Moving-horizon nonlinear least squares-based multirobot cooperative perception

Aamir Ahmad, Heinrich Bulthoff

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publication2015 European Conference on Mobile Robots, ECMR 2015 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781467391634
Publication statusPublished - 2015 Nov 10
Externally publishedYes
EventEuropean Conference on Mobile Robots, ECMR 2015 - Lincoln, United Kingdom
Duration: 2015 Sep 22015 Sep 4


OtherEuropean Conference on Mobile Robots, ECMR 2015
CountryUnited Kingdom


  • Cost function
  • Mathematical model
  • Noise measurement
  • Robots
  • Target tracking
  • Time measurement

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering

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