Simulation of simultaneous localization and mapping using point cloud data

Abstract

This paper presents a simulation study on Simultaneous Localization and Mapping (SLAM) using point cloud data derived from the Light Detection and Ranging (LiDAR) technology. Methods like simulation are useful to simplify the process of learning algorithms, particularly when collecting and annotating large volumes of real data are both impractical and expensive. In this study, a map of a given environment was constructed using the Robotic Operating System (ROS) platform with Gazebo Simulator (GS). The paper begins by presenting the most currently popular algorithms that are widely used in SLAM namely the Extended Kalman Filter, Graph SLAM and Fast SLAM. The simulation of the Robot Operating System in MATLAB is also presented. The study performed the simulations by using standard SLAM with Turtlebot and Husky robots. Husky robot was further compared with the Adaptive Monte Carlo Localization (ACML) algorithm. The results showed that Hector SLAM could achieve the goal faster than ACML algorithm in a pre-defined map. Further studies in this field with other SLAM algorithms would certainly be beneficial to many parties due to the overwhelming demands of robotic applications.