Summary
This paper is concerned with assessing the quality of work-space maps. While there has been much work in recent years on building maps of field settings, little attention has been given to endowing a machine with introspective competencies which would allow assessing the reliability/plausibility of the representation. We classify regions in 3D point-cloud maps into two binary classes — “plausible” or “suspicious”. In this paper we concentrate on the classification of urban maps and use a Conditional Random Fields to model the intrinsic qualities of planar patches and crucially, their relationship to each other. A bipartite labelling of the map is acquired via application of the Graph Cut algorithm. We present results using data gathered by a mobile robot equipped with a 3D laser range sensor while operating in a typical urban setting.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Anguelov, D., Taskar, B., Chatalbashev, V., Koller, D., Gupta, D., Heitz, G., Ng, A.: Discriminative learning of markov random fields for segmentation of 3d scan data. In: CVPR 2005: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 2, pp. 169–176. IEEE Computer Society, Washington (2005)
Besag, J.: Statistical analysis of non-lattice data 24, 179–195 (1975)
Besag, J.: On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society 48(3), 259–302 (1986)
Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, New York (2006)
Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1124–1137 (2004)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1222–1239 (2001)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Geman, S., Geman, D.: Stochastic relaxation, gibbs distribution and the bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI 6(6), 721–741 (1984)
Gutmann, J., Konolige, K.: Incremental mapping of large cyclic environments. In: Int. Symposium on Computational Intelligence in Robotics and Automation (1999)
Hähnel, D., Triebel, R., Burgard, W., Thrun, S.: Map building with mobile robots in dynamic environments. In: Proc. of the IEEE International Conference on Robotics and Automation (ICRA) (2003)
Kinderman, R., Snell, J.: Markov random fields and their applications. American Mathematical Society, Providence, RI (1980)
Kolmogorov, V., Zabin, R.: What energy functions can be minimized via graph cuts? Pattern Analysis and Machine Intelligence. IEEE Transactions on 26(2), 147–159 (2004)
Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proc. 18th International Conf. on Machine Learning, pp. 282–289. Morgan Kaufmann, San Francisco (2001)
Leonard, J.J., Durrant-Whyte, H.F., Cox, I.J.: Dynamic map building for an autonomous mobile robot. Int. J. Rob. Res., 11(4), 286–298 (1992)
Liao, L., Fox, D., Kautz, H.: Extracting places and activities from gps traces using hierarchical conditional random fields. The International Journal of Robotics Research 26(1), 119–134 (2007)
Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., Rother, C.: A comparative study of energy minimization methods for markov random fields. In: European Conference on Computer Vision, vol. 2, pp. 16–29 (2006)
Triebel, R., Kersting, K., Burgard, W.: Robust 3D scan point classification using associative markov networks. In: Proc. of the International Conference on Robotics and Automation (ICRA) (2006)
Viola, P., Jones, M.: Robust real-time object detection. International Journal of Computer Vision (2002)
Wallach, H.M.: Conditional random fields: An introduction (2004)
Weingarten, J., Gruener, G., Siegwart, R.: A fast and robust 3D feature extraction algorithm for structured environment reconstruction. In: Proc. of the 11th International Conference on Advanced Robotics (ICAR) (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Chandran-Ramesh, M., Newman, P. (2008). Assessing Map Quality Using Conditional Random Fields. In: Laugier, C., Siegwart, R. (eds) Field and Service Robotics. Springer Tracts in Advanced Robotics, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75404-6_4
Download citation
DOI: https://doi.org/10.1007/978-3-540-75404-6_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75403-9
Online ISBN: 978-3-540-75404-6
eBook Packages: EngineeringEngineering (R0)