@inproceedings{faae27f3229e4302be36bad63df853d1,
title = "Moving-horizon nonlinear least squares-based multirobot cooperative perception",
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.",
keywords = "Cost function, Mathematical model, Noise measurement, Robots, Target tracking, Time measurement",
author = "Aamir Ahmad and B{\"u}lthoff, {Heinrich H.}",
note = "Funding Information: The first author{\textquoteright}s research leading to the results in this article has received funding from the People Programme (Marie Curie Actions) of the European Union{\textquoteright}s Seventh Framework Programme (FP7/2007–2013) under REA grant agreement no. 626050 . ; European Conference on Mobile Robots, ECMR 2015 ; Conference date: 02-09-2015 Through 04-09-2015",
year = "2015",
month = nov,
day = "10",
doi = "10.1109/ECMR.2015.7324197",
language = "English",
series = "2015 European Conference on Mobile Robots, ECMR 2015 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2015 European Conference on Mobile Robots, ECMR 2015 - Proceedings",
}