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Object identification in dynamic environment using sensor fusion

机译:使用传感器融合的动态环境中的对象识别

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Multisensor data fusion is highly applicable in robotics applications because the relationships among objects and events changes due to the change in orientation of robot, snag in sensory information, sensor range and environmental conditions etc. High level and low level image processing in machine vision are widely involved to investigate object identification in complex application. Due to the limitations of vision technology still it is difficult to identify the objects in certain environments. A new technique of object identification using sonar sensor fusion has been proposed. This paper explains the computational account of the data fusion using Bayesian and neural network to recognize the shape of object in the dynamic environment.
机译:多传感器数据融合在机器人应用中非常适用,因为物体和事件之间的关系由于机器人的方向的变化而变化,感官信息,传感器范围和环境条件等。机器视觉中的高电平和低电平图像处理是广泛的参与调查复杂应用中的对象识别。由于视觉技术的局限性仍然难以识别某些环境中的物体。已经提出了一种使用声纳传感器融合的对象识别技术。本文介绍了使用贝叶斯和神经网络的数据融合的计算账户,以识别动态环境中的物体的形状。

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