Abstract
We propose an algorithm for object recognition in indoor service robots. The problem of object recognition is one of the key challenges in the creation of realistic robotic services. Despite great advancements in the past, sufficiently accurate object recognition for service robots in real-world environments remains problematic. Our algorithm uses image and range data information that is available on a service robotic platform to execute the segmentation and classification steps. The segmentation decision rule is applied to correctly segment objects even in overlapped placements. In the classification step, the bag of words is employed with feature descriptors that are constructed from image and range information of segmented regions. In experiments, a working service robotic platform recognizes objects of similar shapes and colors. In addition, we test the recognition capability of overlapped objects. The results demonstrate the feasibility of the proposed algorithm.
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References
Sundar, H., Silver, D., Gagvani, N., Dickinson, S.: Skeleton based shape matching and retrieval. In: Int. Conf. on Shape Modeling and Applications, p. 130 (2003)
Li, X., Guskov, I.: 3D object recognition from range images using pyramid matching. In: IEEE Int. Conf on Computer Vision, pp. 1–6 (2007)
Tangelder, J., Veltkamp, R.: A survey of content based 3D shape retrieval methods. In: Int. Conf. on Shape Modeling and Applications, pp. 145–156 (2004)
Dance, C., Willamowski, J., Fan, L., Bray, C., Csurka, G.: Visual categorization with bags of keypoints. In: ECCV Int. Workshop on Stat. Learn. in Comp. Vision (2004)
Gould, S., Baumstarck, P., Quigley, M., Ng, A.Y., Koller, D.: Integrating visual and range data for robotic object detection. In: Workshop on Multi-camera and Multi-modal Sensor Fusion (2008)
Quigley, M., et al.: High-accuracy 3D sensing for mobile manipulation improving object detection and door opening. In: IEEE Int. Conf. on Rob. & Autom. (2009)
Marton, Z.-C., et al.: Probabilistic categorization of kitchen objects in table settings with a composite sensor. In: IEEE/RSJ Int. Conf. Intell. Rob. & Syst. (2009)
Nüchter, A., Surman, H., Hertzberg, J.: Automatic classification of objects in 3D laser range scans. In: Conf. on Intelligent Autonomous Systems, pp. 963–970 (2004)
Ng, A.N., Gould, S., Quigley, M., Saxena, A., Berger, E.: STAIR: The STanford Artificial Intelligence Robot project. In: Proc. AAAI Robotics Workshop (2007)
Jain, A., Kemp, C.C.: EL-E: an assistive mobile manipulator that autonomously fetches objects from flat surfaces. Auton. Rob. 28(1), 45–64 (2010)
Rusu, R.B., et al.: Rea-time perception-guided motion planning for a personal robot. In: IEEE/RSJ Int. Conf. on Intell. Robt. And Sys., pp. 4245–4252 (2009)
Unnikrishnan, R., Hebert, M.: Fast extrinsic calibration of a laser rangefinder to a camera. Technical Report CMU-RI-TR-05-09, Robotics Institute, Carnegie Mellon University (2005)
Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. on Patt. Anal. & Mach. Intell. 22(11), 1330–1334 (2000)
Klasing, K., Wollherr, D., Buss, M.: Realtime segmentation of range data using continuous nearest neighbors. In: IEEE Int. Conf. on Rob. & Autom., pp. 2431–3436 (2009)
Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 3rd edn., pp. 635–639. Academic Press (2006)
Mishra, A., Aloimonos, Y., Fah, C.L.: Active segmentation with fixation. In: IEEE Int. Conf. on Comp. Vision, pp. 468–475 (2009)
Gonzalez, R., Woods, R.: Digital Image Processing, pp. 518–519, 549. Addison-Wesley Publishing Company (1992)
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S., et al.: Top 10 algorithms in data mining. Knowledge and Information Systems 14(1), 6–8 (2008)
Sahlgren, M., Cöster, R.: Using bag-of-concepts to improve the performance of support vector machines in text categorization. In: Conf. on Computational Linguistics, pp. 487–513 (2004)
Yang, J., Jiang, Y.G., Hauptmann, A.G., Ngo, C.W.: Evaluating bag-of-visual-words representations in scene classification. In: Int. Workshop on Multimedia Information Retrieval, pp. 197–206 (2007)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2008 (VOC 2008) Results (2008)
Lalonde, J.F., Vandapel, N., Huber, D.F., Hebert, M.: Natural terrain classification using three-dimensional ladar data for ground robot mobility. Journal of Field Robotics 23(10), 839–861 (2006)
Bauer, J., Sunderhauf, N., Protzel, P.: Comparing several implementations of two recently published feature detectors. In: Proc. of the Int. Conf. on Intell. Rob. & Syst. (2007)
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Lee, H., Lee, K., Jo, S. (2013). Overlapped Object Recognition Using Range and Image Data for a Service Robot. In: Kim, JH., Matson, E., Myung, H., Xu, P. (eds) Robot Intelligence Technology and Applications 2012. Advances in Intelligent Systems and Computing, vol 208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37374-9_43
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DOI: https://doi.org/10.1007/978-3-642-37374-9_43
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