Simulation of simultaneous localization and mapping using 3D point cloud data

Shuzlina Abdul-Rahman (Universiti Teknologi MARA)
Mohamad Soffi Abd Razak (Universiti Teknologi MARA)
Aliya Hasanah Binti Mohd Mushin (Universiti Teknologi MARA)
Raseeda Hamzah (Universiti Teknologi MARA)
Nordin Abu Bakar (Universiti Teknologi MARA)
Zalilah Abd Aziz (Universiti Teknologi MARA)

Indonesian Journal of Electrical Engineering and Computer Science
Vol 16, No 2: November 2019

Abstract—This paper presents a simulation study of Simultaneous Localization and Mapping (SLAM) using 3D point cloud data from Light Detection and Ranging (LiDAR) technology.  Methods like simulation is useful to simplify the process of learning algorithms particularly when collecting and annotating large volumes of real data is impractical and expensive. In this study, a map of a given environment was constructed in Robotic Operating System platform with Gazebo Simulator. The paper begins by presenting the most currently popular algorithm that are widely used in SLAM namely Extended Kalman Filter, Graph SLAM and Fast SLAM. The study performed the simulations by using standard SLAM with Turtlebot and Husky robots. Husky robot was further compared with ACML algorithm. The results showed that Hector SLAM could reach the goal faster than ACML algorithm in a pre-defined map. Further studies in this field with other SLAM algorithms would certainly beneficial to many parties due to the demands of robotic application.