Simultaneous Localization and Mapping of Mobile Robot using GMapping Algorithm

The main aspect of the mobile robot system is the ability to localize itself accurately and simultaneously, create an map of the unknown environment. Simultaneous localization and mapping (SLAM) is required for autonomous navigation. The framework of SLAM is based on Rao-Blackwellized Particle Filter (RBPF) that is applied through G-Mapping algorithm. The 2D LIDAR scanner and odometer is used as main sensor in the simulated robot model. The RBPF algorithm and the data of the LIDAR and odometer are fused to create the 2D map of the simulated environment as well as localization and navigation of the robot in unknown environment. Experiment Implementation is carried in Robotic Operating system (ROS) and gazebo simulator.