Abstract
Topological maps are vital for fast and accurate localization in large environments. Sparse topological maps can be constructed by partitioning a sequence of images acquired by a robot, according to their appearance. All images in a partition have similar appearance and are represented by a node in a topological map. In this paper, we present a topological mapping framework which makes use of image sequence partitioning (ISP) to produce sparse maps. The framework facilitates coarse loop closure at node level and a finer loop closure at image level. Hierarchical inverted files (HIF) are proposed which are naturally adaptable to our sparse topological mapping framework and enable efficient loop closure. Computational gain attained in loop closure with HIF over sparse topological maps is demonstrated. Experiments are performed on outdoor environments using an omni-directional camera.
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References
Kosecka, J., Murillo, A.C., Campos, P., Guerrero, J.J.: Gist vocabularies in omnidirectional images for appearance based mapping and localization. In: 10th IEEE Workshop on Omnidirectional Vision, Camera Networks and Non-classical Cameras (OMNIVIS), held with Robotics, Science and Systems (2010)
Angeli, A., Filliat, D., Doncieux, S., Meyer, J.-A.: A fast and incremental method for loop-closure detection using bags of visual words. IEEE Transactions On Robotics, Special Issue on Visual SLAM (2008)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110(3), 346–359 (2008)
Cummins, M., Newman, P.: FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance. The International Journal of Robotics Research 27(6), 647–665 (2008)
Cummins, M., Newman, P.: Highly scalable appearance-only slam fab-map 2.0. In: Robotics Science and Systems (RSS), Seattle, USA (June 2009)
Koseck, J., Li, F., Yang, X.: Global localization and relative positioning based on scale-invariant keypoints. Robotics and Autonomous Systems 52(1), 27–38 (2005)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Nister, D., SteweniusScalable, H.: recognition with a vocabulary tree. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2006, vol. 2, pp. 2161–2168 (2006)
Nourani-Vatani, N., Pradalier, C.: Scene change detection for topological localization and mapping. In: IEEE/RSJ Intl. Conf. on Intelligent Robotics and Systems, IROS (2010)
Tapus, A., Siegwart, R.: Incremental robot mapping with fingerprints of places. In: IEEE/RSJ Intl. Conf. on Intelligent Robotics and Systems (IROS), pp. 2429–2434 (2005)
Valgren, C., Duckett, T., Lilienthal, A.: Incremental spectral clustering and its application to topological mapping. In: In Proc. IEEE Int. Conf. on Robotics and Automation, pp. 4283–4288 (2007)
Zivkovic, Z., Booij, O., Kröse, B.: From images to rooms. Robot. Auton. Syst. 55, 411–418 (2007)
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Korrapati, H., Courbon, J., Mezouar, Y. (2013). Topological Mapping with Image Sequence Partitioning. In: Lee, S., Yoon, KJ., Lee, J. (eds) Frontiers of Intelligent Autonomous Systems. Studies in Computational Intelligence, vol 466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35485-4_3
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DOI: https://doi.org/10.1007/978-3-642-35485-4_3
Publisher Name: Springer, Berlin, Heidelberg
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