Mapping And Localization System Pada Mobile Robot Menggunakan Metode SLAM Berbasis LiDAR
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Jun 16, 2021
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Abstract
Permasalahan utama dari autonomous robot atau robot otonom untuk bernavigasi adalah bagaimana robot dapat mengenali lingkungan sekitar. Oleh karena itu, penelitian ini berfokus pada perancangan sistem mapping dan lokalisasi menggunakan metode Simultaneous Localization And Mapping (SLAM) yang diimplementasikan pada mobile robot jenis omnidirectional atau holonomic menggunakan sensor LiDAR. Penelitian ini mengusulkan sistem mapping dan lokalisasi untuk mengenali lingkungan sekitar dengan membuat peta lingkungan menggunakan algoritma google cartographer yang dikombinasikan dengan metode eulerdometry yaitu kombinasi antara odometry dengan data euler orientation dari sensor IMU. Pengujian dilakukan dengan menguji setiap sensor seperti IMU dan LiDAR, dan menguji instegrasi sistem termasuk metode eulerdometry dan sistem mapping dan lokalisasi dengan algoritma google cartographer yang dikombinasikan dengan metode eulerdometry. Hasil pengujian dari sistem mapping dan lokalisasi menunjukkan hasil yang optimal dan mampu mengenali kondisi lingkungan sekitar robot meskipun masih terdapat noise pada peta yang sudah dibuat.
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DOI: 10.1109/ICARCV.2018.8581131
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https://arxiv.org/pdf/1909.05214
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DOI: 10.5194/isprs-archives-XLII-2-W3-543-2017
http://uilis.unsyiah.ac.id/oer/files/original/f8bdc5ed4ad9d6c70b7923b1a7e19a7e.pdf
Borenstein, J., H. R. Everett, L. Feng, and D. Wehe. 1997. Mobile Robot Positioning - Sensors and Techniques. Invited Paper for the Journal of Robotic Systems, Special Issue on Mobile Robots, Vol.14, No.4, pp. 231-249.
DOI:10.1002/(SICI)1097-4563(199704)14:4%3C231::AID-ROB2%3E3.0.CO;2-R
Wang, X., H. Pan, K. Guo, X. Yang, and S. Luo. 2020. The evolution of LiDAR and its application in high precision measurement. IOP Conference Series: Earth and Environmental Science 502 012008.
DOI: 10.1088/1755-1315/502/1/012008
Li, Y. and J. Ibanez-Guzman. 2020. Lidar for Autonomous Driving. IEEE Signal Processing Magazine Vol.37, Issue 4, pp. 50-61.
DOI: 10.1109/MSP.2020.2973615
Yang, J., Y. Li, L. Cao, Y. Jiang, L. Sun, and Q. Xie. 2019. A Survey of SLAM Research based on LiDAR Sensors. International Journal of Sensors 2019; 1(1): 1003.
http://www.remedypublications.com/open-access/a-survey-of-slam-research-based-on-lidar-sensors-4870.pdf
Bosch Sensortec. 2016. BNO055, Intelligent 9-Axis Absolute Orientation Sensor. Datasheet BST-BNO055-DS000-14.
https://www.mouser.com/pdfdocs/BST_BNO055_DS000_14.pdf
Aji, W. S. 2019. Kendali Berputar pada Robot Kontes Robot Abu Indonesia dengan kendali PID dan IMUBNO055. Buletin Ilmiah Sarjana, Teknik Elektro Vol.2, No.1, April 2020, pp.14~23.
http://journal2.uad.ac.id/index.php/biste/article/view/987/pdf
Langley, R. 2016. Development of a Self-Balancing Robot utilizing FPGA. Engineering Honors Thesis, School of Engineering and Information Technology, Murdoch University, Perth, Western Australia.
http://researchrepository.murdoch.edu.au/id/eprint/38687
Taufiqurrohman, M. and N. F. Sari. 2018. Odometry Method and Rotary Encoder for Wheeled Soccer Robot. IOP Conference Series: Materials Science and Engineering 407 (2018) 012103.
DOI: 10.1088/1757-899X/407/1/012103
Phunopas, A. and S. Inoue. 2018. Motion Improvement of Four-Wheeled Omnidirectional Mobile Robots for Indor Terrain. Journal of Robotics, Networking and Artificial Life, Vol.4, No.4, March 2018 275-282.
DOI:10.2991/jrnal.2018.4.4.4
Sofwan, A., H. R. Mulyana, H. Afrisal, and A. Goni. 2019. Development of Omni-Wheeled Mobile Robot Based-on Inverse Kinematics and Odometry. 2019 6th International Conference on Information Technology, Computer and Electrical Engineering (ICITACEE), 26-27 Sept. 2019, Semarang, Indonesia.
DOI: 10.1109/ICITACEE.2019.8904418
Rijalusalam, D. U. and I. Iswanto. 2021. Implementation Kinematics Modeling and Odometry of Four Wheel Mobile Robot on The Trajectory Planning and Motion Control Based Microcontroller. Journal of Robotics and Control (JRC), Vol.2, Issue 5, Sept. 2021.
DOI: https://doi.org/10.18196/jrc.25121
Fahmizal, D. U. Rijalussalam, M. Budiyanto, and A. Mayub. 2019. Trajectory Tracking pada Robot Omni dengan Metode Odometry. Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI), Vol.8, No.1, Feb. 2019.
DOI: http://dx.doi.org/10.22146/jnteti.v8i1.488
Khairudding, A. R., M. S. Talib, and H. Haron. 2015. Reviow on Simultaneous Localization and Mapping (SLAM). 2015 IEEE International Conference on Control System, Computing and Engineering, 27-29 Nov. 2015, Penang, Malaysia.
DOI: 10.1109/ICCSCE.2015.7482163
Aerts, P. and E. Demeester. 2017. Benchmarking of 2D-Slam Algorithms. ACRO Research Group, KU Leuven, Departement of Mechanical Engineering, Campus Diepenbeek.
http://www.acro.be/downloadvrij/Benchmark_2D_SLAM.pdf
Filipenko, M. and I. Afanasyev. 2018. Comparison of Various SLAM Systems for Mobile Robot in an Indoor Environment. 9th IEEE International Conference on Intelligent Systems 2018. 25-27 Sept. 2018, Medeira, Portugal.
DOI: 10.1109/IS.2018.8710464
Vanelli, B. 2019. Comparison and Benchmarking for SLAM in Mobile Robots. Automation and Control Engineering of the Universidade Federal de Santa Catarina.
https://repositorio.ufsc.br/handle/123456789/196836
Yagfarov, R., M. Ivanou, and I. Afanasyev. 2018. Map Comparison of Lidar-based 2D SLAM Algorithms Using Precise Ground Truth. 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) 18-21 Nov. 2018, Singapore.
DOI: 10.1109/ICARCV.2018.8581131
Huang, B., J. Zhao, and J. Liu. 2020. A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Network. arXiv:1909.05214.
https://arxiv.org/pdf/1909.05214
Hess, W., D. Kohler, H. Rapp, and D. Andor. 2016. Real-Time Loop Closure in 2D LIDAR SLAM. 2016 IEEE International Conference on Robotics and Automation (ICRA) 16-21 May 2016 Stockholm, Sweden.
DOI: 10.1109/ICRA.2016.7487258
Zhi, C. and S. Xiumin. 2019. Research on Cartographer Algorithm based on Low Cost Lidar. International Journal of engineering Research and Technology (IJERT), Vol.8 Issue 10, Oct. 2019.
DOI: 10.17577/IJERTV8IS100060
Nuchter, A., M. Bleier, J. Schauer, and P. Janotta. 2017. Improving Google's Cartographer 3D Mapping By Continuous-Time SLAM. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W3, 2017.
DOI: 10.5194/isprs-archives-XLII-2-W3-543-2017