Abstract
In order to solve the problem of indoor place recognition for indoor service robot, a novel algorithm, clustering of features and images (CFI), is proposed in this work. Different from traditional indoor place recognition methods which are based on kernels or bag of features, with large margin classifier, CFI proposed in this work is based on feature matching, image similarity and clustering of features and images. It establishes independent local feature clusters by feature cloud registration to represent each room, and defines image distance to describe the similarity between images or feature clusters, which determines the label of query images. Besides, it improves recognition speed by image scaling, with state inertia and hidden Markov model constraining the transition of the state to kill unreasonable wrong recognitions and achieves remarkable precision and speed. A series of experiments are conducted to test the algorithm based on standard databases, and it achieves recognition rate up to 97% and speed is over 30 fps, which is much superior to traditional methods. Its impressive precision and speed demonstrate the great discriminative power in the face of complicated environment.
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The work was supported by National Natural Science Foundation of China (Nos. 61305103 and 61473103), Natural Science Foundation Heilongjiang province (No.QC2014C072), and Postdoctoral Science Foundation of Heilongjiang (No. LBH-Z14108), Self- Planned Task of State Key Laboratory of Robotics and System (HIT)(No. SKLRS201609B).
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Ke Wang received the Ph.D. degree in control theory and control engineering from Dalian University of Technology, China in 2008. He is currently a lecturer at Robotics Institute, Harbin Institute of Technology, China.
His research interests include computer vision, image processing, simultaneous localization and mapping (SLAM) and their applications in robotics.
Xue-Xiong Long received the B. Sc. degree in mechanical engineering from Xi’an Jiaotong University, China in 2015. At present, he is a master student in Harbin Institute of Technology (HIT), China.
His research interest is indoor place recognition which involves with features extraction and matching, nearest neighbor search, SVMs and clustering.
Rui-Feng Li received the B. Sc. degree in mechanical engineering from Harbin Institute of Technology, China in 1998, and received the M. Sc. and Ph.D. degrees in mechatronics from Harbin Institute degrees of Technology of China, in 1991 and 1997, respectively. He joined Robotics Institute of HIT in 1991. He is now a professor and a Ph.D. degree supervisor at State Key Laboratory of Robotics and System, HIT and a vice leader at Robotics Institute, China.
His research interest is intelligent robots industrial robots.
Li-Jun Zhao received the B. Sc. degree in mechatronics engineering from Beijing Institute of Technology (BIT), China in 1996, and received the M. Sc. and Ph.D. degrees in mechatronics engineering from Harbin Institute of Technology of China, China in 2002 and 2009, respectively. He joined Robotics Institute of HIT in 2002 and State Key Laboratory of Robotics and System, HIT, China in 2007.
His research interest includes intelligent robots motion control, SLAM and object recognition technology.
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Wang, K., Long, XX., Li, RF. et al. A discriminative algorithm for indoor place recognition based on clustering of features and images. Int. J. Autom. Comput. 14, 407–419 (2017). https://doi.org/10.1007/s11633-017-1081-z
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DOI: https://doi.org/10.1007/s11633-017-1081-z