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This paper presents evaluation of various contemporary interest point detector and descriptor pairs in the context of robot navigation. The robustness of the detectors and descriptors is assessed using publicly available datasets: the first gathered from the camera mounted on the industrial robot [17] and the second gathered from the mobile robot [20]. The most efficient detectors and descriptors for the visual robot navigation are selected.
Słowa kluczowe
Rocznik
Tom
Strony
11--20
Opis fizyczny
Bbiliogr,. 21 poz., rys.
Twórcy
autor
- Poznań University of Technology, Institute of Control and Information Engineering, ul. Piotrowo 3A, 60-965 Poznań, Poland
autor
- Poznań University of Technology, Institute of Control and Information Engineering, ul. Piotrowo 3A, 60-965 Poznań, Poland
autor
- Poznań University of Technology, Institute of Control and Information Engineering, ul. Piotrowo 3A, 60-965 Poznań, Poland
autor
- Poznań University of Technology, Institute of Control and Information Engineering, ul. Piotrowo 3A, 60-965 Poznań, Poland
Bibliografia
- [1] D. Scaramuzza, F. Fraundorfer, “Visual Odometry:Part I – The First 30 Years and Fundamentals”, IEEE Robotics and Automation Magazine, vol. 18(4), 2011,pp. 80–92.
- [2] F. Fraundorfer, D. Scaramuzza, “Visual Odometry:Part II – Matching, Robustness and Applications”,IEEE Robotics and Automation Magazine, vol. 19(2),2012, pp. 78–90.
- [3] A. J. Davison, I. Reid, N. Molton and O. Stasse,“MonoSLAM: Real-Time Single Camera SLAM”,IEEE Trans. PAMI, vol. 29(6), 2007, pp. 1052–1067.
- [4] A. Schmidt, A. Kasinski, “The Visual SLAM System for a Hexapod Robot”, Lecture Notes in Computer Science, vol. 6375, 2010, pp. 260–267.
- [5] E. Rosten, T. Drummond, “Machine learning for highspeed corner detection”. In: Proc. of European Conf. on Computer Vision, 2006, pp. 430–443.
- [6] H. Bay, A. Ess, T. Tuytelaars, L. Van Gool, “SURF: Speeded Up Robust Features”, Computer Vision and Image Understanding, vol. 110(3), 2008, pp. 346–359.
- [7] M. Agrawal, K. Konolige, M.R. Blas, “CenSurE: Center surround extremas for realtime feature detection and matching”, Lecture Notes in Computer Science, vol. 5305, 2008, pp. 102–115.
- [8] M. Calonder, V. Lepetit, C. Strecha, and P. Fua, “BRIEF: Binary Robust Independent Elementary Features”. In: Proceedings of ECCV, 2010, pp. 778–792.
- [9] E. Rublee, V. Rabaud, K. Konolige, G. R. Bradski, “ORB: An efficient alternative to SIFT or SURF”. In: Proc. ICCV, 2011, pp. 2564–2571.
- [10] A. Alahi, R. Ortiz, P. Vandergheynst, “FREAK: Fast Retina Keypoint”. In: Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2012.
- [11] M. Kraft, A. Schmidt, A. Kasinski, “High-speed image feature detection using FPGA implementation of FAST algorithm”. In: Proc. VISAPP, 2008, pp. 174–179.
- [12] M. Kraft, M. Fularz, A. Kasinski, “System on chip coprocessors for high speed image feature detection and matching”. In: Proc. of Advances Concepts for Intelligent Vision Systems, 2011, pp. 599–610.
- [13] M. Kraft, A. Schmidt, “Simplifying SURF feature descriptor to achieve real-time performance”. In: Proc. Computer Recognition Systems, 2011, pp. 431–440.
- [14] Ó. Martínez, A. Gil, M. Ballesta, O. Reinoso, “Interest Point Detectors for Visual SLAM”. In: Proc. of the Conference of the Spanish Association for Artificial Intelligence, 2007.
- [15] M. Ballesta, A. Gil, Ó. Martínez, O. Reinoso, “Local Descriptors for Visual SLAM”. In: Proc. Workshop on Robotics and Mathematics, 2007.
- [16] A. Schmidt, M. Kraft, A. Kasinski, “An evaluation of image feature detectors and descriptors for robot navigation”, Lecture Notes in Computer Science, vol. 6375, 2010, pp. 251–259.
- [17] H. Aanas, A. L. Dahl, K. S. Pedersen, “Interesting Interest Points – A Comparative Study of Interest Point Performance on a Unique data set”, International Journal of Computer Vision, vol. 97, 2011, pp. 18–35.
- [18] A. L. Dahl, H. Aanas, K. S. Pedersen, “Finding the Best Feature Detector-Descriptor Combination”. In: Proc. of 3DIMPVT, 2011.
- [19] D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, vol. 60(2), 2004, pp. 91–110.
- [20] J. Sturm, N. Engelhard, F. Endres, W. Burgard, D. Cremer, “Towards a benchmark for RGB-D SLAM evaluation”. In: Proc. of the RGB-D Workshop on Advanced Reasoning with Depth Cameras at Robotics: Science and Systems Conf. (RSS), 2011.
- [21] J. Shi, C. Tomasi, “Good Features to Track”. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, 1994, pp. 593–600.
Typ dokumentu
Bibliografia
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