Mapping and comparison the quality of 2D and 3D map from mobile robot
Abstract: 318
| 
PDF: 292 
##plugins.themes.academic_pro.article.main##
Author
Keywords:
Slam robot
depth camera RGBD
lidar sensor
2D and 3D map
ROS2
Abstract
The study introduces a method for mapping and compares the quality of 2D and 3D maps created by mobile robots indoor environment. A mobile robot integrating an embedded computer, lidar sensors, and an RGBD camera was developed in this study. The SLAM process is conducted through ROS2 which helps the controller observe the robot's working status. The experiment was conducted on two separate maps with different properties including hidden corners and open spaces. The results showed that, there were differences in both experiments. The result shows that creating maps using lidar is highly accurate with an accuracy rate of 97%. In the case of 3D maps, the study shows that, there is a large difference when the robot moves in a straight line and a circular motion with an accuracy rate of 89% and 64%, respectively. The results of this study support more effective use of sensors for future research.
References
-
[1] Z. Ibragimov and I. M. Afanasyev, "Comparison of ROS-based visual SLAM methods in homogeneous indoor environment”, 14th Workshop on Positioning, Navigation and Communications (WPNC), 2017, pp. 1-6.
[2] Abdelrasoul, A. B. S. H. Saman, and P. Sebastian, "A quantitative study of tuning ROS gmapping parameters and their effect on performing indoor 2D SLAM”, 2nd IEEE International Symposium on Robotics and Manufacturing Automation (ROMA), 2016, pp. 1-6.
[3] Park and G. Lee, "Mapping and localization of cooperative robots by ROS and SLAM in unknown working area”, 56th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), 2017, pp. 858-861.
[4] Rojas-Fernández, D. Mújica-Vargas, M. Matuz-Cruz, and D. López-Borreguero, "Performance comparison of 2D SLAM techniques available in ROS using a differential drive robot”, International Conference on Electronics, Communications and Computers (CONIELECOMP), 2018, pp. 50-58.
[5] V. Nguyen, C. T. Pham, and T. M. Pham, “Modelling and Controlling a Non-holonomic Wheeled Mobile Robot with Lateral Slip”, Viet Nam science journal, Vol 4, no 1, pp. 1-6, 2015.
[6] C. Tran and T. T. V. Nguyen, “Control stability multi-directional mobile robot” (in Vietnamese), The 22st National Conference on Electronics, Communications and Information Technology (REV-ECIT 2019), Hanoi, 2019, pp. 144-149.
[7] Macenski and I. Jambrecic, “SLAM toolbox: SLAM for the dynamic world”, The Journal of open source software, Vol 6, no 61, 2021.
[8] Labbé and F. Michaud, “Multi-Session Visual SLAM for Illumination-Invariant Re-Localization in Indoor Environments”, in Frontiers in Robotics and AI, vol. 9, 2022.
[9] H. Luu, C. T. Vo, N. M. N Trinh, and K. N. Nguyen, “Design ros-based slam mobile robot four wheels drive”, Thai Nguyen jounral of science and technology, vol. 227, no. 011, pp. 42-49, 2022.
[10] M. N, Do. N. T, Vu. V. Q, and Ngo. N. V, “A method of obstacle avoidance for amr robot in warehouse automation”, Thai Nguyen jounral of science and technology, vol. 228, no. 02, pp. 62-69, 2022.
[11] L. H. Pham and T. D. Nguyen, “Constructing local orbit for self-operating robot in agricultural green house based on ros”, Vietnam journal of agricultural science, vol. 21, no. 10, pp. 1282-1293, 2023.
[12] M. Thi and T. H. Nguyen, “The development of mapping, covering and control strategies for a vaccum cleaner robot”, JST: smart systems and devices, vol. 31, no. 1, pp. 059-067, 2021.
[13] Wang, B. Tao, Z. Gong, W. Yu, and Z. Yin, "A Mobile Robotic 3-D Measurement Method Based on Point Clouds Alignment for Large-Scale Complex Surfaces”, in IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1-11, 2021.
[14] Jiang, S. Wang, S. Wang, Z. Yi, M. Zhang, and Xi. Lv, “Autonomous navigation system of greenhouse mobile robot based on 3D lidar and 2D lidar Slam”. Journal of Front. Plant Sci., Vol 13, 2022. https://doi.org/10.3389/fpls.2022.815218
[15] Labbé and F. Michaud, “RTAB-Map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation”. J Field Robotics. Vol. 36, no. 2, pp. 416-446, 2019. https://doi.org/10.1002/rob.21831
Read more
##plugins.themes.academic_pro.article.sidebar##
Published
Oct 31, 2024
Download
How to Cite
Lưu Trọng Hiếu, and Nguyễn Hữu Cường. “Mapping and Comparison the Quality of 2D and 3D Map from Mobile Robot”. The University of Danang - Journal of Science and Technology, vol. 22, no. 10, Oct. 2024, pp. 13-17, https://jst-ud.vn/jst-ud/article/view/9295.
