蒋萍, 孟庆浩, 曾明, 李飞, 李吉功. 一种新的移动机器人气体泄漏源视觉搜寻方法[J]. 机器人, 2009, 31(5): 397-403,409.
引用本文: 蒋萍, 孟庆浩, 曾明, 李飞, 李吉功. 一种新的移动机器人气体泄漏源视觉搜寻方法[J]. 机器人, 2009, 31(5): 397-403,409.
JIANG Ping, MENG Qinghao, ZENG Ming, LI Fei, LI Jigong. A Novel Visual Search Method for Gas Leakage Source Based on Mobile Robot[J]. ROBOT, 2009, 31(5): 397-403,409.
Citation: JIANG Ping, MENG Qinghao, ZENG Ming, LI Fei, LI Jigong. A Novel Visual Search Method for Gas Leakage Source Based on Mobile Robot[J]. ROBOT, 2009, 31(5): 397-403,409.

一种新的移动机器人气体泄漏源视觉搜寻方法

A Novel Visual Search Method for Gas Leakage Source Based on Mobile Robot

  • 摘要: 为提高视觉信息的处理效率,将视觉注意机制引入到特定目标(泄漏源)的搜寻过程中,在此基础上提出了一种新的基于任务驱动视觉注意机制(TBVAM)的气体泄漏源搜寻方法.该方法分为3步完成:首先,确定能有效凸显目标物的对比映射图合并权值系数;其次,当获取实际场景的图像信息后,利用训练得到的权值系数加权合并不同特征及尺度的对比映射图,得到少数几个可疑目标区域,并结合激光测距信息计算出可疑目标所在的位置;最后,驱动机器人对可疑目标区域进行排查,通过判断嗅觉传感器检测到的气体浓度是否大于给定阈值确定其是否找到气体泄漏源.实验结果表明,所提出算法能显著提高对气体泄漏源的搜寻效率.

     

    Abstract: Visual attention mechanism is introduced into the search process of special target(source of gas leakage)in order to improve the efficiency of visual information processing.On this basis,a novel search method of gas leakage source is proposed by using task-based visual attention mechanism(TBVAM).The method is divided into three steps.First of all, the combined weight coefficients of contrast maps that can efficiently highlight the target object in the scene are determined. Secondly,when information of the real scene's image is captured,a few suspected target areas are obtained by combining contrast maps of different features and scales with the trained weight coefficients,and the positions of the suspected targets are calculated with laser range information.Lastly,the robot approaches the suspected target areas and the gas leakage source is identified when the odor concentration detected by olfactory sensor is bigger than a given threshold.Experimental results show that the proposed algorithm can significantly improve the search efficiency of the gas leakage source.

     

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