Simultaneous Localization and Mapping System on the Basis of a CoreSLAM Approach

Baramiya, Denis; D’yakov, M. S.; Kuzikovskii, S. A.; Lavrentyev, Mikhail

doi:10.3103/s8756699017060097

Cited by 1 publication

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“…Firstly, SLAM is presented in 1986 which is considered as one of the AI techniques that benefit from the mobile robot's navigation in at unknown environment [14], to construct a new map for this environment so as other robots can navigate it smoothly in the future [15]. So, the SLAM problem can define as a case study of a chicken-oregg problem [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Developing a real time navigation for the mobile robots at unknown environments

Abdulredah

Kadhim

2020

IJEECS

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<p><span>This research deals with the feasibility of a mobile robot to navigate and discover its location at unknown environments, and then constructing maps of these navigated environments for future usage. In this work, we proposed a modified Extended Kalman Filter- Simultaneous Localization and Mapping (EKF-SLAM) technique which was implemented for different unknown environments containing a different number of landmarks. Then, the detectable landmarks will play an important role in controlling the overall navigation process and EKF-SLAM technique’s performance. MATLAB simulation results of the EKF-SLAM technique come with better performance as compared with an odometry approach performance in terms of measuring the mean square error, especially when increasing the number of landmarks. After that, we simulate and evaluate a mobile robot platform named TurtleBot2e in Gazebo simulator software to achieve the using of the SLAM technique for a different environment using the Rviz library which was built on Robot Operating System in Linux. The main conclusion comes with this work is the simulation and implementation of the SLAM technique using two software platforms separately (MATLAB and ROS) in different unknown environments containing a different number of landmarks so a few number of landmark will make the mobile robot loses its path.</span></p>

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Section: Introductionmentioning

confidence: 99%