Abstract
Location is an elemental problem for mobile robotics due the importance of determining a position of the robot in space. This knowledge along with the environment map are basic information for robot mobility. In this paper, a new approach for navigation and location of mobile robots on topological maps using classification with reject option in attributes obtained from a Structural Co-occurrence Matrix (SCM) is proposed. Furthermore, we compare our approach with others state-of-the-art extractors, such as Statistical Moments, Gray-Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP). Structural Co-Occurrence Matrix was evaluated with the Average, Gaussian, Laplacian and Sobel filters. Regarding to classifiers, Bayesian classifier, Multilayer Perceptron (MLP) and Support Vector Machines (SVM) were analyzed. The descriptors Scale Invariant Feature Transform (SIFT) and Speed Up Robust Features (SURF) were also used. According to results, SCM was the fastest feature extractor with 0.117 s and accuracy of 100% in navigation test, showing the relevance of our approach in the mobile robot localization.
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References
Farinella, G.M., Ravì, D., Tomaselli, V., Guarnera, M., Battiato, S.: Representing scenes for real-time context classification on mobile devices. Pattern Recogn. 48(4), 1086–1100 (2015)
Farinella, G.M., Battiato, S.: Scene classification in compressed and constrained domain. IET Comput. Vis. 5(5), 320–334 (2011)
Jiang, R., Yang, S., Ge, S.S.: Geometric map-assisted localization for mobile robots based on uniform-Gaussian distribution. IEEE Robot. Autom. Lett. 2(2), 789–795 (2017)
Jiang, J.-H., Le, H.-L., Shie, S.-C.: Lightweight topological-based map matching for indoor navigation. In: 30th International Conference on Advanced Information Networking and Applications Workshops AINA, pp. 908–913, March 2016
Ferreira, J.F., Amorim, I., Rocha, R.P., Dias, J.: T-SLAM: registering topological and geo-metric maps for robot localization in large environments. In: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems MFI, pp. 392–398, August 2008
Lee, W., Chung, W.: Position estimation using multiple low-cost GPS receivers for outdoor mobile robots. In: 12th International Conference on Ubiquitous Robots and Ambient Intelligence, pp. 460–461. IEEE, October 2015
Diop, M., Ong, L.Y., Lim, T.S.: A computer vision-aided motion sensing algorithm for mobile robot’s indoor navigation. In: 14th International Workshop on Advanced Motion Control, pp. 400–405. IEEE, April 2016
Song, Z., Jiang, G., Huang, C.: A survey on indoor positioning technologies. In: Zhou, Q. (ed.) ICTMF 2011. CCIS, vol. 164, pp. 198–206. Springer, Heidelberg (2011). doi:10.1007/978-3-642-24999-0_28
Bonin-Font, F., Ortiz, A., Oliver, G.: Visual navigation for mobile robots: a survey. J. Intell. Rob. Syst. 53(3), 263–296 (2008)
Bezerra, G.L., Ferreira, D.S., Rebouças, P.P., Sombra, F.N.: Rotation-invariant feature extraction using a structural co-occurrence matrix. Measurement 94(2), 406–415 (2016)
Haralick, R.M., Shanmugam, K.S., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Flusser, J., Zitova, B., Suk, T.: Moments and Moment Invariants in Pattern Recognition. Wiley Publishing, Chichester (2009)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Pearson Prentice Hall, New Jersey (2010)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346–359 (2008)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic Press, USA (2008)
Haykin, S.: Neural Networks and Learning Machines. Prentice Hall, McMaster University, Canada (2008)
Vapnik, V.N.: Statistical Learning Theory. Wiley, Nova Jersey (1998)
Crammer, K., Singer, Y.: On the algorithmic implementation of multiclass kernel-based vector machines. J. Mach. Learn. Res. 2, 265–292 (2001)
Duan, K.-B., Keerthi, S.S.: Which is the best multiclass SVM method? An empirical study. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds.) MCS 2005. LNCS, vol. 3541, pp. 278–285. Springer, Heidelberg (2005). doi:10.1007/11494683_28
Furnari, A., Farinella, G.M., Battiato, S.: Recognizing personal locations from egocentric videos. IEEE Trans. Hum.-Mach. Syst. 47(1), 6–18 (2017)
Marinho, L.B., Almeida, J.S., Souza, J.W.M., Albuquerque, V.H.C., Rebouças Filho, P.P.: A novel mobile robot localization approach based on topological maps using classification with reject option in omnidirectional images. Expert Syst. Appl. 72, 1–17 (2016)
Nunes, T.M., Coelho, A.L., Lima, C.A., Papa, J.P., de Albuquerque, V.H.C.: EEG signal classification for epilepsy diagnosis via optimum path forest - a systematic assessment. Neurocomputing 136, 103–123 (2014)
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da Silva, S.P.P., Marinho, L.B., Almeida, J.S., Rebouças Filho, P.P. (2017). A Novel Approach for Mobile Robot Localization in Topological Maps Using Classification with Reject Option from Structural Co-occurrence Matrix. In: Felsberg, M., Heyden, A., Krüger, N. (eds) Computer Analysis of Images and Patterns. CAIP 2017. Lecture Notes in Computer Science(), vol 10424. Springer, Cham. https://doi.org/10.1007/978-3-319-64689-3_1
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