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Automatic Navigation Map Generation for Mobile Robots in Urban Environments (2403.13431v1)

Published 20 Mar 2024 in cs.RO

Abstract: A fundamental prerequisite for safe and efficient navigation of mobile robots is the availability of reliable navigation maps upon which trajectories can be planned. With the increasing industrial interest in mobile robotics, especially in urban environments, the process of generating navigation maps has become of particular interest, being a labor intensive step of the deployment process. Automating this step is challenging and becomes even more arduous when the perception capabilities are limited by cost considerations. This paper proposes an algorithm to automatically generate navigation maps using a typical navigation-oriented sensor setup: a single top-mounted 3D LiDAR sensor. The proposed method is designed and validated with the urban environment as the main use case: it is shown to be able to produce accurate maps featuring different terrain types, positive obstacles of different heights as well as negative obstacles. The algorithm is applied to data collected in a typical urban environment with a wheeled inverted pendulum robot, showing its robustness against localization, perception and dynamic uncertainties. The generated map is validated against a human-made map.

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References (25)
  1. L. Ranieri, S. Digiesi, B. Silvestri, and M. Roccotelli, “A Review of Last Mile Logistics Innovations in an Externalities Cost Reduction Vision,” Sustainability, vol. 10, no. 3, p. 782, Mar. 2018.
  2. M. Labbé and F. Michaud, “Long-term online multi-session graph-based SPLAM with memory management,” Autonomous Robots, vol. 42, no. 6, pp. 1133–1150, Aug. 2018.
  3. W. Hess, D. Kohler, H. Rapp, and D. Andor, “Real-time loop closure in 2D LIDAR SLAM,” in 2016 IEEE International Conference on Robotics and Automation (ICRA).   Stockholm, Sweden: IEEE, May 2016, pp. 1271–1278.
  4. A. Ivanov and M. Campbell, “Uncertainty Constrained Robotic Exploration: An Integrated Exploration Planner,” IEEE Transactions on Control Systems Technology, vol. 27, no. 1, pp. 146–160, Jan. 2019.
  5. J. Mei, Y. Yu, H. Zhao, and H. Zha, “Scene-Adaptive Off-Road Detection Using a Monocular Camera,” IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 1, pp. 242–253, Jan. 2018.
  6. O. Mayuku, B. W. Surgenor, and J. A. Marshall, “A Self-Supervised Near-to-Far Approach for Terrain-Adaptive Off-Road Autonomous Driving,” in 2021 IEEE International Conference on Robotics and Automation (ICRA).   Xi’an, China: IEEE, May 2021, pp. 14 054–14 060.
  7. À. Santamaria-Navarro, E. H. Teniente, M. Morta, and J. Andrade-Cetto, “Terrain Classification in Complex Three-dimensional Outdoor Environments: Terrain Classification in Complex 3D Outdoor Environments,” Journal of Field Robotics, vol. 32, no. 1, pp. 42–60, Jan. 2015.
  8. M. Bellone, G. Reina, L. Caltagirone, and M. Wahde, “Learning Traversability From Point Clouds in Challenging Scenarios,” IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 1, pp. 296–305, Jan. 2018.
  9. A. Y. Hata and D. F. Wolf, “Outdoor Mapping Using Mobile Robots and Laser Range Finders,” in 2009 Electronics, Robotics and Automotive Mechanics Conference (CERMA).   Cuernavaca, Morelos, Mexico: IEEE, Sep. 2009, pp. 209–214.
  10. ——, “Terrain mapping and classification using Support Vector Machines,” in 2009 6th Latin American Robotics Symposium (LARS 2009).   Valparaiso, Chile: IEEE, Oct. 2009, pp. 1–6.
  11. C. Chen, Y. He, F. Gu, C. Bu, and J. Han, “A Real-time relative probabilistic mapping algorithm for high-speed off-road autonomous driving,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   Hamburg, Germany: IEEE, Sep. 2015, pp. 6252–6258.
  12. D. Ferguson, A. Morris, D. Hähnel, C. Baker, Z. Omohundro, C. Reverte, S. Thayer, C. Whittaker, W. Whittaker, W. Burgard, and S. Thrun, “An Autonomous Robotic System for Mapping Abandoned Mines,” in Advances in Neural Information Processing Systems, S. Thrun, L. Saul, and B. Schölkopf, Eds., vol. 16.   MIT Press, 2004.
  13. M. Montemerlo and S. Thrun, “Large-Scale Robotic 3-D Mapping of Urban Structures,” in Experimental Robotics IX.   Berlin, Heidelberg: Springer Berlin Heidelberg, 2006, vol. 21, pp. 141–150, series Title: Springer Tracts in Advanced Robotics.
  14. Y. Pan, X. Xu, Y. Wang, X. Ding, and R. Xiong, “GPU accelerated real-time traversability mapping,” in 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), Dec. 2019, pp. 734–740.
  15. H. Lee and W. Chung, “A Self-Training Approach-Based Traversability Analysis for Mobile Robots in Urban Environments,” in 2021 IEEE International Conference on Robotics and Automation (ICRA).   Xi’an, China: IEEE, May 2021, pp. 3389–3394.
  16. A. Pfrunder, P. V. K. Borges, A. R. Romero, G. Catt, and A. Elfes, “Real-time autonomous ground vehicle navigation in heterogeneous environments using a 3D LiDAR,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   Vancouver, BC: IEEE, Sep. 2017, pp. 2601–2608.
  17. L. Chen, J. Yang, and H. Kong, “Lidar-histogram for fast road and obstacle detection,” in 2017 IEEE International Conference on Robotics and Automation (ICRA).   Singapore, Singapore: IEEE, May 2017, pp. 1343–1348.
  18. J. Ahtiainen, T. Stoyanov, and J. Saarinen, “Normal Distributions Transform Traversability Maps: LIDAR-Only Approach for Traversability Mapping in Outdoor Environments: Normal Distributions Transform Traversability Maps,” Journal of Field Robotics, vol. 34, no. 3, pp. 600–621, May 2017.
  19. H. Roncancio, M. Becker, A. Broggi, and S. Cattani, “Traversability analysis using terrain mapping and online-trained Terrain type classifier,” in 2014 IEEE Intelligent Vehicles Symposium Proceedings.   MI, USA: IEEE, Jun. 2014, pp. 1239–1244.
  20. F. Parravicini, M. Corno, and S. Savaresi, “Robust State Observers for Two Wheeled Inverted Pendulum under wheel-slip,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC).   Auckland, New Zealand: IEEE, Oct. 2019, pp. 1525–1530.
  21. ——, “Extended target tracking for autonomous street crossing,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 15 440–15 445, 2020.
  22. H. Moravec and A. Elfes, “High resolution maps from wide angle sonar,” in 1985 IEEE International Conference on Robotics and Automation Proceedings, vol. 2, Mar. 1985, pp. 116–121.
  23. A. Elfes, “Using occupancy grids for mobile robot perception and navigation,” Computer, vol. 22, no. 6, pp. 46–57, Jun. 1989, conference Name: Computer.
  24. A. Hornung, K. M. Wurm, M. Bennewitz, C. Stachniss, and W. Burgard, “OctoMap: an efficient probabilistic 3D mapping framework based on octrees,” Autonomous Robots, vol. 34, no. 3, pp. 189–206, Apr. 2013.
  25. R. B. Rusu and S. Cousins, “3D is here: Point Cloud Library (PCL),” in 2011 IEEE International Conference on Robotics and Automation, May 2011, pp. 1–4, iSSN: 1050-4729.

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