Cooperative SLAM for multiple UGVs navigation using SVSF filter. (2nd January 2017)

• Record Type: Journal Article
• Authors: Demim, Fethi; Nemra, Abdelkrim; Louadj, Kahina; Hamerlain, Mustapha; Bazoula, Abdelouahab
• Is Part Of: Automatika. Volume 58:Number 1(2017)
• Page Start: 119
• Page End: 129
• Publication Date: 2017-01-02
• Subjects: Localization -- map building -- SVSF filter -- sensor fusion -- autonomous navigation -- cooperative SLAM -- mobile robots; Automatic control -- Periodicals; 629.805
• DOI: 10.1080/00051144.2017.1372123
• Languages: English
• ISSNs: 0005-1144

Abstract

ABSTRACT: 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 2 D laser telemeter sensors. Good results are obtained by the Cooperative SVSF-SLAM algorithm compared with the Cooperative EKF-SLAM.