralizedsystem, and allows delays and latency in the communications.The sparseness of the information matrix maintains the communicationsoverhead at a reasonable level. The method isapplied to cooperative tracking and some results in disastermanagement scenarios are shown. In this kind of scenarios thetarget might move in both open field and indoor areas, so fusionof data provided by heterogeneous sensors is beneficial.

BibTeX Entry

@INPROCEEDINGS{capitan_icra09,
  author = {Capitan, J. and Merino, L. and Caballero, F. and Ollero, A.},
  title = {{D}elayed-{S}tate {I}nformation {F}ilter for {C}ooperative {D}ecentralized
	{T}racking},
  booktitle = ICRA,
  year = {2009},
  doi = {10.1109/ROBOT.2009.5152469},
   abstract={This paper presents a decentralized data fusion
approach to perform cooperative perception with data gathered
from heterogeneous sensors, which can be static or carried by
robots. Particularly, a Decentralized Delayed-State Extended
Information Filter (DDSEIF) is described, where full state
trajectories are considered to fuse the information. This permits
to obtain an estimation equal to that obtained by a centralized
system, and allows delays and latency in the communications.
The sparseness of the information matrix maintains the communications
overhead at a reasonable level. The method is
applied to cooperative tracking and some results in disaster
management scenarios are shown. In this kind of scenarios the
target might move in both open field and indoor areas, so fusion
of data provided by heterogeneous sensors is beneficial.},
}

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