Next Article in Journal
Flexible Piezoelectric Energy Harvesting from Mouse Click Motions
Next Article in Special Issue
An Improved Otsu Threshold Segmentation Method for Underwater Simultaneous Localization and Mapping-Based Navigation
Previous Article in Journal
Developing a Penetrometer-Based Mapping System for Visualizing Silage Bulk Density from the Bunker Silo Face
Previous Article in Special Issue
Pedestrian Detection at Day/Night Time with Visible and FIR Cameras: A Comparison
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(7), 1040; doi:10.3390/s16071040

Filtering Based Adaptive Visual Odometry Sensor Framework Robust to Blurred Images

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
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Gabriel Oliver-Codina, Nuno Gracias and Antonio M. López
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)
View Full-Text   |   Download PDF [4320 KB, uploaded 5 July 2016]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top