基于三维激光测量系统的点云拼接理论与试验研究
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摘要
大型高温锻件在线测量技术是锻造加工过程中的重要环节。与传统接触式锻件测量相比,非接触式测量具有测量速度快,精度高,可重复性好等优点。激光测距传感器是典型的非接触式测量工具,广泛应用于场景重构、定位与导航、目标识别与跟踪等领域,非常适合尺度范围广、场地条件恶劣的大型锻件热态工况下快速在线几何尺寸、形状测量应用。
为了对具有大空间尺度、复杂多曲面的大型锻件进行多方位的尺寸测量,必须从不同视角下进行采集表面点云数据,然后通过拼接重建锻件整体形貌,该内容对在线测量的应用起到至关重要的作用。因此,针对三维点云的点元、线元特征提取,空间匹配以及点云拼接等方面的研究是十分必要的,为后续曲面重构、关键尺寸测量等提供保证。围绕三维激光测量系统的点云拼接内容,本文主要在以下几个方面开展研究工作:
1、基于中心轴线空间匹配的点云拼接研究。针对轴类、筒节等锻件空间任意截面为椭圆的特点,基于测量系统激光测距传感器扫描平面内锻件分割数据,提出轴线线元提取及空间匹配的算法;在与操作机的配合工作下,通过绕轴旋转操作实现表面无明显几何特征的轴类锻件多视测量点云拼接。
2、基于分层平面特征点的点云拼接算法研究。分析测量系统自下而上、从右到左的分层扫描特点,充分利用数据点之间内在几何拓扑信息,结合角点提取、聚类分析等相关理论对扫描截面内目标对象的二维数据进行特征处理,省去复杂的三维空间特征计算。不仅为实现基于边界特征直线的点云拼接奠定了基础,而且通过分割出边界之间重叠点云,进一步为之后的精拼接算法提供了保证。
3、基于移动最小二乘曲面特征的点云拼接。基于空间曲面隐式表达的微分几何理论,结合前人对移动最小二乘法曲面拟合方法的研究结果,提取点云模型中空间不变特征量并引入欧氏距离有序关系约束,在不确定操作机工况的情况下从三维空间的角度对点元进行匹配处理,进一步为实现锻件在线测量提供有力的、具有普适性的点云拼接方法。
本文以大型锻件非接触式在线测量为研究背景,利用三维激光测量系统逐行逐列扫描方式,完成了三种针对不同形式锻件内外特征在不同工况下的三维点云拼接研究,并通过试验得到验证。上述研究方法和结果可对激光测量系统点云融合、曲面重构等后续研究提供理论基础,具有一定的科研价值和实际工程意义。
On-site measuring technology of large forgings plays a key role in open die forgings process. Non-contact measuring instruments for forging measurement have the advantage of fast online measurement, high precision and stable repeatability over contact ones. Laser range sensor is a type of non-contact measuring tools, which can be applied to varying industrial fields with respects to scene reconstruction, registration and navigation, object recognition and tracking, etc. And it’s especially suitable for fast online measurement of large objects in complicated working condition such as the open die forgings process in hot status.
To implement multidirectional and wide-range measurement of forgings, it’s necessary to collect points cloud datasets of forging surfaces from multiple views. So the registration of different points cloud datasets, which contributes effectively to the complete surface reconstruction, plays an indispensable role in measuring application. Therefore, researches on feature point/line primitives’extraction, matching and registration of points cloud datasets are essential procedures to surface reconstruction and dimensional measurement. With regards to points cloud registration of 3D laser measurement system, the dissertation is focusing on:
1、Points cloud registration algorithm based on matching axis of cylinder. With respect to the geometry that any cross section of axial forgings or vessel course is in the form of ellipse, the segmented dataset in the scanning plane can provide the centroid of ellipse located on the axis, which is the feature line to be matched in the proposed registration method. So by working with forging manipulator, points cloud of axial forgings without the surface features can be registered around the axis.
2、Points cloud registration algorithm based on the feature points extracted from scanning planes. On the analysis of 3D laser measurement system’s scanning both from bottom to top and from right to left, the advantage of geometrical topological information of datasets can provide the feature points extraction method in both vertical and horizontal scanning planes using hierarchical clustering method, which is easier than surface fitting procedure. Thus all the extracted points belonging to the feature lines of edges are the foundation of points clouds registration after being matched. And the overlapped datasets between the edges can be accurately registered by ICP algorithm with higher precision.
3、Points cloud registration based on differential geometries of moving least surfaces(MLS). The differential geometry of surface implicit functions provides geometric feature invariants of points cloud datasets. By introducing the constraints of sequenced distance vectors between features, the point primitives can be mapped in space without information of forging manipulator’s working conditions, which is effective and flexible in the on-site measuring application.
Considering on-site non-contact measurement of large-scale forgings as research background, three kinds of points cloud registration algorithms in the context of both intrinsic and extrinsic feature properties have been proposed and verified by experiments, in the framework of scanning row by row or column by column. The proposed algorithms provide the foundation for points cloud merging and surface reconstruction of 3D laser measurement system and broaden its application with widely scientific value and practical significance.
为了对具有大空间尺度、复杂多曲面的大型锻件进行多方位的尺寸测量,必须从不同视角下进行采集表面点云数据,然后通过拼接重建锻件整体形貌,该内容对在线测量的应用起到至关重要的作用。因此,针对三维点云的点元、线元特征提取,空间匹配以及点云拼接等方面的研究是十分必要的,为后续曲面重构、关键尺寸测量等提供保证。围绕三维激光测量系统的点云拼接内容,本文主要在以下几个方面开展研究工作:
1、基于中心轴线空间匹配的点云拼接研究。针对轴类、筒节等锻件空间任意截面为椭圆的特点,基于测量系统激光测距传感器扫描平面内锻件分割数据,提出轴线线元提取及空间匹配的算法;在与操作机的配合工作下,通过绕轴旋转操作实现表面无明显几何特征的轴类锻件多视测量点云拼接。
2、基于分层平面特征点的点云拼接算法研究。分析测量系统自下而上、从右到左的分层扫描特点,充分利用数据点之间内在几何拓扑信息,结合角点提取、聚类分析等相关理论对扫描截面内目标对象的二维数据进行特征处理,省去复杂的三维空间特征计算。不仅为实现基于边界特征直线的点云拼接奠定了基础,而且通过分割出边界之间重叠点云,进一步为之后的精拼接算法提供了保证。
3、基于移动最小二乘曲面特征的点云拼接。基于空间曲面隐式表达的微分几何理论,结合前人对移动最小二乘法曲面拟合方法的研究结果,提取点云模型中空间不变特征量并引入欧氏距离有序关系约束,在不确定操作机工况的情况下从三维空间的角度对点元进行匹配处理,进一步为实现锻件在线测量提供有力的、具有普适性的点云拼接方法。
本文以大型锻件非接触式在线测量为研究背景,利用三维激光测量系统逐行逐列扫描方式,完成了三种针对不同形式锻件内外特征在不同工况下的三维点云拼接研究,并通过试验得到验证。上述研究方法和结果可对激光测量系统点云融合、曲面重构等后续研究提供理论基础,具有一定的科研价值和实际工程意义。
On-site measuring technology of large forgings plays a key role in open die forgings process. Non-contact measuring instruments for forging measurement have the advantage of fast online measurement, high precision and stable repeatability over contact ones. Laser range sensor is a type of non-contact measuring tools, which can be applied to varying industrial fields with respects to scene reconstruction, registration and navigation, object recognition and tracking, etc. And it’s especially suitable for fast online measurement of large objects in complicated working condition such as the open die forgings process in hot status.
To implement multidirectional and wide-range measurement of forgings, it’s necessary to collect points cloud datasets of forging surfaces from multiple views. So the registration of different points cloud datasets, which contributes effectively to the complete surface reconstruction, plays an indispensable role in measuring application. Therefore, researches on feature point/line primitives’extraction, matching and registration of points cloud datasets are essential procedures to surface reconstruction and dimensional measurement. With regards to points cloud registration of 3D laser measurement system, the dissertation is focusing on:
1、Points cloud registration algorithm based on matching axis of cylinder. With respect to the geometry that any cross section of axial forgings or vessel course is in the form of ellipse, the segmented dataset in the scanning plane can provide the centroid of ellipse located on the axis, which is the feature line to be matched in the proposed registration method. So by working with forging manipulator, points cloud of axial forgings without the surface features can be registered around the axis.
2、Points cloud registration algorithm based on the feature points extracted from scanning planes. On the analysis of 3D laser measurement system’s scanning both from bottom to top and from right to left, the advantage of geometrical topological information of datasets can provide the feature points extraction method in both vertical and horizontal scanning planes using hierarchical clustering method, which is easier than surface fitting procedure. Thus all the extracted points belonging to the feature lines of edges are the foundation of points clouds registration after being matched. And the overlapped datasets between the edges can be accurately registered by ICP algorithm with higher precision.
3、Points cloud registration based on differential geometries of moving least surfaces(MLS). The differential geometry of surface implicit functions provides geometric feature invariants of points cloud datasets. By introducing the constraints of sequenced distance vectors between features, the point primitives can be mapped in space without information of forging manipulator’s working conditions, which is effective and flexible in the on-site measuring application.
Considering on-site non-contact measurement of large-scale forgings as research background, three kinds of points cloud registration algorithms in the context of both intrinsic and extrinsic feature properties have been proposed and verified by experiments, in the framework of scanning row by row or column by column. The proposed algorithms provide the foundation for points cloud merging and surface reconstruction of 3D laser measurement system and broaden its application with widely scientific value and practical significance.
引文
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