Abstract
Purpose
Agricultural mobile robots using Global Navigation Satellite System (GNSS)–based signal for navigation can be easily occluded and the position and attitude errors calculated by inertial navigation sensor (INS) will accumulate over time, which will seriously affect functions like localization and navigation.
Method
Therefore, based on the above shortcomings, this research uses 2D LIDAR (two-dimensional, Light Detection and Ranging) SLAM (Simultaneous Localization and Mapping) as the scheme for the outdoor positioning of the mobile robot. The Cartographer SLAM algorithm was selected in this study and was operated under the ROS (Robot Operating System) platform.
Results
After a series of comparative experiments, it can be concluded that the positioning accuracy at the normal speed of 1.5 km/h is about 0.2–0.3 m considering the error of human manipulation and the accuracy of attitude around 3–4 degrees.
Conclusion
This 2D LIDAR-based localization framework helps for developing an autonomous navigation system for the static agricultural environment that can be operated under GNSS denied environment.
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References
Backman, J., Oksanen, T., & Visala, A. (2012). Navigation system for agricultural machines: Nonlinear model predictive path tracking. Computers and Electronics in Agriculture, 82, 32–43.
Bechar, A., & Vigneault, C. (2016). Agricultural robots for field operations: Concepts and components. Biosystems Engineering, 149, 94–111.
Borenstein, J., Everett, H. R., Feng, L., & Wehe, D. (1997). Mobile robot positioning: Sensors and techniques. Journal of Robotic Systems, 14(4), 231–249.
Bresson, G., Alsayed, Z., Yu, L., & Glaser, S. (2017). Simultaneous localization and mapping: A survey of current trends in autonomous driving. IEEE Transactions on Intelligent Vehicles, 2(3), 194–220.
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., & Leonard, J. J. (2016). Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age. IEEE Transactions on Robotics, 32(6), 1309–1332.
Chan, S. H., Wu, P. T., & Fu, L. C. (2018, October). Robust 2D indoor localization through laser SLAM and visual SLAM fusion. In 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 1263–1268). Miyazaki, Japan: IEEE.
Chatila, R., & Laumond, J. P. (1985, March). Position referencing and consistent world modeling for mobile robots. In Proceedings. 1985 IEEE International Conference on Robotics and Automation (Vol. 2, pp. 138–145). St. Louis, MO, USA: IEEE.
Cheein, F. A. A., & Carelli, R. (2013). Agricultural robotics: Unmanned robotic service units in agricultural tasks. IEEE Industrial Electronics Magazine, 7(3), 48–58.
Durrant-Whyte, H., & Bailey, T. (2006). Simultaneous localization and mapping: Part I. IEEE Robotics & Automation Magazine, 13(2), 99–110.
Fountas, S., Mylonas, N., Malounas, I., Rodias, E., Hellmann Santos, C., & Pekkeriet, E. (2020). Agricultural Robotics for Field Operations. Sensors, 20(9), 2672.
Goel, P., Roumeliotis, S. I., & Sukhatme, G. S. (1999, October). Robust localization using relative and absolute position estimates. In Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No. 99CH36289) (Vol. 2, pp. 1134–1140). Kyongju, Korea (South): IEEE.
Google-Cartographer: Cartographer tuning methodology. Tuning methodology — Cartographer ROS documentation. Retrieved March 3, 2020, from https://google-cartographer-ros.readthedocs.io/en/latest/tuning.html.
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.
Grisetti, G., Kümmerle, R., Stachniss, C., & Burgard, W. (2010). A tutorial on graph-based SLAM. IEEE Intelligent Transportation Systems Magazine, 2(4), 31–43.
Hachour, O. (2008). Path planning of autonomous mobile robot. International Journal of Systems Applications, Engineering & Development, 2(4), 178–190.
Hess, W., Kohler, D., Rapp, H., & Andor, D. (2016, May). Real-time loop closure in 2D LIDAR SLAM. In 2016 IEEE international conference on robotics and automation (ICRA) (pp. 1271–1278). Stockholm, Sweden: IEEE.
Huang, S., & Dissanayake, G. (1999). Robot localization: An introduction. In Wiley Encyclopedia of Electrical and Electronics Engineering (pp. 1–10).
Kim, J. Y. (2020). Roadmap to high throughput phenotyping for plant breeding. Journal of Biosystems Engineering, 45, 43–55.
Kim, W. S., Lee, W. S., & Kim, Y. J. (2020). A review of the applications of the internet of things (IoT) for agricultural automation. Journal of Biosystems Engineering, 45, 385–400.
Linz, A., Brunner, D., Fehrmann, J., Herlitzius, T., Keicher, R., Ruckelshausen, A., & Schwarz, H. P. (2017). Modelling environment for an electrical driven selective sprayer robot in orchards. Advances in Animal Biosciences, 8(2), 848–853.
Panzieri, S., Pascucci, F., & Ulivi, G. (2002). An outdoor navigation system using GPS and inertial platform. IEEE/ASME Transactions on Mechatronics, 7(2), 134–142.
Post, M. A., Bianco, A., & Yan, X. T. (2017, July). Autonomous navigation with ROS for a mobile robot in agricultural fields. In 14th International Conference on Informatics in Control, Automation and Robotics (ICINCO).
Ros, G., Sappa, A., Ponsa, D., & Lopez, A. M. (2012). Visual slam for driverless cars: A brief survey. Intelligent Vehicles Symposium (IV) Workshops, 2, 1–6.
ROS Kinetic Kame. (2016). Kinetic-ROS wiki. Retrieved March 3,2020 from ROS.org: http://wiki.ros.org/kinetic
Rovira-Más, F., Chatterjee, I., & Sáiz-Rubio, V. (2015). The role of GNSS in the navigation strategies of cost-effective agricultural robots. Computers and Electronics in Agriculture, 112, 172–183.
Shalal, N., Low, T., McCarthy, C., & Hancock, N. (2015). Orchard mapping and mobile robot localisation using on-board camera and laser scanner data fusion–Part B: Mapping and localisation. Computers and Electronics in Agriculture, 119, 267–278.
Smith, R., Self, M., & Cheeseman, P. (1988). Estimating uncertain spatial relationships in robotics. Machine intelligence and pattern recognition (Vol. 5, pp. 435–461). North-Holland.
Sobczak, Ł, Filus, K., Domańska, J., & Domański, A. (2022). Finding the best hardware configuration for 2D SLAM in indoor environments via simulation based on Google Cartographer. Scientific Reports, 12(1), 18815.
Taketomi, T., Uchiyama, H., & Ikeda, S. (2017). Visual SLAM algorithms: A survey from 2010 to 2016. IPSJ Transactions on Computer Vision and Applications, 9(1), 1–11.
Woodall, W. (2018) rviz-ROS wiki. Retrieved March 3,2020 from ROS.org: http://wiki.ros.org/rviz
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). Guangzhou, China: IEEE.
Acknowledgements
The authors would like to thank the Technical University of Dresden for supporting this research.
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Appendix
Appendix
Lua Configuration file of Cartographer in ROS related to the tuning process
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Raikwar, S., Yu, H. & Herlitzius, T. 2D LIDAR SLAM Localization System for a Mobile Robotic Platform in GPS Denied Environment. J. Biosyst. Eng. 48, 123–135 (2023). https://doi.org/10.1007/s42853-023-00176-y
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DOI: https://doi.org/10.1007/s42853-023-00176-y