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Matching monocular lightweight features using N-gram techniques for topological location identification

  • Jaime Boal (a1), Álvaro Sánchez-Miralles (a1) and Manuel Alvar (a1)
Summary

In SLAM (simultaneous localization and mapping), the topological paradigm provides a more natural and compact solution that scales better with the size of the environment. Computer vision has always been regarded as the ideal sensor technology for topological feature extraction and description and several methods have been proposed in the literature, but they are either time-consuming, require plenty of different sensors, or are very sensitive to perceptual aliasing, all of which limit their application scope.

This paper presents a fast-to-compute collection of features extracted from monocular images, and an adaptive matching procedure for location identification in structured indoor environments inspired by the natural language processing field. Although only dominant vertical lines, color histograms, and a reduced number of keypoints are employed in this paper, the matching framework introduced allows for the incorporation of almost any other type of feature. The results of the experiments carried out in a home and an office environment suggest that the proposed method could be used for real-time topological scene recognition even if the environment changes moderately over time. Due to the combination of complementary features, high precision can be achieved within reasonable computation time by using weaker but faster descriptors.

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Corresponding author
*Corresponding author. E-mail: jaime.boal@iit.upcomillas.es
References
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1.Agrawal, M., Konolige, K. and Blas, M. R., “CenSurE: Center surround Extremas for Realtime Feature Detection and MatchingIn: European Conference on Computer Vision Lecture Notes in Computer Science, vol. 5305. (Forsyth, D., Torr, P. and Zisserman, A., eds.) (Springer, 2008) pp. 102115.
2.Angeli, A., Doncieux, S., Meyer, J.-A. and Filliat, D., “Incremental Vision-Based Topological SLAM”, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Nice, France (Sep. 22–26, 2008) pp. 1031–036.
3.Bay, H., Ess, A., Tuytelaars, T. and van Gool, L., “SURF: Speeded up robust features”, Comput. Vis. Image Underst. 110 (3), 346359 (2008).
4.Bologna, G., Deville, B. and Pun, T., “Blind Navigation Along a Sinuous Path by Means of the See ColOr Interface”, In: Proceedings of the International Work–Conference on the Interplay Between Natural and Artificial Computation. Part II: Bioinspired Applications in Artificial and Natural Computation (Mira, J., Álvarez, J. R., de la Paz, F. and Ferrández, J. M., eds.) (Springer-Verlag, Santiago de Compostela, Spain, 2009) pp. 235243.
5.Booij, O., Terwijn, B., Zivkovic, Z. and Kröse, B., “Navigation Using an Appearance Based Topological Map”, Proceedings of the IEEE International Conference on Robotics and Automation, Rome, Italy (Apr. 10–14, 2007) pp. 39273932.
6.Bradski, G., “The OpenCV library”, (2000). http://opencv.willowgarage.com.
7.Brooks, R. A., “Elephants don't play chess”, Robot. Auton. Syst. 6, 315 (1990).
8.Cha, S.-H., “Comprehensive survey on distance/similarity measures between probability density functions”, Int. J. Math. Models Methods Appl. Sci. 1 (4), 300307 (2007).
9.Comaniciu, D., Ramesh, V. and Meer, P., “Kernel-based object tracking”, IEEE Trans. Pattern Anal. Mach. Intell. 25 (5), 564577 (2003).
10.Cummins, M. and Newman, P., “FAB-MAP: Probabilistic localization and mapping in the space of appearance,” Int. J. Robot. Res. 27 (6), 647665 (2008).
11.Fraundorfer, F., Engels, C. and Nistér, D., “Topological mapping, localization and navigation using image collections”, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, USA (Oct. 29–Nov. 2, 2007) pp. 38723877.
12.Horvath, M., “ShapeGrid macro—Isometricland”, (2008). http://isometricland.net/povray/povray.php; last accessed: Nov. 21, 2013.
13.Jurafsky, D. and Martin, J. H., Speech and Language Processing (Prentice Hall, New Jersey 2009).
14.Kailath, T., “The divergence and Bhattacharyya distance measures in signal selection”, IEEE Trans. Commun. Technol. 15 (1), 5260 (1967).
15.Lamon, P., Nourbakhsh, I., Jensen, B. and Siegwart, R., “Deriving and Matching Image Fingerprint Sequences for Mobile Robot Localization”, Proceedings of the IEEE International Conference on Robotics and Automation, Seoul, Korea (May 21–26, 2001) pp. 16091614.
16.LeCam, L., Asymptotic Methods in Statistical Decision Theory (Springer-Verlag, New York, 1986).
17.Liu, M., Scaramuzza, D., Pradalier, C., Siegwart, R. and Chen, Q., “Scene Recognition with Omnidirectional Vision for Topological Map Using Lightweight Adaptive Descriptors.” Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, USA (Oct. 11–15, 2009) pp. 116121.
18.Liu, M. and Siegwart, R., “DP-FACT: Towards Topological Mapping and Scene Recognition with Color for Omnidirectional CameraIEEE International Conference on Robotics and Automation, Saint Paul, USA (May 14–18, 2012) pp. 35033508.
19.Lowe, D. G.. “Distinctive image features from scale-invariant keypoints.,” International Journal of Computer Vision, 60 (2), 91110 (2004).
20.Luo, J., Pronobis, A., Caputo, B. and Jensfelt, P., “Incremental Learning for Place Recognition in Dynamic Environments”, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, USA (Oct. 29–Nov. 2, 2007) pp. 721728.
21.Magimai-Doss, M., Hakkani-Tür, D., Çetin, Ö., Shriberg, E., Fung, J. and Mirghafori, N., “Entropy Based Classifier Combination for Sentence Segmentation”, IEEE International Conference on Acoustics, Speech and Signal Processing, Honolulu, HI, USA vol. 4 (Apr. 15–20, 2007) pp. IV-189192.
22.Matas, J., Chum, O., Urban, M. and Pajdla, T., “Robust Wide Baseline Stereo from Maximally Stable Extremal Regions”, Proceedings of the British Machine Vision Conference, Cardiff, UK, vol. 1 (Sep. 2–5, 2002) pp. 384393.
23.Needleman, S. B. and Wunsch, C. D., “A general method applicable to the search for similarities in the amino acid sequence of two proteins”, J. Mol. Biol. 48, 443453 (1970).
24.Neubeck, A. and van Gool, L., “Efficient Non-Maximum Suppression”, Proceedings of the International Conference on Pattern Recognition, Hong Kong, China, vol. 3 (Aug. 20–24, 2006) pp. 850855.
25.Ramos, F. T., Upcroft, B., Kumar, S. and Durrant-Whyte, H. F.. “A Bayesian Approach for Place Recognition.” Proceedings of the IJCAI Workshop Reasoning with Uncertainty in Robotics, Edinburgh, Scotland (Jul. 30, 2005).
26.Rublee, E., Rabaud, V., Konolige, K. and Bradski, G., “ORB: An Efficient Alternative to SIFT or SURF,” Proceedings of the IEEE International Conference on Computer Vision, Barcelona, Spain (2011) pp. 25642571.
27.Russell, S. and Norvig, P., Artificial Intelligence: A Modern Approach, 3rd ed. (Pearson Education, New Jersey, 2010).
28.Sabatta, D. G., “Vision-Based Topological Map Building and Localisation Using Persistent Features”, Robotics and Mechatronics Symposium, Bloemfontein, South Africa (Nov. 11, 2008) pp. 16.
29.Stankiewicz, B. J. and Kalia, A. A., “Acquisition of structural versus object landmark knowledge”, J. Exp. Psychol.: Human Perception and Performance 33 (2), 378390 (2007).
30.Tapus, A., Topological SLAM – Simultaneous Localization and Mapping with Fingerprints of Places PhD thesis (Lausanne, Switzerland: École Polytechnique Fédérale de Lausanne, 2005).
31.Tapus, A. and Siegwart, R., “Incremental Robot Mapping with Fingerprints of Places”, Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, AB, Canada (Aug. 2–6, 2005) pp. 24292434.
32.Tseng, D.-C. and Chang, C.-H., “Color Segmentation Using Perceptual Attributes”, Proceedings of the IAPR International Conference on Pattern Recognition, The Hage, Netherlands, vol. 3 (Aug. 30–Sep. 3, 1992) pp. 228231.
33.Ulrich, I. and Nourbakhsh, I., “Appearance-Based Place Recognition for Topological Localization”, Proceedings of the IEEE International Conference on Robotics and Automation, San Francisco, USA (2000) pp. 10231029.
34.Werner, F., Sitte, J. and Maire, F., “Topological map induction using neighbourhood information of places”, Auton. Robots 32 (4), 405418 (2012).
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Robotica
  • ISSN: 0263-5747
  • EISSN: 1469-8668
  • URL: /core/journals/robotica
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