Moving-horizon nonlinear least squares-based multirobot cooperative perception
详细信息 查看全文
- 作者：Aamir Ahmad ; aamir.ahmad@tuebingen.mpg.de" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Heinrich H. Bü ; lthoff heinrich.buelthoff@tuebingen.mpg.de" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae
- 关键词：Nonlinear least squares ; Cooperative localization and target tracking ; Soccer robots ; Multirobot datasets
- 刊名：Robotics and Autonomous Systems
- 出版年：2016
- 出版时间：September 2016
- 年：2016
- 卷：83
- 期：Complete
- 页码：275-286
- 全文大小：1259 K
文摘
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.