Skip to main content

Towards Semi-autonomous Robotic Inspection and Mapping in Confined Spaces with the EspeleoRobô

Abstract

Autonomous mobile devices operating in confined environments, such as pipes, underground tunnel systems, and cave networks, face multiple open challenges from the robotics perspective. Those challenges, such as mobility, localization, and mapping in GPS denied scenarios, are receiving particular attention from the academy and industry. One example is the Brazilian mining company Vale S.A., which is employing a robot – EspeleoRobô (SpeleoRobot) – to access restricted and dangerous areas for human workers. The EspeleoRobô is a robot initially designed for natural cave inspection during teleoperated missions. It is now being used to monitor other types of confined environments, such as dam galleries and other restrained or dangerous areas. This paper describes the platform in its current version and the pipeline used for semi-autonomous inspection in confined environments. The pipeline includes photorealistic mapping techniques, Simultaneous Localization and Mapping (SLAM) with LiDAR, path planning based on mobility optimization, and navigation control using vector fields to reduce operator dependency of the robot operation. The proposed concept was validated in simulations with a realistic underground tunnel system and in representative real-world scenarios. The results endorse the viability of using the proposed concept for real deployments.

This is a preview of subscription content, access via your institution.

Availability of Data and Material

Algorithms are already published on GitHub. Data-sets can be made available upon acceptance and publication.

References

  1. Booz, A.: Unearthing the subterranean environment. https://www.subtchallenge.com/ (2018)

  2. Murphy, R.R.: Disaster robotics (2014)

  3. Azpurua, H., Rocha, F., Garcia, G., Santos, A.S., Cota, E., Barros, L.G., Thiago, A.S., Gustavo P., Gustavo M.F.: EspeleoRobô - a robotic device to inspect confined environments. In: 2019 19th Int. Conf. on Advanced Robotics (ICAR), IEEE (2019)

  4. Saranli, U., Buehler, M., Koditschek, D.E.: Rhex: A simple and highly mobile hexapod robot. Int. J. Robot. Res. 20(7), 616–631 (2001)

    Article  Google Scholar 

  5. Freitas, G.M., Rocha, F.A.S., Torre, M.P., Frederico, F.J.A., Ramos, V.R., Nogueira, L.E.C., Santos, A.S., Cota, E., Miola, W., Reis, M.A.D., Costa, B.L.S., Ledezma, L.C.M., Evangelista, R.P., Alcantara, P.X., Lima, R.T., De Souza, T.P., Brandi, I.V., Araujo, R.N., Gomes, M.F.M., Garcia, G.C.: Multi-terrain inspection robotic device and methods for configuring and guiding the same - pct/br2018/050025 (2018)

  6. Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., Ng, A.Y.: Ros: an open-source robot operating system. In: ICRA Workshop, vol. 3, pp. 5. Kobe (2009)

  7. RocketM Datasheet: Ubiquiti Networks Inc. https://dl.ubnt.com/datasheets/rocketm/RocketM_DS.pdf (2015)

  8. Antena móvel ASA-900CI: ARS Eletrônica Industrial Ltda. http://www.arseletronica.com.br/fileuploader/download/download/?d=0&file=custom%2Fupload%2FFile-1562681992.pdf (2018)

  9. WiFi for Industrial Internet of Things – Reseiwe: RESEIWE A/S, https://reseiwe.com/wifi-for-industrial-internet-of-things-2/ (2017)

  10. Murphy, R.R., Tadokoro, S., Kleiner, A.: Disaster robotics. In: Springer Handbook of Robotics, pp. 1577–1604. Springer (2016)

  11. Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., Leonard, J.J.: Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age. IEEE Trans. Robot. 32(6), 1309–1332 (2016)

    Article  Google Scholar 

  12. Siciliano, B., Khatib, O.: Springer handbook of robotics. Springer, Berlin (2016)

    Book  Google Scholar 

  13. Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping: part i. IEEE Robot. Automat. Mag. 13(2), 99–110 (2006)

    Article  Google Scholar 

  14. Fuentes-Pacheco, J., Ascencio, J., Rendon-Mancha, J.: Visual simultaneous localization and mapping: A survey. Artif. Intell. Rev. 43, 11 (2015)

    Article  Google Scholar 

  15. Filipenko, M., Afanasyev, I.: Comparison of various slam systems for mobile robot in an indoor environment. In: Int. Conf. on Intelligent Systems (IS), pp. 400–407. IEEE (2018)

  16. Hess, W., Kohler, D., Rapp, H., Andor, D.: Real-time loop closure in 2d lidar slam. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 1271–1278. IEEE (2016)

  17. Droeschel, D., Behnke, S.: Efficient continuous-time slam for 3d lidar-based online mapping. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1–9. IEEE (2018)

  18. Engel, J., Schöps, T., Cremers, D.: Lsd-slam: Large-scale direct monocular slam. In: European Conf. on Computer Vision, pp 834–849, Springer (2014)

  19. Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: Orb-slam: a versatile and accurate monocular slam system. IEEE Trans. Robot. 31(5), 1147–1163 (2015)

    Article  Google Scholar 

  20. Labbé, M., Michaud, F.: Rtab-map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation. J. Field Robot. 36(2), 416–446 (2019)

    Article  Google Scholar 

  21. Labbe, M., Michaud, F.: Appearance-based loop closure detection for online large-scale and long-term operation. IEEE Trans. Robot. 29(3), 734–745 (2013)

    Article  Google Scholar 

  22. Rezende, A.M.C., Júnior, G.P.C., Fernandes, R., Miranda, V.R.F., Azpúrua, H., Pessin, G., Gustavo, M.F.: Indoor localization and navigation control strategies for a mobile robot designed to inspect confined environments. In: 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), pp. 1427–1433. IEEE (2020)

  23. Yagfarov, R., Ivanou, M., Afanasyev, I.: Map comparison of lidar-based 2d slam algorithms using precise ground truth. In: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 1979–1983. IEEE (2018)

  24. Ji, Z., Singh, S.: Low-drift and real-time lidar odometry and mapping. Auton. Robot. 41(2), 401–416 (2017)

    Article  Google Scholar 

  25. Koide, K., Miura, J., Menegatti, E.: A portable three-dimensional lidar-based system for long-term and wide-area people behavior measurement. Int. J. Adv. Robot. Syst. 16(2), 1729881419841532 (2019)

    Article  Google Scholar 

  26. Tixiao Shan, Brendan Englot: Lego-loam: Lightweight and ground-optimized lidar odometry and mapping on variable terrain. In: 2018 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), pp 4758–476., IEEE (2018)

  27. Wolf, P.R., Dewitt, B.A., Wilkinson, B.E.: Elements of photogrammetry with applications in GIS, 4th ed. 696 pp. ISBN 9780071761123 (2014)

  28. Low, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 91–110. https://www.cs.ubc.ca/~lowe/papers/ijcv04.pdf (2004)

  29. Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  30. Wu, C., Agarwal, S., Curless, B., Seitz, S.M.: Multicore bundle adjustment. In: CVPR 2011, pp 3057–306. IEEE (2011)

  31. Agarwal, S., Mierle, K., et al.: Ceres solver. http://ceres-solver.org

  32. Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2, pp 2161–2168 (2006)

  33. Moulon, P., Monasse, P., Marlet, R.: Global fusion of relative motions for robust, accurate and scalable structure from motion. In: 2013 IEEE International Conference on Computer Vision, pp. 3248–3255 (2013)

  34. Hirschmuller, H.: Accurate and efficient stereo processing by semi-global matching and mutual information. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol 2, pp 807–814 (2005)

  35. Jancosek, M., Pajdla, T.: Exploiting visibility information in surface reconstruction to preserve weakly supported surfaces. International Scholarly Research Notices, 2014: 1–20. ISSN 2356-7872 (2014)

  36. Bruno, L., Sylvain, P., Nicolas, R., Maillot, J.: Least squares conformal maps for automatic texture atlas generation. In: Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’02, pp. 362–371 (2002)

  37. AliceVision: Meshroom: A 3D reconstruction software. https://github.com/alicevision/meshroom (2018)

  38. Moulon, P., Monasse, P., Renaud, M.: Adaptive structure from motion with a contrario model estimation. In: Proceedings of the Asian Computer Vision Conference (ACCV 2012), pp 257–270. Springer, Berlin (2012)

  39. Shewchuk, J.R.: Delaunay refinement algorithms for triangular mesh generation. Comput. Geom. 22(1-3), 21–74 (2002)

    MathSciNet  Article  Google Scholar 

  40. Shekhar, R., Fayyad, E., Yagel, R., Cornhill, J.F.: Octree-based decimation of marching cubes surfaces. In: Proceedings of the 7th conference on Visualization’96, pp. 335–ff IEEE Computer Society Press (1996)

  41. Kazhdan, M., Klein, A., Dalal, K., Hoppe, H.: Unconstrained isosurface extraction on arbitrary octrees. Symp. Geom. Process. 7, 256–263 (2007)

    Google Scholar 

  42. Carr, J.C., Beatson, R.K., McCallum, B.C., Fright, W.R., McLennan, T.J., Mitchell, T.J.: Smooth surface reconstruction from noisy range. In: Proceedings of the 1st international conference on Computer graphics and interactive techniques in Australasia and South East Asia, pp 119–ff ACM (2003)

  43. Kazhdan, M., Hoppe, H.: Screened poisson surface reconstruction. ACM Trans. Graph. (TOG) 32(3), 29 (2013)

    Article  Google Scholar 

  44. Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., Stuetzle, W.: Surface reconstruction from unorganized points. 26, ACM (1992)

  45. Fabri, A., Sylvain, P.: CGAL The Computational geometry algorithms library. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems, pp. 538–53. ACM (2009)

  46. Liepa, P.: Filling holes in meshes. In: Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on geometry processing, pp. 200–205. Eurographics Association (2003)

  47. Shepard, D.: A two-dimensional interpolation function for irregularly-spaced data. In: Proceedings of the 1968 23rd ACM national conference, pp. 517–524. ACM (1968)

  48. Phong, B.T.: Illumination for computer generated pictures. Commun. ACM 18(6), 311–317 (1975)

    Article  Google Scholar 

  49. Macenski, S., Martin, F., White, R., Clavero, J.G.: The marathon 2: A navigation system. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2020)

  50. Karaman, S., Frazzoli, E.: Sampling-based algorithms for optimal motion planning. Int. J. Robot. Res. 30(7), 846–894 (2011). https://doi.org/10.1177/0278364911406761

    Article  Google Scholar 

  51. Miranda, V.R.F., Mozelli, L.A., Neto, A.A., Freitas, G.M.: On the robust longitudinal trajectory tracking for load transportation vehicles on uneven terrains. In: 2019 19th International Conference on Advanced Robotics (ICAR), pp 320–325. IEEE (2019)

  52. Sousa, R.L.S., Forte, M.D.N., Nogueira, F.G., Torrico, B.C.: Trajectory tracking control of a nonholonomic mobile robot with differential drive. In: IEEE Biennial Congress of Argentina (ARGENCON), pp. 1–6. IEEE, p 2016 (2016)

  53. Cho, N., Kim, Y., Park, S.: Three-dimensional nonlinear differential geometric path-following guidance law. J. Guid. Control Dyn. 38(12), 2366–2385 (2015). https://doi.org/10.2514/1.G001060

    Article  Google Scholar 

  54. Gonçalves, V.M., Pimenta, L.C.A., Maia, C.A., Dutra, B.C.O., Pereira, G.A.S.: Vector fields for robot navigation along time-varying curves in n-dimensions. IEEE Trans. Robot. 26(4), 647–659 (2010). ISSN 1552-3098

    Article  Google Scholar 

  55. Rezende, A.M.C., Junioŕ, G.P.C., Fernandes, R., Miranda, V.R.F., Azpúrua, H., Pessin, G., Freitas, G.M.: Indoor localization and navigation control strategies for a mobile robot designed to inspect confined environments. In: 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), pp. 1427–1433 (2020)

  56. Santos, A.S., Azpúrua, H.I.P., Pessin, G., Freitas, G.M.: Path planning for mobile robots on rough terrain. In: 2018 Latin American Robotic Symposium, 2018 Brazilian Symposium on Robotics (SBR) and Workshop on Robotics in Education (WRE), pp. 265–270. IEEE, p 2018 (2018)

  57. Siciliano, B., Sciavicco, L., Villani, L., Oriolo, G.: Robotics: modelling, planning and control. Springer Science & Business Media (2010)

  58. Chaimowicz, L., Michael, N., Kumar, V.: Controlling swarms of robots using interpolated implicit functions. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation. pp. 2487–2492 (2005)

  59. Carrillo, H., Dames, P., Kumar, V., Castellanos, J.A.: Autonomous robotic exploration using a utility function based on rényi’s general theory of entropy. Auton. Robot. 42(2), 235–256 (2018)

    Article  Google Scholar 

Download references

Funding

- Instituto Tecnológico Vale (ITV);

- Vale S.A.;

- Conselho Nacional de Desenvolvimento Cientíıfico e Tecnológico (CNPq);

- Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG);

- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).

Author information

Authors and Affiliations

Authors

Contributions

General work conduction: H.A. and G.M.F.; Conceptualization: H.A., G.P. and G.M.F.; Robot platform: L.G.D.B., H.A., J.D., F.R. and G.M.F.; Online SLAM: G.P.C.J., R.F. and G.M.F.; Photogrammetry and mesh reconstruction: G.P., H.A., L.W.R.F and E.R.N; Navigation: H.A., F.L.M.S., G.M.F. and D.G.M.; Control: A.R., V.M., H.A. and G.M.F.; Experimental methodology: H.A., A.R., G.P., G.P.C.J., R.F., V.M., L.W.R.F., J.D., F.R., F.L.M.S. and G.M.F.; Conceived images and graphics: H.A., A.R., G.P., G.P.C.J., R.F., V.M., L.W.R.F., F.L.M.S. and L.G.D.B.; Work supervision: G.P. and G.M.F.; All authors reviewed and wrote the manuscript.

Corresponding author

Correspondence to Héctor Azpúrua.

Ethics declarations

Conflict of Interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The authors would like to thank Ramon Araú jo and the speleology team of Vale S.A. due to the support in developing this project. The authors would also like to thank the colleagues from Mina du Veloso for their help in accessing the area to perform experimental validation. This work was supported by Instituto Tecnológico Vale (ITV), Vale S.A., Universidade Federal de Minas Gerais (UFMG), FAPEMIG, Conselho Nacional de Desenvolvimento Científico e Tecnológico - Brasil (CNPq), and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. Dr. Gustavo Pessin acknowledges MCTIC/CNPq-Brazil processes 429096/2018-6 and 308425/2017-0. Dr. Gustavo M. Freitas also acknowledges MCTIC/CNPq-Brazil processes 443209/2015-4 and 402725/2018-2.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Azpúrua, H., Rezende, A., Potje, G. et al. Towards Semi-autonomous Robotic Inspection and Mapping in Confined Spaces with the EspeleoRobô. J Intell Robot Syst 101, 69 (2021). https://doi.org/10.1007/s10846-021-01321-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10846-021-01321-5

Keywords

  • Subterranean exploration with mobile robots
  • 3D reconstruction and mapping
  • GPS-denied localization
  • path planning in rugged terrains
  • Vector field control