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DCEGen: Dense Clutter Environment Generation Tool for Autonomous 3D Exploration and Coverage Algorithms Testing

  • Evgeni Denisov
  • Artur Sagitov
  • Roman LavrenovEmail author
  • Kuo-Lan Su
  • Mikhail Svinin
  • Evgeni Magid
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11659)

Abstract

Autonomous exploration and coverage in 3D environments recently has became a rapidly developing research field. Emerging 3D reconstruction methods, designed specifically for exploration and coverage, allows capturing an environment in a greater details. However, not much work addresses certain difficulties inherent to dense clutter environments. We observed those difficulties and made an attempt that seeks to expand the applicability of such methods to more demanding scenarios. Automating the process of testing and evaluation by designing a dense clutter environment generation algorithm (DCEGen) allows us to measure comparative performance of available algorithms. We focus on path-planning algorithms used in an unmanned ground vehicles. The algorithm was implemented and verified using Gazebo simulator.

Keywords

Mobile robot Gazebo simulation ROS Dense clutter environment 3D environment reconstruction Autonomous exploration and coverage algorithm Next-best-view 

Notes

Acknowledgments

This work was supported by the Russian Foundation for Basic Research (RFBR), project ID 19-58-70002.

References

  1. 1.
    Blender: Free and open source 3d creation. https://www.blender.org/
  2. 2.
    Adán, A., Quintana, B., Vázquez, A.S., Olivares, A., Parra, E., Prieto, S.: Towards the automatic scanning of indoors with robots. Sensors (Basel) 15(5), 11551–11574 (2015)CrossRefGoogle Scholar
  3. 3.
    Afanasyev, I., Sagitov, A., Magid, E.: ROS-based SLAM for a Gazebo-simulated mobile robot in image-based 3D model of indoor environment. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2015. LNCS, vol. 9386, pp. 273–283. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-25903-1_24CrossRefGoogle Scholar
  4. 4.
    Andreychuk, A., Yakovlev, K.: Path finding for the coalition of co-operative agents acting in the environment with destructible obstacles. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds.) ICR 2018. LNCS (LNAI), vol. 11097, pp. 13–22. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-99582-3_2CrossRefGoogle Scholar
  5. 5.
    Bircher, A., Kamel, M., Alexis, K., Oleynikova, H., Siegwart, R.: Receding horizon “next-best-view” planner for 3D exploration. In: IEEE International Conference on Robotics and Automation (ICRA) (2016)Google Scholar
  6. 6.
    Bircher, A., Kamel, M., Alexis, K., Oleynikova, H., Siegwart, R.: Receding horizon path planning for 3D exploration and surface inspection. Auton. Robot. 42(2), 291–306 (2018)CrossRefGoogle Scholar
  7. 7.
    Dang, T., Parachristos, C., Alexis, K.: Visual saliency-aware receding horizon autonomous exploration with application to aerial robotics. In: IEEE International Conference on Robotics and Automation (ICRA) (2018)Google Scholar
  8. 8.
    Engel, J., Schöps, T., Cremers, D.: LSD-SLAM: large-scale direct monocular SLAM. In: IEEE European Conference on Computer Vision (ECCV), pp. 834–849 (2014)CrossRefGoogle Scholar
  9. 9.
    Galceran, E., Carreras, M.: A survey on coverage path planning for robotics. Robot. Auton. Syst. 61(12), 1258–1276 (2013)CrossRefGoogle Scholar
  10. 10.
    González-Banos, H.H., Latombe, J.C.: Navigation strategies for exploring indoor environments. Int. J. Robot. Res. 21(10–11), 829–848 (2002)CrossRefGoogle Scholar
  11. 11.
    Heng, L., Gotovos, A., Krause, A., Pollefeys, M.: Efficient visual exploration and coverage with a micro aerial vehicle in unknown environments. In: IEEE International Conference on Robotics and Automation (ICRA) (2015)Google Scholar
  12. 12.
    Hornung, A.: Octomap\(\_\)mapping ros package. wiki.ros.org/octomap_mapping/
  13. 13.
    LaValle, S.M.: Rapidly-exploring random trees a new tool for path planning. Technical report (1998)Google Scholar
  14. 14.
    Lavrenov, R., Matsuno, F., Magid, E.: Modified spline-based navigation: guaranteed safety for obstacle avoidance. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds.) ICR 2017. LNCS (LNAI), vol. 10459, pp. 123–133. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66471-2_14CrossRefGoogle Scholar
  15. 15.
    Lavrenov, R., Zakiev, A.: Tool for 3D Gazebo map construction from arbitrary images and laser scans. In: 2017 10th International Conference on Developments in eSystems Engineering (DeSE), pp. 256–261. IEEE (2017)Google Scholar
  16. 16.
    Magid, E., Tsubouchi, T., Koyanagi, E., Yoshida, T.: Static balance for rescue robot navigation: losing balance on purpose within random step environment. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 349–356. IEEE (2010)Google Scholar
  17. 17.
    Mendez, O., Hadfield, S., Pugeault, N., Bowden, R.: Taking the scenic route to 3D: optimising reconstruction from moving cameras. In: IEEE International Conference on Computer Vision (ICCV) (2017)Google Scholar
  18. 18.
    Meng, Z., Qin, H., Chen, Z., Chen, X., Sun, H., Lin, F., Ang Jr., M.H.: A 2-stage optimized next view planning framework for 3-d unknown environment exploration and structural reconstruction. IEEE Robot. Autom. Lett. 2(3), p1680–1687 (2017)CrossRefGoogle Scholar
  19. 19.
    Panov, A.I., Yakovlev, K.: Behavior and path planning for the coalition of cognitive robots in smart relocation tasks. In: Kim, J.-H., Karray, F., Jo, J., Sincak, P., Myung, H. (eds.) Robot Intelligence Technology and Applications 4. AISC, vol. 447, pp. 3–20. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-31293-4_1CrossRefGoogle Scholar
  20. 20.
    Rodríguez-Teiles, F.G., Pérez-Alcocer, R., Maldonado-Ramírez, A., Torres-Méndez, L.A., Dey, B.B., Martínez-García, E.A.: Vision-based reactive autonomous navigation with obstacle avoidance: towards a non-invasive and cautious exploration of marine habitat. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 3813–3818. IEEE (2014)Google Scholar
  21. 21.
    Ronzhin, A., Saveliev, A., Basov, O., Solyonyj, S.: Conceptual model of cyberphysical environment based on collaborative work of distributed means and mobile robots. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds.) ICR 2016. LNCS (LNAI), vol. 9812, pp. 32–39. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-43955-6_5CrossRefGoogle Scholar
  22. 22.
    Senarathne, P.G.C.N., Wang, D.: Towards autonomous 3D exploration using surface frontiers. In: IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) (2016)Google Scholar
  23. 23.
    Shabat, Y.B., Fischer, A.: Design of adaptive porous micro-structures using curvature analysis for additive manufacturing. In: the 25th CIRP Design conference, Haifa, Israel (2015)Google Scholar
  24. 24.
    Shimchik, I., Sagitov, A., Afanasyev, I., Matsuno, F., Magid, E.: Golf cart prototype development and navigation simulation using ROS and Gazebo. In: MATEC Web of Conferences, vol. 75. EDP Sciences (2016)Google Scholar
  25. 25.
    Weise, T., Leibe, B., Van Gool, L.: Fast 3D scanning with automatic motion compensation. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2007)Google Scholar
  26. 26.
    Yakovlev, K., Khithov, V., Loginov, M., Petrov, A.: Distributed control and navigation system for quadrotor UAVs in GPS-denied environments. In: Filev, D., Jabłkowski, J., Kacprzyk, J., Krawczak, M., Popchev, I., Rutkowski, L., Sgurev, V., Sotirova, E., Szynkarczyk, P., Zadrozny, S. (eds.) Intelligent Systems 2014. AISC, vol. 323, pp. 49–56. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-11310-4_5CrossRefGoogle Scholar
  27. 27.
    Yang, M.D., Chao, C.F., Huang, K.S., Lu, L.Y., Chen, Y.P.: Image-based 3D scene reconstruction and exploration in augmented reality. Autom. Constr. 33, 48–60 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Higher Institute for Information Technology and Intelligent Systems (ITIS)Kazan Federal UniversityKazanRussian Federation
  2. 2.Department of Electrical EngineeringNational Yunlin University of Science and TechnologyTainanTaiwan
  3. 3.Robot Dynamics and Control Laboratory, College of Information Science and EngineeringRitsumeikan UniversityKusatsuJapan

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