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  Moving-horizon Nonlinear Least Squares-based Multirobot Cooperative Perception

Ahmad, A., & Bülthoff, H. (2016). Moving-horizon Nonlinear Least Squares-based Multirobot Cooperative Perception. Robotics and Autonomous Systems, 83, 275-286. doi:10.1016/j.robot.2016.06.002.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0000-7982-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-89C5-4
Genre: Journal Article

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 Creators:
Ahmad, A1, 2, Author              
Bülthoff, HH1, 2, 3, Author              
Affiliations:
1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
3Project group: Cybernetics Approach to Perception & Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_2528701              

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 Abstract: In this article we present an online estimator for multirobot cooperative localization and target tracking based on nonlinear least squares minimization. Our method not only makes the rigorous optimization-based approach applicable online but also allows the estimator to be stable and convergent. We do so by employing a moving horizon technique to nonlinear least squares minimization and a novel design of the arrival cost function that ensures stability and convergence of the estimator. Through an extensive set of real robot experiments, we demonstrate the robustness of our method as well as the optimality of the arrival cost function. The experiments include comparisons of our method with i) an extended Kalman filter-based online-estimator and ii) an offline-estimator based on full-trajectory nonlinear least squares.

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 Dates: 2016-09
 Publication Status: Published in print
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 Rev. Method: -
 Identifiers: DOI: 10.1016/j.robot.2016.06.002
BibTex Citekey: Ahmad2016
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Title: Robotics and Autonomous Systems
  Other : Robotics and autonomous systems : international journal
Source Genre: Journal
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Publ. Info: Amsterdam : Elsevier
Pages: - Volume / Issue: 83 Sequence Number: - Start / End Page: 275 - 286 Identifier: ISSN: 0921-8890
CoNE: https://pure.mpg.de/cone/journals/resource/954925565691