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Fusion of Multiple Ultrasonic Sensor Data and Image Data for Measuring an Object's Motion

机译:融合多个超声传感器数据和图像数据以测量对象的运动

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摘要

Robot sensing requires two types of observation – intensive and wide-angle. We selected multiple ultrasonic sensors for intensive observation and an image sensor for wide-angle observation in measuring a moving object's motion with sensors in two kinds of fusion – one fusing multiple ultrasonic sensor data and the other fusing the two types of sensor data. The fusion of multiple ultrasonic sensor data takes advantage of object movement from a measurement range of an ultrasonic sensor to another sensor's range. They are formulated in a Kalman filter framework. Simulation and experiments demonstrate the effectiveness and applicability to an actual robot system.
机译:机器人感应需要两种观察方式:密集观察和广角观察。我们选择了两种超声波传感器进行密集观察,而将图像传感器用于广角观察,使用两种融合传感器来测量运动对象的运动,一种融合多种超声传感器数据,另一种融合两种传感器数据。多个超声传感器数据的融合利用了对象从超声传感器的测量范围到另一个传感器范围的运动。它们在卡尔曼滤波器框架中制定。仿真和实验证明了其在实际机器人系统中的有效性和适用性。

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