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A robust, multi-hypothesis approach to matching occupancy grid maps

Published online by Cambridge University Press:  11 January 2013

Jose-Luis Blanco ,
Javier González-Jiménez  and
Juan-Antonio Fernández-Madrigal
Jose-Luis Blanco*
Affiliation:
Department of Engineering, University of Almería, Almería, Spain
Javier González-Jiménez
Affiliation:
Department of System Engineering and Automation, University of Málaga, Malaga, Spain
Juan-Antonio Fernández-Madrigal
Affiliation:
Department of System Engineering and Automation, University of Málaga, Malaga, Spain
*
*Corresponding author. E-mail: joseluisblancoc@gmail.com
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Summary

This paper presents a new approach to matching occupancy grid maps by means of finding correspondences between a set of sparse features detected in the maps. The problem is stated here as a special instance of generic image registration. To cope with the uncertainty and ambiguity that arise from matching grid maps, we introduce a modified RANSAC algorithm which searches for a dynamic number of internally consistent subsets of feature pairings from which to compute hypotheses about the translation and rotation between the maps. By providing a (possibly multi-modal) probability distribution of the relative pose of the maps, our method can be seamlessly integrated into large-scale mapping frameworks for mobile robots. This paper provides a benchmarking of different detectors and descriptors, along extensive experimental results that illustrate the robustness of the algorithm with a 97% success ratio in loop-closure detection for ~1700 matchings between local maps obtained from four publicly available datasets.

Keywords

Mobile robotsSLAMRobot localizationPose estimation and registrationnavigation
Type
Articles
Information
Robotica , Volume 31 , Issue 5 , August 2013 , pp. 687 - 701
Copyright
Copyright © Cambridge University Press 2013 

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References

1.Bay, H., Tuytelaars, T. and Van Gool, L., “Surf: Speeded up robust features,’ Lecture Notes Comput. Sci. 3951, 404 (2006).CrossRefGoogle Scholar
2.Besl, P. J. and McKay, N. D., “A method for registration of 3-D shapes,’ IEEE Trans. Pattern Anal. Mach. Intell. 14 (2), 239–256 (1992).CrossRefGoogle Scholar
3.Birk, A. and Carpin, S., “Merging occupancy grid maps from multiple robots,’ IEEE Proc. 94 (7), 1384 (2006).CrossRefGoogle Scholar
4.Blanco, J.-L., “A tutorial on se(3) transformation parameterizations and on-manifold optimization,’ Technical report, University of Malaga (Sep. 2010).Google Scholar
5.Blanco, J.-L., Fernández-Madrigal, J.-A. and Gonzalez, J., “Towards a unified Bayesian approach to hybrid metric-topological SLAM,’ IEEE Trans. Robot. 24 (2), 259–270 (2008).CrossRefGoogle Scholar
6.Blanco, J.-L., Gonzalez, J. and Fernández-Madrigal, J.-A., “Subjective local maps for hybrid metric-topological SLAM,’ Robot. Auton. Syst. 57 (1), 64–74 (2009).CrossRefGoogle Scholar
7.Blanco, J.-L., González-Jiménez, J. and Fernández-Madrigal, J.-A., “An alternative to the Mahalanobis distance for determining optimal correspondences in data association,’ IEEE Trans. Robot. 28 (4) (2012).CrossRefGoogle Scholar
8.Bosse, M., Newman, P., Leonard, J., Soika, M., Feiten, W. and Teller, S., “An Atlas Framework for Scalable Mapping,’ In: Proceedings of the IEEE International Conference on Robotics and Automation, vol. 2 (2003) pp. 1899–1906.Google Scholar
9.Davison, A. J., Reid, I., Molton, N. and Stasse, O., “MonoSLAM: Real-time single camera SLAM,’ IEEE Trans. Pattern Anal. Mach. Intell. 29 (6), 1052–1067 (2007).CrossRefGoogle ScholarPubMed
10.Duckett, T. and Nehmzow, U., “Mobile robot self-localisation using occupancy histograms and a mixture of Gaussian location hypotheses,’ Robot. Auton. Syst. 34 (2–3), 119–130 (2001).CrossRefGoogle Scholar
11.Elfes, A., “Using occupancy grids for mobile robot perception and navigation,’ Computer 22 (6), 46–57 (1989).CrossRefGoogle Scholar
12.Estrada, C., Neira, J. and Tardos, J. D., “Hierarchical SLAM: Real-time accurate mapping of large environments,’ IEEE Trans. Robot. 21 (4), 588–596 (2005).CrossRefGoogle Scholar
13.Fischler, M. A. and Bolles, R. C., “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,’ Commun. ACM 24 (6), 381–395 (1981).CrossRefGoogle Scholar
14.Gil, A., Mozos, O. M., Ballesta, M. and Reinoso, O., “A comparative evaluation of interest point detectors and local descriptors for visual slam,’ Mach. Vision Appl. 21 (6), 905–920 (2010).CrossRefGoogle Scholar
15.Grisetti, G., Tipaldi, G. D., Stachniss, C., Burgard, W. and Nardi, D., “Fast and accurate SLAM with Rao-Blackwellized particle filters,’ Robot. Auton. Syst. 55 (1), 30–38 (2007).CrossRefGoogle Scholar
16.Gutmann, J. S. and Konolige, K., “Incremental mapping of large cyclic environments,’ In: Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation (1999) pp. 318–325.Google Scholar
17.Harris, C. and Stephens, M., “A combined corner and edge detector,’ In: Proceedings of Alvey Vision Conference, vol. 15 (1988) pp. 147–151.Google Scholar
18.Hartley, R. and Zisserman, A., Multiple View Geometry in Computer Vision (Cambridge University Press, Cambridge, 2003).Google Scholar
19.Hess, R., “An open-source SIFTLibrary,’ In: Proceedings of the international conference on Multimedia, (2010) pp. 1493–1496.Google Scholar
20.Horn, B. K. P., “Closed-form solution of absolute orientation using unit quaternions,’ J. Opt. Soc. Am. A, 4 (4), 629–642 (1987).CrossRefGoogle Scholar
21.Howard, A. and Roy, N., The robotics data set repository (radish) (2003). available at: http://radish.sourceforge.net/Google Scholar
22.Lazebnik, S., Schmid, C. and Ponce, J., “A sparse texture representation using affine-invariant regions,’ In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2 (2003) pp. 319–324.Google Scholar
23.Lowe, D. G., “Object recognition from local scale-invariant features,’ In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2 (1999) pp. 1150–1157.CrossRefGoogle Scholar
24.Lucas, B. D. and Kanade, T., “An iterative image registration technique with an application to stereo vision,’ Proc. DARPA Image Understanding Workshop. 121, 130 (1981).Google Scholar
25.Martínez, J. L., González, J., Morales, J., Mandow, A. and García-Cerezo, A., “Mobile robot motion estimation by 2D scan matching with genetic and iterative closest point algorithms,’ J. Field Robot. 23 (Jan. 2006) pp. 21–34.CrossRefGoogle Scholar
26.Mikolajczyk, K. and Schmid, C., “An affine invariant interest point detector,’ In: Proceedings of European Conference on Computer Vision, vol. 1 (2002) pp. 128–142.Google Scholar
27.Mikolajczyk, K. and Schmid, C., “A performance evaluation of local descriptors,’ IEEE Trans. Pattern Anal. Mach. Intell. 27 (10), 1615–1630 (2005).CrossRefGoogle ScholarPubMed
28.Neira, J. and Tardós, J. D., “Data association in stochastic mapping using the joint compatibility test,’ IEEE Trans. Robot. Autom. 17 (6), 890–897 (2001).CrossRefGoogle Scholar
29.Nieto, J. I., Guivant, J. E. and Nebot, E. M., “The hybrid metric maps (HYMMS): A novel map representation for DenseSLAM,’ In: Proceedings of the IEEE International Conference on Robotics and Automation (2004) pp. 391–396.Google Scholar
30.Runnalls, A. R., “Kullback–Leibler approach to Gaussian mixture reduction,’ IEEE Trans. Aerosp. Electron. Syst. 43 (3), 989–999 (2007).CrossRefGoogle Scholar
31.Saeedi, P., Lawrence, P. D. and Lowe, D. G., “Vision-based 3-D trajectory tracking for unknown environments,’ IEEE Trans. Robot. 22 (1), 119–136 (2006).CrossRefGoogle Scholar
32.Se, S., Lowe, D. and 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, vol. 1 (2001) pp. 414–420.Google Scholar
33.Shekhar, C., Govindu, V. and Chellappa, R., “Multisensor image registration by feature consensus,’ Pattern Recogn. 32 (1), 39–52 (1999).CrossRefGoogle Scholar
34.Shi, J. and Tomasi, C., “Good features to track,’ In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (1994) pp. 593–600.Google Scholar
35.Tamimi, H., Andreasson, H., Treptow, A., Duckett, T. and Zell, A., “Localization of mobile robots with omnidirectional vision using particle filter and iterative SIFT,’ Robot. Auton. Syst. 54, 758–765 (2006).CrossRefGoogle Scholar
36.Thrun, S., “Learning occupancy grid maps with forward sensor models,’ Auton. Robot. 15 (2), 111–127 (2003).CrossRefGoogle Scholar
37.Thrun, S., Burgard, W. and Fox, D., Probabilistic Robotics (MIT Press, Cambridge, MA (USA), 2005).Google Scholar
38.Zitova, B. and Flusser, J., “Image registration methods: A survey,’ Image Vis. Comput. 21 (11), 977–1000 (2003).CrossRefGoogle Scholar