NDLI: The real-time video stabilization for the rescue robot

Content Provider	IEEE Xplore Digital Library
Author	Jen-Pin Hsiao Cheng-Chung Hsu Tzu-Chiang Shih Pau-Lo Hsu Syh-Shiuh Yeh Bor-Chyun Wang
Copyright Year	2009
Description	Author affiliation: Department of Electrical and Control Engineering, National Chiao-Tung University, Hsinchu, 300 Taiwan, ROC (Jen-Pin Hsiao; Cheng-Chung Hsu; Tzu-Chiang Shih; Pau-Lo Hsu) \|\| Department of Mechanical Engineering, National Taipei University of Technology, 10608 Taiwan, ROC (Syh-Shiuh Yeh) \|\| Department of Computer Science and Information Engineering, China University of Technology, Hukuo, 330 Taiwan, ROC (Bor-Chyun Wang)
Abstract	When rescue robots navigate in a rough terrain, significant vibration of the video occurs unavoidably and a video stabilization system is proposed in this paper to reduce the disturbance on the visual system of the present rescue robots. The Kalman filter is applied to estimate the motion vector of the robot and the false estimation occurrence can be thus greatly reduced by applying analysis of correlation and variance of the motion vector estimation. With a hierarchical searching algorithm implemented on the TI DSP 6437, the frame rate can be improved from 10 fps to 28 fps to realize the real-time video stabilization and furthermore, the cooperative mission of multiple rescue robots has been achieved by applying the developed visual servo technique. The proposed virtual bounded motion control algorithm further leads the robots approaching the target precisely with cooperation of multiple rescue robots. The demo video is available at: http://lab816.cn.nctu.edu.tw/DemoVideo/. video stabilization
Starting Page	4364
Ending Page	4369
File Size	780954
Page Count	6
File Format	PDF
ISBN	9784907764340
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-18
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	SICE
Subject Keyword	Robot sensing systems Mobile robots Motion estimation Optical noise Visual servoing Navigation Motion control Wireless sensor networks Vibrations Motion analysis visual servo rescue robot Kalman filter multi-vision
Content Type	Text
Resource Type	Article

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