Abstract
In this paper we present an integrated approach for efficient online 3D semantic map building of urban environments and the subsequent extraction of qualitative spatial relationships between the different objects in the scene. We split this process into three stages, where we combine a state of the art image segmentation and classification algorithm with an online clustering algorithm to obtain a coherent representation of the environment. Finally, a graph representation is extracted which can then be used for spatial reasoning and human robot interaction. We present first results from data collected by a mobile robot which operates in city areas.
Keywords
- Point Cloud
- Mobile Robot
- Urban Environment
- Spatial Relation
- Conditional Random Field
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Bazeille, S., Filliat, D.: Incremental topo-metric slam using vision and robot odometry. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 4067–4073. IEEE (2011)
Doh, N.L., Lee, K., Chung, W.K., Cho, H.: Simultaneous localisation and mapping algorithm for topological maps with dynamics. Control Theory & Applications, IET 3(9), 1249–1260 (2009)
Elfes, A.: Using occupancy grids for mobile robot perception and navigation. Computer 22(6), 46–57 (1989)
Thrun, S., Bücken, A.: Integrating grid-based and topological maps for mobile robot navigation. In: Proceedings of the National Conference on Artificial Intelligence, pp. 944–951 (1996)
Zender, H., Martínez Mozos, O., Jensfelt, P., Kruijff, G.J.M., Burgard, W.: Conceptual spatial representations for indoor mobile robots. Robotics and Autonomous Systems 56(6), 493–502 (2008)
Galindo, C., Fernández-Madrigal, J.A., González, J., Saffiotti, A.: Robot task planning using semantic maps. Robotics and Autonomous Systems 56, 955–966 (2008)
Skubic, M., Perzanowski, D., Blisard, S., Schultz, A., Adams, W., Bugajska, M., Brock, D.: Spatial language for human-robot dialogs. IEEE Systems, Man, and Cybernetics, Part C: Applications and Reviews 34, 154–167 (2004)
Vasudevan, S., Siegwart, R.: Bayesian space conceptualization and place classification for semantic maps in mobile robotics. Robotics and Autonomous Systems 56(6), 522–537 (2008)
Pronobis, A., Mozos, O.M., Caputo, B., Jensfelt, P.: Multi-modal semantic place classification. The International Journal of Robotics Research 29, 298–320 (2010)
Nüchter, A., Hertzberg, J.: Towards semantic maps for mobile robots. Robotics and Autonomous Systems 56(11), 915–926 (2008)
Rusu, R.B., Marton, Z.C., Blodow, N., Holzbach, A., Beetz, M.: Model-based and learned semantic object labeling in 3d point cloud maps of kitchen environments. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2009, pp. 3601–3608. IEEE (2009)
Wolf, D.F., Sukhatme, G.S.: Semantic mapping using mobile robots. IEEE Transactions on Robotics 24(2), 245–258 (2008)
Douillard, B., Fox, D., Ramos, F., Durrant-Whyte, H.: Classification and semantic mapping of urban environments. International Journal of Robotics Research 30(1), 5–32 (2011)
Kuipers, B.: The spatial semantic hierarchy. Artificial Intelligence 119(1-2), 191–233 (2000)
He, X., Zemel, R.S., Carreira-Perpinán, M.A.: Multiscale conditional random fields for image labeling. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 2, pp. 695–702. IEEE (2004)
Sturgess, P., Alahari, K., Ladicky, L., Torr, P.H.S.: Combining appearance and structure from motion features for road scene understanding. In: BMVC (2009)
Posner, I., Cummins, M., Newman, P.: A generative framework for fast urban labeling using spatial and temporal context. Auton. Robots 26(2-3), 153–170 (2009)
Ladicky, L., Sturgess, P., Russell, C., Sengupta, S., Bastanlar, Y., Clocksin, W., Torr, P.: Joint optimisation for object class segmentation and dense stereo reconstruction (2010)
Geiger, A., Lauer, M., Urtasun, R.: A generative model for 3d urban scene understanding from movable platforms. In: CVPR (2011)
Geiger, A., Wojek, C., Urtasun, R., Geiger, A., Lauer, M., Urtasun, R., Geiger, A., Ziegler, J., Stiller, C., Lenz, P., et al.: Joint 3d estimation of objects and scene layout. In: Neural Information Processing Systems
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Computing Surveys (CSUR) 31(3), 264–323 (1999)
Xu, R., Wunsch, D., et al.: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3), 645–678 (2005)
Rusu, R., Marton, Z., Blodow, N., Holzbach, A., Beetz, M.: Model-based and learned semantic object labeling in 3D point cloud maps of kitchen environments. In: IROS (2009)
Wolf, D., Howard, A., Sukhatme, G.S.: Towards geometric 3d mapping of outdoor environments using mobile robots. In: IROS. IEEE (2005)
Valencia, R., Teniente, E.H., Trulls, E., Andrade-Cetto, J.: 3D mapping for urban service robots. In: IROS, IEEE (2009)
Marton, Z.C., Rusu, R.B., Beetz, M.: On fast surface reconstruction methods for large and noisy datasets. In: ICRA (2009)
Klasing, K., Wollherr, D., Buss, M.: Realtime segmentation of range data using continuous nearest neighbors. In: ICRA (2009)
Zlatanova, S., Rahman, A.A., Shi, W.: Topological models and frameworks for 3D spatial objects. In: Computers & Geosciences, pp. 419–428 (2004)
Jungert, E.: Qualitative spatial reasoning for determination of object relations using symbolic interval projections. In: Proceedings 1993 IEEE Symposium on Visual Languages, pp. 83–87 (August 1993)
Li, C., Lu, J., Yin, C., Ma, L.: Qualitative spatial representation and reasoning in 3d space. In: Intelligent Computation Technology and Automation, ICICTA 2009, vol. 1, pp. 653–657 (2009)
Skubic, M., Perzanowski, D., Schultz, A., Adams, W.: Using spatial language in a human-robot dialog. In: Proceedings of the 2002 International Conference on IEEE Robotics and Automation, ICRA 2002, vol. 4, pp. 4143–4148. IEEE (2002)
Moratz, R., Tenbrink, T., Bateman, J.A., Fischer, K.: Spatial Knowledge Representation for Human-Robot Interaction. In: Freksa, C., Brauer, W., Habel, C., Wender, K.F. (eds.) Spatial Cognition III. LNCS (LNAI), vol. 2685, pp. 263–286. Springer, Heidelberg (2003)
Bloch, I., Saffiotti, A.: On the representation of fuzzy spatial relations in robot maps. In: Intelligent Systems for Information Processing, pp. 47–57 (2002)
Bauer, A., Gonsior, B., Wollherr, D., Buss, M.: Heuristic rules for human-robot interaction based on principles from linguistics - asking for directions. In: AISB Convention - Symposium on New Frontiers in Human-Robot Interaction (2009)
Sj, K., Aydemir, A., Moerwald, T., Zhou, K., Jensfelt, P.: Mechanical support as a spatial abstraction for mobile robots. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4894–4900. IEEE (2010)
Sj, K., Pronobis, A., Jensfelt, P.: Functional topological relations for qualitative spatial representation. In: The 15th International Conference on Advanced Robotics (2011)
Tenorth, M.: Knowledge Processing for Autonomous Robots. PhD thesis, Technische Universitaet Muenchen (2011)
Lafferty, J., McCallum, A., Pereira, F.C.N.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data (2001)
Kumar, S., Hebert, M.: Discriminative random fields: A discriminative framework for contextual interaction in classification. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, pp. 1150–1157. IEEE (2003)
Vishwanathan, S.V.N., Schraudolph, N.N., Schmidt, M.W., Murphy, K.P.: Accelerated training of conditional random fields with stochastic gradient methods. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 969–976. ACM (2006)
Wainwright, M.J., Jaakkola, T.S., Willsky, A.S.: Tree-based reparameterization framework for analysis of sum-product and related algorithms. IEEE Transactions on Information Theory 49(5), 1120–1146 (2003)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 888–905 (2000)
Levinshtein, A., Stere, A., Kutulakos, K.N., Fleet, D.J., Dickinson, S.J., Siddiqi, K.: Turbopixels: Fast superpixels using geometric flows. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(12), 2290–2297 (2009)
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: Slic superpixels. Technical Report 149300 EPFL (June 2010)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Bhatia, A., Snyder, W.E., Bilbro, G.: Stacked integral image. In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 1530–1535. IEEE (2010)
Zhang, C., Wang, L., Yang, R.: Semantic Segmentation of Urban Scenes Using Dense Depth Maps. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6314, pp. 708–721. Springer, Heidelberg (2010)
Spiliopoulou, M., Ntoutsi, I., Theodoridis, Y., Schult, R.: Monic: modeling and monitoring cluster transitions. In: Proceedings of the 12th SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 706–711. ACM (2006)
Wurm, K.M., Hornung, A., Bennewitz, M., Stachniss, C., Burgard, W.: Octomap: A probabilistic, flexible, and compact 3d map representation for robotic systems. In: Proc. of the ICRA 2010 Workshop on Best Practice in 3D Perception and Modeling for Mobile Manipulation (2010)
del Pobil, A.P., Escrig, M.T., Jaen, J.A.: An attempt towards a general representation paradigm for spatial reasoning. In: International Conference on Systems, Man and Cybernetics, ‘Systems Engineering in the Service of Humans’, Conference Proceedings, vol. 1, pp. 215–220 (October 1993)
Brostow, G.J., Fauqueur, J., Cipolla, R.: Semantic object classes in video: A high-definition ground truth database. Pattern Recognition Letters (2008)
Geiger, A., Ziegler, J., Stiller, C.: Stereoscan: Dense 3d reconstruction in real-time. In: IEEE Intelligent Vehicles Symposium, Baden-Baden, Germany (2011)
Bauer, A., Klasing, K., Xu, T., Sosnowski, S., Lidoris, G., Muhlbauer, Q., Zhang, T., Rohrmuller, F., Wollherr, D., Kuhnlenz, K., et al.: The autonomous city explorer project. In: IEEE International Conference on Robotics and Automation, ICRA 2009, pp. 1595–1596. IEEE (2009)
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Mitsou, N. et al. (2012). Online Semantic Mapping of Urban Environments. In: Stachniss, C., Schill, K., Uttal, D. (eds) Spatial Cognition VIII. Spatial Cognition 2012. Lecture Notes in Computer Science(), vol 7463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32732-2_4
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DOI: https://doi.org/10.1007/978-3-642-32732-2_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32731-5
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