JPH01191012A - Three-dimensional data correcting method - Google Patents

Abstract

PURPOSE:To remove variation components contained in original data completely by finding points of intersections of connection lines drawn among point group data in a net shape and a plane containing an ideal scanning line, and using the found points of intersections as corrected point group data. CONSTITUTION:Point group data which is measured by a three-dimensional position measuring instrument, etc., are inputted with a command from a console panel 1 and point group data Si (i=1-N) on one scanning line are stored in measurement data read memory 3 in order; and points on the other point group data which are shortest in the distance to lines connecting respective points of one of adjacent point group data Si on one scanning line and respective points of the other point group data are found and coupling information consisting of connection lines Cik (k=1, 2,...) connecting points with the shortest distances is found and stored in a memory 4. Further, the plane which contains the ideal scanning line is found and the points of intersections of the plane and connection lines of the point group data being distributed in the net shape are found, and outputted as the corrected point group data.

Description

DETAILED DESCRIPTION OF THE INVENTION (Field of Industrial Application) The present invention relates to a three-dimensional data correction method for point group data in a three-dimensional space obtained by a three-dimensional position measuring device or the like.

(Prior art) It is possible to obtain point cloud data of the model shape by bringing a probe of a three-dimensional position measuring device into contact with the model surface with one axis fixed and scanning it multiple times at predetermined intervals. can. For example, as shown in FIG. 7, xtliIb is fixed, the scanning (measuring) direction is set to YIli111 direction, and by scanning the probe ST 5 times on the surface of the model M at a measurement interval a and performing predetermined interpolation, point cloud data can be obtained. In this point cloud data, each point is connected as a series of fine straight line sections in the measurement direction (shown in S, S
,...,s5).

Methods of using such point cloud data include using it as processing information as it is, and shape feature recognition that extracts only the corners and valleys of the measured shape (for example, using corner MC of model M shown in Figure 7 as Extraction as shown in Figure 8 (solid line)) or shape operation that extends the end of the measured shape along the shape (for example, extending the end ME of the model M shown in Figure 7 as shown in Figure 9 (solid line) Department)) etc.

(Problem to be Solved by the Invention) In order to improve the accuracy of point cloud data obtained by measuring with a probe such as the above-mentioned three-dimensional position measuring machine, it is necessary to completely fix one axis of the probe. be. However, it is physically impossible to completely fix the data, resulting in point cloud data that includes fluctuating components. Then, when the above-described shape feature recognition is performed using point cloud data including such fluctuation components (for example, Figure 1O), the portion that should be recognized may be erroneously extracted (for example, the solid line portion shown in Figure 11), or Further, when the shape is manipulated, there is a problem in that it expands in random directions (for example, the solid line shown in FIG. 12).

The present invention was made in view of the above-mentioned circumstances, and an object of the present invention is to perform accurate shape feature recognition, stable shape manipulation, etc. even if the point cloud data of the model shape contains a fluctuation component. The object of the present invention is to provide a three-dimensional data modification method that can perform the following steps.

(Means for Solving the Problems) The present invention relates to a three-dimensional data correction method for point cloud data in a three-dimensional space obtained by a three-dimensional position measuring device, etc., and the above object of the present invention is to 3 obtained by scanning the model surface multiple times at predetermined intervals.
Input the point cloud data in the dimensional space, and draw a connecting line between each point on the line connecting each point of one point cloud data to each point of the other point cloud data of the adjacent point cloud data for one scan. find a plane that includes an ideal scanning line during measurement with the measuring tip, find a line connecting each point of the point group data in the scanning direction, and the intersection of the connecting line and the plane, This is achieved by using the obtained intersection points as corrected point cloud data.

(Function) The three-dimensional data correction method of the present invention finds the intersections between the connections laid out in a mesh between the dotted data and the plane containing the ideal scanning line, and uses the found intersections as correction point group data. Therefore, it is possible to completely remove the fluctuation components contained in any point cloud data.

(Example) FIG. 1 is a block diagram showing an example of a device that realizes the three-dimensional data correction method of the present invention, in which point cloud data is measured by a three-dimensional position measuring device or the like based on commands from the operation panel 1. Input point group data S+(i-1, 2,...
N) are sequentially stored in the measurement data reading memory 3, and
A point on the other point cloud data where the distance from each point of one point cloud data of one scan of adjacent frames 5l-1+sl to the line connecting each point of the other point cloud data is the shortest. , and each connecting line C1k (k
-1,2,...), (m-1,2
, . It consists of a processing device 2 that sequentially finds the intersections of the connections of the group data (connections in the measurement direction and the connecting lines) and outputs the found intersections to the outside as corrected point group data.

In such a configuration, the operation will be explained using the flowchart shown in FIG. 2 and an example of the point cloud data shown in FIG. The point cloud data for one scan is read in and sequentially stored in the measurement data reading memory 3 (step St). Then, it is checked in step S2) whether the storage of the point group data has been completed, and if the storage of the point group data has not been completed, the process returns to step S1 and the above-described operation is repeated. On the other hand, in the judgment step S2, when the storage of the point cloud data "1-1n" I is completed, one point cloud data of the point cloud data for one scan of the adjacent frame, for example, St-+(St ) is the point P3° (Q
3°) and calculate the bond line C13 (C14) for all points, and each bond PJC-th~C
The combined information CTI consisting of I5 is stored in the combined information memory 4. Note that points that are clearly recognized as corners of the shape (
For example, P2 and Q2) the starting point of 9 point cloud data (for example, P2 and Q2)
, and Ql), the connecting line at the end point (for example, P4 and Q,) connects each point without finding the shortest distance.
゛ CI I + CI2 , (:15. Then, this operation is performed sequentially from the point cloud data for the first scan (steps 53 to S5), and ends up to the point cloud data for the final (N) scan. Step S to confirm whether or not
6) If the point group data for the Nth scan has not been completed, the process returns to step S4 and the above-described operation is repeated. On the other hand, in the judgment step S6, when the point cloud data for the Nth scan is completed, for example, S
Plane PL that includes the ideal scanning line during the i-th measurement
, find the intersections R and -R8 between this plane PCI and the connections of the point cloud data spread out in a net shape, and output the obtained intersections R4 to R8 to the outside as corrected point cloud data. The measurements are performed sequentially starting from the ideal scanning line used in the first measurement (steps 57 to 511). Then, it is checked whether the ideal scanning line for the final (N) measurement has been completed (step 512), and if the ideal scanning line for the Nth measurement has not been completed, then step 512) is performed. Return to step S8 and repeat the above operation, and then
When the ideal scanning line for the th measurement is completed, all processing ends.

4 to 6 show the case where the correction method according to the present invention is applied to the changed point cloud data shown in FIG. 1O. First, as shown in FIG. 4, connect lines (dotted lines) at each point of the point cloud data are found and meshed to find a plane PL3 that includes the ideal scanning line for the third measurement, for example.
The intersection of this plane PL3 and each connection (black circle shown in Figure 5)
By calculating , corrected point cloud data can be obtained.

In this way, by correcting each point group data S,° to S5', highly accurate point group data S1' to S5'' as shown in FIG. 6 can be obtained.

(Effects of the Invention) As described above, according to the three-dimensional data correction method of the present invention,
Accurate shape feature recognition and stable shape manipulation can be performed even with point cloud data that contains fluctuating components.
It is possible to stably supply products with high dimensional accuracy and reduce manufacturing costs.

[Brief explanation of the drawing]

FIG. 1 is a block diagram showing an example of a device that implements the three-dimensional data correction method of the present invention, FIG. 2 is a flowchart explaining its operation, and FIG. 3 explains the three-dimensional data correction method of the present invention. A perspective view showing an example of point cloud data, FIGS. 4 to 6 are perspective views showing the process when the three-dimensional data correction method of the present invention is applied to an example of point cloud data, and FIG. 7 is a perspective view of a model. A perspective view illustrating how to obtain an example of point cloud data, Figures 8 and 9 are perspective views each illustrating how to use point cloud data, and Figure 1O shows an example of point cloud data that includes a fluctuation component. The perspective view, FIG. 11, and FIG. 12 are perspective views showing the case where the usage method shown in FIG. 8 and FIG. 9 is applied to the point cloud data containing a fluctuation component, respectively. DESCRIPTION OF SYMBOLS 1...Operation panel, 2...Processing device, 3...Measurement data reading memory, 4...Coupling information memory. Stem 2nd 3rd rii1 Mu 4 Box 5 Nagi 6 Figure 6 Figure 9 Mouth 11

Claims (1)

[Claims] 1. Input the point cloud data in a three-dimensional space obtained by scanning the measuring probe multiple times at predetermined intervals on the model surface, and calculate each of the point cloud data of one of the point cloud data for one adjacent scan. Find a connecting line from each point to a point on the line connecting each point of the other point cloud data, find a surface that includes the ideal scanning line when measuring with the probe, and calculate each point of the point cloud data. A method for correcting three-dimensional data, characterized in that the intersections between a line connecting the two in the scanning direction and the connecting line and the surface are determined, and the obtained points of intersection are used as corrected point group data.