Moving-horizon Nonlinear Least Squares-based Multirobot Cooperative Perception


Conference Paper


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.

Author(s): Ahmad, A and Bülthoff, HH
Pages: 1-8
Year: 2015
Publisher: IEEE

Department(s): Perceiving Systems
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/ECMR.2015.7324197
Event Name: 7th European Conference on Mobile Robots (ECMR 2015)
Event Place: Lincoln, UK


  title = {Moving-horizon Nonlinear Least Squares-based Multirobot Cooperative Perception},
  author = {Ahmad, A and B{\"u}lthoff, HH},
  pages = {1-8},
  publisher = {IEEE},
  year = {2015}