ROS ve Gazebo Kullanılarak Geometrik Cisimlerin SLAM Performansına Etkisinin İncelenmesi

Yıl 2021, Cilt: 8 Sayı: 3, 1441 - 1454, 30.09.2021

Hamza Aydemir Mehmet Tekerek Mehmet Gök

https://doi.org/10.31202/ecjse.943364

Öz

Robotların uygulama alanını genişletmek amacıyla bilinmeyen ortamlarda da çalışabilen otonom mobil robot geliştirme çalışmaları devam etmektedir. Otonom bir mobil robotun gezinme için bir çevre haritasına ve haritaya göre konum bilgilerine ihtiyacı vardır. Eş Zamanlı Konum Belirleme ve Haritalama (SLAM), bir otonom mobil robotun, tutarlı bir harita oluştururken konumunu belirlemek için bu haritayı kullanabileceği bir tahmin sürecidir. Bu çalışmanın amacı, geometrik nesnelerin SLAM performansı üzerindeki etkisini incelemektir. Bu doğrultuda Gazebo ortamında eşkenar üçgen prizma, kare prizma ve silindir içeren üç farklı deney alanı tasarlanmıştır. Dördüncü deney alanı, çalışmada kullanılan üç geometrik nesnenin tümünü içermektedir. SLAM algoritması, TurtleBot3 Waffle Pi robotu kullanılarak test edilmiştir. Dört deney alanının haritalama süreleri karşılaştırıldığında, en hızlı üçgen prizma ve en yavaş karma deney alanının haritasının oluşturulduğu görülmüştür. Haritada yapılan ölçümlerde gerçek ölçülere en yakın haritanın üçgen harita olduğu görülmüştür. Elde edilen bulgular, cisimlerin geometrik şekillerinin SLAM performansını direkt olarak etkilediğini göstermektedir.

Anahtar Kelimeler

SLAM, Gmapping, ROS, Gazebo.

Examining of the Effect of Geometric Objects on SLAM Performance Using ROS and Gazebo

Öz

Development effort on Autonomous mobile robots that can operate in unknown environments are ongoing in order to extend the application area of the robots. In order to navigate, an autonomous mobile robot needs a map of the environment and location information relative to the map. Simultaneous Localization and Mapping (SLAM) is a prediction process in which the autonomous mobile robot can use this map to determine its position while building a consistent map. The purpose of this study is to examine the effect of geometric objects on SLAM performance. In this direction, three different experimental areas including equilateral triangular prisms, square prisms and cylinders are designed in a Gazebo. The fourth experiment area includes all three geometric objects used in the study. SLAM algorithm was tested by using the TurtleBot3 Waffle Pi robot. When the mapping times of the four experimental areas were compared, it was seen that the fastest scenario is achieved within triangular-only objects and the slowest within mixed prism. In terms of measures, the map including the triangular prisms is the closest to the actual measures of the simulated area. The obtained results show that the shapes of the geometric objects directly affect the performance of SLAM.

Anahtar Kelimeler

SLAM, Gmapping, ROS, Gazebo.

Kaynakça

• [1] A. Elfes, Using occupancy grids for mobile robot perception and navigation, Computer, 22(6), pp. 46-57, 1989.
• [2] J. A. Castellanos, J. Neira ve J. D. Tard´os, Limits to the consistency of EKF-based SLAM, 5th IFAC/EURON Symposium on Intelligent Autonomous Vehicles, Lisboa, 2004.
• [3] R. A. Brooks, A Robust Layered Control Syste For A Mobile Robot, IEEE Journal of Robotics and Automation, 1, pp. 14-23, 1986.
• [4] J. L. Crowley, Navigation for an Intelligent Mobile Robot, IEEE Journal of Robotics and Automation, 1, pp. 31-41, 1985.
• [5] S. Saeedi, M. Trentini, M. Seto ve H. Li, Space Robotics , Part II Editorial, Journal of Field Robotics, 24(4), pp. 273-274, 2007.
• [6] Z. Xuexi, L. Guokun, F. Genping, X. Dongliang ve L. Shiliu, SLAM Algorithm Analysis of Mobile Robot Based on LIDAR, 38th Chinese Control Conference (CCC), Guangzhou, 2019.
• [7] J. M. Santos, D. Portugal ve R. P. Rocha, An Evaluation of 2D SLAM Techniques Available in Robot Operating System, IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), Linkoping. Sweden, 2013.
• [8] G. Jiang, L. Yin, S. Jin, C. Tian , X. Ma ve Y. Ou , A Simultaneous Localization and Mapping (SLAM) Framework for 2.5D Map Building Based on Low-Cost LiDAR and Vision Fusion, Applied Sciences, 9(10), p. 2105, 2019.
• [9] Q. Gao, H. Jia, Y. Liu ve X.-c. Tian, Design of Mobile Robot Based on Cartographer SLAM Algorithm, 2nd International Conference on Informatics, Control and Automation (ICA 2019), Phuket Island, Thailand, 2019.
• [10] D. Singh, E. Trivedi, Y. Sharma ve V. Niranjan, TurtleBot: Design and Hardware Component Selection, International Conference on Computing, Power and Communication Technologies (GUCON), India, 2018.
• [11] A. Singandhupe ve H. M. La, A Review of SLAM Techniques and Security in Autonomous Driving, Third IEEE International Conference on Robotic Computing (IRC), Naples, 2019.
• [12] M. Zhang, H. Qin, M. Lan, J. Lin, S. Wang, K. Liu, F. Lin ve B. M. Chen, A high fidelity simulator for a quadrotor UAV using ROS and Gazebo, 41st Annual Conference of the IEEE Industrial Electronics Society (IECON), Yokohama, 2015.
• [13] K. Takaya, T. Asai, V. Kroumov ve F. Smarandache, Simulation Environment for Mobile Robots Testing Using ROS and Gazebo, içinde 20th International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, 2016.
• [14] W. Qian, Z. Xia, J. Xiong, Y. Gan, Y. Guo, S. Weng, H. Deng, Y. Hu ve J. Zhang, Manipulation task simulation using ROS and Gazebo, IEEE International Conference on Robotics and Biomimetics (ROBIO), Bali, 2015.
• [15] W. Yao, W. Dai, J. Xiao, H. Lu ve Z. Zheng, A simulation system based on ROS and Gazebo for RoboCup Middle Size League, IEEE International Conference on Robotics and Biomimetics (ROBIO), Zhuhai, 2015.
• [16] K. Takaya, T. Asai, V. Kroumov ve F. Smarandache, 20th International Conference on System Theory, Control and Computing (ICSTCC), IEEE, Sinaia, 2016.
• [17] E. Ackerman, IEEE Spectrum, 26 March 2013. [Online]. Available: https://spectrum.ieee.org/automaton/robotics/diy/interview-turtlebot-inventors-tell-us-everything-about-the-robot.
• [18] ROBOTIS, ROBOTIS e-Manuel, ROBOTIS, [Online]. Available: http://emanual.robotis.com/docs/en/platform/turtlebot3/overview/.
• [19] C. Stachniss, G. Grisetti ve W. Burgard, Information Gain-based Exploration Using Rao-Blackwellized Particle Filters, Robotics: Science and Systems, 2, pp. 65-72, 2005.
• [20] A. Doucet, N. d. Freitas ve N. Gordon, An introduction to sequential Monte Carlo methods, New York: Springer, 2001, pp. 3-14.
• [21] Y. Pyo, H. Cho, R. Jung ve T. Lim, ROS robot programming, Seoul: Robotis, 2017.
• [22] J. Hörner, Map-merging for muşti-robot system, Thesis, 2016.
• [23] ROS Wiki, Map_server Package, 2020. [Online]. Available: http://wiki.ros.org/map_server.
• [24] ROS Wiki, Navigation Stack, 2018. [Online]. Available: http://wiki.ros.org/navigation/Tutorials/RobotSetup.
• [25] B. Gerkey, slam_gmapping, docs.ros.org, 2015 . [Online]. Available: http://docs.ros.org/en/hydro/api/gmapping/html/index.html.
• [26] G. Popovic, J. Orsulic, D. Miklic ve S. Bogdan, Rao-Blackwellized Particle Filter SLAM with Prior Map: An Experimental Evaluation, Advances in Intelligent Systems and Computing, 2018.
• [27] T. Lemaire, S. Lacroix ve J. Sol`a, A practical 3D bearing-only SLAM algorithm, IEEE/RSJ International Conference on Intelligent Robots and Systems IROS, 2005.
• [28] T. Bailey ve H. Durrant-Whyte, Simultaneous localization and mapping (SLAM): Part II, IEEE Robotics and Automation Magazine, 13(3), pp. 108-117, 2006.
• [29] G. Dissanayake, H. Durrant-Whyte ve T. Bailey, A Computationally Efficient Solution to the Simultaneous Localisation and Map Building (SLAM) Problem, IEEE lntemational Conference on Robotics & Automation, San Francisco, 2000.
• [30] S. E. Hadji, S. Kazi, T. H. Hing ve M. S. Mohamed Ali, A Review: Simultaneous Localization and Mapping Algorithms, Jurnal Teknologi, 73(2), pp. 25-29, 2015.
• [31] S. Zaman, W. Slany ve G. Steinbauer, ROS-based mapping, localization and autonomous navigation using a Pioneer 3-DX robot and their relevant issues, Saudi International Electronics, Communications and Photonics Conference (SIECPC), Riyadh, 2011.
• [32] A. Li, X. Ruan, J. Huang, X. Zhu ve F. Wang, Review of vision-based Simultaneous Localization and Mapping, IEEE 3rd Information Technology,Networking, Electronic and Automation Control Conference (ITNEC 2019), Chengdu, 2019.