Commenced in January 2007
Paper Count: 32297
Robot Operating System-Based SLAM for a Gazebo-Simulated Turtlebot2 in 2d Indoor Environment with Cartographer Algorithm
Authors: Wilayat Ali, Li Sheng, Waleed Ahmed
Abstract:The ability of the robot to make simultaneously map of the environment and localize itself with respect to that environment is the most important element of mobile robots. To solve SLAM many algorithms could be utilized to build up the SLAM process and SLAM is a developing area in Robotics research. Robot Operating System (ROS) is one of the frameworks which provide multiple algorithm nodes to work with and provide a transmission layer to robots. Manyof these algorithms extensively in use are Hector SLAM, Gmapping and Cartographer SLAM. This paper describes a ROS-based Simultaneous localization and mapping (SLAM) library Google Cartographer mapping, which is open-source algorithm. The algorithm was applied to create a map using laser and pose data from 2d Lidar that was placed on a mobile robot. The model robot uses the gazebo package and simulated in Rviz. Our research work's primary goal is to obtain mapping through Cartographer SLAM algorithm in a static indoor environment. From our research, it is shown that for indoor environments cartographer is an applicable algorithm to generate 2d maps with LIDAR placed on mobile robot because it uses both odometry and poses estimation. The algorithm has been evaluated and maps are constructed against the SLAM algorithms presented by Turtlebot2 in the static indoor environment.
Keywords: SLAM, ROS, navigation, localization and mapping, Gazebo, Rviz, Turtlebot2, SLAM algorithms, 2d Indoor environment, Cartographer.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 950
 C. Stachniss, J. J. Leonard, and S. Thrun, "Simultaneous localization and mapping," in Springer Handbook of Robotics: Springer, 2016, pp. 1153-1176.
 C. Cadena et al., "Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age," vol. 32, no. 6, pp. 1309-1332, 2016.
 M. Quigley et al., "ROS: an open-source Robot Operating System," in ICRA workshop on open source software, 2009, vol. 3, no. 3.2, p. 5: Kobe, Japan.
 S. Kohlbrecher, O. Von Stryk, J. Meyer, and U. Klingauf, "A flexible and scalable slam system with full 3d motion estimation," in 2011 IEEE international symposium on safety, security, and rescue robotics, 2011, pp. 155-160: IEEE.
 W. Hess, D. Kohler, H. Rapp, and D. Andor, "Real-time loop closure in 2D LIDAR SLAM," in 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016, pp. 1271-1278: IEEE.
 G. Grisetti, C. Stachniss, and W. J. I. t. o. R. Burgard, "Improved techniques for grid mapping with rao-blackwellized particle filters," vol. 23, no. 1, pp. 34-46, 2007.
 H. I. M. A. Omara and K. S. M. Sahari, "Indoor mapping using kinect and ROS," in 2015 International Symposium on Agents, Multi-Agent Systems and Robotics (ISAMSR), 2015, pp. 110-116: IEEE.
 I. Z. Ibragimov and I. M. Afanasyev, "Comparison of ROS-based visual SLAM methods in homogeneous indoor environment," in 2017 14th Workshop on Positioning, Navigation and Communications (WPNC), 2017, pp. 1-6: IEEE.
 M. Filipenko and I. Afanasyev, "Comparison of various slam systems for mobile robot in an indoor environment," in 2018 International Conference on Intelligent Systems (IS), 2018, pp. 400-407: IEEE.
 A. Gabdullin, G. Shvedov, M. Ivanou, and I. Afanasyev, "Analysis of onboard sensor-based odometry for a quadrotor uav in outdoor environment," in Int. Conf. on Artif. Life and Robotics (ICAROB), 2018.
 R. K. Megalingam, C. R. Teja, S. Sreekanth, and A. J. I. J. P. A. M. Raj, "ROS based autonomous indoor navigation simulation using SLAM algorithm," vol. 118, no. 7, pp. 199-205, 2018.
 B. M. da Silva, R. S. Xavier, and L. M. Gonçalves, "Mapping and Navigation for Indoor Robots under ROS: An Experimental Analysis," 2019.
 F. Duchoň et al., "Verification of SLAM Methods Implemented in ROS," 2019.
 J. M. Santos, D. Portugal, and R. P. Rocha, "An evaluation of 2D SLAM techniques available in robot operating system," in 2013 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), 2013, pp. 1-6: IEEE.
 A. Filatov, A. Filatov, K. Krinkin, B. Chen, and D. Molodan, "2d slam quality evaluation methods," in 2017 21st Conference of Open Innovations Association (FRUCT), 2017, pp. 120-126: IEEE.
 R. Yagfarov, M. Ivanou, and I. Afanasyev, "Map comparison of lidar-based 2d slam algorithms using precise ground truth," in 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), 2018, pp. 1979-1983: IEEE.
 M. Abouzahir, A. Elouardi, R. Latif, S. Bouaziz, A. J. R. Tajer, and A. Systems, "Embedding SLAM algorithms: Has it come of age?," vol. 100, pp. 14-26, 2018.
 R. Munguia, B. Castillo-Toledo, and A. J. S. Grau, "A robust approach for a filter-based monocular simultaneous localization and mapping (SLAM) system," vol. 13, no. 7, pp. 8501-8522, 2013.
 C. Zhi and S. Xiumin, "Research on Cartographer Algorithm based on Low Cost Lidar."