MOBİL ROBOTLAR İÇİN ROS KULLANILARAK 2B SLAM ALGORİTMALARININ KARŞILAŞTIRILMASI

Year 2023, Volume: 24 Issue: 2, 29 - 38, 28.12.2023

İrem Mertyuz , Oğuz Yakut , Beyda Taşar

https://doi.org/10.59314/tujes.1347214

Abstract

Bu çalışmada iç mekanlarda kullanılmak üzere tasarlanan mobil robotlar için SLAM algoritmalarının uygulamaları gerçekleştirilmiştir. SLAM uygulamaları ROS kullanılarak Turtlebot3 Burger Robot ile yapılmıştır. Robot üzerine monte edilen LİDAR sensör verisi kullanılarak GMapping, Hector SLAM, Frontier SLAM ve Karto Slam olmak üzere dört farklı SLAM algoritması tasarlanan bir parkur içerinde çalıştırılmıştır. Deneysel çalışmalardan elde edilen haritalarla her algoritmanın SSIM değeri hesaplanarak haritaların kalite ve doğruluğu analiz edilmiştir.

Keywords

Gmapping, Hector, Karto, Frontier, SSIM

COMPARISON OF 2D SLAM ALGORITHMS USING ROS FOR MOBILE ROBOTS

Abstract

In this study, the applications of SLAM algorithms for mobile robots designed to be used indoors are carried out. SLAM applications were made with Turtlebot3 Burger Robot using ROS. Using the LIDAR sensor data mounted on the robot, four different SLAM algorithms, namely GMapping, Hector SLAM, Frontier SLAM and Karto Slam, were run in a designed track. The quality and accuracy of the maps were analyzed by calculating the SSIM value of each algorithm with the maps obtained from the experimental studies.

Keywords

Gmapping, Hector, Karto, Frontier, SSIM

References

• Aerts, P., Demester E. (2017). Benchmarking of 2D-Slam Algorithms. A Validation fort he TETRA Project Ad Usum Navigantium.
• Bhargava, M., Mehta, R., Adhikari, C. D., & Sivanathan, K. (2021, July). Towards development of performance metrics for benchmarking SLAM algorithms. In Journal of Physics: Conference Series (Vol. 1964, No. 6, p. 062115). IOP Publishing.
• Dhaoui, R. (2022). Vergleich LIDAR-basierter 2D-SLAM-Algorithmen auf ein TurtleBot3 auf Basis des Robot Operating Systems (ROS).
• Filatov, A., Filatov, A., Krinkin, K., Chen, B., & Molodan, D. (2017, November). 2d slam quality evaluation methods. In 2017 21st Conference of Open Innovations Association (FRUCT) (pp. 120-126). IEEE Mobil Robotlar İçin ROS Kullanılarak 2B SLAM Algoritmalarının Karşılaştırılması . Filipenko, M., & Afanasyev, I. (2018, September). Comparison of various slam systems for mobile robot in an indoor environment. In 2018 International Conference on Intelligent Systems (IS) (pp. 400-407). IEEE.
• Giubilato, R., Chiodini, S., Pertile, M., & Debei, S. (2019). An evaluation of ROS-compatible stereo visual SLAM methods on a nVidia Jetson TX2. Measurement, 140, 161-170.
• Grisetti, G., Stachniss, C., & Burgard, W. (2007). Improved techniques for grid mapping with rao-blackwellized particle filters. IEEE transactions on Robotics, 23(1), 34-46.
• Kiran, B., Karthikeyan, S., Pasha, M. S., Manjunatha, K. N., Kumar, S. M., & Moras, S. V. (2022, December). Design and Development of Autonomous Mobile Robot for Mapping and Navigation System. In 2022 IEEE Pune Section International Conference (PuneCon) (pp. 1-5). IEEE.
• Kohlbrecher, S., Von Stryk, O., Meyer, J., & Klingauf, U. (2011, November). A flexible and scalable SLAM system with full 3D motion estimation. In 2011 IEEE international symposium on safety, security, and rescue robotics (pp. 155-160). IEEE.
• Merzlyakov, A., & Macenski, S. (2021, September). A comparison of modern general-purpose visual SLAM approaches. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 9190-9197). IEEE.
• Nguyen, Q. H., Johnson, P., & Latham, D. (2022). Performance Evaluation of ROS-Based SLAM Algorithms for Handheld Indoor Mapping and Tracking Systems. IEEE Sensors Journal, 23(1), 706-714.
• Rojas-Fernández, M., Mújica-Vargas, D., Matuz-Cruz, M., & López-Borreguero, D. (2018, February). Performance comparison of 2D SLAM techniques available in ROS using a differential drive robot. In 2018 International Conference on Electronics, Communications and Computers (CONIELECOMP) (pp. 50-58). IEEE.
• Sankalprajan, P., Sharma, T., Perur, H. D., & Pagala, P. S. (2020, June). Comparative analysis of ROS based 2D and 3D SLAM algorithms for Autonomous Ground Vehicles. In 2020 International Conference for Emerging Technology (INCET) (pp. 1-6). IEEE.
• Santos, J. M., Portugal, D., & Rocha, R. P. (2013, October). An evaluation of 2D SLAM techniques available in robot operating system. In 2013 IEEE international symposium on safety, security, and rescue robotics (SSRR) (pp. 1-6). IEEE.
• Sara, U., Akter, M., & Uddin, M. S. (2019). Image quality assessment through FSIM, SSIM, MSE and PSNR—a comparative study. Journal of Computer and Communications, 7(3), 8-18.
• Sharafutdinov, D., Griguletskii, M., Kopanev, P., Kurenkov, M., Ferrer, G., Burkov, A., ... & Tsetserukou, D. (2023). Comparison of modern open-source visual SLAM approaches. Journal of Intelligent & Robotic Systems, 107(3),
• Singandhupe, A., & La, H. M. (2019, February). A review of slam techniques and security in autonomous driving. In 2019 third IEEE international conference on robotic computing (IRC) (pp. 602-607). IEEE.
• İ. Mertyüz, O. Yakut, B. Taşar, Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4), 600-612.
• Xuexi, Z., Guokun, L., Genping, F., Dongliang, X., & Shiliu, L. (2019, July). SLAM algorithm analysis of mobile robot based on LIDAR. In 2019 Chinese Control Conference (CCC) (pp. 4739-4745). IEEE.
• Yagfarov, R., Ivanou, M., & Afanasyev, I. (2018, November). Map comparison of LIDAR-based 2d slam algorithms using precise ground truth. In 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) (pp. 1979-1983). IEEE. Zamora, E., & Yu, W. (2013). Recent advances on simultaneous localization and mapping for mobile robots. IETE Technical Review, 30(6), 490-496. Zhao, J., Liu, S., & Li, J. (2022). Research and Implementation of Autonomous Navigation for Mobile Robots Based on SLAM Algorithm under ROS. Sensors, 22(11), 4172.