ROS-based 2-D Mapping Using Non-holonomic Differential Mobile Robot
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Abstract
This research proposes a 2-D mapping method by a mobile robot using LIDAR sensor. The mobile robot used is a non-holonomic type with a differential driver designed to operate in an indoor area. The robot applies an occupancy grid map method that uses a probability rule to handle the uncertainties of the sensor. The quality of 2-D occupied map relies on the accuracy of distance measurements by the LIDAR sensor and the accuracy of position estimation. Position estimation is obtained by using the 2-D LIDAR odometry which is based on the laser scan matching technique. This research uses simulation model which has characteristics like real nature. All the robotic software operations are managed by the Robot Operating System (ROS) as one of the most popular software frameworks currently used by robot researchers. The experimental results show that the robot can arrange a 2-D map well which is indicated by the similarity between the reference ground truth and the resulting 2-D map.
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[1]
M. H. Achmad, S. Nuryadi, W. Fadlun, and M. R. Daud, “ROS-based 2-D Mapping Using Non-holonomic Differential Mobile Robot”, INFOTEL, vol. 10, no. 2, pp. 75-82, Jul. 2018.
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References
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[2] Besl, P., & McKay, N. (1992). A Method for Registration of 3-D Shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(2), 239–256. http://doi.org/10.1109/34.121791
[3] Borenstein, J., Everett, H. R., & Feng, L. (1996). Where am I? Sensors and Methods for Mobile Robot Positioning. (J. Borenstein, Ed.)University of Michigan (Vol. 119). The University of Michigan. http://doi.org/10.1017/CBO9781107415324.004
[4] Cummins, M., & Newman, P. (2008). FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance. In The International Journal of Robotics Research (Vol. 27, pp. 647–665). http://doi.org/10.1177/0278364908090961
[5] Grisetti, G., Stachniss, C., & Burgard, W. (2005). Improving Grid-based SLAM with Rao-Blackwellized Particle Filters by Adaptive Proposals and Selective Resampling. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 2432–2437). http://doi.org/10.1109/ROBOT.2005.1570477
[6] Jaulin, L. (2011). Range-only SLAM with occupancy maps: A set-membership approach. IEEE Transactions on Robotics, 27(5), 1004–1010. http://doi.org/10.1109/TRO.2011.2147110
[7] Kohlbrecher, S., Meyer, J., Graber, T., Petersen, K., Klingauf, U., & Von Stryk, O. (2014). Hector open source modules for autonomous mapping and navigation with rescue robots. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8371 LNAI, 624–631. http://doi.org/10.1007/978-3-662-44468-9_58
[8] Kohlbrecher, S., Von Stryk, O., Meyer, J., & Klingauf, U. (2011). A flexible and scalable SLAM system with full 3D motion estimation. In 9th IEEE International Symposium on Safety, Security, and Rescue Robotics, SSRR 2011 (pp. 155–160). http://doi.org/10.1109/SSRR.2011.6106777
[9] Marden, S., & Guivant, J. (2012). Improving the Performance of ICP for Real-Time Applications using an Approximate Nearest Neighbour Search. In Proceedings of Australasian Conference on Robotics and Automation (pp. 1–6).
[10] Milstein, A. (2008). Occupancy Grid Maps for Localization and Mapping. In Motion Planning (pp. 381–408).
[11] Moravec, H. P., & Elfes, A. (1985). High Resolution Maps from Wide Angle Sonar. In IEEE International Conference on Robotics and Automation (Vol. 2, pp. 116–121). http://doi.org/10.1109/ROBOT.1985.1087316
[12] Parsons, S. (2006). Probabilistic Robotics by Sebastian Thrun, Wolfram Burgard and Dieter Fox, MIT Press, 647 pp., $55.00, ISBN 0-262-20162-3. The Knowledge Engineering Review, 21(3), 287. http://doi.org/10.1017/S0269888906210993
[13] Quigley, M., Conley, K., Gerkey, B., FAust, J., Foote, T., Leibs, J., … Mg, A. (2009). ROS: an open-source Robot Operating System. In IEEE International Conference on Robotics and Automation. http://doi.org/http://www.willowgarage.com/papers/ros-open-source-robot-operating-system
[14] Riisgaard, S., & Blas, M. R. (2004). SLAM for Dummies. A Tutorial Approach to Simultaneous Localization and Mapping (Vol. 22). http://doi.org/10.1017/S0025315400002526
[15] Siciliano, B., Khatib, O., & Groen, F. (2004). Mobile Robots in Rough Terrain. Springer Tracts in Advanced Robotics (Vol. 12).
[16] Thrun, S. B. (1993). Exploration and Model Building in Mobile Robot Domains. In Proceedings of the IEEE InternationalConference on NeuralNetworks (pp. 1–12).
[17] Thrun, S. B. (2001). Learning Occupancy Grids with Forward Models. In Proceedings - IEEE/RSJ International Conference on Intelligent Robots and Systems, 2001. (Vol. 3, pp. 1676–1681). http://doi.org/10.1109/IROS.2001.977219
[18] Welch, G., & Bishop, G. (2001). An Introduction to the Kalman Filter. SIGGRAPH 2001, 7(1), 1–16. http://doi.org/10.1.1.117.6808