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.
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References
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)
Borenstein, J.H., Everett, R., Feng, L., Wehe, D.: Mobile robot positioning: sensors and techniques. J. Robot. Syst. 14, 231–249 (1997)
Candy, J.V.: Model-Based Signal Processing. John Wiley & Sons, Inc., New York (2006)
Olson, C.F.: Probabilistic self-localization for mobile robots. IEEE Trans. Robot. Autom. 16(1), 55–66 (2000)
DeSouza, G.N., Kak, A.C.: Vision for mobile robot navigation: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 24(2), 237–267 (2002)
Bonin-Font, F., Ortiz, A., Oliver, G.: Visual navigation for mobile robots: a survey. J. Intell. Rob. Syst. 53(3), 263–296 (2008)
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
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)
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)
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)
Crowley, J.L., Pourraz, F.: Continuity properties of the appearance manifold for mobile robot position estimation. Image Vis. Comput. 19(11), 741–752 (2001)
Pauli, J.: Learning-Based Robot Vision. LNCS, vol. 2048, 292 p. Springer, Heidelberg (2001)
Oore, S., Hinton, G.E., Dudek, G.: A mobile robot that learns its place. Neural Comput. 9, 683–699 (1997)
Thrun, S.: Bayesian landmark learning for mobile robot localization. Mach. Learn. 33(1), 41–76 (1998)
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)
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)
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)
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)
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)
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)
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)
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)
Betke, M., Gurvits, L.: Mobile robot localization using landmarks. IEEE Trans. Robot. Autom. 13, 251–263 (1997)
Sugihara, K.: Some location problems for robot navigation using a single camera. Comput. Vis. Graph. Image Process. 42, 112–129 (1988)
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)
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)
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)
Vlassis, N., Motomura, Y., Krse, B.J.A.: Supervised dimension reduction of intrinsically low-dimensional data. Neural Comput. 14(1), 191–215 (2002)
Se, S., Lowe, D., Little, J.: Vision-based global localization and mapping for mobile robots. IEEE Trans. Rob. 21(3), 364–375 (2005)
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)
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)
Jollie, T.: Principal Component Analysis. Springer, New-York (2002)
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)
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
Melzer, T., Reiter, M., Bischof, H.: Appearance models based on kernel canonical correlation analysis. Pattern Recogn. 36(9), 1961–1973 (2003)
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)
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)
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
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)
Saul, L.K., Roweis, S.T.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)
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
Scholkopf, B., Smola, A., Muller, K.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 10(5), 1299–1319 (1998)
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)
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)
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)
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)
Tibshirani, R.: Regression shrinkage and selection via the lasso: a retrospective. J. Roy. Stat. Soc.: Ser. B (Methodol.) 73(3), 273–282 (2011)
Ribeiro, M.I.: Kalman and extended Kalman filters: Concept, derivation and properties. Institute for Systems and Robotics, Technical report, 44 p. (2004)
Herbert, B., Andreas, E., Tuytelaars, T., Gool, L.V.: Speeded-Up Robust Features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)
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)
Ma, Y., Fu, Y. (eds.): Manifold Learning Theory and Applications. CRC Press, London (2011)
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)
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)
Stone, C.J.: Optimal rates of convergence for nonparametric estimators. Ann. Stat. 8, 1348–1360 (1980)
Stone, C.J.: Optimal global rates of convergence for nonparametric regression. Ann. Stat. 10, 1040–1053 (1982)
Lee, J.M.: Manifolds and Differential Geometry. Graduate Studies in Mathematics, vol. 107. American Mathematical Society, Providence (2009)
Lee, J.M.: Introduction to Smooth Manifolds. Springer, New York (2003)
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
Bernstein, A.V., Kuleshov, A.P.: Manifold Learning: generalizing ability and tangent proximity. Int. J. Softw. Inf. 7(3), 359–390 (2013)
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
Golub, G.H., Van Loan, C.F.: Matrix Computation, 3rd edn. Johns Hopkins University Press, Baltimore (1996)
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)
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)
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)
Burnaev, E., Zaytsev, A.: Surrogate modeling of mutlifidelity data for large samples. J. Commun. Technol. Electron. 60(12), 1348–1355 (2016)
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The research was supported solely by the Russian Science Foundation grant (project 14-50-00150).
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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
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DOI: https://doi.org/10.1007/978-3-319-62416-7_20
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