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http://dx.doi.org/10.5302/J.ICROS.2015.15.0027

An Object Recognition Method Based on Depth Information for an Indoor Mobile Robot  

Park, Jungkil (Division of Electronics and Information Engineering, Chonbuk National University)
Park, Jaebyung (Division of Electronics and Information Engineering, Chonbuk National University)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.21, no.10, 2015 , pp. 958-964 More about this Journal
Abstract
In this paper, an object recognition method based on the depth information from the RGB-D camera, Xtion, is proposed for an indoor mobile robot. First, the RANdom SAmple Consensus (RANSAC) algorithm is applied to the point cloud obtained from the RGB-D camera to detect and remove the floor points. Next, the removed point cloud is classified by the k-means clustering method as each object's point cloud, and the normal vector of each point is obtained by using the k-d tree search. The obtained normal vectors are classified by the trained multi-layer perceptron as 18 classes and used as features for object recognition. To distinguish an object from another object, the similarity between them is measured by using Levenshtein distance. To verify the effectiveness and feasibility of the proposed object recognition method, the experiments are carried out with several similar boxes.
Keywords
object recognition; depth; point cloud; Levenshtein distance; multi-layer neural network;
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Times Cited By KSCI : 3  (Citation Analysis)
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1 C. S. Lee, E. S. Park, J. H. Lee, J. H. Kim, and H. K. Kim, "Pillar and vehicle classification using ultrasonic sensors and statistical regression method," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 20, no. 4, pp. 428-436, 2014.   DOI
2 D. G. Lowe, "Object recognition from local scale-invariant features," Proc. of the seventh International Conference on Computer Vision, vol. 2, pp. 1150-1157, Sep. 1999.
3 Y. S. Jeon, J. G. Choi, and J. O. Lee, "Development of a SLAM system for small UAVs in indoor environments using gaussian processes," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 20, no. 11, pp. 1098-1102, 2014.   DOI
4 H. Bay, A. Ess, T. Tuytelaars, and L. Van Gool, "Surf: Speeded up robust features," Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346-359, 2008.   DOI
5 K. S. Lee, D. H. Kim, S. M. Rho, and E. J. Hwang, "Improving matching performance of SURF using color and relative position," The Journal of Korea Navigation Institute (in Korean), vol. 16, no. 2, pp. 394-399, 2012.
6 L. C. Caron, Y. Song, D. Filliat, and A. Gepperth, "Neural network based 2D/3D fusion for robotic object recognition," Proc. of European Symposium on Artificial Neural Networks, pp. 127-132, Apr. 2014.
7 M. Blum, J. T. Springenberg, J. Wulfing, and M. Riedmiller, "A learned feature desciptor for object recognition in RGB-D data," Proc. of IEEE International Conference on Robotics and Automation, pp. 1298-1303, May 2012.
8 H. Y. Lee, et al., "IR image segmentation using GrabCut," Journal of Korean Institute of Intelligent Systems (in Korean), vol. 21, no. 2, pp. 260-267, 2011.   DOI
9 R. B. Rusu and S. Cousins, "3D is here: Point Cloud Library (PCL)," Proc. of IEEE International Conference on Robotics and Automation, Sanghai, China, pp. 1-4, May 2011.
10 M. A. Fischler and R. C. Bolles, "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography," Communications of the ACM, vol. 24, no. 6, pp. 381-395, 1981.   DOI
11 R. B. Rusu, "Semantic 3D object maps for everyday manipulation in human living environments," KI-Kunstliche Intelligenz, vol. 24, no. 4, pp. 345-348, 2010.   DOI
12 K. Klasing, D. Althoff, D. Wolherr, and M. Buss, "Comparison of surface normal estimation methods for range sensing applications," Proc. of IEEE International Conference on Robotics and Automation, pp. 3206-3211, May 2009.
13 M. Caudill and C. Butler, "Understanding neural networks," Computer Explorations, MIT Press, 1992.
14 R. O. Duda, P. E. Hart, and D. G. Stork, "Pattern classification," Wiley Interscience, 2000.
15 V. I. Levenshtein, "Binary codes capable of correcting deletions, insertions and reversals," Doklady Akademii. Nauk SSSR, vol. 10, no. 8, pp. 707-710, 1966.
16 H. Zhang and Z. Wang, "A comprehensive review of stability analysis of continuous-time recurrent neural networks," IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 7, pp. 1229-1262, 2014.   DOI