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

作者：Wu, Jun-Jun; Guan, Yi-Sheng^*; Zhang, Hong; Zhou, Xue-Feng; Su, Man-Jia

来源：Acta Automatica Sinica, 2014, 40(2): 267-276.

DOI：10.3724/SP.J.1004.2014.00267

• Anomaly detection
• Detection framework
• Humanoid robot
• Motion blur
• No reference methods
• Unsupervised method
• Visual Navigation
• Visual navigation systems

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.

• 出版日期2014-2
• 单位华南理工大学; university of alberta; The University of Alberta