{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:19:37Z","timestamp":1760242777206,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2016,7,5]],"date-time":"2016-07-05T00:00:00Z","timestamp":1467676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s16071040","type":"journal-article","created":{"date-parts":[[2016,7,5]],"date-time":"2016-07-05T10:06:19Z","timestamp":1467713179000},"page":"1040","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Filtering Based Adaptive Visual Odometry Sensor Framework Robust to Blurred Images"],"prefix":"10.3390","volume":"16","author":[{"given":"Haiying","family":"Zhao","sequence":"first","affiliation":[{"name":"Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China"},{"name":"Mobile Media and Cultural Calculation Key Laboratory of Beijing, Century College, Beijing University of Posts and Telecommunications, Beijing 102101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojia","family":"Xie","sequence":"additional","affiliation":[{"name":"Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiyi","family":"Liao","sequence":"additional","affiliation":[{"name":"Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xixi","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,7,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1016\/j.robot.2013.11.008","article-title":"An integrated stereo visual odometry for robotic navigation","volume":"62","author":"Azartash","year":"2014","journal-title":"Robot. 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