APA 6th Edition Demim, F., Nemra, A., Louadj, K., Hamerlain, M. i Bazoula, A. (2017). Cooperative SLAM for multiple UGVs navigation using SVSF filter. Automatika, 58 (1), 119-129. https://doi.org/10.1080/00051144.2017.1372123
MLA 8th Edition Demim, Fethi, et al. "Cooperative SLAM for multiple UGVs navigation using SVSF filter." Automatika, vol. 58, br. 1, 2017, str. 119-129. https://doi.org/10.1080/00051144.2017.1372123. Citirano 19.10.2021.
Chicago 17th Edition Demim, Fethi, Abdelkrim Nemra, Kahina Louadj, Mustapha Hamerlain i Abdelouahab Bazoula. "Cooperative SLAM for multiple UGVs navigation using SVSF filter." Automatika 58, br. 1 (2017): 119-129. https://doi.org/10.1080/00051144.2017.1372123
Harvard Demim, F., et al. (2017). 'Cooperative SLAM for multiple UGVs navigation using SVSF filter', Automatika, 58(1), str. 119-129. https://doi.org/10.1080/00051144.2017.1372123
Vancouver Demim F, Nemra A, Louadj K, Hamerlain M, Bazoula A. Cooperative SLAM for multiple UGVs navigation using SVSF filter. Automatika [Internet]. 2017 [pristupljeno 19.10.2021.];58(1):119-129. https://doi.org/10.1080/00051144.2017.1372123
IEEE F. Demim, A. Nemra, K. Louadj, M. Hamerlain i A. Bazoula, "Cooperative SLAM for multiple UGVs navigation using SVSF filter", Automatika, vol.58, br. 1, str. 119-129, 2017. [Online]. https://doi.org/10.1080/00051144.2017.1372123
Sažetak The aim of this paper is to present a cooperative simultaneous localization and mapping (CSLAM) solution based on a laser telemeter. The proposed solution gives the opportunity to a group of unmanned ground vehicles (UGVs) to construct a large map and localize themselves without any human intervention. Many solutions proposed to solve this problem, most of them are based on the sequential probabilistic approach, based around Extended Kalman Filter (EKF)
or the Rao-Blackwellized particle filter. In our work, we propose a new alternative to avoid these limitations, a novel alternative solution based on the smooth variable structure filter (SVSF) to solve the UGV SLAM problem is proposed. This version of SVSF-SLAM algorithm uses a boundary layer width vector and does not require covariance derivation. The new algorithm has been developed to implement the SVSF filter for CSLAM. Our contribution deals with adapting the SVSF to solve the CSLAM problem for multiple UGVs. The algorithms developed in this work were implemented using a swarm of mobile robots Pioneer 3–AT. Two mapping approaches, point-based and line-based, are implemented and validated experimentally using 2D laser telemeter sensors. Good results are obtained by the Cooperative SVSF-SLAM algorithm compared with the Cooperative EKF-SLAM.