仿人机器人视觉导航中的实时性运动模糊探测器设计

吴俊君; 管贻生; 张宏; 周雪峰; 苏满佳

doi:10.3724/SP.J.1004.2014.00267

仿人机器人视觉导航中的实时性运动模糊探测器设计

doi: 10.3724/SP.J.1004.2014.00267

1.
华南理工大学机械与汽车工程学院广州 510640, 中国;
2.
广东工业大学机电工程学院广州 510006, 中国;
3.
阿尔伯塔大学计算科学系埃德蒙顿 AB T2G2E8, 加拿大

基金项目:

国家自然科学基金（50975089）;中国博士后科学基金（2012M521600）资助

详细信息

作者简介:
吴俊君华南理工大学博士研究生.主要研究方向为移动机器人视觉导航，同时定位与地图构建.E-mail：junjun-wu@hotmail.com

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出版历程
- 收稿日期: 2013-03-12
- 修回日期: 2013-08-01
- 刊出日期: 2014-02-20

A Real-time Method for Motion Blur Detection in Visual Navigation with a Humanoid Robot

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

Funds:

Supported by National Natural Science Foundation of China (50975089) and China Postdoctoral Science Foundation (2012M5 21600)

摘要

摘要: 针对仿人机器人视觉导航系统的鲁棒性受到运动模糊制约的问题，提出一种基于运动模糊特征的实时性异常探测方法. 首先定量地分析运动模糊对视觉导航系统的负面影响，然后研究仿人机器人上图像的运动模糊规律，在此基础上对图像的运动模糊特征进行无参考的度量，随后采用无监督的异常探测技术，在探测框架下对时间序列上发生的图像运动模糊特征进行聚类分析，实时地召回数据流中的模糊异常，以增强机器人视觉导航系统对运动模糊的鲁棒性. 仿真实验和仿人机器人实验表明：针对国际公开的标准数据集和仿人机器人NAO数据集，方法具有良好的实时性（一次探测时间0.1s）和有效性（召回率98.5%，精确率90.7%）. 方法的探测框架对地面移动机器人亦具有较好的普适性和集成性，可方便地与视觉导航系统协同工作.
- 仿人机器人 /
- 视觉导航 /
- 鲁棒性 /
- 运动模糊 /
- 异常探测
Abstract: 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 (0.1s 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.
- Humanoid robot /
- visual navigation /
- robustness /
- motion blur /
- anomaly detection

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