A real-time method for motion blur detection in visual navigation with a humanoid robot

被引：0

作者：

Wu, Jun-Jun ^{[1
]}

Guan, Yi-Sheng ^{[2
]}

Zhang, Hong ^{[3
]}

Zhou, Xue-Feng ^{[1
]}

Su, Man-Jia ^{[1
]}

机构：

[1] School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China

[2] School of Mechanical and Electronic Engineering, Guangdong University of Technology, Guangzhou 510006, China

[3] Department of Computing Science, University of Alberta, Edmonton AB T2G2E8, Canada

来源：

Zidonghua Xuebao/Acta Automatica Sinica | 2014年 / 40卷 / 02期

关键词：

Anthropomorphic robots - Feature extraction - Obstacle detectors - Image enhancement;

D O I：

10.3724/SP.J.1004.2014.00267

中图分类号：

TP24 [机器人技术];

学科分类号：

080202 ; 1405 ;

摘要：

To address the problem about robustness of humanoid robot visual navigation due to motion blur, a real-time method of motion blur detection based on motion blur feature is proposed. The negative impact of motion blur on visual navigation is analyzed, the motion blur law is studied and a no-reference method is then used to measure the motion blur feature of images captured by the robot. An unsupervised method is employed to cluster the blur features of images in the time sequence in an detection framework for recalling the anomaly from observations. The purpose is to improve the robustness of visual navigation to motion blur. Simulation and experiment on humanoid robot verify that the proposed method is real-time (0.1 s per detecting) and effective (recall: 98.5%, precision: 90.7%) for an open standard dataset and the dataset acquired by NAO. The detection framework of the proposed method is universal and can be integrate with a robot visual navigation system. Copyright © 2014 Acta Automatica Sinica. All rights reserved.

引用

页码：267 / 276