Decentralized Delayed State Information Filter (DDSIF): a new approach for cooperative decentralized tracking

J. Capitan, L. Merino, F. Caballero, and A. Ollero. Decentralized Delayed State Information Filter (DDSIF): a new approach for cooperative decentralized tracking. Robotics and Autonomous Systems, 59:376–388, 2011.

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Abstract

This paper presents a decentralized data fusion approach to perform cooperativeperception with data gathered from heterogeneous sensors, which canbe static or carried by robots. Particularly, a Decentralized Delayed-StateInformation Filter (DDSIF) is described, where full-state trajectories (thatis, delayed states) are considered to fuse the information. This approachallows obtaining an estimation equal to that provided by a centralized systemand reduces the impact of communications delays and latency into theestimation. The sparseness of the information matrix maintains the communicationoverhead at a reasonable level. The method is applied to cooperativetracking and some results in disaster management scenarios are shown. Inthis kind of scenarios the target might move in both open field and indoorareas, so fusion of data provided by heterogeneous sensors is beneficial. Thepaper also shows experimental results with real data and integrating severalsources of information.

BibTeX Entry

@ARTICLE{capitan_ras11,
  author = {J. Capitan and L. Merino and F. Caballero and A. Ollero},
  title = {{Decentralized Delayed State Information Filter (DDSIF): a new approach for cooperative decentralized tracking}},
  journal = {Robotics and Autonomous Systems},
  year = {2011},
  volume = {59},
  issue = {6},
  pages = {376--388},
  doi = {10.1016/j.robot.2011.02.001},
  issn = {0921-8890},
  keywords = {Cooperative robotics},
   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
Information Filter (DDSIF) is described, where full-state trajectories (that
is, delayed states) are considered to fuse the information. This approach
allows obtaining an estimation equal to that provided by a centralized system
and reduces the impact of communications delays and latency into the
estimation. The sparseness of the information matrix maintains the communication
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. The
paper also shows experimental results with real data and integrating several
sources of information.}
}

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