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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参考文献(29)

[1]	Cummins M, Newman P. Appearance-only SLAM at large scale with FAB-MAP 2.0. The International Journal of Robotics Research, 2011, 30(9): 1100-1123
[2]	Granström K, Schön T B, Nieto J I, Ramos F T. Learning to close loops from range data. The International Journal of Robotics Research, 2011, 30(14): 1728-1754
[3]	Sun Yao, Zhang Qiang, Wan Lei. Small autonomous underwater vehicle navigation system based on adaptive UKF algorithm. Acta Automatica Sinica, 2011, 37(3): 342-353(孙尧, 张强, 万磊. 基于自适应UKF算法的小型水下机器人导航系统. 自动化学报, 2011, 37(3): 342-353)
[4]	Guo Shuai, Ma Shu-Gen, Li Bin, Wang Ming-Hui, Wang Yue-Chao. A data association approach based on multi-rules in VorSLAM. Acta Automatica Sinica, 2013, 39(6): 883-894(郭帅, 马书根, 李斌, 王明辉, 王越超. VorSLAM 算法中基于多规则的数据关联方法. 自动化学报, 2013, 39(6): 883-894)
[5]	Sun Rong-Chuan, Ma Shu-Gen, Li Bin, Wang Ming-Hui, Wang Yue-Chao. Simultaneous localization and sampled environment mapping based on a divide-and-conquer ideology. Acta Automatica Sinica, 2010, 36(12): 1697-705(孙荣川, 马书根, 李斌, 王明辉, 王越超. 基于分治法的同步定位与环境采样地图创建. 自动化学报, 2010, 36(12): 1697-1705)
[6]	Bonin-Font F, Ortiz A, Olive G. Visual navigation for mobile robots: a survey. Journal of Intelligent and Robotic Systems, 2008, 53(3): 263-296
[7]	Droeschel D, Holz D, Stuckler J, Behnke S. Using time-of-flight cameras with active gaze control for 3D collision avoidance. In: Proceedings of the 2010 International Conference on Robotics and Automation. Anchorage, AK: IEEE, 2010. 4035-4040
[8]	Gemeiner P, Ponweiser W, Vincze M. Real-time SLAM with a high-speed CMOS camera. In: Proceedings of the 14th International Conference on Image Analysis and Processing. Modena: IEEE, 2007. 297-302
[9]	Lee H S, Kwon J, Lee K M. Simultaneous localization, mapping and deblurring. In: Proceedings of the 2011 IEEE International Conference on Computer Vision. Barcelona: IEEE, 2011. 1203-1210
[10]	Fu S Y, Zhang Y C, Cheng L, Liang Z Z, Hou Z G, Tan M. Motion based image deblur using recurrent neural network for power transmission line inspection robot. In: Proceedings of the 2006 International Joint Conference on Neural Networks. Vancouver, BC: IEEE, 2006. 3854-3859
[11]	Williams B P, Klein G, Reid I. Real-time SLAM relocalisation. In: Proceedings of the 11th International Conference on Computer Vision. Rio de Janeiro: IEEE, 2007. 1-8
[12]	Williams B, Klein G, Reid I. Automatic relocalization and loop closing for real-time monocular SLAM. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(9): 1699-1712
[13]	Pretto A, Menegatti E, Bennewitz M, Burgard W, Pagello E. A visual odometry framework robust to motion blur. In: Proceedings of the 2009 International Conference on Robotics and Automation. Kobe: IEEE, 2009. 2250-2257
[14]	Hornung A, Bennewitz M, Strasdat H. Efficient vision-based navigation. Autonomous Robots, 2010, 29(2): 137-149
[15]	Liu R T, Li Z R, Jia J Y. Image partial blur detection and classification. In: Proceedings of the 2008 International Conference on Computer Vision Pattern Recognition. Anchorage, AK: IEEE, 2008. 1-8
[16]	Ciancio A, da Costa A L N T, da Silva E A B, Said A, Samadani R, Obrador P. No-reference blur assessment of digital pictures based on multifeature classifiers. IEEE Transactions on Image Process, 2011, 20(1): 64-75
[17]	Dash R, Sa P, Majhi B. RBFN based motion blur parameter estimation. In: Proceedings of the 2009 IEEE International Conference on in Advanced Computer Control. Singapore: IEEE, 2009. 327-331
[18]	Mittal A, Moorthy A K, Bovik A C. No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing, 2012, 21(12): 4695-4708
[19]	Yang K C, Clark C G, Das P. Motion blur detecting by support vector machine. In: Proceedings of Mathematical Methods in Pattern and Image Analysis. California, USA: SPIE, 2005, 5916: 261-273
[20]	Hsu P, Chen B Y. Blurred image detection and classification. Advances in Multimedia Modeling Lecture Notes in Computer Science. Berlin: Springer-Verlag, 2008. 277-286
[21]	Hansen B C, Hess R F. Discrimination of amplitude spectrum slope in the fovea and parafovea and the local amplitude distributions of natural scene imagery. Journal of Vision, 2006, 6(7): 696-711
[22]	Roth S, Black M J. Fields of experts: a framework for learning image priors. In: Proceedings of the 2005 Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA: IEEE, 2005. 860-867
[23]	Zhang N F, Vladar A, Postek M T, Larrabee R D. A kurtosis-based statistical measure for two-dimensional processes and its applications to image sharpness. In: Proceedings of the 2003 Section on Physical and Engineering Sciences. Alexandria, VA: IEEE, 2003. 4730-4736
[24]	Caviedes J, Oberti F. A new sharpness metric based on local kurtosis, edge and energy information. Signal Processing: Image Communication, 2004, 19(2): 147-161
[25]	Liu Y, Zhang H. Visual loop closure detection with a compact image descriptor. In: Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vilamoura: IEEE, 2012. 1051-1056
[26]	Perona P, Malik J. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(7): 629-639
[27]	Marziliano P, Dufaux F, Winkler S, Ebrahimi T. A no-reference perceptual blur metric. In: Proceedings of the 2002 International Conference on Image Processing. Rochester, NY, USA: IEEE, 2002. Ⅲ-57-Ⅲ-60
[28]	Zhang H, Li B, Yang D. Keyframe detection for appearance-based visual SLAM. In: Proceedings of the 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems. Taipei, China: IEEE, 2010. 2071-2076
[29]	Cummins M, Newman P. Fab-map: probabilistic localization and mapping in the space of appearance. The International Journal of Robotics Research, 2008, 27(6): 647-665