Simultaneous localization and mapping for mobile robots using rp-lidar

Autonomous robots are the mechanisms by which they can interpret and decide on the data they receive. Autonomous robots are used to perform necessary operations in areas that are dangerous or difficult to reach or accessible by people. In such a case, the autonomous robot must first know its surroundings and its position in order to be able to do the necessary work. If it is a known environment, the previously prepared map is loaded to autonomous robot. On the other hand, if it is an unknown one, it must simultaneously determine both the map of the environment and its own location. For this reason, when entering an unknown environment, an autonomous mobile robotic is needed that will both map out the environment and determine its own location. In this thesis, firstly, the necessary steps for the modeling of autonomous mobile robots were given. Secondly, the process of Simultaneous Localization And Mapping (SLAM) was realized for a virtual environment in MATLAB at Windows 7. Thirdly, it was performed SLAM in the Robot Operating System (ROS) at Linux. For this purpose, Turtlebot mobile robot, manufactured by Kobuki Company, was used. In this application, Turtlebot computer was manually controlled via USB interface. The LIDAR sensor, which measures the laser distance, was used to extract the environment map and determine the position of the robot. Particle Filter based on Kalman Filter that is one of the statistical estimation methods was used for SLAM. For this method, the Gmapping algorithm was loaded into the ROS. Finally, the control of Turtlebot was performed autonomously using ROS and the map of the experimental environment was also determined by determining its position at the same time. All applications were successfully carried out. The results obtained were illustrated in figures.