Hostname: page-component-cb9f654ff-9b74x Total loading time: 0 Render date: 2025-08-22T00:46:32.864Z Has data issue: false hasContentIssue false
  • English
  • Français

Development of a Method for Data Dimensionality Reduction in Loop Closure Detection: An Incremental Approach

Published online by Cambridge University Press:  17 July 2020

Leandro A. S. Moreira Open the ORCID record for Leandro A. S. Moreira [Opens in a new window] ,
Claudia M. Justel ,
Jauvane C. de Oliveira  and
Paulo F. F. Rosa
Leandro A. S. Moreira*
Affiliation:
Laboratório Nacional de Computação Científica, Brazil. E-mail: jauvane@acm.org Instituto Militar de Engenharia, Brazil. E-mails: cjustel@ime.eb.br, rpaulo@ime.eb.br
Claudia M. Justel
Affiliation:
Instituto Militar de Engenharia, Brazil. E-mails: cjustel@ime.eb.br, rpaulo@ime.eb.br
Jauvane C. de Oliveira
Affiliation:
Laboratório Nacional de Computação Científica, Brazil. E-mail: jauvane@acm.org
Paulo F. F. Rosa
Affiliation:
Instituto Militar de Engenharia, Brazil. E-mails: cjustel@ime.eb.br, rpaulo@ime.eb.br
*
*Corresponding author. E-mail: leandromoreira75@gmail.com
Get access Rights & Permissions [Opens in a new window]

Summary

This article proposes a method for incremental data dimensionality reduction in loop closure detection for robotic autonomous navigation. The approach uses dominant eigenvector concept for: (a) spectral description of visual datasets and (b) representation in low dimension. Unlike most other papers on data dimensionality reduction (which is done in batch mode), our method combines a sliding window technique and coordinate transformation to achieve dimensionality reduction in incremental data. Experiments in both simulated and real scenarios were performed and the results are suitable.

Keywords

SLAMLoop closure detectionIncremental dimensionality reductionMobile robotsRobot localization

Information

Type
Articles
Information
Robotica , Volume 39 , Issue 4 , April 2021 , pp. 557 - 571
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Leonard, J. J. and Durrant-Whyte, H. F., “Simultaneous Map Building and Localization for an Autonomous Mobile Robot”, Intelligent Robots and Systems’ 91.’Intelligence for Mechanical Systems, Proceedings IROS’91, IEEE/RSJ International Workshop (1991) 1442–1447.Google Scholar
Thrun, S., Burgard, W. and Fox, D., Probabilistic Robotics (Massachusetts Institute of Technology Press, Cambridge, MA, USA, 2005).Google Scholar
Lowe, D. G., “Distinctive image features from scale-invariant keypoints”, Int. J. Comput. Vis. 60(2), 91–110 (2004).CrossRefGoogle Scholar
Bay, H. and Van Gool, L., “Surf: Speeded Up Robust Features”, Proceedings of 9th European Conference on Computer Vision (2006) pp. 404–417.Google Scholar
Coifman, R. and Lafon, S., “Diffusion maps”, Appl. Comput. Harmonic Anal. 21(1), 5–30 (2006).CrossRefGoogle Scholar
Zavlanos, M. M., Egerstedt, M. B. and Pappas, G. J., “Graph-theoretic connectivity control of mobile robot networks”, Proc. IEEE 99(9), 1525–1540 (2011).CrossRefGoogle Scholar
Olson, E., Walter, M., Leonard, J. and Teller, S., “Single-Cluster Graph Partitioning for Robotics Applications”, Proceedings of Robotics Science and Systems (2005) pp. 265–272.Google Scholar
Valgren, C., Duckett, T and Lilienthal, A., “Incremental Spectral Clustering and its Application to Topological Mapping”, Proceedings of IEEE International Conference on Robotics and Automation (2007) pp. 4283–4288.Google Scholar
Ng, A., Jordan, M. and Weiss, Y., “On Spectral Clustering: Analysis and an Algorithm”, Proceedings of Advances in Neural Information Processing Systems (2001) pp. 849–856.Google Scholar
Verma, D. and Meila, M., “A Comparison of Spectral Clustering Algorithms”, University of Washington, Tech. Rep. UW-CSE-03-05-01 (2003).Google Scholar
Blanco, J. L., Gonzales, J. and Fernández-Madrigal, J. A., “Consistent Observation Grouping for Generating Metric-Topological Maps that Improves Robot Localization”, Proceedings of International Conference on Robotics and Automation (2006) pp. 818–823.Google Scholar
Shi, J. and Malik, J., “Normalized cuts and image segmentation”, IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000).Google Scholar
Forster, C., Sabatta, D., Siegwart, R. and Scaramuzza, D., “RFID-Based Hybrid Metric-Topological SLAM for GPS-Denied Environments”, Proceedings of International Conference on Robotics and Automation (2013) pp. 5228–5234.Google Scholar
Yairi, T., “Map Building without Localization by Dimensionality Reduction Techniques”, Proceedings of the 24th International Conference on Machine Learning (2007) pp. 1071–1078.Google Scholar
Tenenbaum, J., Silva, V. and Langford, J., “A global geometric framework for nonlinear dimensionality reduction”, Science 290(5500), 2319–2323 (2000).CrossRefGoogle ScholarPubMed
Roweis, S. and Saul, L., “Nonlinear dimensionality reduction by locally linear embedding”, Science 290(5500), 2323–2326 (2000).CrossRefGoogle ScholarPubMed
Belkin, M. and Niyogi, P., “Laplacian Eigenmaps for dimensionality reduction and data representation”, Neural Comput. 15(6), 1373–1396 (2003).CrossRefGoogle Scholar
Newman, P. M., Cole, D. M. and Ho, K. L., “Outdoor SLAM Using Visual Appearance and Laser Ranging”, IEEE International Conference on Robotics and Automation (ICRA) (2006) pp. 1180–1187.Google Scholar
Cummins, M. and Newman, P., “FAB-MAP: Probabilistic localization and mapping in the space of appearance”, Int. J. Robot. Res. 27(6), 647–665 (2008).CrossRefGoogle Scholar
Cadena, C., Gálvez-López, D., Ramos, F. and Tardós, J. D., “Robust Place Recognition with Stereo Cameras”, IEEE International Conference on Intelligent Robots and Systems (IROS) (2010) pp. 5182–5189.Google Scholar
Calonder, M., Lepetit, V., Strecha, C. and Fua, P., “BRIEF: Binary Robust Independent Elementary Features”, Proceedings of 11th European Conference on Computer Vision (2010) pp. 778–792.Google Scholar
Oliva, A. and Torralba, A., “Modeling the shape of the scene: A holistic representation of the spatial envelope”, Int. J. Comput. Vis. 42(3), 145–175 (2001).CrossRefGoogle Scholar
Sünderhauf, N. and Protzel, P., “BRIEF-Gist Closing the Loop by Simple Means”, International Conference on Intelligent Robots and Systems (IROS) (2011) pp. 1234–1241.Google Scholar
Arroyo, R., Alcantarilla, P. F., Bergasa, L. M., Torres, J. J. Y. and Gamez, S., “Bidirectional Loop Closure Detection on Panoramas for Visual Navigation”, 2014 IEEE Intelligent Vehicles Symposium Proceedings (2014) pp. 1378–1383.Google Scholar
Garcia-Fidalgo, E. and Ortiz, A., “On the Use of Binary Feature Descriptors for Loop Closure Detection”, 2014 IEEE Emerging Technology and Factory Automation (2014) pp. 1–8.Google Scholar
Moreira, L., Justel, C. and Rosa, P., “Experimental Implementation of Loop Closure Detection Using Data Dimensionality Reduction by Spectral Method”, Proceedings of the 2017 IEEE International Conference on Industrial Technology (ICIT) (2017) pp. 797–802.Google Scholar
Mikolajczyk, K. and Schmid, C., “A performance evaluation of local descriptors”, IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005).CrossRefGoogle ScholarPubMed
Freeman, W. T. and Adelson, E. H., “The design and use of steerable filters”, IEEE Trans. Pattern Anal. Mach. Intell. 13(9), 891–906 (1991).CrossRefGoogle Scholar
Koenderink, J. J. and van Doom, A. J., “Representation of Local Geometry in the Visual System”, Biological Cybernetics 55(6), 367–375 (1987).CrossRefGoogle ScholarPubMed
Schaffalitzky, F. and Zisserman, A., “Multi-view Matching for Unordered Image Sets”, Proceedings of the 7th European Conference on Computer Vision-Part I (2002) pp. 414–431.Google Scholar
van Gool, L. J., Luc, and Moons, T. and Ungureanu, D., “Affine/Photometric Invariants for Planar Intensity Patterns”, Proceedings of the 4th European Conference on Computer Vision-Volume I, ECCV 96 (1996) pp. 642–651.Google Scholar
Gil, A., Mozos, O. M., Ballesta, M. and Reinoso, O., “A comparative evaluation of interest point detectors and local descriptors for visual SLAM”, Mach. Vis. Appl. 21(6), 905–920 (2010).CrossRefGoogle Scholar
Harris, C. and Stephens, M., “A Combined Corner and Edge Detector”, Proceedings of Fourth Alvey Vision Conference (1988) pp. 147–151.Google Scholar
Smith, S., “A New Class of Corner Finder”, Proceedings of the 3rd British Machine Vision Conference (1992) pp. 139–148.Google Scholar
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 (2002) pp. 384–393.Google Scholar
Hartmann, J., Klussendorff, J. and Maehle, E., “A Comparison of Feature Descriptors for Visual SLAM”, IEEE European Conference on Mobile Robots (ECMR) (2013) pp. 56–61.Google Scholar
Leutenegger, S., Chli, M. and Siegwart, R. Y., “BRISK: Binary Robust Invariant Scalable Keypoints”, Proceedings of the 2011 International Conference on Computer Vision (2011) pp. 2548–2555.Google Scholar
Ortiz, R., “FREAK: Fast Retina Keypoint”, Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2012) pp. 510–517.Google Scholar
Sturm, J., Magnenat, S., Engelhard, N., Pomerleau, F., Colas, F., Cremers, D., Siegwart, R. and Burgard, W., “Towards a Benchmark for RGB-D SLAM Evaluation”, RGB-D Workshop on Advanced Reasoning with Depth Cameras at Robotics: Science and Systems Conference (RSS) (2011).Google Scholar
Pearson, K., “On lines and planes of closest fit to systems of points in space”, Philos. Mag. 2(6), 559–572 (1901).CrossRefGoogle Scholar
Hotelling, H., “Analysis of a complex of statistical variables into principal components”, J. Edu. Psychol. 24(6), 417–441 (1933).CrossRefGoogle Scholar
Jolliffe, J., Principal Component Analysis (Springer Verlag, New York, NY, USA, 1986).CrossRefGoogle Scholar
Schölkopf, B., Smola, A. J. and Müller, K. R., “Nonlinear component analysis as a kernel eigenvalue problem”, Neural Comput. 10(5), 1299–1319 (1998).Google Scholar
Boser, B., Bernhard, E., Guyon, I. and Vapnik, V., “A Training Algorithm for Optimal Margin Classifiers”, Proceedings of the Fifth Annual Workshop on Computational Learning Theory (1992) pp. 144–152.Google Scholar
Fukunaga, K., Introduction to Statistical Pattern Recognition (Academic Press, San Diego, CA, USA, 1990).Google Scholar
Duda, R., Hart, P. and Stork, D., Pattern Classification (Wiley Interscience, New York, NY, USA, 2001).Google Scholar
Shin, Y. and Park, C., “Analysis of Correlation Based Dimension Reduction Methods”, Int. J. Appl. Math. Comput. Sci. 21(3), 549–558 (2011).CrossRefGoogle Scholar
Dijkstra, E., “A note on two problems in connexion with graphs”, Numer. Math. 1(1), 269–271 (1959).CrossRefGoogle Scholar
Chen, Y. F., Liu, S. Y., Liu, M., Miller, J. and How, J. P, “Motion Planning with Diffusion Maps”, International Conference on Intelligent Robots and Systems (IROS) (2011) pp. 1423–1430.Google Scholar
Johnstone, I. M., “On the distribution of the largest eigenvalue in principal components analysis”, Ann. Stat. 29(2), 295–327 (2001).CrossRefGoogle Scholar
Lafon, S., Keller, Y. and Coifman, R., “Data fusion and multicue data matching by diffusion maps”, IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1784–1797 (2006).CrossRefGoogle ScholarPubMed
Williams, C. and Seeger, M., “Using the Nyström method to speed up kernel machines”, Neural Inf. Process. Syst. 13, 682–688 (2001).Google Scholar
Jia, P., Yin, J., Huang, X. and Hu, D., “Incremental Laplacian eigenmaps by preserving adjacent information between data points”, J. Pattern Recognit. Lett. 30(16), 1457–1463 (2009).CrossRefGoogle Scholar
Shmueli, Y., Sipola, T., Shabat, G. and Averbuch, A., “Using Affinity Perturbations to Detect Web Traffic Anomalies”, Proceedings of the 10th International Conference on Sampling Theory and Applications (SampTA) (2013) pp. 444–447.Google Scholar
Shmueli, Y., Wolf, G. and Averbuch, A., “Updating kernel methods in spectral decomposition by affinity perturbations”, Linear Alg. Appl. 437(6), 1356–1365 (2012).CrossRefGoogle Scholar