An improved chebyshev flatness measurement method and system based on local ordering

Abstract

The application discloses an improved Chebyshev flatness measurement method and system based on local ordering, which comprises the following steps: inputting a depth image, performing fast plane fitting on a point set in the depth image to obtain a spatial fitting plane; calculating the distance of each point in the point set to the spatial fitting plane; sorting each point according to the absolute value of the distance of each point to the spatial fitting plane and a local ordering method, and selecting a specified number of points; and calculating the flatness according to the selected specified number of points. The application can efficiently, quickly and accurately obtain the flatness parameter by taking the depth image as a processing object and selecting the points in the specified distribution range of the depth image for flatness calculation, and has high popularization and application value.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of flatness measurement, and in particular to an improved Chebyshev flatness measurement method and system based on local sorting. BACKGROUND

[0002] In recent years, computer vision, target detection, three-dimensional data scanning and other computer technologies have developed rapidly, and various application researches based on depth images and three-dimensional point cloud data have become a research hotspot in the field of computer vision.

[0003] Flatness is an important indicator for measuring the flatness and smoothness of the 3D surface of an object, and is commonly used in measurement industry applications. Currently, the evaluation methods for flatness error mainly include the minimum containment method, the maximum straight line method, the three-far-point method, the diagonal line method, the least squares method and the Chebyshev method. When different methods are used for evaluation, the fitting difference of ordered discrete points in the process will directly affect the reliability of the final result. Therefore, the non-uniformity of flatness evaluation methods and the imperfection of the methods themselves bring great instability and uncertainty to the final evaluation results. At the same time, when calculating the flatness of a specified area, due to the limitation of the sensor, there may be many outliers, noise points, environmental points and non-target area points in the measurement area, which also have a great impact on the true flatness result.

[0004] The above information is given as background information only to assist with an understanding of the present disclosure, and should not be taken as an acknowledgement or admission that any of the above information forms part of the prior art with respect to the present disclosure. SUMMARY

[0005] The present application provides an improved Chebyshev flatness measurement method and system based on local sorting to solve the problems of the prior art.

[0006] To achieve the above object, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides an improved Chebyshev flatness measurement method based on local sorting, which comprises:

[0008] Inputting a depth image and performing fast plane fitting according to a point array in the depth image to obtain a spatial fitting plane;

[0009] Calculating the distance of each point in the point array to the spatial fitting plane;

[0010] According to the absolute value of the distance of each point to the spatial fitting plane and the local sorting method, sorting each point, and selecting a specified number of points;

[0011] Calculating the flatness according to the selected specified number of points.

[0012] Further, in the improved Chebyshev flatness measurement method based on local sorting, the step of inputting a depth image and performing fast plane fitting on a point set in the depth image to obtain a spatial fitting plane comprises:

[0013] The depth image is inputted, and a set value is filled in positions where no data is collected in the depth image.

[0014] The point set in the depth image is subjected to fast plane fitting to obtain a spatial fitting plane.

[0015] Further, in the improved Chebyshev flatness measurement method based on local sorting, the step of performing fast plane fitting on a point set in the depth image to obtain a spatial fitting plane comprises:

[0016] The depth image is subjected to ROI cropping, and a part of the point set is subjected to fast plane fitting to obtain a spatial fitting plane.

[0017] Further, in the improved Chebyshev flatness measurement method based on local sorting, the step of sorting each point according to the absolute value of the distance of each point to the spatial fitting plane and the local sorting method and selecting a specified number of points comprises:

[0018] Each point is sorted according to the absolute value of the distance of each point to the spatial fitting plane and the local sorting method.

[0019] A certain number of points with the largest absolute value of the distance are removed.

[0020] From the remaining points, the m points farthest above the spatial fitting plane and the n points farthest below the spatial fitting plane are selected.

[0021] Further, in the improved Chebyshev flatness measurement method based on local sorting, the step of calculating flatness according to the selected specified number of points comprises:

[0022] The average value of the absolute value of the distance of the selected specified number of points to the spatial fitting plane is calculated to obtain flatness.

[0023] In a second aspect, the application provides an improved Chebyshev flatness measurement system based on local sorting, which comprises:

[0024] A plane fitting module for inputting a depth image and performing fast plane fitting on a point set in the depth image to obtain a spatial fitting plane.

[0025] a distance obtaining module, configured to obtain distances of points in the point set to the spatial fitting plane;

[0026] a distance sorting module, configured to sort the points according to absolute values of the distances corresponding to the points and a local sorting method, and select a specified number of points;

[0027] a flatness calculation module, configured to calculate flatness according to the selected specified number of points.

[0028] Further, in the improved Chebyshev flatness measurement system based on local sorting, the plane fitting module is specifically configured to:

[0029] input a depth image, and fill a set of invalid values in positions where no data is collected in the depth image;

[0030] perform fast plane fitting according to a point set in the depth image to obtain a spatial fitting plane.

[0031] Further, in the improved Chebyshev flatness measurement system based on local sorting, the plane fitting module performs the step of performing fast plane fitting according to the point set in the depth image to obtain a spatial fitting plane, which specifically includes:

[0032] perform ROI cropping on the depth image, and select a part of the point set to perform fast plane fitting to obtain a spatial fitting plane.

[0033] Further, in the improved Chebyshev flatness measurement system based on local sorting, the distance sorting module is specifically configured to:

[0034] sort the points according to absolute values of the distances corresponding to the points and a local sorting method;

[0035] remove a certain number of points with the largest absolute values of the distances;

[0036] select the farthest m points above the spatial fitting plane and the farthest n points below the spatial fitting plane from the remaining points.

[0037] Further, in the improved Chebyshev flatness measurement system based on local sorting, the flatness calculation module is specifically configured to:

[0038] calculate an average value of absolute values of distances of the selected specified number of points to the spatial fitting plane to obtain flatness.

[0039] Compared with the prior art, the present application has the following beneficial effects:

[0040] The application provides an improved Chebyshev flatness measurement method and system based on local ordering, which takes a depth image as a processing object, selects points in a specified distribution range in the depth image for flatness calculation, and can efficiently, quickly and accurately obtain flatness parameters, and has high popularization and application value. BRIEF DESCRIPTION OF DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0042] Figure 1 is a flowchart of an improved Chebyshev flatness measurement method based on local ordering provided by the first embodiment of the present application;

[0043] Figure 2 is a depth image of a workpiece surface mentioned by the first embodiment of the present application;

[0044] Figure 3 is a schematic diagram of the depth image after sampling in the form of point cloud mentioned by the first embodiment of the present application;

[0045] Figure 4 is a schematic diagram of a space fitting plane mentioned by the first embodiment of the present application;

[0046] Figure 5 is a schematic diagram of the removed points mentioned by the first embodiment of the present application;

[0047] Figure 6 is a schematic diagram of the inclusive plane and the space fitting plane mentioned by the first embodiment of the present application;

[0048] Figure 7 is a functional module schematic diagram of an improved Chebyshev flatness measurement system based on local ordering provided by the second embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, features and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the following described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0050] In the description of the present application, it needs to be understood that when one component is considered to be "connected" to another component, it can be directly connected to the other component or there can be a component arranged in the middle. When one component is considered to be "arranged on" another component, it can be directly arranged on the other component or there can be a component arranged in the middle.

[0051] In addition, the terms "long", "short", "inner", "outer", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, which is only for the convenience of describing the present application, and does not indicate or imply that the device or element referred to must have this particular orientation, be constructed in this particular orientation, and cannot be understood as a limitation of the present application.

[0052] The technical solutions of the present application will be further illustrated below in conjunction with the drawings and through specific embodiments.

[0053] Embodiment one

[0054] In view of the defects of the existing flatness measurement technology, the applicant, based on many years of rich practical experience and professional knowledge in designing and manufacturing such products, and in cooperation with the application of theory, actively researches and innovates, in the hope of creating a technology that can solve the defects in the prior art, so that the flatness measurement technology is more practical. After continuous research, design, and repeated trial of samples and improvement, the present application is finally created.

[0055] Please refer to Figure 1 The flowchart of an improved Chebyshev flatness measurement method based on local sorting provided by the embodiment one of the present application, which is suitable for real-time processing of flatness calculation in industrial 3D projects. The method is executed by an improved Chebyshev flatness measurement system based on local sorting, which can be realized by software and / or hardware. The method specifically includes the following steps:

[0056] S101, input the depth image, and perform fast plane fitting according to the point array in the depth image to obtain a spatial fitting plane.

[0057] In this embodiment, the step S101 can be further refined to include the following steps:

[0058] Input the depth image, and fill in a set of invalid values at the positions where no data is collected in the depth image;

[0059] Perform fast plane fitting according to the point array in the depth image to obtain a spatial fitting plane.

[0060] It should be noted that the target function for obtaining the spatial fitting plane is composed of the square sum of several functions, and the best function matching of the data is found by minimizing the square sum of errors. The unknown data can be easily obtained by using the least square method, and the square sum of errors between the obtained data and the actual data is minimized. The plane fitting used in this paper samples the TBB acceleration, which can quickly perform plane fitting and obtain a spatial plane by participating in plane fitting with all point arrays.

[0061] In this embodiment, the step of performing fast plane fitting on the point array in the depth image to obtain a spatial fitting plane can be further refined as follows:

[0062] The ROI of the depth image is cropped, and a part of the point array is selected for fast plane fitting to obtain a spatial fitting plane.

[0063] S102, obtain the distance of each point in the point array to the spatial fitting plane.

[0064] It should be noted that the distance of each point in the point array to the spatial fitting plane is positive for points above the spatial fitting plane and negative for points below the spatial fitting plane.

[0065] S103, sort each point according to the absolute value of the distance of each point to the spatial fitting plane and a local sorting method, and select a specified number of points.

[0066] In this embodiment, the step S103 can be further refined to include the following steps:

[0067] Sort each point according to the absolute value of the distance of each point to the spatial fitting plane;

[0068] Remove a certain number of points with the largest absolute value of distance;

[0069] From the remaining points, select the m points farthest above the spatial fitting plane and the n points farthest below the spatial fitting plane.

[0070] It should be noted that the absolute values of the distances of the points above and below the spatial fitting plane to the plane are sorted, and the local sorting method nth_element is used in this embodiment, with an average time complexity of O(n). Then, according to the upper and lower removal threshold, a certain number of points with the largest absolute value of distance to the spatial fitting plane are removed.

[0071] The upper and lower rejection thresholds respectively represent the rejection of the points deviating farthest above and below the spatial fitting plane by a certain proportion. On the basis of the remaining points after rejection, the m points farthest above the spatial fitting plane and the n points farthest below the spatial fitting plane are further selected to participate in the final flatness calculation.

[0072] S104, calculating flatness according to the selected specified number of points.

[0073] In the embodiment, the step S104 can be further refined as follows:

[0074] The average value of the absolute values of the distances of the selected specified number of points to the spatial fitting plane is calculated to obtain the flatness. It should be noted that the basic method for calculating the flatness according to the selected remaining points in the embodiment is Chebyshev flatness algorithm. Two mutually parallel spatial planes are gradually approached to reduce the number of point sets between the two parallel planes, and the point sets not sandwiched by the two parallel planes are the rejected point sets, and finally the and points above and below the plane are selected from far to near to participate in the final flatness parameter calculation.

[0075] In addition, the embodiment can obtain the rejected point set information and the point set information participating in the calculation of the flatness parameter at the same time.

[0076] In the embodiment, the Chebyshev method refers to the process of the size of the minimum containing area of the containing ideal element (the plane estimated by fitting), that is, the above four steps. The uneven workpiece actually includes two containing planes, which can be referred to as the two dashed planes in the three planes in Figure 6 , which are offset by a certain distance, such as Figure 6 P i in i , x i and y i are the same, but z i is different, that is, e . Figure 6 The middle solid line plane in is the spatial fitting plane, and the measurement of the flatness in the embodiment is based on the spatial fitting plane.

[0077] Figure 2 Exemplarily, the embodiment uses a complete depth image for effect verification, as shown in a depth map of a workpiece surface. For convenience of display, the depth map is sampled to be displayed in the form of point cloud as shown in Figure 3 .

[0078] First, all points are used to fit a plane to get a spatial fitting plane using a fast plane fitting algorithm, as shown in Figure 4 The center point of the fitting plane is (1249, 1591, -0.138939); the normal vector is (-0.00011146, -0.000041748, 0.999999);

[0079] Second, the distances of each point in the input point set to the spatial fitting plane are calculated, and then the distances are sorted, and the 10% of points with the largest absolute distance values are removed according to the upper and lower rejection thresholds. The removed points are shown by the black points in Figure 5 After the removal, the 100 points farthest above the spatial fitting plane and the 100 points farthest below the fitting plane are further selected to participate in the final flatness calculation. The 100 points above the fitting plane are shown by the gray points in the upper part of Figure 5 The 100 points below the fitting plane are shown by the gray points in the lower part of Figure 5 .

[0080] Further, the 100 points above and below the spatial fitting plane are calculated to participate in the flatness calculation, and the average of the absolute values of the distances of the 200 points to the plane is obtained, that is, the flatness.

[0081] The improved Chebyshev flatness measurement method based on local sorting provided by the embodiment of the application can efficiently, quickly and accurately obtain the flatness parameter by taking a depth image as a processing object and selecting points in a specified distribution range of the depth image for flatness calculation, and has high popularization and application value.

[0082] Embodiment two

[0083] Please refer to Figure 7 , Figure 7 The function module schematic diagram of the improved Chebyshev flatness measurement system based on local sorting provided by the embodiment two of the application is suitable for executing the improved Chebyshev flatness measurement method based on local sorting provided by the embodiment of the application. The system specifically includes the following modules:

[0084] The plane fitting module 201 is used for inputting a depth image and performing fast plane fitting according to a point set in the depth image to obtain a spatial fitting plane;

[0085] The distance calculation module 202 is used for calculating the distance of each point in the point set to the spatial fitting plane;

[0086] The distance sorting module 203 is used for sorting each point according to the absolute value of the distance corresponding to each point and a local sorting method, and selecting a specified number of points;

[0087] The flatness calculation module 204 is configured to calculate the flatness according to the selected specified number of points.

[0088] Preferably, the plane fitting module 201 is specifically configured to:

[0089] input a depth image and fill a set of invalid values in positions where no data is collected in the depth image;

[0090] perform fast plane fitting according to the point set in the depth image to obtain a spatial fitting plane.

[0091] Preferably, the plane fitting module 201 performs the step of performing fast plane fitting according to the point set in the depth image to obtain a spatial fitting plane, and the step specifically includes:

[0092] perform ROI cropping on the depth image and select a part of the point set to perform fast plane fitting to obtain a spatial fitting plane.

[0093] Preferably, the distance sorting module 203 is specifically configured to:

[0094] sort the points according to the absolute values of the distances corresponding to the points and a local sorting method;

[0095] remove a certain number of points with the largest absolute values of the distances;

[0096] select the m points farthest above the spatial fitting plane and the n points farthest below the spatial fitting plane from the remaining points.

[0097] Preferably, the flatness calculation module 204 is specifically configured to:

[0098] calculate the average value of the absolute values of the distances corresponding to the selected specified number of points to the spatial fitting plane to obtain the flatness.

[0099] The improved Chebyshev flatness measurement system based on local sorting provided by the embodiment of the present application can efficiently, quickly and accurately obtain the flatness parameter by taking a depth image as a processing object and selecting points in a specified distribution range in the depth image for flatness calculation, and has high application value.

[0100] The system described above can perform the method provided by any embodiment of the present application and has the corresponding functional modules and beneficial effects of performing the method.

[0101] In view of the foregoing, after reading this detailed description, those skilled in the art realize that the preceding detailed description is illustrative only and not limiting of the application as claimed. Although not explicitly described for reason of brevity, it is well understood that by one of ordinary skill in the art that the here disclosed application is intended to include any and all modifications, improvements or changes to the embodiments described herein. Such modifications, improvements or changes are intended to fall within the spirit and scope of the here disclosed application and appended claims.

[0102] In addition, certain terms have been used herein for the purpose of reference only and thus are not intended to be limiting, for example "one embodiment", "an embodiment", and / or "some embodiments". Moreover, as used herein "exemplary" is intended to mean serving as an example, instance, or illustration. The following description is intended only by way of example and thus should not be understood as limiting. In the description, same reference numbers are used to describe the same elements throughout several embodiments unless otherwise described.

[0103] It is to be understood that the foregoing description is directed to individual embodiments for the purpose of aiding comprehension of one feature, and that for the purpose of simplifying the present application, the application has combined various features in a single embodiment, drawing or description thereof. However, this does not imply that combinations of features are necessary and that a person skilled in the art, upon reading the present application, cannot extract a part of the features as a separate embodiment. That is, the embodiments in the present application can also be understood as an integration of multiple sub-embodiments. And the content of each sub-embodiment is also valid when there are less than all the features of a single foregoing disclosed embodiment.

[0104] Each patent, patent application, publication of a patent application, and other material, for example articles, books, specifications, publications, documents, items, and the like which can be cited in the above specification are hereby incorporated by reference for all purposes to the same extent as if each were individually so denoted. All content, except any prosecution file history that may be associated with any of the same, now or later associated with the present document, is especially incorporated by reference in its entirety for all purposes to the same extent as if each were individually so denoted. In the event of any inconsistency between the content of the present document and any incorporated material, the content of the present document will control.

[0105] Finally, it should be understood that the embodiments of the application disclosed herein are illustrative of the principles of the present application. Other modifications that are obvious within the spirit and principles of the application are intended to be within the scope of the application. Accordingly, the disclosure of embodiments of the application is intended to be illustrative, but not limiting, of the scope of the application. Those of skill in the art could readily devise their own modifications to the specific embodiments disclosed in this application without departing from the spirit and principles of the application. Consequently, the application is intended to be limited only by the spirit and scope of the claims, including the equivalents thereof.

Claims

1. An improved Chebyshev flatness measurement method based on local ranking, characterized in that, The method comprises: inputting a depth image, and performing fast plane fitting on a point set in the depth image to obtain a spatial fitting plane; calculating distances of each point in the point set to the spatial fitting plane; sorting each point according to an absolute value of the distance of each point to the spatial fitting plane and a local sorting method, and selecting a specified number of points; calculating flatness according to the selected specified number of points.

2. The method of claim 1, wherein, The step of inputting a depth image and performing fast plane fitting on a point set in the depth image to obtain a spatial fitting plane comprises: inputting a depth image, and filling a set of invalid values in positions where no data is collected in the depth image; performing fast plane fitting on a point set in the depth image to obtain a spatial fitting plane.

3. The method of claim 2, wherein, The step of performing fast plane fitting on a point set in the depth image to obtain a spatial fitting plane comprises: performing ROI cropping on the depth image, and performing fast plane fitting on a part of the point set to obtain a spatial fitting plane.

4. The method of claim 1, wherein, The step of sorting each point according to an absolute value of the distance of each point to the spatial fitting plane and a local sorting method, and selecting a specified number of points comprises: sorting each point according to an absolute value of the distance of each point to the spatial fitting plane and a local sorting method; removing a certain number of points with the largest absolute value of the distance; selecting the most distant m points above the spatial fitting plane and the most distant n points below the spatial fitting plane from the remaining points.

5. The method of claim 1, wherein, The step of calculating flatness according to the selected specified number of points comprises: calculating an average value of the absolute values of the distances of the selected specified number of points to the spatial fitting plane to obtain flatness.

6. An improved Chebyshev flatness measurement system based on local ranking, characterized in that, The system comprises: a plane fitting module configured to input a depth image, and perform fast plane fitting on a point set in the depth image to obtain a spatial fitting plane; a distance calculation module configured to calculate distances of each point in the point set to the spatial fitting plane; a distance sorting module configured to sort each point according to an absolute value of the distance of each point to the spatial fitting plane and a local sorting method, and select a specified number of points; a flatness calculation module configured to calculate flatness according to the selected specified number of points.

7. The improved Chebyshev flatness measurement system based on local ranking of claim 6, wherein, The plane fitting module is specifically configured to: input a depth image, and fill a set of invalid values in positions where no data is collected in the depth image; perform fast plane fitting on a point set in the depth image to obtain a spatial fitting plane.

8. The improved Chebyshev flatness measurement system based on local ranking of claim 7, wherein, The plane fitting module performs the step of performing fast plane fitting on a point set in the depth image to obtain a spatial fitting plane, and the step specifically comprises: performing ROI cropping on the depth image, and performing fast plane fitting on a part of the point set to obtain a spatial fitting plane.

9. The improved Chebyshev flatness measurement system based on local ranking of claim 6, wherein, The distance sorting module is specifically configured to: sort each point according to an absolute value of the distance of each point to the spatial fitting plane and a local sorting method; remove a certain number of points with the largest absolute value of the distance; and select the most distant m points above the spatial fitting plane and the most distant n points below the spatial fitting plane from the remaining points. From the remaining points, the furthest m points above the spatial fitting plane and the furthest n points below the spatial fitting plane are selected.

10. The improved Chebyshev flatness measurement system based on local ranking of claim 6, wherein, The flatness calculation module is specifically configured to: Calculate the average value of the absolute values of the distances of the selected specified number of points to the spatial fitting plane to obtain the flatness.

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