Particle Filter-based Localization of a Mobile Robot by Using a Single Lidar Sensor under SLAM in ROS Environment

Abstract

One of the most popular issues in autonomous mobile robots is mapping, localizing and autonomous navigation. In this paper, Adaptive Monte Carlo Localization (AMCL) as particle filters method is presented to show how effectively it localizes the mobile robot in an indoor environment. During the simulations, the robot is localized and autonomously navigates in Gazebo and Rviz environment. The simulation results demonstrated that the particles in the filter quickly converge on the pose and the robot was successfully able to follow the path to reach its goal position. Simulations for a mobile robot to be localized in two different environments, static and dynamic, were carried out. The robot was successful in reaching its goal every time. Simulation results thus point out that AMCL performed effectively in these environments.