Skip to main content
SpringerLink
Log in

Mobile Robot Localization via Machine Learning

  • Conference paper
  • First Online:
Machine Learning and Data Mining in Pattern Recognition (MLDM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10358))

Abstract

We consider an appearance-based robot self-localization problem in the machine learning framework. Using recent manifold learning techniques, we propose a new geometrically motivated solution. The solution includes estimation of the robot localization mapping from the appearance manifold to the robot localization space, as well as estimation of the inverse mapping for image modeling. The latter allows solving the robot localization problem as a Kalman filtering problem.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Talluri, R., Aggarwal, J.K.: Position estimation techniques for an autonomous mobile robot – A review. In: Chen, C.H., Pau, L.F., Wang, P.S.P. (eds.) Handbook of Pattern Recognition and Computer Vision, chap. 4.4, pp. 769–801. World Scientific, Singapore (1993)

    Google Scholar 

  2. Borenstein, J.H., Everett, R., Feng, L., Wehe, D.: Mobile robot positioning: sensors and techniques. J. Robot. Syst. 14, 231–249 (1997)

    Article  Google Scholar 

  3. Candy, J.V.: Model-Based Signal Processing. John Wiley & Sons, Inc., New York (2006)

    Google Scholar 

  4. Olson, C.F.: Probabilistic self-localization for mobile robots. IEEE Trans. Robot. Autom. 16(1), 55–66 (2000)

    Article  Google Scholar 

  5. DeSouza, G.N., Kak, A.C.: Vision for mobile robot navigation: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 24(2), 237–267 (2002)

    Article  Google Scholar 

  6. Bonin-Font, F., Ortiz, A., Oliver, G.: Visual navigation for mobile robots: a survey. J. Intell. Rob. Syst. 53(3), 263–296 (2008)

    Article  Google Scholar 

  7. Kröse, B.J.A., Vlassis, N., Bunschoten, R.: Omnidirectional vision for appearance-based robot localization. In: Hager, G.D., Christensen, H.I., Bunke, H., Klein, R. (eds.) Sensor Based Intelligent Robots. LNCS, vol. 2238, pp. 39–50. Springer, Heidelberg (2002). doi:10.1007/3-540-45993-6_3

    Chapter  Google Scholar 

  8. Krose, B.J.A., Vlassis, N., Bunschoten, R., Motomura, Y.: A probabilistic model for appearance-based robot localization. Image Vis. Comput. 19, 381–391 (2001)

    Article  Google Scholar 

  9. Saito, M., Kitaguchi, K.: Appearance based robot localization using regression models. In: Proceedings of 4th IFAC-Symposium on Mechatronic Systems, vol. 2, pp. 584–589 (2006)

    Google Scholar 

  10. Hamm, J., Lin, Y., Lee, D.D.: Learning nonlinear appearance manifolds for robot localization. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2005), pp. 1239–1244 (2005)

    Google Scholar 

  11. Crowley, J.L., Pourraz, F.: Continuity properties of the appearance manifold for mobile robot position estimation. Image Vis. Comput. 19(11), 741–752 (2001)

    Article  Google Scholar 

  12. Pauli, J.: Learning-Based Robot Vision. LNCS, vol. 2048, 292 p. Springer, Heidelberg (2001)

    Google Scholar 

  13. Oore, S., Hinton, G.E., Dudek, G.: A mobile robot that learns its place. Neural Comput. 9, 683–699 (1997)

    Article  Google Scholar 

  14. Thrun, S.: Bayesian landmark learning for mobile robot localization. Mach. Learn. 33(1), 41–76 (1998)

    Article  MATH  Google Scholar 

  15. Krose, B.J.A., Bunschoten, R.: Probabilistic localization by appearance models and active vision. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 1999), Detroit, Michigan, pp. 2255–2260 (1999)

    Google Scholar 

  16. Vlassis, N., Krose, B.J.A.: Robot environment modeling via principal component regression. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 1999), pp. 677–682 (1999)

    Google Scholar 

  17. Se, S., Lowe, D., Little, J.: Local and global localization for mobile robots using visual landmarks. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2001), pp. 414–420 (2001)

    Google Scholar 

  18. Hayet, J., Lerasle, F., Devy, M.: Visual landmarks detection and recognition for mobile robot navigation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2003), vol. 2, pp. 313–318 (2003)

    Google Scholar 

  19. Bunschoten, R., Krose, B.J.A.: 3-D scene reconstruction from cylindrical panoramic images. In Proceedings of the 9th International Symposium on Intelligent Robotic Systems (SIRS-2001), pp. 199–205 (2001)

    Google Scholar 

  20. Gluckman, J., Nayar, S.K.: Ego-motion and omnidirectional cameras. In: Proceedings of the Sixth International Conference on Computer Vision (ICCV 1998), pp. 999–1005 (1998)

    Google Scholar 

  21. Colin de Verdiere, V., Crowley, J.L.: Local appearance space for recognition of navigation landmarks. J. Robot. Auton. Syst. 32(1–2), 61–89 (2000)

    Article  Google Scholar 

  22. Dudek, G., Jugessur, D.: Robust place recognition using local appearance based methods. In: Proceedings of the International Conference on Robotics and Automation (ICRA 2000), pp. 1030–1035 (2000)

    Google Scholar 

  23. Betke, M., Gurvits, L.: Mobile robot localization using landmarks. IEEE Trans. Robot. Autom. 13, 251–263 (1997)

    Article  Google Scholar 

  24. Sugihara, K.: Some location problems for robot navigation using a single camera. Comput. Vis. Graph. Image Process. 42, 112–129 (1988)

    Article  Google Scholar 

  25. Sim, R., Dudek, G.: Robot positioning using learned landmarks. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 1998), vol. 2, pp. 1060–1065 (1998)

    Google Scholar 

  26. Friedman, A.: Robot localization using landmarks. In: Mathematics in Industrial Problems. The IMA Volumes in Mathematics and its Applications, vol. 67(7), pp. 86–94. Springer, New York (1995)

    Google Scholar 

  27. Jogan, M., Leonardis, A.: Robust localization using panoramic view-based recognition. In: Proceedings of the 15th International Conference on Pattern Recognition (ICPR 2000), pp. 136–139. IEEE Computer Society (2000)

    Google Scholar 

  28. Vlassis, N., Motomura, Y., Krse, B.J.A.: Supervised dimension reduction of intrinsically low-dimensional data. Neural Comput. 14(1), 191–215 (2002)

    Google Scholar 

  29. Se, S., Lowe, D., Little, J.: Vision-based global localization and mapping for mobile robots. IEEE Trans. Rob. 21(3), 364–375 (2005)

    Article  Google Scholar 

  30. Cobzas, D., Zhang, H.: Cylindrical panoramic image-based model for robot localization. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Maui, HI, pp. 1924–1930 (2001)

    Google Scholar 

  31. Crowley, J.L., Wallner, F., Schiele, B.: Position estimation using principal components of range data. In: Proceedings of the 1998 IEEE International Conference on Robotics and Automation, vol. 4, pp. 3121–3128 (1998)

    Google Scholar 

  32. Jollie, T.: Principal Component Analysis. Springer, New-York (2002)

    Google Scholar 

  33. Peres-Neto, P.R., Jackson, D.A., Somers, K.M.: How many principal components? stopping rules for determining the number of non-trivial axes revisited. Comput. Stat. Data Anal. 49(4), 974–997 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  34. Härdle, W.K., Simar, L.: Canonical correlation analysis. In: Applied Multivariate Statistical Analysis, pp. 443–454. Springer, Heidelberg (2015). doi:10.1007/978-3-662-45171-7_16

    Google Scholar 

  35. Melzer, T., Reiter, M., Bischof, H.: Appearance models based on kernel canonical correlation analysis. Pattern Recogn. 36(9), 1961–1973 (2003)

    Article  MATH  Google Scholar 

  36. Skocaj, D., Leonardis, A.: Appearance-based localization using CCA. In: Proceedings of the of the 9th Computer Vision Winter Workshop (CVWW 2004), pp. 205–214 (2004)

    Google Scholar 

  37. Se, S., Lowe, D., Little, J.: Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks. Int. J. Robot. Res. 21(8), 735–758 (2002)

    Article  Google Scholar 

  38. Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  39. Vlassis, N., Motomura, Y., Krose, B.J.A.: Supervised linear feature extraction for mobile robot localization. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2000), vol. 4, pp. 2979–2984 (2000)

    Google Scholar 

  40. Saul, L.K., Roweis, S.T.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)

    Article  Google Scholar 

  41. Wang, K., Wang, W., Zhuang, Y.: Appearance-based map learning for mobile robot by using generalized regression neural network. In: Liu, D., Fei, S., Hou, Z.-G., Zhang, H., Sun, C. (eds.) ISNN 2007. LNCS, vol. 4491, pp. 834–842. Springer, Heidelberg (2007). doi:10.1007/978-3-540-72383-7_97

    Chapter  Google Scholar 

  42. Scholkopf, B., Smola, A., Muller, K.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10(5), 1299–1319 (1998)

    Article  Google Scholar 

  43. Wu, H., Wu, Y.-X., Liu, C.-A., Yang, G.-T., Qin, S.-Y.: Fast robot localization approach based on manifold regularization with sparse area features. Cognitive Comput. 8(5), 856–876 (2016)

    Article  Google Scholar 

  44. Do, H.N., Jadaliha, M., Choi, J., Lim, C.Y.: Feature selection for position estimation using an omnidirectional camera. Image Vis. Comput. 39, 1–9 (2015)

    Article  Google Scholar 

  45. Do, H.N., Choi, J., Lim, C.Y., Maiti, T.: Appearance-based localization using Group LASSO regression with an indoor experiment. In: Proceedings of the 2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM 2015), pp. 984–989 (2015)

    Google Scholar 

  46. Do, H.N., Choi, J.: Appearance-based outdoor localization using group lasso regression. In: Proceedings of the ASME Dynamic Systems and Control Conference (DSCC 2015), vol. 3, 8 p. (2015)

    Google Scholar 

  47. Tibshirani, R.: Regression shrinkage and selection via the lasso: a retrospective. J. Roy. Stat. Soc.: Ser. B (Methodol.) 73(3), 273–282 (2011)

    Article  MathSciNet  Google Scholar 

  48. Ribeiro, M.I.: Kalman and extended Kalman filters: Concept, derivation and properties. Institute for Systems and Robotics, Technical report, 44 p. (2004)

    Google Scholar 

  49. Herbert, B., Andreas, E., Tuytelaars, T., Gool, L.V.: Speeded-Up Robust Features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)

    Article  Google Scholar 

  50. Obozinski, G., Wainwright, M.J., Jordan, M.I., et al.: Support union recovery in high-dimensional multivariate regression. Annal. Stat. 39(1), 1–47 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  51. Ma, Y., Fu, Y. (eds.): Manifold Learning Theory and Applications. CRC Press, London (2011)

    Google Scholar 

  52. Kuleshov, A.P., Bernstein, A.V.: Manifold learning in data mining tasks. In: Perner, P. (ed.) MLDM 2014. LNCS, vol. 8556, pp. 119–133. Springer, Heidelberg (2014)

    Google Scholar 

  53. Kuleshov, A.P., Bernstein, A.V.: Statistical learning on manifold-valued data. In: Perner, P. (ed.) MLDM 2016. LNCS, vol. 9729, pp. 311–325. Springer International Publishing, Switzerland (2016)

    Google Scholar 

  54. Stone, C.J.: Optimal rates of convergence for nonparametric estimators. Ann. Stat. 8, 1348–1360 (1980)

    Article  MathSciNet  MATH  Google Scholar 

  55. Stone, C.J.: Optimal global rates of convergence for nonparametric regression. Ann. Stat. 10, 1040–1053 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  56. Lee, J.M.: Manifolds and Differential Geometry. Graduate Studies in Mathematics, vol. 107. American Mathematical Society, Providence (2009)

    Google Scholar 

  57. Lee, J.M.: Introduction to Smooth Manifolds. Springer, New York (2003)

    Book  Google Scholar 

  58. Bernstein, A.V., Kuleshov, A.P.: Tangent bundle manifold learning via Grass-mann & Stiefel eigenmaps. In: arxiv:1212.6031v1 [cs.LG], pp. 1–25 (2012), December 2012

  59. Bernstein, A.V., Kuleshov, A.P.: Manifold Learning: generalizing ability and tangent proximity. Int. J. Softw. Inf. 7(3), 359–390 (2013)

    Google Scholar 

  60. Kuleshov, A., Bernstein, A.: Incremental construction of low-dimensional data representations. In: Schwenker, F., Abbas, H.M., El Gayar, N., Trentin, E. (eds.) ANNPR 2016. LNCS, vol. 9896, pp. 55–67. Springer, Cham (2016). doi:10.1007/978-3-319-46182-3_5

    Chapter  Google Scholar 

  61. Golub, G.H., Van Loan, C.F.: Matrix Computation, 3rd edn. Johns Hopkins University Press, Baltimore (1996)

    MATH  Google Scholar 

  62. Kuleshov, A.P., Bernstein, A.V.: Regression on high-dimensional inputs. In: Workshops Proceedings volume of the IEEE International Conference on Data Mining (ICDM 2016), pp. 732–739. IEEE Computer Society, USA (2016)

    Google Scholar 

  63. Burnaev, E., Belyaev, M., Kapushev, E.: Computationally efficient algorithm for Gaussian Processes based regression in case of structured samples. Comput. Math. Math. Phys. 56(4), 499–513 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  64. Burnaev, E., Panov, M., Zaytsev, A.: Regression on the basis of nonstationary Gaussian processes with Bayesian regularization. J. Commun. Technol. Electron. 61(6), 661–671 (2016)

    Article  Google Scholar 

  65. Burnaev, E., Zaytsev, A.: Surrogate modeling of mutlifidelity data for large samples. J. Commun. Technol. Electron. 60(12), 1348–1355 (2016)

    Article  Google Scholar 

Download references

Acknowledgments

The research was supported solely by the Russian Science Foundation grant (project 14-50-00150).

Author information

Authors and Affiliations

  1. Skolkovo Institute of Science and Technology, Moscow, Russia

    Alexander Kuleshov, Alexander Bernstein & Evgeny Burnaev

  2. Kharkevich Institute for Information Transmission Problems RAS, Moscow, Russia

    Alexander Bernstein & Evgeny Burnaev

Authors
  1. Alexander Kuleshov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexander Bernstein
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Evgeny Burnaev
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Evgeny Burnaev .

Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Leipzig, Sachsen, Germany

    Petra Perner

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Kuleshov, A., Bernstein, A., Burnaev, E. (2017). Mobile Robot Localization via Machine Learning. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2017. Lecture Notes in Computer Science(), vol 10358. Springer, Cham. https://doi.org/10.1007/978-3-319-62416-7_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-62416-7_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62415-0

  • Online ISBN: 978-3-319-62416-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation