Skip to main content
SpringerLink
Log in

Continuous Probabilistic SLAM Solved via Iterated Conditional Modes

  • Research Article
  • Published:
International Journal of Automation and Computing Aims and scope Submit manuscript

Abstract

This article proposes a simultaneous localization and mapping (SLAM) version with continuous probabilistic mapping (CP-SLAM), i.e., an algorithm of simultaneous localization and mapping that avoids the use of grids, and thus, does not require a discretized environment. A Markov random field (MRF) is considered to model this SLAM version with high spatial resolution maps. The mapping methodology is based on a point cloud generated by successive observations of the environment, which is kept bounded and representative by including a novel recursive subsampling method. The CP-SLAM problem is solved via iterated conditional modes (ICM), which is a classic algorithm with theoretical convergence over any MRF. The probabilistic maps are the most appropriate to represent dynamic environments, and can be easily implemented in other versions of the SLAM problem, such as the multi-robot version. Simulations and real experiments show the flexibility and excellent performance of this proposal.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. H. Durrant-Whyte, T. Bailey. Simultaneous localization and mapping: Part I. IEEE Robotics & Automation Magazine, vol. 13, no. 2, pp. 99–110, 2006. DOI: https://doi.org/10.1109/MRA.2006.1638022.

    Google Scholar 

  2. T. S. Ho, Y. C. Fai, E. S. Lee Ming. Simultaneous localization and mapping survey based on filtering techniques. In Proceedings of the 10th Asian Control Conference, IEEE, Kota Kinabalu, Malaysia, pp. 1–6, 2015. DOI: https://doi.org/10.1109/ASCC.2015.7244836.

    Google Scholar 

  3. Y. Yang, F. Qiu, H. Li, L. Zhang, M. L. Wang, M. Y. Fu. Large-scale 3D semantic mapping using stereo vision. International Journal of Automation and Computing, vol. 15, no. 2, pp. 194–206, 2018. DOI: https://doi.org/10.1007/s11633-018-1118-y.

    Google Scholar 

  4. A. Yassin, Y. Nasser, M. Awad, A. Al-Dubai, R. Liu, C. Yuen, R. Raulefs, E. Aboutanios. Recent advances in indoor localization: A survey on theoretical approaches and applications. IEEE Communications Surveys & Tutorials, vol. 19, no. 2, pp. 1327–1346, 2017. DOI: https://doi.org/10.1109/COMST.2016.2632427.

    Google Scholar 

  5. X. Yuan, J. F. Martínez-Ortega, J. A. Sánchez Fernández, M. Eckert. AEKF-SLAM: A new algorithm for robotic underwater navigation. Sensors, vol. 17, no. 5, Article number 1174, 2017. DOI: https://doi.org/10.3390/s17051174.

    Google Scholar 

  6. H. Roh, J. Jeong, A. Kim. Aerial image based heading correction for large scale SLAM in an urban canyon. IEEE Robotics and Automation Letters, vol. 2, no. 4, pp. 2232–2239, 2017. DOI: https://doi.org/10.1109/LRA.2017.2725439.

    Google Scholar 

  7. P. Kim, J. D. Chen, Y. K. Choc. SLAM-driven robotic mapping and registration of 3D point clouds. Automation in Construction, vol. 89, pp. 38–48, 2018. DOI: https://doi.org/10.1016/j.autcon.2018.01.009.

    Google Scholar 

  8. S. Thrun, W. Burgard, D. Fox. Probabilistic Robotics (Intelligent Robotics and Autonomous Agents), Cambridge, USA: The MIT Press, 2005.

    MATH  Google Scholar 

  9. P. Xu, G. Dherbomez, E. Hery, A. Abidli, P. Bonnifait. System architecture of a driverless electric car in the grand cooperative driving challenge. IEEE Intelligent Transportation Systems Magazine, vol. 10, no. 1, pp. 47–59, 2018. DOI: https://doi.org/10.1109/MITS.2017.2776135.

    Google Scholar 

  10. J. C. Trujillo, R. Munguia, E. Guerra, A. Grau. Cooperative monocular-based SLAM for multi-UAV systems in GPS-denied environments. Sensors, vol. 18, no. 5, Article number 1351, 2018. DOI: https://doi.org/10.3390/s18051351.

    Google Scholar 

  11. J. Li, M. Kaess, R. M. Eustice, M. Johnson-Roberson. Pose-graph SLAM using forward-looking sonar. IEEE Robotics and Automation Letters, vol. 3, no. 3, pp. 2330–2337, 2018. DOI: https://doi.org/10.1109/LRA.2018.2809510.

    Google Scholar 

  12. R. Mur-Artal, J. D. Tardós. ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Transactions on Robotics, vol. 33, no. 5, pp. 1255–1262, 2017. DOI: https://doi.org/10.1109/TRO.2017.2705103.

    Google Scholar 

  13. L. T. Hsu, Y. Wada, Y. L. Gu, S. Kamijo. Rectification of 3D building models based on GPS signal collected by vehicle. In Proceedings of IEEE International Conference on Vehicular Electronics and Safety, IEEE, Yokohama, Japan, 2015. DOI: https://doi.org/10.1109/ICVES.2015.7396907.

    Google Scholar 

  14. F. Auat Cheein, G. Steiner, G. P. Paina, R. Carelli. Optimized EIF-SLAM algorithm for precision agriculture mapping based on stems detection. Computers and Electronics in Agriculture, vol. 78, no. 2, pp. 195–207, 2011. DOI: https://doi.org/10.1016/j.compag.2011.07.007.

    Google Scholar 

  15. J. A. Hesch, D. G. Kottas, S. L. Bowman, S. I. Roumeliotis. Consistency analysis and improvement of vision-aided inertial navigation. IEEE Transactions on Robotics, vol. 30, no. 1, pp. 158–176, 2014. DOI: https://doi.org/10.1109/TRO.2013.2277549.

    Google Scholar 

  16. G. P. Huang, A. I. Mourikis, S. I. Roumeliotis. A quadratic-complexity observability-constrained unscented Kalman filter for SLAM. IEEE Transactions on Robotics, vol. 29, no. 5, pp. 1226–1243, 2013. DOI: https://doi.org/10.1109/TRO.2013.2267991.

    Google Scholar 

  17. J. Gimenez, D. Herrera, S. Tosetti, R. Carelli. Optimization methodology to fruit grove mapping in precision agriculture. Computers and Electronics in Agriculture, vol. 116, pp. 88–100, 2015. DOI: https://doi.org/10.1016/j.compag.2015.06.013.

    Google Scholar 

  18. Y. Xu, T. Shen, X. Y. Chen, L. L. Bu, N. Feng. Predictive adaptive Kalman filter and its application to INS/UWB-integrated human localization with missing UWB-based measurements. International Journal of Automation and Computing, to be published. DOI: https://doi.org/10.1007/s11633-018-1157-4.

    Google Scholar 

  19. P. J. Huber. Robust Statistics, New York, USA: Wiley-Interscience, 1981.

    Book  Google Scholar 

  20. D. M. Rosen, C. DuHadway, J. J. Leonard. A convex relaxation for approximate global optimization in simultaneous localization and mapping. In Proceedings of IEEE International Conference on Robotics and Automation, IEEE, Seattle, USA, 2015. DOI: https://doi.org/10.1109/ICRA.2015.7140014.

    Google Scholar 

  21. I. J. Cox. A review of statistical data association techniques for motion correspondence. International Journal of Computer Vision, vol. 10, no. 1, pp. 53–66, 1993. DOI: https://doi.org/10.1007/BF01440847.

    Google Scholar 

  22. M. Adams, B. N. Vo, R. Mahler, J. Mullane. SLAM gets a PHD: New concepts in map estimation. IEEE Robotics & Automation Magazine, vol. 21, no. 2, pp. 26–37, 2014. DOI: https://doi.org/10.1109/MRA.2014.2304111.

    Google Scholar 

  23. J. Folkesson, H. I. Christensen. Graphical SLAM - a self-correcting map. In Proceedings of IEEE International Conference on Robotics and Automation, IEEE, New Orleans, USA, pp. 383–389, 2004. DOI: https://doi.org/10.1109/ROBOT.2004.1307180.

    Google Scholar 

  24. R. G. Cowell, A. P. Dawid, S. L. Lauritzen, D. J. Spiegelhalter, Probabilistic Networks and Expert Systems, New York, USA: Springer, 1999. DOI: https://doi.org/10.1007/b97670.

    MATH  Google Scholar 

  25. J. M. Hammersley, P. Clifford. Markov Field on Finite Graphs and Lattices, Technical Report, Department of Statistics, Oxford University, Oxford, UK, 1971.

    Google Scholar 

  26. R. Kindermann, J. L. Snell. Markov Random Fields and Their Applications, Providence, USA: American Mathematical Society, 1980.

    MATH  Google Scholar 

  27. S. Z. Li. Markov Random Field Modeling in Image Analysis, 3rd ed., London, USA: Springer, 2009. DOI: https://doi.org/10.1007/978-1-84800-279-1.

    MATH  Google Scholar 

  28. F. Dellaert, M. Kaess. Square root SAM: Simultaneous localization and mapping via square root information smoothing. International Journal of Robotics Research, vol. 25, no. 12, pp. 1181–1203, 2006. DOI: https://doi.org/10.1177/0278364906072768.

    MATH  Google Scholar 

  29. M. Jadaliha, J. Choi. Fully Bayesian simultaneous localization and spatial prediction using Gaussian Markov random fields (GMRFs). In Proceedings of American Control Conference, IEEE, Washington, USA, pp. 4592–4597, 2013. DOI: https://doi.org/10.1109/ACC.2013.6580547.

    Google Scholar 

  30. Y. Latif, C. Cadena, J. Neira. Robust graph SLAM back-ends: A comparative analysis. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Chicago, USA, pp. 2683–2690, 2014. DOI: https://doi.org/10.1109/IROS.2014.6942929.

    Google Scholar 

  31. N. Carlevaris-Bianco, M. Kaess, R. M. Eustice. Generic node removal for factor-graph SLAM. IEEE Transactions on Robotics, vol. 30, no. 6, pp. 1371–1385, 2014. DOI: https://doi.org/10.1109/TRO.2014.2347571.

    Google Scholar 

  32. B. Suger, G. D. Tipaldi, L. Spinello, W. Burgard. An approach to solving large-scale slam problems with a small memory footprint. In Proceedings of IEEE International Conference on Robotics and Automation, IEEE, Hong Kong, China, pp. 3632–3637, 2014. DOI: https://doi.org/10.1109/ICRA.2014.6907384.

    Google Scholar 

  33. M. Mazuran, G. D. Tipaldi, L. Spinello, W. Burgard. Non-linear graph sparsification for SLAM. In Proceedings of Robotics: Science and Systems, RSS, Berkeley, USA, 2014. DOI: https://doi.org/10.15607/RSS.2014.X.040.

    Google Scholar 

  34. S. Choudhary, V. Indelman, H. I. Christensen, F. Dellaert. Information-based reduced landmark SLAM. In Proceedings of IEEE International Conference on Robotics and Automation, IEEE, Seattle, USA, 2015. DOI: https://doi.org/10.1109/ICRA.2015.7139839.

    Google Scholar 

  35. G. Dissanayake, S. B. Williams, H. Durrant-Whyte, T. Bailey. Map management for efficient simultaneous localization and mapping (SLAM). Autonomous Robots, vol. 12, no. 3, pp. 267–286, 2002. DOI: https://doi.org/10.1023/A:1015217631658.

    MATH  Google Scholar 

  36. J. Besag. On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society, Series B, vol. 48, no. 3, pp. 259–302, 1986.

    MathSciNet  MATH  Google Scholar 

  37. J. Gimenez, A. Amicarelli, J. M. Toibero, F. di Sciascio, R. Carelli. Iterated conditional modes to solve simultaneous localization and mapping in Markov random fields context. International Journal of Automation and Computing, vol. 15, no. 3, pp. 310–324, 2018. DOI: https://doi.org/10.1007/s11633-017-1109-4.

    Google Scholar 

  38. S. Kullback, R. A. Leibler. On information and sufficiency. Annals of Mathematical Statistics, vol. 22, no. 1, pp. 79–86, 1951. DOI: https://doi.org/10.1214/aoms/1177729694.

    MathSciNet  MATH  Google Scholar 

  39. Z. I. Botev, J. F. Grotowski, D. P. Kroese. Kernel density estimation via diffusion. The Annals of Statistics, vol. 38, no. 5, pp. 2916–2957, 2010. DOI: https://doi.org/10.1214/10-AOS799.

    MathSciNet  MATH  Google Scholar 

  40. M. P. Wand, M. C. Jones. Comparison of smoothing parameterizations in bivariate kernel density estimation. Journal of the American Statistical Association, vol. 88, no. 422, pp. 520–528, 1993. DOI: https://doi.org/10.1080/01621459.1993.10476303.

    MathSciNet  MATH  Google Scholar 

  41. J. Gimenez, S. Tosetti, L. Salinas, R. Carelli. Bounded memory probabilistic mapping of out-of-structure objects in fruit crops environments. Computers and Electronics in Agriculture, vol. 115, pp. 11–20, 2018. DOI: https://doi.org/10.1016/j.com-pag.2018.05.018.

    Google Scholar 

  42. Z. I. Botev. Kernel density estimation using Matlab, 2007. [Online], Available: https://la.mathworks.com/matlab-central/fileexchange/17204-kernel-density-estimation?focused=5829342&tabfunction, 2007.

    Google Scholar 

  43. V. H. Andaluz, F. Roberti, J. M. Toibero, R. Carelli, B. Wagner. Adaptive dynamic path following control of an unicycle-like mobile robot. In Proceedings of the 4th International Conference on Intelligent Robotics and Applications, Springer, Aachen, Germany, pp. 563–574, 2011. DOI: https://doi.org/10.1007/978-3-642-25486-4_56.

    Google Scholar 

  44. H. Secchi, R. Carelli, V. Mut. An experience on stable control of mobile robots. Latin American Applied Research, vol. 33, no. 4, pp. 379–385, 2003.

    Google Scholar 

Download references

Acknowledgements

This research was financed by Argentinean National Council for Scientific Research (CONICET) and the National University of San Juan (UNSJ) of Argentina. We also thank NVIDIA Corporation for their support.

Author information

Authors and Affiliations

  1. Automatics Institute (INAUT), National University of San Juan - CONICET, San Juan, J5400ARL, Argentina

    J. Gimenez, A. Amicarelli, J. M. Toibero, F. di Sciascio & R. Carelli

Authors
  1. J. Gimenez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Amicarelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. M. Toibero
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. F. di Sciascio
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. R. Carelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J. M. Toibero.

Additional information

Recommended by Associate Editor De Xu

J. Gimenez received the B. Sc. degree in mathematics from the National University of San Juan (UNSJ), Argentina in 2009, and the Ph. D. degree in mathematics from the National University of Córdoba (UNC), Argentina in 2014. Currently, he is an assistant researcher of the Argentinean National Council for Scientific Research (CONICET), and an adjunct professor in the Institute of Automatics, UNSJ-CONICET, Argentina.

His research interests include probabilistic and statistical implementations of robotics, such as SLAM algorithms.

A. Amicarelli received the Dipl-Ing degree in chemical engineering from the National University of San Juan, Argentina in 2000, and the Ph. D. degree in control systems from the Institute of Automatics (INAUT) of the same University in 2007. Her main works are related with state estimations of complex non-linear systems and with bioprocess control. She is adjunct researcher of the Argentinean National Council for Scientific Research (CONICET) from 2012.

Her research interests include systems modeling, process control and state estimation to control porpoises.

J. M. Toibero received the B. Eng. degree in electronic engineering from the Facultad Tecnológica Nacional (UTN-Paraná) of Argentina, Argentina in 2002, and the Ph. D. degree in control systems from the Institute of Automatics (INAUT), the National University of San Juan, Argentina in 2007. His main works are related to nonlinear control of robotic platforms and robotics applications in agriculture. He is with the National Council for Scientific and Technological Research (CONICET) of Argentina since 2011, actually he is adjunct researcher. He leads different technological projects and his cur- rent scientific research is at the Institute of Automatics (INAUT) of San Juan, Argentina.

His research interests include wheeled mobile robots, manipulators force/impedance, switched, hybrid, nonlinear control methods applied to automatic control and visual servoing.

F. di Sciascio received B. Sc. degree in electromechanical engineering with orientation in electronics from the National University of Buenos Aires (UBA), Argentina in 1986. He received the M. Sc. degree in engineering of control Systems in 1994, and the Ph. D. degree in engineering in 1997, from the Institute of Automatics (INAUT), National University of San Juan (UNSJ), Argentina. Since 1987, he is a professor and researcher at the INAUT and he is currently a full professor in charge of the subjects artificial intelligence complements and identification and adaptive control (degree in electronic engineering) in the Department of Electronics and Automation, at the same time, he is a professor of the postgraduate degree at the INAUT, Argentina.

His research interests include modelling, identification and estimation in dynamical systems, and technology developments in automatic process control.

R. Carelli received B. Sc. degree in engineering from the National University of San Juan, Argentina in 1976, and the Ph. D. degree in electrical engineering from the National University of Mexico (UNAM), Mexico in 1989. He is a full professor at the National University of San Juan and a senior researcher of the National Council for Scientific and Technical Research, Argentina. He is Director of the Automatics Institute, National University of San Juan, Argentina. He is a Senior Member of IEEE and a Member of Argentine Association of Automatic Control — International Federation of Automatic Control (AA-DECA-IFAC).

His research interests include robotics, manufacturing systems, adaptive control and artificial intelligence applied to automatic control.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gimenez, J., Amicarelli, A., Toibero, J.M. et al. Continuous Probabilistic SLAM Solved via Iterated Conditional Modes. Int. J. Autom. Comput. 16, 838–850 (2019). https://doi.org/10.1007/s11633-019-1186-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11633-019-1186-7

Keywords

Search

Navigation