Abstract
ABSTRACTThe 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.
Highlights
In the last few years, the simultaneous localization and mapping (SLAM) became an important topic of research in the robotics community
The sampling rates used for each sensor and filter are as follows: fodom 1⁄4 flaser 1⁄4 fEKFÀSLAM 1⁄4 fSVSFÀSLAM 1⁄4 10 Hz: The simulation results provided in Figures 6, 7 and 8 represent the estimated unmanned ground vehicles (UGVs) position obtained using the Extended Kalman Filter (EKF), and smooth variable structure filter (SVSF) filters, respectively, with sv =
We studied a number of technical problems that are necessary to be solved in order to increase UGVs autonomy
Summary
In the last few years, the simultaneous localization and mapping (SLAM) became an important topic of research in the robotics community. The foremost problems of cooperative SLAM (CSLAM) to be solved are in the computational efficiency parts, software/network performance, and robustness to modelling errors. Some other solutions for CSLAM, cooperative visual SLAM [4], are based on an Extended Kalman filter. The latter is very sensitive to outliers and suffers from linearization. The work presented in [11] is a part of research work on autonomous navigation for multiple robots based on local submap strategy. In [12], a vision-based position drift minimization method is proposed for cooperative navigation of quad-rotor and ground robot.
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