Clustering and PCA for Reconstructing Two Perpendicular Planes Using Ultrasonic Sensors
Clustering and PCA for Reconstructing Two Perpendicular Planes Using Ultrasonic Sensors
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DOI：
10.5772/55606
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发表时间：
2013-04
影响因子：
2.3
通讯作者：
L. Spedicato;N. Giannoccaro;G. Reina;M. Bellone
中科院分区：
文献类型：
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作者：
L. Spedicato;N. Giannoccaro;G. Reina;M. Bellone
在本文中，作者利用声纳传感器检测两个正交面板的角，他们提出了一种策略，精确重建的表面。为了将四个传感器的线性阵列指向期望的位置，数字电机的运动被适当地控制。当传感器指向平面之间的交叉点时，由于多次反射，观察到更长的飞行时间。必须排除所有相关距离，这就是为什么引入基于输出信号能量的指标。聚类技术允许将数据集划分为三个聚类，指标选择包含误报信息的子集。其余的距离进行了校正，以便考虑到方向性，它们允许在三维空间中绘制两组点。为了排除离群值，每个集合都通过由主成分分析（PCA）定义的置信椭圆进行过滤。基于主方向和方差获得最佳拟合平面。实验测试和结果表明，这种新方法的有效性。
In this paper, the authors make use of sonar transducers to detect the corner of two orthogonal panels and they propose a strategy for accurately reconstructing the surfaces. In order to point a linear array of four sensors at the desired position, the motion of a digital motor is appropriately controlled. When the sensors are directed towards the intersection between the planes, longer times of flight are observed because of multiple reflections. All the concerned distances have to be excluded and that is why an indicator based on the output signal energy is introduced. A clustering technique allows for the partitioning of the dataset in three clusters and the indicator selects the subset containing misrepresented information. The remaining distances are corrected so as to take into consideration the directivity and they permit the plotting of two sets of points in a three-dimensional space. In order to leave out the outliers, each set is filtered by means of a confidence ellipsoid which is defined by the Principal Component Analysis (PCA). The best-fit planes are obtained based on the principal directions and the variances. Experimental tests and results are shown demonstrating the effectiveness of this new approach.