CN113674278B - Hub point cloud obtaining method, device and equipment and computer readable storage medium - Google Patents
Hub point cloud obtaining method, device and equipment and computer readable storage medium Download PDFInfo
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Abstract
The application discloses a method, a device and equipment for obtaining a hub point cloud and a computer readable storage medium, wherein first hub point cloud data of a target hub positioned on a rotary table are obtained; rotating the rotary table to obtain second hub point cloud data of the target hub positioned on the rotated rotary table; and determining target hub point cloud data of the target hub based on the first hub point cloud data and the second hub point cloud data so as to perform burr detection on the target hub according to the target hub point cloud data. According to the method and the device, the point cloud data of the first hub and the point cloud data of the second hub of the target hub at different positions in the rotary table can be obtained, the point cloud data of the first hub and the point cloud data of the second hub are determined to be complete and high-precision point cloud data serving as the point cloud data of the target hub, and therefore whether burrs exist on the target hub and the positions where the burrs are accurately detected can be accurately detected according to the point cloud data of the target hub, and the polishing efficiency of the burrs of the hub is improved.
Description
Technical Field
The present application relates to the field of industrial manufacturing technologies, and in particular, to a method, an apparatus, a device, and a computer-readable storage medium for obtaining a hub point cloud.
Background
The flying development of car makes the demand to wheel hub improve greatly, and in wheel hub manufacturing process, because machining's technology restriction, wheel hub inevitably can produce the burr at present, and the burr belongs to wheel hub's surface defect, consequently need polish the wheel hub burr. At present, the existing solutions for polishing the hub according to the teaching track through a robot are low in burr and random in position, the accuracy of the point cloud of the hub is low when the point cloud of the hub is obtained through a single large-view structured light three-dimensional sensor, the position of the burr on the hub cannot be accurately determined based on the obtained point cloud of the hub, all positions where burrs possibly exist in the hub are required to be polished once when the burr is polished according to the established teaching track, and the polishing efficiency of the current hub burr is low.
Disclosure of Invention
The application mainly aims to provide a method, a device and equipment for obtaining hub point cloud and a computer readable storage medium, and aims to solve the technical problem that the grinding efficiency of the current hub burrs is low due to the fact that the accuracy of the obtained hub point cloud is low.
In order to achieve the above object, an embodiment of the present application provides a method for obtaining a hub point cloud, where the method for obtaining a hub point cloud includes:
acquiring first hub point cloud data of a target hub positioned on a rotary table;
rotating the rotary table to obtain second hub point cloud data of the target hub of the rotary table after rotation;
and determining target hub point cloud data of the target hub based on the first hub point cloud data and the second hub point cloud data so as to perform burr detection on the target hub according to the target hub point cloud data.
Preferably, the step of determining target hub point cloud data of the target hub based on the first hub point cloud data and the second hub point cloud data comprises:
performing data conversion on the first hub point cloud data according to a turntable calibration result to obtain third hub point cloud data;
and determining target hub point cloud data of the target hub according to the second hub point cloud data and the third hub point cloud data.
Preferably, the step of determining target hub point cloud data of the target hub according to the second hub point cloud data and the third hub point cloud data comprises:
determining fourth hub point cloud data and fifth hub point cloud data based on Euclidean distances between point data in the second hub point cloud data and point data in the third hub point cloud data;
and determining target hub point cloud data of the target hub according to the second hub point cloud data, the third hub point cloud data, the fourth hub point cloud data and the fifth hub point cloud data.
Preferably, the step of determining the target hub point cloud data of the target hub according to the second hub point cloud data, the third hub point cloud data, the fourth hub point cloud data and the fifth hub point cloud data includes:
registering the fourth hub point cloud data and the fifth hub point cloud data according to a preset registration algorithm to obtain sixth hub point cloud data and seventh hub point cloud data;
and determining target hub point cloud data of the target hub according to the second hub point cloud data, the third hub point cloud data, the sixth hub point cloud data and the seventh hub point cloud data.
Preferably, the step of determining the target hub point cloud data of the target hub according to the second hub point cloud data, the third hub point cloud data, the sixth hub point cloud data and the seventh hub point cloud data comprises:
performing point cloud adjustment on the third hub point cloud data according to the sixth hub point cloud data and the seventh hub point cloud data to obtain eighth hub point cloud data;
and determining target hub point cloud data of the target hub according to the eighth hub point cloud data and the second hub point cloud data.
Preferably, the step of determining the target hub point cloud data of the target hub according to the eighth hub point cloud data and the second hub point cloud data comprises:
combining the eighth hub point cloud data with the second hub point cloud data to obtain ninth hub point cloud data;
and eliminating the data which is the same as the fifth hub point cloud data in the ninth hub point cloud data to obtain the target hub data of the target hub.
Preferably, before the step of acquiring the first hub point cloud data of the target hub located on the turntable, the method further includes:
determining whether the turntable is calibrated;
and if the rotary table is not calibrated, calibrating the rotary table by using the rotary shaft to obtain a rotary table calibration result.
In order to achieve the above object, the present application further provides a hub point cloud obtaining apparatus, which includes:
the first acquisition module is used for acquiring first hub point cloud data of a target hub positioned on the rotary table;
the second acquisition module is used for rotating the rotary table and acquiring second hub point cloud data of the target hub of the rotary table after rotation;
the determining module is used for determining target hub point cloud data of the target hub based on the first hub point cloud data and the second hub point cloud data so as to perform burr detection on the target hub according to the target hub point cloud data.
Further, in order to achieve the above object, the present application also provides a hub point cloud obtaining apparatus, where the hub point cloud obtaining apparatus includes a memory, a processor, and a hub point cloud obtaining program stored in the memory and operable on the processor, and the hub point cloud obtaining program implements the steps of the hub point cloud obtaining method when executed by the processor.
Further, to achieve the above object, the present application also provides a computer readable storage medium, where a hub point cloud obtaining program is stored on the computer readable storage medium, and when the hub point cloud obtaining program is executed by a processor, the steps of the hub point cloud obtaining method are implemented.
Further, to achieve the above object, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the above hub point cloud obtaining method are implemented.
The embodiment of the application provides a method, a device and equipment for obtaining a hub point cloud and a computer readable storage medium, and the method comprises the steps of obtaining first hub point cloud data of a target hub positioned on a rotary table; rotating the rotary table to obtain second hub point cloud data of the target hub of the rotary table after rotation; and determining target hub point cloud data of the target hub based on the first hub point cloud data and the second hub point cloud data so as to perform burr detection on the target hub according to the target hub point cloud data. According to the method and the device, the point cloud data of the first hub and the point cloud data of the second hub of the target hub at different positions in the rotary table can be obtained, the point cloud data of the first hub and the point cloud data of the second hub are determined to be complete and high-precision point cloud data serving as the point cloud data of the target hub, and therefore whether burrs exist on the target hub and the positions where the burrs are accurately detected can be accurately detected according to the point cloud data of the target hub, and the polishing efficiency of the burrs of the hub is improved.
Drawings
FIG. 1 is a schematic structural diagram of a hardware operating environment according to an embodiment of a hub point cloud obtaining method of the present application;
FIG. 2 is a schematic flow chart of a first embodiment of a hub point cloud obtaining method according to the present application;
FIG. 3 is a schematic view of a first scenario of a hub point cloud obtaining system according to a first embodiment of the present application;
FIG. 4 is a schematic diagram of a second scenario of the hub point cloud obtaining system according to the first embodiment of the present application;
FIG. 5 is a schematic flow chart of a second embodiment of the hub point cloud obtaining method according to the present application;
fig. 6 is a functional module schematic diagram of a preferred embodiment of the hub point cloud obtaining apparatus according to the present application.
The implementation, functional features and advantages of the objectives of the present application will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The embodiment of the application provides a method, a device and equipment for obtaining a hub point cloud and a computer readable storage medium, and the method comprises the steps of obtaining first hub point cloud data of a target hub positioned on a rotary table; rotating the rotary table to obtain second hub point cloud data of the target hub of the rotary table after rotation; and determining target hub point cloud data of the target hub based on the first hub point cloud data and the second hub point cloud data so as to perform burr detection on the target hub according to the target hub point cloud data. According to the method and the device, the point cloud data of the first hub and the point cloud data of the second hub of the target hub at different positions in the rotary table can be obtained, the point cloud data of the first hub and the point cloud data of the second hub are determined to be complete and high-precision point cloud data serving as the point cloud data of the target hub, and therefore whether burrs exist on the target hub and the positions where the burrs are accurately detected can be accurately detected according to the point cloud data of the target hub, and the polishing efficiency of the burrs of the hub is improved.
As shown in fig. 1, fig. 1 is a schematic structural diagram of a hub point cloud obtaining apparatus in a hardware operating environment according to an embodiment of the present application.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for the convenience of description of the present application, and have no specific meaning by themselves. Thus, "module", "component" or "unit" may be used mixedly.
The hub point cloud acquisition equipment can be a PC (personal computer), a tablet personal computer, a portable computer and other mobile terminal equipment.
As shown in fig. 1, the hub point cloud obtaining apparatus may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the hub point cloud acquisition device configuration shown in fig. 1 does not constitute a limitation of the hub point cloud acquisition device and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and a hub point cloud obtaining program.
In the device shown in fig. 1, the network interface 1004 is mainly used for connecting to a backend server and performing data communication with the backend server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and the processor 1001 may be configured to call the hub point cloud obtaining program stored in the memory 1005, and perform the following operations:
acquiring first hub point cloud data of a target hub positioned on a rotary table;
rotating the rotary table to obtain second hub point cloud data of the target hub of the rotary table after rotation;
and determining target hub point cloud data of the target hub based on the first hub point cloud data and the second hub point cloud data so as to perform burr detection on the target hub according to the target hub point cloud data.
Further, the step of determining target hub point cloud data for the target hub based on the first hub point cloud data and the second hub point cloud data comprises:
performing data conversion on the first hub point cloud data according to a turntable calibration result to obtain third hub point cloud data;
and determining target hub point cloud data of the target hub according to the second hub point cloud data and the third hub point cloud data.
Further, the step of determining target hub point cloud data of the target hub according to the second hub point cloud data and the third hub point cloud data comprises:
determining fourth hub point cloud data and fifth hub point cloud data based on Euclidean distances between point data in the second hub point cloud data and point data in the third hub point cloud data;
and determining target hub point cloud data of the target hub according to the second hub point cloud data, the third hub point cloud data, the fourth hub point cloud data and the fifth hub point cloud data.
Further, the step of determining the target hub point cloud data of the target hub according to the second hub point cloud data, the third hub point cloud data, the fourth hub point cloud data and the fifth hub point cloud data includes:
registering the fourth hub point cloud data and the fifth hub point cloud data according to a preset registration algorithm to obtain sixth hub point cloud data and seventh hub point cloud data;
and determining target hub point cloud data of the target hub according to the second hub point cloud data, the third hub point cloud data, the sixth hub point cloud data and the seventh hub point cloud data.
Further, the step of determining the target hub point cloud data of the target hub according to the second hub point cloud data, the third hub point cloud data, the sixth hub point cloud data and the seventh hub point cloud data comprises:
performing point cloud adjustment on the third hub point cloud data according to the sixth hub point cloud data and the seventh hub point cloud data to obtain eighth hub point cloud data;
and determining target hub point cloud data of the target hub according to the eighth hub point cloud data and the second hub point cloud data.
Further, the step of determining the target hub point cloud data of the target hub according to the eighth hub point cloud data and the second hub point cloud data comprises:
combining the eighth hub point cloud data with the second hub point cloud data to obtain ninth hub point cloud data;
and eliminating the data which is the same as the fifth hub point cloud data in the ninth hub point cloud data to obtain the target hub data of the target hub.
Further, prior to the step of acquiring first hub point cloud data of a target hub located on a turntable, the processor 1001 may be configured to call a hub point cloud acquisition program stored in the memory 1005 and perform the following operations:
determining whether the turntable is calibrated;
and if the rotary table is not calibrated, calibrating the rotary table by using the rotary shaft to obtain a rotary table calibration result.
For a better understanding of the above technical solutions, exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
Referring to fig. 2, a first embodiment of the present application provides a schematic flow chart of a hub point cloud obtaining method. In this embodiment, the hub point cloud obtaining method includes the following steps:
step S10, acquiring first hub point cloud data of a target hub positioned on a rotary table;
the method for acquiring the wheel hub point cloud in the embodiment is applied to a wheel hub point cloud acquiring system, and referring to fig. 3, fig. 3 is a schematic view of a first scene of the wheel hub point cloud acquiring system in the first embodiment of the present application, the wheel hub point cloud acquiring system comprises a platform 1, a turntable 3, a line laser three-dimensional sensor 4 and a motor track 5, and it can be understood that a wheel hub 2 in fig. 3 is placed on the turntable 3. The platform 1 is used for bearing the rotary table 3, the line laser three-dimensional sensor 4, the motor track 5 and the wheel hub 2, the rotary table 3 is placed on the platform 1, and the rotary table 3 is used for placing the wheel hub 2 and can rotate; the motor track 5 is used for driving the linear laser three-dimensional sensor 4 to move; the line laser three-dimensional sensor 4 scans a passing area while moving along with the motor track 5 to obtain point cloud data of a scanned object (for example, a hub in the present embodiment), and the point cloud data is composed of a plurality of point data of the scanned object. The line laser three-dimensional sensor 4 in this embodiment is a sensor with high precision and a scanning view smaller than that of the hub, and therefore point cloud data obtained by scanning the hub once by the line laser three-dimensional sensor 4 is part of the point cloud data of the hub.
It can be understood that, at present, a single large-view structured light three-dimensional sensor can be adopted to obtain a wheel hub point cloud and detect the position of burrs in a wheel hub based on the point cloud, but because the size of the wheel hub is large, the precision of the single large-view structured light three-dimensional sensor capable of covering the whole wheel hub is often difficult to reach 1 mm, and the precision requirement of wheel hub burr polishing is less than 0.2 mm, so that the wheel hub posture can be randomly placed by adopting the structured light three-dimensional sensor to obtain the wheel hub posture only, the specific burr position cannot be recognized, the burr polishing still needs to be carried out according to a complete teaching track, and if a plurality of high-precision three-dimensional sensors (line laser or structured light) can be combined to obtain the high-precision wheel hub point cloud, but the high-precision three-dimensional sensor is expensive, and the cost can be greatly increased by using the plurality of sensors. Based on the method, the wheel hub point cloud data which are relatively complete and high in precision can be obtained by scanning a single high-precision line laser three-dimensional sensor twice under the condition of considering the cost, whether burrs exist in the wheel hub can be accurately detected according to the obtained wheel hub point cloud data, if the burrs exist, the positions of the burrs can be further accurately detected, the burrs can be conveniently and accurately polished, and the polishing efficiency of the wheel hub burrs is improved.
Specifically, on the one hand, when the user has the demand of performing burr detection and burr polishing on the hub, the hub to be detected is placed on the rotary table 3, and then the scanning instruction is sent to the hub point cloud acquisition system through the starting motor track 5, so that the hub point cloud acquisition system obtains the complete and high-precision point cloud data of the hub by scanning the hub on the rotary table 3. On the other hand, the hub point cloud obtaining system receives a scanning instruction sent by a user, controls the motor track 5 to operate and simultaneously starts the line laser three-dimensional sensor 4 to operate, so that the line laser three-dimensional sensor 4 moves from an initial position along with the motor track 5 and performs laser scanning on a passing area, specifically, scans a target hub placed on the turntable 3 to obtain first hub point cloud data including partial point cloud data of the target hub, and the first hub point cloud data includes at least half of the target hub and less than the whole point cloud data, referring to fig. 4, fig. 4 is a second scene schematic diagram of the hub point cloud obtaining system in the first embodiment of the present application, as can be seen from fig. 4, in the present embodiment, the laser three-dimensional sensor 4 moves from the initial position along with the motor track 5 and performs laser scanning on the passing area to obtain first hub point cloud data of the target hub located on the turntable 3 in the scanning area, wherein the ratio between the scanned first hub point cloud data and the complete hub point cloud data is greater than or equal to 0.5 and less than 1. The complete and high-precision wheel hub point cloud data of the target wheel hub are determined on the basis of the first wheel hub point cloud data to serve as the target wheel hub point cloud data, and then whether burrs exist on the target wheel hub or not can be accurately detected according to the target wheel hub point cloud data, the position where the burrs are accurately detected when the burrs exist is detected, and the polishing efficiency of the wheel hub burrs is improved.
Step S20, rotating the turntable to obtain second hub point cloud data of the target hub of the turntable after rotation;
after acquiring the first hub point cloud data of the target hub located on the turntable 3, the line laser three-dimensional sensor 4 moves towards the initial position along with the motor track 5 (at this time, the point cloud is not scanned), and at the same time, the turntable 3 rotates by a preset angle, so that the target hub placed on the turntable rotates by a preset angle correspondingly along with the rotation of the turntable 3, wherein the preset angle is 180 degrees in the embodiment. Further, the laser three-dimensional sensor 4 moves from the initial position along with the motor track 5 and performs laser scanning on the passing area to obtain second hub point cloud data of the target hub on the turntable 3 after rotating by a preset angle (180 degrees in this embodiment), wherein the second hub point cloud data is the same as the first hub point cloud data in number, and includes point cloud data of at least half of the target hub and smaller than the whole. The complete and high-precision point cloud data of the target hub are determined as the point cloud data of the target hub according to the point cloud data of the first hub and the point cloud data of the father hub, and then whether burrs exist on the target hub or not and the positions of the burrs are accurately detected according to the point cloud data of the target hub, so that the polishing efficiency of the burrs of the hub is improved.
Step S30, determining target hub point cloud data of the target hub based on the first hub point cloud data and the second hub point cloud data, and performing burr detection on the target hub according to the target hub point cloud data.
After the first hub point cloud data and the second hub point cloud data of the target hub are obtained, data conversion is performed on the first hub point cloud data according to a turntable calibration result obtained by calibrating the turntable, and third hub point cloud data after data conversion is obtained, that is, the third hub point cloud data is point cloud data obtained by rotating the first hub point cloud data. And determining fourth hub point cloud data and fifth hub point cloud data according to the Euclidean distance between the point data in the third hub point cloud data and the point data in the second hub point cloud data. Further, the fourth hub point cloud data and the fifth hub point cloud data are registered according to a preset registration algorithm to obtain sixth hub point cloud data and seventh hub point cloud data, wherein the preset registration algorithm is an ICP point cloud fine registration algorithm in this embodiment. And further, calculating eighth hub point cloud data according to a preset calculation formula by combining the third hub point cloud data, the sixth hub point cloud data and the seventh hub point cloud data, wherein the preset calculation formula is used for accurately adjusting the third hub point cloud data. And further combining the eighth hub point cloud data with the second hub point cloud data to obtain ninth hub point cloud data, and then eliminating the data, which is the same as the fifth hub point cloud data, in the ninth hub point cloud data to obtain target hub data of the target hub. The point cloud data with complete and high precision is determined as the target hub point cloud data of the target hub through the first hub point cloud data and the second hub point cloud data, and then whether burrs exist on the target hub or not can be accurately detected according to the target hub point cloud data, and the positions of the burrs are accurately detected when the burrs exist, so that the grinding efficiency of the hub burrs is improved.
The embodiment provides a method, a device, equipment and a medium for obtaining a hub point cloud, which are used for obtaining first hub point cloud data of a target hub positioned on a rotary table; rotating the rotary table to obtain second hub point cloud data of the target hub of the rotary table after rotation; and determining target hub point cloud data of the target hub based on the first hub point cloud data and the second hub point cloud data so as to perform burr detection on the target hub according to the target hub point cloud data. According to the method and the device, the point cloud data of the first hub and the point cloud data of the second hub of the target hub at different positions in the rotary table can be obtained, the point cloud data of the first hub and the point cloud data of the second hub are determined to be complete and high-precision point cloud data serving as the point cloud data of the target hub, and therefore whether burrs exist on the target hub and the positions where the burrs are accurately detected can be accurately detected according to the point cloud data of the target hub, and the polishing efficiency of the burrs of the hub is improved.
Further, referring to fig. 5, a second embodiment of the hub point cloud obtaining method of the present application is proposed based on the first embodiment of the hub point cloud obtaining method of the present application, and in the second embodiment, the step of determining the target hub point cloud data of the target hub based on the first hub point cloud data and the second hub point cloud data includes:
a1, performing data conversion on the first hub point cloud data according to a turntable calibration result to obtain third hub point cloud data;
step A2, determining target hub point cloud data of the target hub according to the second hub point cloud data and the third hub point cloud data.
After the first hub point cloud data and the second hub point cloud data of the target hub are acquired, since the turntable is rotated (rotated by 180 degrees in this embodiment) when the first hub point cloud data is acquired as the second hub point cloud data, the first hub point cloud data is now in a state of being rotated around the turntable normal vector (rotated by 180 degrees in this embodiment). Therefore, in this embodiment, the first hub point cloud data needs to be subjected to data conversion by using a turntable calibration result obtained by calibrating the turntable, where the turntable calibration result includes a turntable rotating shaft coordinate and a turntable normal vector. Specifically, the coordinates of the rotating shaft of the turntable and the normal vector of the turntable are combined into a transformation matrix after the turntable rotates (rotates 180 degrees in this embodiment), and then each point in the first hub point cloud data is subjected to data transformation through the transformation matrix by a preset data transformation formula, so that the data transformation of the first hub point cloud data is completed, and the third hub point cloud data is obtained. The preset data transformation formula is shown as the following formula:
P′=Mp；
wherein, P' is the point data in the third hub point cloud data, P is the point data in the first point cloud data, M is the transformation matrix of the turntable rotating 180 degrees, and M is specifically shown as the following formula:
wherein x, y and z are respectively the horizontal, vertical and vertical coordinates of the normal vector of the turntable, and xC、yC、zCRespectively a horizontal coordinate, a vertical coordinate and a vertical coordinate in the rotating shaft coordinate of the rotary table.
And after the third hub point cloud data is obtained, determining fourth hub point cloud data and fifth hub point cloud data according to the Euclidean distance between the point data in the third hub point cloud data and the point data in the second hub point cloud data. And further, registering the fourth hub point cloud data and the fifth hub point cloud data according to a preset registration algorithm to obtain sixth hub point cloud data and seventh hub point cloud data. And further, calculating eighth hub point cloud data according to a preset calculation formula by combining the third hub point cloud data, the sixth hub point cloud data and the seventh hub point cloud data. And further combining the eighth hub point cloud data with the second hub point cloud data to obtain ninth hub point cloud data, and then eliminating the data, which is the same as the fifth hub point cloud data, in the ninth hub point cloud data to obtain target hub data of the target hub. The point cloud data with complete and high precision is determined as the target hub point cloud data of the target hub through the first hub point cloud data and the second hub point cloud data, and then whether burrs exist on the target hub or not can be accurately detected according to the target hub point cloud data, and the positions of the burrs are accurately detected when the burrs exist, so that the grinding efficiency of the hub burrs is improved.
Further, the step of determining target hub point cloud data of the target hub according to the second hub point cloud data and the third hub point cloud data comprises:
step B1, determining fourth hub point cloud data and fifth hub point cloud data based on Euclidean distance between point data in the second hub point cloud data and point data in the third hub point cloud data;
and step B2, determining target hub point cloud data of the target hub according to the second hub point cloud data, the third hub point cloud data, the fourth hub point cloud data and the fifth hub point cloud data.
As shown in fig. 4, the line laser three-dimensional sensor 4 is still larger than a half target hub although it cannot cover the whole area of the target hub by a single scan, so that there is an overlapping portion between the second hub point cloud data and the third hub point cloud data, the third hub point cloud data and the fifth hub point cloud data which are initially empty are created, for each point data in the third hub point cloud data, the corresponding point data whose euclidean distance is less than 0.3 mm is searched in the second hub point cloud data, if the corresponding point data exists, the point data in the third hub point cloud data is stored in the fourth hub point cloud data, the corresponding point data is stored in the fifth hub point cloud data, and after the search of all the point data in the third hub point cloud data is completed, the fourth hub point cloud data and the fifth hub point cloud data storing the point data are obtained.
And after the fourth hub point cloud data and the fifth hub point cloud data are obtained, registering the fourth hub point cloud data and the fifth hub point cloud data according to a preset registration algorithm to obtain sixth hub point cloud data and seventh hub point cloud data. And further, calculating eighth hub point cloud data according to a preset calculation formula by combining the third hub point cloud data, the sixth hub point cloud data and the seventh hub point cloud data. And further combining the eighth hub point cloud data with the second hub point cloud data to obtain ninth hub point cloud data, and then eliminating the data, which is the same as the fifth hub point cloud data, in the ninth hub point cloud data to obtain target hub data of the target hub. The point cloud data with complete and high precision is determined as the target hub point cloud data of the target hub through the first hub point cloud data and the second hub point cloud data, and then whether burrs exist on the target hub or not can be accurately detected according to the target hub point cloud data, and the positions of the burrs are accurately detected when the burrs exist, so that the grinding efficiency of the hub burrs is improved.
Further, the step of determining the target hub point cloud data of the target hub according to the second hub point cloud data, the third hub point cloud data, the fourth hub point cloud data and the fifth hub point cloud data includes:
step C1, registering the fourth hub point cloud data and the fifth hub point cloud data according to a preset registration algorithm to obtain sixth hub point cloud data and seventh hub point cloud data;
and step C2, determining target hub point cloud data of the target hub according to the second hub point cloud data, the third hub point cloud data, the sixth hub point cloud data and the seventh hub point cloud data.
After the fourth hub point cloud data and the fifth hub point cloud data are obtained, registering the fourth hub point cloud data and the fifth hub point cloud data according to a preset ICP point cloud precise registration algorithm to obtain corresponding sixth hub point cloud data and seventh hub point cloud data, specifically, under the condition that the precision of the rotary table is good, the fourth hub point cloud data and the fifth hub point cloud data are well overlapped, only the ICP point cloud precise registration algorithm is needed to obtain a rotation matrix from the fourth hub point cloud data to the fifth hub point cloud data as the sixth hub point cloud data and a translation matrix as the seventh hub point cloud data, the ICP point cloud precise registration algorithm is used to obtain a process that the rotation matrix from the fourth hub point cloud data to the fifth hub point cloud data is used as the sixth hub point cloud data and the translation matrix as the seventh hub point cloud data, and the existing process that two groups of point cloud data matched by the ICP point cloud precise registration algorithm are registered can be referred to The process is not described in detail in this embodiment. The principle of the ICP point cloud precise registration algorithm is as follows: and respectively finding out the nearest neighbor points in the target point cloud and the source point cloud to be matched according to a certain constraint condition, and then calculating the optimal matching parameters to minimize the error function.
And after the registration is finished to obtain the sixth hub point cloud data and the seventh hub point cloud data, calculating eighth hub point cloud data according to a preset calculation formula by combining the third hub point cloud data, the sixth hub point cloud data and the seventh hub point cloud data. And further combining the eighth hub point cloud data with the second hub point cloud data to obtain ninth hub point cloud data, and then eliminating the data, which is the same as the fifth hub point cloud data, in the ninth hub point cloud data to obtain target hub data of the target hub. The point cloud data with complete and high precision is determined as the target hub point cloud data of the target hub through the first hub point cloud data and the second hub point cloud data, and then whether burrs exist on the target hub or not can be accurately detected according to the target hub point cloud data, and the positions of the burrs are accurately detected when the burrs exist, so that the grinding efficiency of the hub burrs is improved.
Further, the step of determining the target hub point cloud data of the target hub according to the second hub point cloud data, the third hub point cloud data, the sixth hub point cloud data and the seventh hub point cloud data comprises:
d1, performing point cloud adjustment on the third hub point cloud data according to the sixth hub point cloud data and the seventh hub point cloud data to obtain eighth hub point cloud data;
and D2, determining the target hub point cloud data of the target hub according to the eighth hub point cloud data and the second hub point cloud data.
After the registration is completed to obtain the sixth hub point cloud data and the seventh hub point cloud data, inputting the third hub point cloud data, the sixth hub point cloud data and the seventh hub point cloud data into a preset calculation formula, and accurately adjusting the third hub point cloud data by the sixth hub point cloud data and the seventh hub point cloud data, thereby obtaining eighth hub point cloud data representing the accurately adjusted third hub point cloud data. Specifically, the preset calculation formula is shown as follows:
S1′′=R*S1′+T；
wherein, R is the sixth hub point cloud data, T is the seventh hub point cloud data, S1 ' is the third hub point cloud data, and S1 ' ' is the eighth hub point cloud data.
And after the eighth hub point cloud data is obtained, combining the eighth hub point cloud data with the second hub point cloud data to obtain ninth hub point cloud data, and then eliminating the data which is the same as the fifth hub point cloud data in the ninth hub point cloud data to obtain target hub data of the target hub. The point cloud data with complete and high precision is determined as the target hub point cloud data of the target hub through the first hub point cloud data and the second hub point cloud data, and then whether burrs exist on the target hub or not can be accurately detected according to the target hub point cloud data, and the positions of the burrs are accurately detected when the burrs exist, so that the grinding efficiency of the hub burrs is improved.
Further, the step of determining the target hub point cloud data of the target hub according to the eighth hub point cloud data and the second hub point cloud data comprises:
step E1, combining the eighth hub point cloud data and the second hub point cloud data to obtain ninth hub point cloud data;
and E2, removing the data in the ninth hub point cloud data which is the same as the fifth hub point cloud data to obtain the target hub data of the target hub.
After the eighth hub point cloud data is obtained, the eighth hub point cloud data and the second hub point cloud data are combined, specifically, the eighth hub point cloud data and the second hub point cloud data are spliced to obtain ninth hub point cloud data of complete point cloud data containing hubs. However, the ninth hub point cloud data obtained by stitching the eighth hub point cloud data and the second hub point cloud data further includes partially repeated point data, so that the repeated point data in the ninth hub point cloud data needs to be removed, and specifically, the point data identical to the fifth hub point cloud data is searched in the ninth hub point cloud data. It can be understood that the search result for the point data in the ninth hub point cloud data which is the same as the point data in the fifth hub point cloud data will include 2 same point data, and then any point data in the 2 same point data is removed, so that the repeated point data in the ninth hub point cloud data is removed, and the target hub data of all point clouds of the target hub is obtained. Whether burrs exist in the target hub or not can be accurately detected according to the point cloud data of the target hub, the burrs exist at the position where the burrs are accurately detected, and the polishing efficiency of the burrs of the hub is improved.
The embodiment can determine complete and high-precision point cloud data as target hub point cloud data of the target hub through the point cloud data of the first hub and the point cloud data of the second hub, and then can accurately detect whether burrs exist on the target hub or not and accurately detect the positions of the burrs when the burrs exist according to the point cloud data of the target hub, so that the grinding efficiency of the hub burrs is improved.
Further, based on the first embodiment of the method for obtaining a wheel hub point cloud of the present application, a third embodiment of the method for obtaining a wheel hub point cloud of the present application is provided, and in the third embodiment, before the step of obtaining the first wheel hub point cloud data of the target wheel hub located on the turntable, the method further includes:
step F1, determining whether the rotary table is calibrated;
and F2, if the rotary table is not calibrated, calibrating the rotary table to obtain a rotary table calibration result.
It can be understood that if the rotation axis of the turntable 3 is not calibrated, the coordinates of the position of the point cloud of the scanned object before rotation after rotation cannot be accurately determined, and all the point clouds scanned overlap. Therefore, before the motor track 5 drives the moving line laser three-dimensional sensor 4 to scan the passing area, it is further required to determine whether the turntable 3 is calibrated, and specifically, whether a calibration record exists or whether a turntable calibration result exists can be detected, and if the calibration record exists or the turntable calibration result exists, the motor track 5 can drive the moving line laser three-dimensional sensor 4 to scan the passing area when a scanning instruction is received. On the contrary, if there is no calibration record or turntable calibration result, the turntable 3 needs to be calibrated, specifically, the standard calibration ball is fixed on the turntable 3 at a position close to the edge of the turntable, and it is ensured that the standard calibration ball is always within the scanning range of the line laser three-dimensional sensor 4 when the turntable 3 performs cumulative clockwise rotation of 150 degrees for multiple times. And further controlling the line laser three-dimensional sensor 4 to scan along the motor track 5 to obtain a point cloud of a standard calibration ball on the turntable 3, and controlling the line laser three-dimensional sensor 4 to return to an initial position. Controlling the turntable 3 to rotate, so that the standard calibration ball is still in the scanning range of the line laser three-dimensional sensor 4 (stopped in the range of 150 ℃); repeating the steps for N times (wherein N is more than 5), obtaining point clouds of N standard calibration balls at different positions, and fitting a sphere center coordinate for each point cloud through the point clouds of the standard calibration balls. Through the obtained N spherical center coordinates, the center coordinates of the rotation of the standard calibration ball can be fitted to serve as the rotation axis coordinates of the turntable 3, and the normal vector (unit vector) of the turntable 3 is obtained, so that calibration is completed.
The calibration principle is as follows: because the rotating table 3 rotates around the rotating shaft, the rotating shaft cannot change in the rotating process of the rotating table 3, and an object on the rotating table 3 rotates around the rotating shaft along with the rotating table 3, if a linear equation representing the rotating shaft can be solved, and meanwhile, the angle of each rotation of the rotating table 3 is known (without considering the rotation angle error of the rotating table), a new coordinate after the rotating table 3 rotates can be obtained through the rotation angle and the rotating shaft linear equation for each point in the collected point cloud. In summary, the calibration of the rotating shaft of the turntable 3 is actually a linear equation for calibrating the rotating shaft, a three-dimensional point p and a three-dimensional vector v in a three-dimensional space can represent a unique straight line, the center of a three-dimensional circle fitted by the center coordinates of a plurality of standard calibration balls at different angles of the turntable 3 is taken as p (when the standard calibration balls are placed on the turntable 3 at fixed positions and rotate along with the turntable 3, the trajectory of the center of the standard calibration ball is necessarily an arc), and the normal vector thereof is taken as v, the calibration is completed.
Before the step of obtaining the first wheel hub point cloud data of the target wheel hub located on the rotary table, the rotary table is calibrated firstly, so that data conversion can be carried out on the obtained wheel hub point cloud data according to the calibration result of the rotary table, accurate coordinates of each point cloud can be accurately obtained, and then whether burrs exist on the target wheel hub and the positions of the burrs are accurately detected according to the wheel hub point cloud data containing the accurate point cloud coordinates, and the polishing efficiency of the wheel hub burrs is improved.
Further, this application still provides a wheel hub point cloud acquisition device.
Referring to fig. 6, fig. 6 is a functional module schematic diagram of the hub point cloud obtaining apparatus according to the first embodiment of the present application.
The hub point cloud obtaining device comprises:
the first acquisition module 10 is used for acquiring first hub point cloud data of a target hub of the turntable;
a second obtaining module 20, configured to rotate the turntable, and obtain second hub point cloud data of the target hub of the turntable after the turntable rotates;
a determining module 30, configured to determine target hub point cloud data of the target hub based on the first hub point cloud data and the second hub point cloud data, so as to perform burr detection on the target hub according to the target hub point cloud data.
In addition, the present application also provides a computer-readable storage medium, on which a hub point cloud obtaining program is stored, and when being executed by a processor, the hub point cloud obtaining program realizes the steps of the embodiments of the hub point cloud obtaining method.
In addition, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the embodiments of the hub point cloud obtaining method are implemented.
In the embodiments of the hub point cloud obtaining device, the computer readable storage medium, and the computer program product of the present application, all technical features of the embodiments of the hub point cloud obtaining method are included, and the description and explanation contents are basically the same as those of the embodiments of the hub point cloud obtaining method, and are not repeated herein.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present application or a part contributing to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g., a ROM/RAM, a magnetic disk, and an optical disk), and includes a plurality of instructions for enabling a terminal device (which may be a fixed terminal, such as an internet of things smart device including smart homes, such as a smart air conditioner, a smart lamp, a smart power supply, and a smart router, or a mobile terminal, including a smart phone, a wearable networked AR/VR device, a smart sound box, and a network device such as an auto-driven automobile) to execute the method according to the embodiments of the present application.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Claims (8)
1. A hub point cloud obtaining method is characterized by comprising the following steps:
acquiring first hub point cloud data of a target hub positioned on a rotary table;
rotating the rotary table to obtain second hub point cloud data of the target hub of the rotary table after rotation;
determining target hub point cloud data of the target hub based on the first hub point cloud data and the second hub point cloud data, and performing burr detection on the target hub according to the target hub point cloud data;
wherein the step of determining target hub point cloud data for the target hub based on the first hub point cloud data and the second hub point cloud data comprises:
performing data conversion on the first hub point cloud data according to a turntable calibration result to obtain third hub point cloud data;
determining target hub point cloud data of the target hub according to the second hub point cloud data and the third hub point cloud data;
wherein the step of determining target hub point cloud data of the target hub from the second hub point cloud data and the third hub point cloud data comprises:
determining fourth hub point cloud data and fifth hub point cloud data based on Euclidean distances between point data in the second hub point cloud data and point data in the third hub point cloud data;
and determining target hub point cloud data of the target hub according to the second hub point cloud data, the third hub point cloud data, the fourth hub point cloud data and the fifth hub point cloud data.
2. The hub point cloud obtaining method of claim 1, wherein the step of determining the target hub point cloud data of the target hub according to the second, third, fourth and fifth hub point cloud data comprises:
registering the fourth hub point cloud data and the fifth hub point cloud data according to a preset registration algorithm to obtain sixth hub point cloud data and seventh hub point cloud data;
and determining target hub point cloud data of the target hub according to the second hub point cloud data, the third hub point cloud data, the sixth hub point cloud data and the seventh hub point cloud data.
3. The hub point cloud obtaining method of claim 2, wherein the step of determining target hub point cloud data of the target hub from the second, third, sixth and seventh hub point cloud data comprises:
performing point cloud adjustment on the third hub point cloud data according to the sixth hub point cloud data and the seventh hub point cloud data to obtain eighth hub point cloud data;
and determining target hub point cloud data of the target hub according to the eighth hub point cloud data and the second hub point cloud data.
4. The hub point cloud obtaining method of claim 3, wherein the step of determining target hub point cloud data of the target hub from the eighth hub point cloud data and the second hub point cloud data comprises:
combining the eighth hub point cloud data with the second hub point cloud data to obtain ninth hub point cloud data;
and eliminating the data which is the same as the fifth hub point cloud data in the ninth hub point cloud data to obtain the target hub data of the target hub.
5. The hub point cloud acquisition method of claim 1, wherein the step of acquiring first hub point cloud data of a target hub located on a turntable is preceded by:
determining whether the turntable is calibrated;
and if the rotary table is not calibrated, calibrating the rotary table by using the rotary shaft to obtain a rotary table calibration result.
6. A hub point cloud obtaining device is characterized by comprising:
the first acquisition module is used for acquiring first hub point cloud data of a target hub positioned on the rotary table;
the second acquisition module is used for rotating the rotary table and acquiring second hub point cloud data of the target hub of the rotary table after rotation;
the determining module is used for determining target hub point cloud data of the target hub based on the first hub point cloud data and the second hub point cloud data so as to perform burr detection on the target hub according to the target hub point cloud data;
the determining module is further used for performing data conversion on the first hub point cloud data according to the turntable calibration result to obtain third hub point cloud data; determining target hub point cloud data of the target hub according to the second hub point cloud data and the third hub point cloud data;
the determining module is further configured to determine fourth hub point cloud data and fifth hub point cloud data based on an Euclidean distance between point data in the second hub point cloud data and point data in the third hub point cloud data; and determining target hub point cloud data of the target hub according to the second hub point cloud data, the third hub point cloud data, the fourth hub point cloud data and the fifth hub point cloud data.
7. A hub point cloud acquisition apparatus, characterized in that the apparatus comprises a memory, a processor and a hub point cloud acquisition program stored on the memory and executable on the processor, the hub point cloud acquisition program when executed by the processor implementing the steps of the hub point cloud acquisition method as claimed in any one of claims 1 to 5.
8. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a hub point cloud acquisition program which, when executed by a processor, implements the steps of the hub point cloud acquisition method according to any one of claims 1 to 5.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104915986A (en) * | 2015-06-26 | 2015-09-16 | 北京航空航天大学 | Physical three-dimensional model automatic modeling method |
| CN110728623A (en) * | 2019-08-27 | 2020-01-24 | 深圳市华讯方舟太赫兹科技有限公司 | Cloud point splicing method, terminal equipment and computer storage medium |
| CN110992468A (en) * | 2019-11-28 | 2020-04-10 | 贝壳技术有限公司 | Point cloud data-based modeling method, device and equipment, and storage medium |
| CN111507928A (en) * | 2020-03-27 | 2020-08-07 | 福建汇川物联网技术科技股份有限公司 | Point cloud data fusion method, device and equipment and storage medium |
| CN113420384A (en) * | 2021-08-23 | 2021-09-21 | 深圳市信润富联数字科技有限公司 | Method and device for generating wheel hub grinding track |
| CN113436234A (en) * | 2021-08-26 | 2021-09-24 | 深圳市信润富联数字科技有限公司 | Wheel hub burr identification method, electronic device, device and readable storage medium |
-
2021
- 2021-10-22 CN CN202111230004.5A patent/CN113674278B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104915986A (en) * | 2015-06-26 | 2015-09-16 | 北京航空航天大学 | Physical three-dimensional model automatic modeling method |
| CN110728623A (en) * | 2019-08-27 | 2020-01-24 | 深圳市华讯方舟太赫兹科技有限公司 | Cloud point splicing method, terminal equipment and computer storage medium |
| CN110992468A (en) * | 2019-11-28 | 2020-04-10 | 贝壳技术有限公司 | Point cloud data-based modeling method, device and equipment, and storage medium |
| CN111507928A (en) * | 2020-03-27 | 2020-08-07 | 福建汇川物联网技术科技股份有限公司 | Point cloud data fusion method, device and equipment and storage medium |
| CN113420384A (en) * | 2021-08-23 | 2021-09-21 | 深圳市信润富联数字科技有限公司 | Method and device for generating wheel hub grinding track |
| CN113436234A (en) * | 2021-08-26 | 2021-09-24 | 深圳市信润富联数字科技有限公司 | Wheel hub burr identification method, electronic device, device and readable storage medium |
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