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Vision-based topological mapping and localization by means of local invariant features and map refinement

Published online by Cambridge University Press:  09 April 2014

Emilio Garcia-Fidalgo  and
Alberto Ortiz
Emilio Garcia-Fidalgo*
Affiliation:
Department of Mathematics and Computer Science, University of the Balearic Islands, Spain
Alberto Ortiz
Affiliation:
Department of Mathematics and Computer Science, University of the Balearic Islands, Spain
*
*Corresponding author. E-mail: [email protected]
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Summary

We propose an appearance-based approach for topological visual mapping and localization using local invariant features. To optimize running times, matchings between the current image and previously visited places are determined using an index based on a set of randomized kd-trees. We use a discrete Bayes filter for predicting loop candidates, whose observation model is a novel approach based on an efficient matching scheme between features. In order to avoid redundant information in the resulting maps, we also present a map refinement framework, which takes into account the visual information stored in the map for refining the final topology of the environment. These refined maps save storage space and improve the execution times of localizations tasks. The approach is validated using image sequences from several environments and compared with the state-of-the-art FAB-MAP 2.0 algorithm.

Keywords

Topological mapsLoop closureLocalizationMap refinementVisual SLAM
Type
Articles
Information
Robotica , Volume 33 , Issue 7 , August 2015 , pp. 1446 - 1470
Copyright
Copyright © Cambridge University Press 2014 

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References

1. Durrant-Whyte, H. and Bailey, T., “Simultaneous Localisation and Mapping (SLAM): Part I the essential algorithms,” Robot. Autom. Mag. 13 (2), 99–110 (2006).Google Scholar
2. Sivic, J. and Zisserman, A., “Video Google: A Text Retrieval Approach to Object Matching in Videos,” Proceedings of the International Conference on Computer Vision, Nice, France (2003) pp. 1470–1477.Google Scholar
3. Zhang, H., “BoRF: Loop-Closure Detection with Scale Invariant Visual Features,” Proceedings of the International Conference on Robotics and Automation, Shangai, China (2011) pp. 3125–3130.Google Scholar
4. Angeli, A., Doncieux, S., Meyer, J.-A. and Filliat, D., “Incremental Vision-Based Topological SLAM,” Proceedings of the International Conference on Intelligent Robots and Systems, Nice, France (2008) pp. 22–26.Google Scholar
5. Booij, O., Terwijn, B., Zivkovic, Z. and Krose, B., “Navigation Using an Appearance Based Topological Map,” Proceedings of the International Conference on Robotics and Automation, Roma, Italy (Apr. 2007) pp. 3927–3932.Google Scholar
6. Garcia-Fidalgo, E. and Ortiz, A., “Probabilistic Appearance-Based Mapping and Localization Using Visual Features,” Proceedings of the Pattern Recognition and Image Analysis, Lecture Notes in Computer Science, Funchal, Portugal, vol. 7887 (2013) pp. 277–285.Google Scholar
7. Zivkovic, Z., Bakker, B. and Krose, B., “Hierarchical Map Building Using Visual Landmarks and Geometric Constraints,” Proceedings of the International Conference on Intelligent Robots and Systems, Edmonton, Canada (2005) pp. 2480–2485.Google Scholar
8. Ulrich, I. and Nourbakhsh, I., “Appearance-Based Place Recognition for Topological Localization,” Proceedings of the International Conference on Robotics and Automation, San Francisco, USA (2000) pp. 1023–1029.Google Scholar
9. Goedemé, T., Nuttin, M., Tuytelaars, T. and Van Gool, L., “Markerless Computer Vision Based Localization using Automatically Generated Topological Maps,” Proceedings of the European Navigation Conference, Rotterdam, The Netherlands (2004) pp. 235–243.Google Scholar
10. Sabatta, D. G., “Vision-based Topological Map Building and Localisation using Persistent Features,” Proceedings of the Robotics and Mechatronics Symposium, Pretoria, South Africa (2008) pp. 1–6.Google Scholar
11. Kawewong, A., Tongprasit, N., Tungruamsub, S. and Hasegawa, O., “Online and incremental appearance-based SLAM in highly dynamic environments,” Int. J. Robot. Res. 30 (1), 33–55 (2011).Google Scholar
12. Singh, G. and Kosecka, J., “Visual Loop Closing using Gist Descriptors in Manhattan World,” Proceedings of the Omnidirectional Robot Vision Workshop, Held with IEEE ICRA, Anchorage, USA (2010).Google Scholar
13. Murillo, A. C., Sagues, C. and Guerrero, J., “From omnidirectional images to hierarchical localization,” Robot. Auton. Syst. 55, 372–382 (May 2007).Google Scholar
14. 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).Google Scholar
15. Angeli, A., Doncieux, S., Meyer, J.-A. and Filliat, D., “Real-Time Visual Loop-Closure Detection,” Proceedings of the International Conference on Robotics and Automation, Pasadena, USA (2008) pp. 1842–1847.Google Scholar
16. Angeli, A., Filliat, D., Doncieux, S. and Meyer, J.-A., “A fast and incremental method for loop-closure detection using bags of visual words,” IEEE Trans. Robot. 24 (5), 1027–1037 (2008).Google Scholar
17. Zhang, H., “Indexing Visual Features: Real-Time Loop Closure Detection Using a Tree Structure,” Proceedings of the International Conference on Robotics and Automation, Vilamoura, Portugal (2012) pp. 3613–3618.Google Scholar
18. Fraundorfer, F., Engels, C. and Nister, D., “Topological Mapping, Localization and Navigation Using Image Collections,” Proceedings of the International Conference on Intelligent Robots and Systems, San Diego, USA (2007) pp. 3872–3877.Google Scholar
19. Johns, E. and Yang, G.-Z., “Feature Co-Occurrence Maps: Appearance-Based Localisation Throughout the Day,” Proceedings of the International Conference on Robotics and Automation, Karlsruhe, Germany (2013) pp. 3212–3218.Google Scholar
20. Nister, D. and Stewenius, H., “Scalable Recognition with a Vocabulary Tree,” Proceedings of the International Conference on Computer Vision and Pattern Recognition, New York, USA vol. 2 (2006) pp. 2161–2168.Google Scholar
21. Cummins, M. and Newman, P., “Accelerating FAB-MAP with concentration inequalities,” IEEE Trans. Robot. 26 (6), 1042–1050 (2010).Google Scholar
22. Cummins, M. and Newman, P. M., “Appearance-only SLAM at large scale with FAB-MAP 2.0,” Int. J. Robot. Res. 30 (9), 1100–1123 (2011).Google Scholar
23. Glover, A. J., Maddern, W. P., Milford, M. and Wyeth, G. F., “FAB-MAP + RatSLAM: Appearance-based SLAM for multiple times of day,” International Conference on Robotics and Automation, Anchorage, USA, IEEE (2010) pp. 3507–3512.Google Scholar
24. Filliat, D., “A Visual Bag of Words method for Interactive Qualitative Localization and Mapping,” Proceedings of the International Conference on Robotics and Automation, Roma, Italy (2007) pp. 3921–3926.Google Scholar
25. Angeli, A., Doncieux, S., Meyer, J.-A. and Filliat, D., “Visual Topological SLAM and Global Localization,” Proceedings of the International Conference on Robotics and Automation, Kobe, Japan (2009) pp. 4300–4305.Google Scholar
26. Nicosevici, T. and García, R., “On-line Visual Vocabularies for Robot Navigation and Mapping,” Proceedings of the International Conference on Intelligent Robots and Systems, St. Louis, USA (2009) pp. 205–212.Google Scholar
27. 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).Google Scholar
28. Liu, Y. and Zhang, H., “Visual Loop Closure Detection with a Compact Image Descriptor,” Proceedings of the International Conference on Intelligent Robots and Systems, Vilamoura, Portugal (2012) pp. 1051–1056.Google Scholar
29. Siagian, C. and Itti, L., “Rapid biologically-inspired scene classification using features shared with visual attention,” IEEE Trans. Pattern Anal. Mach. Intell. 29 (2), 300–12 (2007).Google Scholar
30. Sünderhauf, N. and Protzel, P., “BRIEF-Gist - Closing the Loop by Simple Means,” Proceedings of the International Conference on Intelligent Robots and Systems, San Francisco, USA (2011) pp. 1234–1241.Google Scholar
31. Bradley, D. M., Patel, R., Vandapel, N. and Thayer, S., “Real-Time Image-Based Topological Localization in Large Outdoor Environments,” Proceedings of the International Conference on Intelligent Robots and Systems, Edmonton, Canada (2005) pp. 3670–3677.Google Scholar
32. Weiss, C., Masselli, A., Tamimi, H. and Zell, A., “Fast Outdoor Robot Localization using Integral Invariants,” Proceedings of the International Conference on Computer Vision Systems, Bielefeld, Germany (2007) 24 pp.Google Scholar
33. Wang, J., Zha, H. and Cipolla, R., “Efficient Topological Localization Using Orientation Adjacency Coherence Histograms,” Proceedings of the International Conference on Pattern Recognition, IEEE Computer Society, Hong Kong, China (2006) pp. 271–274.Google Scholar
34. Badino, H., Huber, D. F. and Kanade, T., “Real-Time Topometric Localization,” Proceedings of the International Conference on Robotics and Automation, IEEE, St. Paul, USA (2012) pp. 1635–1642.Google Scholar
35. Lowe, D. G., “Distinctive Image Features from Scale-Invariant Keypoints,” Int. J. Comput. Vis. 60 (2), 91–110 (2004).Google Scholar
36. Valgren, C., Duckett, T. and Lilienthal, A., “Incremental Spectral Clustering and Its Application to Topological Mapping,” Proceedings of the International Conference on Robotics and Automation, Roma, Italy (Apr. 2007) pp. 10–14.Google Scholar
37. Bacca, B., Salvi, J., Batlle, J. and Cufi, X., “Appearance-based mapping and localisation using feature stability histograms,” Electron. Lett. 46 (16), 1120–1121 (2010).Google Scholar
38. Blanco, J. L., Fernandez-Madrigal, J. A. and Gonzalez, J., “Towards a unified bayesian approach to hybrid metric-topological SLAM,” IEEE Trans. Robot. 24 (2), 259–270 (2008).Google Scholar
39. Zhang, H., Li, B. and Yang, D., “Keyframe Detection for Appearance-Based Visual SLAM,” Proceedings of the International Conference on Intelligent Robots and Systems, Taipei, Taiwan (2010) pp. 2071–2076.Google Scholar
40. Leutenegger, S., Chli, M., and Siegwart, R., “BRISK: Binary Robust Invariant Scalable Keypoints,” Proceedings of the International Conference on Computer Vision, Barcelona, Spain, vol. 0 (2011) pp. 2548–2555.Google Scholar
41. Rublee, E., Rabaud, V., Konolige, K. and Bradski, G., “ORB: An Efficient Alternative to SIFT or SURF,” Proceedings of the International Conference on Computer Vision, Barcelona, Spain (2011) pp. 2564–2571.Google Scholar
42. Alahi, A., Ortiz, R. and Vandergheynst, P., “FREAK: Fast Retina Keypoint,” Proceedings of the International Conference on Computer Vision and Pattern Recognition, Rhode Island, USA (2012) pp. 510–517.Google Scholar