Exploring Mapping and Avoidance Simulation for Mobile Robot

Abstract

The use of mobile robot simulation is widely seen in various purpose. The navigation of the mobile robot is important to make the simulation process success. Simultaneous Localization and Mapping (SLAM) is one of the important features in a mobile robot navigation to enable an autonomous robot to map its surrounding and localize itself in real time. This paper presents a simulation study that investigates the algorithms that could be used in localization and mapping processes based on Karto and GMapping SLAM algorithms. The algorithms were tested on Robot Operating System (ROS) simulation in Linux environment. Experimental results from the Karto SLAM algorithm showed that the map produced is more precise and reliable for localization compared to its counterpart. In addition, the notification of distance is provided to give information on obstacle. Our results showed that Karto SLAM algorithm produced a better position estimation and can determine the direction of where the mobile robot is moving autonomously without colliding in any collision. Further studies on other SLAM algorithms could be done to generate more accurate results to localize the autonomous vehicle.