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Article

Filtering Based Adaptive Visual Odometry Sensor Framework Robust to Blurred Images

by 1,2,†, 1,*,†, 1, 1 and 1
1
Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China
2
Mobile Media and Cultural Calculation Key Laboratory of Beijing, Century College, Beijing University of Posts and Telecommunications, Beijing 102101, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Gabriel Oliver-Codina, Nuno Gracias and Antonio M. López
Sensors 2016, 16(7), 1040; https://doi.org/10.3390/s16071040
Received: 28 March 2016 / Revised: 18 June 2016 / Accepted: 28 June 2016 / Published: 5 July 2016
(This article belongs to the Special Issue Vision-Based Sensors in Field Robotics)
Visual odometry (VO) estimation from blurred image is a challenging problem in practical robot applications, and the blurred images will severely reduce the estimation accuracy of the VO. In this paper, we address the problem of visual odometry estimation from blurred images, and present an adaptive visual odometry estimation framework robust to blurred images. Our approach employs an objective measure of images, named small image gradient distribution (SIGD), to evaluate the blurring degree of the image, then an adaptive blurred image classification algorithm is proposed to recognize the blurred images, finally we propose an anti-blurred key-frame selection algorithm to enable the VO robust to blurred images. We also carried out varied comparable experiments to evaluate the performance of the VO algorithms with our anti-blur framework under varied blurred images, and the experimental results show that our approach can achieve superior performance comparing to the state-of-the-art methods under the condition with blurred images while not increasing too much computation cost to the original VO algorithms. View Full-Text
Keywords: visual odometry; blurred image; adaptive classification; key-frame selection; image gradient distribution visual odometry; blurred image; adaptive classification; key-frame selection; image gradient distribution
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MDPI and ACS Style

Zhao, H.; Liu, Y.; Xie, X.; Liao, Y.; Liu, X. Filtering Based Adaptive Visual Odometry Sensor Framework Robust to Blurred Images. Sensors 2016, 16, 1040. https://doi.org/10.3390/s16071040

AMA Style

Zhao H, Liu Y, Xie X, Liao Y, Liu X. Filtering Based Adaptive Visual Odometry Sensor Framework Robust to Blurred Images. Sensors. 2016; 16(7):1040. https://doi.org/10.3390/s16071040

Chicago/Turabian Style

Zhao, Haiying; Liu, Yong; Xie, Xiaojia; Liao, Yiyi; Liu, Xixi. 2016. "Filtering Based Adaptive Visual Odometry Sensor Framework Robust to Blurred Images" Sensors 16, no. 7: 1040. https://doi.org/10.3390/s16071040

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