Examining of the Effect of Geometric Objects on SLAM Performance Using ROS and Gazebo
Yıl: 2021 Cilt: 8 Sayı: 3 Sayfa Aralığı: 1441 - 1454 Metin Dili: İngilizce DOI: 10.31202/ecjse.943364 İndeks Tarihi: 17-10-2022
Examining of the Effect of Geometric Objects on SLAM Performance Using ROS and Gazebo
Öz: n 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 Gazebo. The fourth experiment area includes all three geometric objects used in the study. When the mapping times of the four experimental areas were compared, it was seen that the fastest scenario is achieved within triangular-only objects (9 min 55 sec) and the slowest within square (10 min 43 sec). In terms of measures, the generated map including the triangular prisms is the closest to the actual measures of the simulated area. Accordingly, the mapping error was calculated as 0.171 m2 per 1 m2 in an interior made of triangular prisms, and 0.682 m2 in an interior made of square prisms. The obtained results show that the shapes of the geometric objects directly affect the performance of SLAM.
Anahtar Kelime: ROS ve Gazebo Kullanılarak Geometrik Cisimlerin SLAM Performansına Etkisinin İncelenmesi
Öz: 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. Dört deney alanının haritalama süreleri karşılaştırıldığında, en hızlı üçgen prizma (9 dakika 55 saniye) ve en yavaş kare (10 dakika 43 saniye) deney alanının haritasının oluşturulduğu görülmüştür. Oluşturulan haritada yapılan ölçümlerde gerçek ölçülere en yakın haritanın üçgen harita olduğu görülmüştür. Buna göre, üçgen prizmadan oluşan bir iç mekânda 1 m2'de haritalama hatası 0,171 m2, kare prizmadan oluşan bir iç mekânda ise 0,682 m2 olarak hesaplanmıştır. Elde edilen bulgular, cisimlerin geometrik şekillerinin SLAM performansını direkt olarak etkilediğini göstermektedir.
Anahtar Kelime: Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
- [1]. Elfes, A., Using occupancy grids for mobile robot perception and navigation, Computer, 1989, 22 (6), 46-57.
- [2]. Castellanos, J. A., Neira J. and Tard ́os J. D., Limits to the consistency of EKF-based SLAM, 5th IFAC/EURON Symposium on Intelligent Autonomous Vehicles, Lisbon, Portugal, (2004).
- [3]. Brooks, R. A., A robust layered control system for a mobile robot, IEEE Journal of Robotics and Automation, 1986, 2 (1), 14-23.
- [4]. Crowley, J. L., Navigation for an intelligent mobile robot, IEEE Journal of Robotics and Automation, 1985, 1 (1), 31-41.
- [5]. Saeedi, S., Trentini, M., Seto, M. and Li, H., Space Robotics, Part II Editorial, Journal of Field Robotics, 2007, 24 (4), 273-274.
- [6]. Xuexi, Z., Guokun, L., Genping, F., Dongliang, X. and Shiliu, L., SLAM algorithm analysis of mobile robot based on lidar, 38th Chinese Control Conference (CCC), Guangzhou, China, (2019).
- [7]. Santos, J. M., Portugal, D. and Rocha, R. P., 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]. Jiang, G., Yin, L., Jin, S., Tian, C., Ma, X. and Ou, Y., A simultaneous localization and mapping (SLAM) framework for 2.5D map building based on low-cost LiDAR and vision fusion, Applied Sciences, 2019, 9 (10), 2105.
- [9]. Gao, Q., Jia, H., Liu, Y. and Tian, X., Design of mobile robot based on cartographer SLAM algorithm, 2nd International Conference on Informatics, Control and Automation (ICA 2019), Phuket Island, Thailand, (2019).
- [10]. Singh, D., Trivedi, E., Sharma, Y. and Niranjan, V., TurtleBot: Design and hardware component selection, International Conference on Computing, Power and Communication Technologies (GUCON), Greater Noida, India, (2018).
- [11]. Zhang, M., Qin, H., Lan, M., Lin, J., Wang, S., Liu, K., Lin, F. and Chen, B. M., A high fidelity simulator for a quadrotor UAV using ROS and Gazebo, 41st Annual Conference of the IEEE Industrial Electronics Society (IECON), Yokohama, Japan, (2015).
- [12]. Takaya, K. Asai, T., Kroumov, V. and Smarandache, F., Simulation environment for mobile robots testing using ROS and Gazebo, 20th International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, Romania, (2016).
- [13]. Qian, W., Xia, Z., Xiong, J., Gan, Y., Guo, Y., Weng, S., Deng, H., Hu, Y. and Zhang, J., Manipulation task simulation using ROS and Gazebo, IEEE International Conference on Robotics and Biomimetics (ROBIO), Bali, (2015).
- [14]. Karalekas, G., Vologiannidis, S. and Kalomiros, J., Europa: A case study for teaching sensors, data acquisition and robotics via a ROS-based educational robot, Sensors, 2020, 20 (9).
- [15]. Ackerman, E., IEEE Spectrum, https://spectrum.ieee.org/automaton/robotics/diy/interview- turtlebot-inventors-tell-us-everything-about-the-robot (accessed 26.05.2021).
- [16]. ROBOTIS, ROBOTIS e-Manuel, http://emanual.robotis.com/docs/en/platform/turtlebot3/overview/ (accessed 26.05., 2021).
- [17]. Stachniss, C., Grisetti, G. and Burgard, W., Information Gain-based exploration using Rao- Blackwellized particle filters, Robotics: Science and Systems, 2005, 2, 65-72.
- [18]. Doucet, A., Freitas, N. d. and Gordon, N., An introduction to sequential Monte Carlo methods, New York: Springer, 2001, 3-14.
- [19]. Pyo, Y., Cho, H., Jung, R. and Lim, T., ROS robot programming, Seoul: Robotis, 2017.
- [20]. Hörner, J., Map-merging for multi-robot system, Barchelor Thesis, Computer Science, Charles University, 2016.
- [21]. Ben-Kiki, O., Evans, C. and Net, I. d., YAML Ain’t Markup Language (YAML™) Version 1.2, https://yaml.org/about.html, (accessed 25.11., 2020).
- [22]. ROS Wiki, Map_server Package, http://wiki.ros.org/map_server, (accessed 26.05., 2021).
- [23]. ROS Wiki, Navigation Stack, http://wiki.ros.org/navigation/Tutorials/RobotSetup, (accessed 26.05., 2021).
- [24]. Gerkey, B., slam_gmapping, http://docs.ros.org/en/hydro/api/gmapping/html/index.html (accessed 26.05., 2021).
- [25]. Popovic, G., Orsulic, J., Miklic, D. and Bogdan, S., Rao-Blackwellized particle filter SLAM with prior map: an experimental evaluation, ROBOT 2017: Third Iberian Robotics Conference, Seville, Spain, (2018).
| APA | Aydemir H, Tekerek M, GÖK M (2021). Examining of the Effect of Geometric Objects on SLAM Performance Using ROS and Gazebo. , 1441 - 1454. 10.31202/ecjse.943364 |
| Chicago | Aydemir Hamza,Tekerek Mehmet,GÖK Mehmet Examining of the Effect of Geometric Objects on SLAM Performance Using ROS and Gazebo. (2021): 1441 - 1454. 10.31202/ecjse.943364 |
| MLA | Aydemir Hamza,Tekerek Mehmet,GÖK Mehmet Examining of the Effect of Geometric Objects on SLAM Performance Using ROS and Gazebo. , 2021, ss.1441 - 1454. 10.31202/ecjse.943364 |
| AMA | Aydemir H,Tekerek M,GÖK M Examining of the Effect of Geometric Objects on SLAM Performance Using ROS and Gazebo. . 2021; 1441 - 1454. 10.31202/ecjse.943364 |
| Vancouver | Aydemir H,Tekerek M,GÖK M Examining of the Effect of Geometric Objects on SLAM Performance Using ROS and Gazebo. . 2021; 1441 - 1454. 10.31202/ecjse.943364 |
| IEEE | Aydemir H,Tekerek M,GÖK M "Examining of the Effect of Geometric Objects on SLAM Performance Using ROS and Gazebo." , ss.1441 - 1454, 2021. 10.31202/ecjse.943364 |
| ISNAD | Aydemir, Hamza vd. "Examining of the Effect of Geometric Objects on SLAM Performance Using ROS and Gazebo". (2021), 1441-1454. https://doi.org/10.31202/ecjse.943364 |
| APA | Aydemir H, Tekerek M, GÖK M (2021). Examining of the Effect of Geometric Objects on SLAM Performance Using ROS and Gazebo. El-Cezerî Journal of Science and Engineering, 8(3), 1441 - 1454. 10.31202/ecjse.943364 |
| Chicago | Aydemir Hamza,Tekerek Mehmet,GÖK Mehmet Examining of the Effect of Geometric Objects on SLAM Performance Using ROS and Gazebo. El-Cezerî Journal of Science and Engineering 8, no.3 (2021): 1441 - 1454. 10.31202/ecjse.943364 |
| MLA | Aydemir Hamza,Tekerek Mehmet,GÖK Mehmet Examining of the Effect of Geometric Objects on SLAM Performance Using ROS and Gazebo. El-Cezerî Journal of Science and Engineering, vol.8, no.3, 2021, ss.1441 - 1454. 10.31202/ecjse.943364 |
| AMA | Aydemir H,Tekerek M,GÖK M Examining of the Effect of Geometric Objects on SLAM Performance Using ROS and Gazebo. El-Cezerî Journal of Science and Engineering. 2021; 8(3): 1441 - 1454. 10.31202/ecjse.943364 |
| Vancouver | Aydemir H,Tekerek M,GÖK M Examining of the Effect of Geometric Objects on SLAM Performance Using ROS and Gazebo. El-Cezerî Journal of Science and Engineering. 2021; 8(3): 1441 - 1454. 10.31202/ecjse.943364 |
| IEEE | Aydemir H,Tekerek M,GÖK M "Examining of the Effect of Geometric Objects on SLAM Performance Using ROS and Gazebo." El-Cezerî Journal of Science and Engineering, 8, ss.1441 - 1454, 2021. 10.31202/ecjse.943364 |
| ISNAD | Aydemir, Hamza vd. "Examining of the Effect of Geometric Objects on SLAM Performance Using ROS and Gazebo". El-Cezerî Journal of Science and Engineering 8/3 (2021), 1441-1454. https://doi.org/10.31202/ecjse.943364 |
