CN114612452B - Identification method and system for bar, electronic equipment and readable storage medium - Google Patents
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Abstract
The invention relates to the technical field of three-dimensional modeling, and discloses a method, a system, electronic equipment and a readable storage medium for identifying bars.
Description
Technical Field
The present invention relates to the field of three-dimensional modeling technologies, and in particular, to a method and a system for identifying a rod, an electronic device, and a readable storage medium.
Background
At present, under the promotion of national intelligent manufacturing strategy, warehouse logistics in the steel industry has entered an intelligent rapid development stage. Along with the continuous development of storage logistics, the intelligent storage technology is widely applied to materials with regular shapes such as steel coils and slabs and materials with relatively low standardization such as bars and wires. However, since the materials of the bars are not provided with a fixed stacking saddle in the warehouse area, the materials are often stored in a mode of stacking bar bundles in a groined shape to improve warehouse capacity, and on the other hand, since the bar bundles are often transported by an electromagnetic crane, the bar bundles are easy to receive the abnormal stacking state of forked ends, overlapped ends and the like due to the influence of magnetic force, so that potential safety hazards exist in the bar materials in the warehouse area. But compared with a steel coil warehouse or a slab warehouse, the bar material in the bar warehouse is larger in size, and the conditions of stacking and lifting simultaneously by multiple bundles exist, so that the intelligent difficulty of the bar warehouse is higher, and the intelligent warehouse technology also provides higher requirements for the environment sensing technology of the bar warehouse.
Therefore, an identification method for the bars is needed to determine the coordinate position of the bar bundle, realize the intellectualization of the bar library, and further facilitate the management of the bars by users and improve the management efficiency.
Disclosure of Invention
The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview, and is intended to neither identify key/critical elements nor delineate the scope of such embodiments, but is intended as a prelude to the more detailed description that follows.
In view of the shortcomings of the prior art, the invention discloses a method, a system, electronic equipment and a readable storage medium for identifying bars, so as to determine the coordinate position of a bar bundle, realize the intellectualization of a bar library, and further facilitate the management of the bars by a user and improve the management efficiency.
The invention discloses a bar identification method, which comprises the following steps: acquiring contour point cloud data corresponding to two ends in a target bar bundle; determining arc characteristic parameters corresponding to the contour point cloud data, and performing contour segmentation on the contour point cloud data based on the parameter change period of the arc characteristic parameters to obtain arc segment point cloud data corresponding to each end; and determining vertex coordinate data corresponding to each end according to the arc segment point cloud data, and determining vector feature data corresponding to the target bar bundle according to the vertex coordinate data corresponding to each end.
Optionally, acquiring contour point cloud data corresponding to two ends in the target bar bundle includes: along with the movement of a mobile device, the outlines of two ends in the target bar bundle are scanned by scanning equipment arranged on the mobile device respectively, so that device displacement data corresponding to the mobile device and original point cloud data corresponding to the ends are obtained; determining a real-time displacement vector according to the device displacement data, and converting the original point cloud data from a sensor coordinate system to a system coordinate system based on the real-time displacement vector to obtain conversion point cloud data corresponding to each end; and extracting contour point cloud data corresponding to each end from the conversion point cloud data through a straight-through filtering algorithm.
Optionally, the raw point cloud data is converted from a sensor coordinate system to a system coordinate system by the following formula: In the/> For converting point cloud data based on a system coordinate system,/>For the original point cloud data based on the sensor coordinate system, R is a rotation matrix, T 1 is a relative displacement vector between the mobile device and the scanning device, and T 2 is a real-time displacement vector corresponding to the mobile device.
Optionally, when the scanning device scans any end of the target bar bundle, the scanning direction of the scanning device is perpendicular to a connecting line segment between two ends of the target bar bundle.
Optionally, performing contour segmentation on the contour point cloud data based on the parameter variation period of the arc characteristic parameter to obtain arc segment point cloud data corresponding to each end, where the contour segmentation includes: performing contour segmentation on the contour point cloud data according to an arc segmentation model to obtain segmentation point cloud data corresponding to the contour point cloud data, wherein the arc segmentation model is obtained by training a preset neural network model through contour sample data with segmentation position labels; and determining arc segment point cloud data from the segmentation point cloud data based on a comparison result between the parameter change period of the arc characteristic parameter and a preset period threshold.
Optionally, determining vertex coordinate data corresponding to each end according to the arc segment point cloud data includes: determining whether the arc segment point cloud data meets an expected standard according to a preset evaluation algorithm; if the arc segment point cloud data meets the expected standard, establishing an end head circular model corresponding to each end head according to the arc segment point cloud data; and extracting arc radius characteristic information corresponding to each end from the end circular model, and determining vertex coordinate data corresponding to each end according to the arc radius characteristic information.
Optionally, the method further comprises: acquiring a bar bundle set to be tested, wherein the bar bundle set to be tested comprises a plurality of bar bundles to be tested which are stacked according to a groined shape; determining a bar bundle to be detected which is positioned at the topmost layer in the bar bundle set to be detected as a top bar bundle, and acquiring contour point cloud data, vertex coordinate data and vector feature data corresponding to each top bar bundle; performing data fitting based on contour point cloud data, vertex coordinate data and vector feature data of each top bar bundle, and establishing a bar bundle stacking model corresponding to the top bar bundles; determining an abnormal stacking state between the top bundles of bars based on the bundle stacking model; uploading at least one of contour point cloud data, vertex coordinate data, vector feature data, a bar bundle stacking model and an abnormal stacking state to a server side.
The invention discloses an identification system for bars, comprising: the acquisition module is used for acquiring contour point cloud data corresponding to two ends in the target bar bundle; the segmentation module is used for determining arc characteristic parameters corresponding to the contour point cloud data, and carrying out contour segmentation on the contour point cloud data based on the parameter change period of the arc characteristic parameters to obtain arc segment point cloud data corresponding to each end; the determining module is used for determining vertex coordinate data corresponding to each end according to the arc segment point cloud data and determining vector feature data corresponding to the target bar bundle according to the vertex coordinate data corresponding to each end.
The invention discloses an electronic device, comprising: a processor and a memory; the memory is used for storing a computer program, and the processor is used for executing the computer program stored in the memory so as to enable the electronic equipment to execute the method.
The present invention discloses a computer-readable storage medium having stored thereon a computer program: the computer program, when executed by a processor, implements the method described above.
The invention has the beneficial effects that:
The method comprises the steps of determining arc characteristic parameters corresponding to contour point cloud data by acquiring contour point cloud data corresponding to two ends in a target bar bundle, conducting contour segmentation on the contour point cloud data based on parameter change periods of the arc characteristic parameters to obtain arc segment point cloud data corresponding to the ends, determining vertex coordinate data corresponding to the ends according to the arc segment point cloud data, and determining vector characteristic data corresponding to the target bar bundle according to vertex coordinate data corresponding to the ends. Therefore, vector characteristic data of the target bar bundle can be obtained through the contour point cloud data corresponding to the two ends of the target bar bundle, the coordinate position of the bar bundle is further determined, the intellectualization of the bar library is realized, a user can manage bars conveniently, the management efficiency is improved, and meanwhile, the robustness, the convenience and the instantaneity of a storage sensing system can be improved, so that the storage logistics operation safety is guaranteed.
Drawings
FIG. 1 is a flow chart of an identification method for a rod in an embodiment of the invention;
FIG. 2-a is a schematic diagram of a rod warehousing system according to an embodiment of the invention;
FIG. 2-b is a front view of a laser scanner in an embodiment of the invention;
FIG. 2-c is a schematic illustration of the effect of one bar treatment in an embodiment of the present invention;
FIG. 3 is a flow chart of another method for identifying a rod in an embodiment of the invention;
FIG. 4 is a schematic diagram of an identification system for a rod in accordance with an embodiment of the present invention;
Fig. 5 is a schematic diagram of an electronic device in an embodiment of the invention.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that, without conflict, the following embodiments and sub-samples in the embodiments may be combined with each other.
It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present invention by way of illustration, and only the components related to the present invention are shown in the drawings and are not drawn according to the number, shape and size of the components in actual implementation, and the form, number and proportion of the components in actual implementation may be arbitrarily changed, and the layout of the components may be more complicated.
In the following description, numerous details are set forth in order to provide a more thorough explanation of embodiments of the present invention, it will be apparent, however, to one skilled in the art that embodiments of the present invention may be practiced without these specific details, in other embodiments, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the embodiments of the present invention.
The terms first, second and the like in the description and in the claims of the embodiments of the disclosure and in the above-described figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe embodiments of the present disclosure. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion.
The term "plurality" means two or more, unless otherwise indicated.
In the embodiment of the present disclosure, the character "/" indicates that the front and rear objects are an or relationship. For example, A/B represents: a or B.
The term "and/or" is an associative relationship that describes an object, meaning that there may be three relationships. For example, a and/or B, represent: a or B, or, A and B.
Referring to fig. 1, an embodiment of the present disclosure provides an identification method for a rod, including:
Step S101, contour point cloud data corresponding to two ends in a target bar bundle are obtained;
step S102, determining arc characteristic parameters corresponding to the contour point cloud data, and performing contour segmentation on the contour point cloud data based on the parameter change period of the arc characteristic parameters to obtain arc segment point cloud data corresponding to each end;
And step S103, determining vertex coordinate data corresponding to each end according to the arc segment point cloud data, and determining vector feature data corresponding to the target bar bundle according to the vertex coordinate data corresponding to each end.
By adopting the identification method for the bars, which is provided by the embodiment of the disclosure, the contour point cloud data corresponding to the contour point cloud data are obtained, the arc characteristic parameters corresponding to the contour point cloud data are determined, the contour segmentation is performed on the contour point cloud data based on the parameter change period of the arc characteristic parameters, the arc segment point cloud data corresponding to each end are obtained, then the vertex coordinate data corresponding to each end are determined according to the arc segment point cloud data, and the vector characteristic data corresponding to the target bar bundle are determined according to the vertex coordinate data corresponding to each end. Therefore, vector characteristic data of the target bar bundle can be obtained through the contour point cloud data corresponding to the two ends of the target bar bundle, the coordinate position of the bar bundle is further determined, the intellectualization of the bar library is realized, a user can manage bars conveniently, the management efficiency is improved, and meanwhile, the robustness, the convenience and the instantaneity of a storage sensing system can be improved, so that the storage logistics operation safety is guaranteed.
Referring to fig. 2-a, an embodiment of the present disclosure provides a bar stock storage system for implementing the above-mentioned method for identifying bars, including a bar bundle stack 201, a moving device 202, a scanning device 203, and an electronic device 204. The bar bundle stack comprises a plurality of bar bundles, and the bar bundles are stacked according to a groined shape; the moving device 202 comprises a cart 2021 and a cart 2022, wherein the cart is arranged on the cart to form an unmanned traveling crane, and the moving direction of the cart is perpendicular to the moving direction of the cart; the scanning device 203 comprises two laser scanners 2031, which are mounted on the longitudinal side and the lateral side of the trolley, respectively, the front view of which is shown in fig. 2-b; the electronic device 204 is connected with the laser scanner through an ethernet port, and is used for implementing the method for identifying the bar, and providing external data interaction service through the ethernet port, and the bar processing effect of the method for identifying the bar is shown in fig. 2-c.
Optionally, acquiring contour point cloud data corresponding to two ends in the target bar bundle includes: along with the movement of the mobile device, the outlines of two ends in the target bar bundle are scanned by scanning equipment arranged on the mobile device respectively, so that device displacement data corresponding to the mobile device and original point cloud data corresponding to each end are obtained; determining a real-time displacement vector according to the device displacement data, and converting the original point cloud data from a sensor coordinate system to a system coordinate system based on the real-time displacement vector to obtain conversion point cloud data corresponding to each end; and extracting contour point cloud data corresponding to each end from the conversion point cloud data through a straight-through filtering algorithm.
Optionally, the profile point cloud data is used to characterize the profile characteristics of the end of the target bundle of bars.
In some embodiments, as the unmanned crane moves, scanning two ends of a target bar bundle in the bar bundle stack from top to bottom through a laser scanner to obtain device displacement data corresponding to a moving device of the cart and the trolley and original point cloud data corresponding to each end, sending the device displacement data and the original point cloud data to electronic equipment, determining a real-time displacement vector by the electronic equipment according to the device displacement data, converting the original point cloud data from a sensor coordinate system to a system coordinate system based on the real-time displacement vector to obtain conversion point cloud data corresponding to each end, and extracting contour point cloud data corresponding to each end from the conversion point cloud data through a straight-through filtering algorithm.
In the acquisition process of the laser point cloud, due to the influence of the effective distance of the acquisition equipment, the reflected laser point cloud data of a target with a far distance is in an excessively discrete state, the possibility of identification is not provided, points outside the area range of the target bar bundle are filtered through a straight-through filtering algorithm (the background can be removed under the condition that the background is a certain distance from the foreground), and the accuracy of the direction identification of the target bar bundle is improved.
Optionally, the raw point cloud data is converted from the sensor coordinate system to the system coordinate system by the following formula:
In the method, in the process of the invention, For converting point cloud data based on a system coordinate system,/>For the original point cloud data based on the sensor coordinate system, R is a rotation matrix, T 1 is a relative displacement vector between the mobile device and the scanning device, and T 2 is a real-time displacement vector corresponding to the mobile device.
In some embodiments, a rotation matrix, a relative displacement vector between the mobile device and the scanning device is obtained through a preset sensor calibration method, wherein the rotation matrix is used for correcting a scanning angle of the scanning device; and determining a real-time displacement vector according to the device displacement data. Because the scanning device has an installation angle, the scanning angle of the scanning device is deviated, the original point cloud data obtained through the scanning device is inaccurate, and the scanning angle of the scanning device is corrected through the rotation matrix, so that the data deviation of the original point cloud data is eliminated, the accuracy and the reliability of the conversion point cloud data are improved, and the direction identification accuracy of the target bar bundle is further improved.
Optionally, when the scanning device scans any end of the target bar bundle, the scanning direction of the scanning device is perpendicular to a connecting line segment between two ends of the target bar bundle.
In some embodiments, when the placement direction of the target bar bundle is transverse, scanning each end of the target bar bundle with a laser scanner scanning along the longitudinal direction; when the arrangement direction of the target bar bundle is longitudinal, scanning each end of the target bar bundle by a laser scanner scanning along the transverse direction.
Optionally, contour segmentation is performed on the contour point cloud data based on a parameter variation period of the arc characteristic parameter to obtain arc segment point cloud data corresponding to each end, including: performing contour segmentation on the contour point cloud data according to an arc segmentation model to obtain segmentation point cloud data corresponding to the contour point cloud data, wherein the arc segmentation model is obtained by training a preset neural network model through contour sample data with segmentation position labels; and determining arc segment point cloud data from the segmentation point cloud data based on a comparison result between a parameter change period of the arc characteristic parameter and a preset period threshold.
In some embodiments, a periodic variation rule is formed between the arcs of the end heads of the target bar bundles, the arc characteristic parameters of the end heads of the target bar bundles are used for representing the curvature of clustered arc sections of the end heads of the target bar bundles, the parameter variation period of the arc characteristic parameters is used for representing the arc scale or the outer diameter of the bar bundles, and the segmentation point cloud data which are larger than a preset period threshold value is filtered through the parameter variation period of the arc characteristic parameters to obtain arc section point cloud data, wherein the preset period threshold value is 300mm.
In some embodiments, the circular arc segmentation model is a YOLO v3 network model.
Optionally, determining vertex coordinate data corresponding to each end according to the arc segment point cloud data includes: determining whether the arc segment point cloud data meets the expected standard according to a preset evaluation algorithm; if the arc segment point cloud data meets the expected standard, establishing an end head circular model corresponding to each end head according to the arc segment point cloud data; and extracting the arc radius characteristic information corresponding to each end from the end circular model, and determining vertex coordinate data corresponding to each end according to the arc radius characteristic information.
Optionally, the preset evaluation algorithm is a RANSAC algorithm, i.e. a random sampling consistency method, wherein the input of the RANSAC algorithm is a set of observations, a parameterized model that can be interpreted or adapted to the observations. RANSAC achieves this goal by iteratively selecting a set of random subsets of data, the selected subset being assumed to be intra-office points, and validating as follows: firstly, randomly supposing a small group of local points as initial values, and then fitting a model by using the local points, wherein the model is suitable for the suppositional local points, and all unknown parameters can be calculated from the suppositional local points; testing all other data by using the obtained model, and if a certain point is suitable for the estimated model, considering the certain point as an intra-local point, and expanding the intra-local point; if there are enough points to be classified as hypothetical intra-local points, then the estimated model is reasonable enough; then, the model is re-estimated by using all assumed local points, because the model is estimated only by the initial assumed local points, and needs to be updated after the subsequent expansion; finally, the model is evaluated by estimating the error rate of the local points and the model. The whole process is iterated once, the process is repeated a fixed number of times, and each time the model generated has two outcomes: or because there are too few points in the office, the model of the last time is not discarded yet; or is selected for use as being better than existing models. Therefore, whether the arc segment point cloud data meets the expected standard or not is judged through the RANSAC algorithm, unreasonable model parameters are removed, accuracy of the arc segment point cloud data of the target bar bundle is improved, and reliability of vector feature data corresponding to the target bar bundle is further improved.
Optionally, determining vector feature data corresponding to the target bar bundle according to vertex coordinate data corresponding to each end includes: respectively obtaining vertex coordinate data A 1(x1,y1,z1)、A2(x2,y2,z2 corresponding to each end in the target bar bundle; determining vector characteristic data corresponding to the target bar bundle according to vertex coordinate data of two ends in the target bar bundleI.e. a direction vector is calculated that characterizes the orientation of the target bundle.
Optionally, the method further comprises: acquiring a bar bundle set to be tested, wherein the bar bundle set to be tested comprises a plurality of bar bundles to be tested which are stacked according to a groined shape; determining the bar bundle to be detected which is positioned at the topmost layer in the bar bundle set to be detected as a top bar bundle, and acquiring contour point cloud data, vertex coordinate data and vector feature data corresponding to each top bar bundle; performing data fitting based on contour point cloud data, vertex coordinate data and vector feature data of each top bar bundle, and establishing a bar bundle stacking model corresponding to the top bar bundles; determining an abnormal stacking state between top bundles of bars based on the bundle stacking model; uploading at least one of contour point cloud data, vertex coordinate data, vector feature data, a bar bundle stacking model and an abnormal stacking state to a server side.
Alternatively, the abnormal stacked state includes branching, sagging, and the like between the target bar bundles; the server side comprises an intelligent warehousing system.
In some embodiments, the topmost bundle of bars to be tested in the set of bundles of bars to be tested is the bundle of bars to be tested that is furthest from the ground or the bundle of bars to be tested that is closest to the scanning device.
As shown in fig. 3, an embodiment of the present disclosure provides an identification method for a rod, including:
step S301, acquiring a bar bundle set to be tested;
the bar bundle set to be tested comprises a plurality of bar bundles to be tested which are stacked according to a groined shape;
Step S302, along with the movement of the mobile device, the outlines of two ends in the target bar bundle are scanned by scanning equipment installed on the mobile device respectively, so as to obtain device displacement data corresponding to the mobile device and original point cloud data corresponding to each end;
the target bar bundle is the bar bundle to be tested positioned at the topmost layer in the bar bundle set to be tested;
Step S303, determining a real-time displacement vector according to the device displacement data, and converting original point cloud data from a sensor coordinate system to a system coordinate system based on the real-time displacement vector to obtain conversion point cloud data corresponding to each end;
step S304, extracting contour point cloud data corresponding to each end from the conversion point cloud data through a straight-through filtering algorithm;
Step S305, performing contour segmentation on the contour point cloud data according to the arc segmentation model to obtain segmented point cloud data corresponding to the contour point cloud data, and determining arc segment point cloud data from the segmented point cloud data based on a comparison result between a parameter change period of the arc characteristic parameter and a preset period threshold;
The method comprises the steps that an arc segmentation model is obtained by training a preset neural network model through contour sample data with segmentation position labels;
Step S306, if the arc segment point cloud data meets the expected standard, establishing an end head circular model corresponding to each end head according to the arc segment point cloud data;
determining whether the arc segment point cloud data meets the expected standard according to a preset evaluation algorithm;
step S307, extracting the corresponding arc radius characteristic information of each end from the end circular model;
step S308, determining vertex coordinate data corresponding to each end according to the arc radius characteristic information;
Step S309, determining vector feature data corresponding to the target bar bundle according to vertex coordinate data corresponding to each end;
Step S310, carrying out data fitting based on contour point cloud data, vertex coordinate data and vector feature data of each top bar bundle, and establishing a bar bundle stacking model corresponding to the top bar bundles;
step S311 of determining an abnormal stacking state between top bundles of bars based on the bundle stacking model;
Step S312, uploading at least one of the contour point cloud data, the vertex coordinate data, the vector feature data, the bar bundle stacking model and the abnormal stacking state to the server side.
By adopting the identification method for the bars, which is provided by the embodiment of the disclosure, the contour point cloud data corresponding to the contour point cloud data are obtained, the arc characteristic parameters corresponding to the contour point cloud data are determined, the contour segmentation is performed on the contour point cloud data based on the parameter change period of the arc characteristic parameters, the arc segment point cloud data corresponding to each end are obtained, then the vertex coordinate data corresponding to each end are determined according to the arc segment point cloud data, and the vector characteristic data corresponding to the target bar bundle are determined according to the vertex coordinate data corresponding to each end. Therefore, vector characteristic data of the target bar bundle can be obtained through the contour point cloud data corresponding to the two ends of the target bar bundle, the coordinate position of the bar bundle is further determined, the intellectualization of the bar library is realized, a user can manage bars conveniently, the management efficiency is improved, and meanwhile, the robustness, the convenience and the instantaneity of a storage sensing system can be improved, so that the storage logistics operation safety is guaranteed.
As shown in conjunction with fig. 4, an embodiment of the present disclosure provides an identification system for a rod, including an acquisition module 401, a segmentation module 402, and a determination module 403. The acquisition module is used for acquiring contour point cloud data corresponding to two ends in the target bar bundle. The segmentation module is used for determining arc characteristic parameters corresponding to the contour point cloud data, and carrying out contour segmentation on the contour point cloud data based on the parameter change period of the arc characteristic parameters to obtain arc segment point cloud data corresponding to each end. The determining module is used for determining vertex coordinate data corresponding to each end according to the arc segment point cloud data and determining vector feature data corresponding to the target bar bundle according to the vertex coordinate data corresponding to each end.
By adopting the identification system for bars provided by the embodiment of the disclosure, the contour point cloud data corresponding to the contour point cloud data are obtained by acquiring the contour point cloud data corresponding to two ends in the target bar bundle, the arc characteristic parameters corresponding to the contour point cloud data are determined, the contour segmentation is performed on the contour point cloud data based on the parameter change period of the arc characteristic parameters to obtain the arc segment point cloud data corresponding to each end, then the vertex coordinate data corresponding to each end is determined according to the arc segment point cloud data, and the vector characteristic data corresponding to the target bar bundle is determined according to the vertex coordinate data corresponding to each end. Therefore, vector characteristic data of the target bar bundle can be obtained through the contour point cloud data corresponding to the two ends of the target bar bundle, the coordinate position of the bar bundle is further determined, the intellectualization of the bar library is realized, a user can manage bars conveniently, the management efficiency is improved, and meanwhile, the robustness, the convenience and the instantaneity of a storage sensing system can be improved, so that the storage logistics operation safety is guaranteed.
As shown in conjunction with fig. 5, an embodiment of the present disclosure provides an electronic device, including: a processor (processor) 500 and a memory (memory) 501; the memory is used for storing a computer program, and the processor is used for executing the computer program stored in the memory, so that the terminal executes any one of the methods in the embodiment. Optionally, the electronic device may also include a communication interface (Communication Interface) 502 and a bus 503. The processor 500, the communication interface 502, and the memory 501 may communicate with each other via the bus 503. The communication interface 502 may be used for information transfer. The processor 500 may call logic instructions in the memory 501 to perform the methods of the embodiments described above.
Further, the logic instructions in the memory 501 may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand alone product.
The memory 501 is a computer readable storage medium that may be used to store a software program, a computer executable program, and program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 500 performs functional applications as well as data processing, i.e. implements the methods of the embodiments described above, by running program instructions/modules stored in the memory 501.
Memory 501 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for functionality; the storage data area may store data created according to the use of the terminal device, etc. Further, the memory 501 may include a high-speed random access memory, and may also include a nonvolatile memory.
By adopting the electronic equipment provided by the embodiment of the disclosure, the contour point cloud data corresponding to the contour point cloud data are obtained, the arc characteristic parameters corresponding to the contour point cloud data are determined, the contour segmentation is carried out on the contour point cloud data based on the parameter change period of the arc characteristic parameters, the arc segment point cloud data corresponding to each end are obtained, then the vertex coordinate data corresponding to each end are determined according to the arc segment point cloud data, and the vector characteristic data corresponding to the target bar bundle are determined according to the vertex coordinate data corresponding to each end. Therefore, vector characteristic data of the target bar bundle can be obtained through the contour point cloud data corresponding to the two ends of the target bar bundle, the coordinate position of the bar bundle is further determined, the intellectualization of the bar library is realized, a user can manage bars conveniently, the management efficiency is improved, and meanwhile, the robustness, the convenience and the instantaneity of a storage sensing system can be improved, so that the storage logistics operation safety is guaranteed.
The disclosed embodiments also provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements any of the methods of the present embodiments.
The computer readable storage medium in the embodiments of the present disclosure may be understood by those of ordinary skill in the art: all or part of the steps for implementing the method embodiments described above may be performed by computer program related hardware. The aforementioned computer program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
The electronic device disclosed in this embodiment includes a processor, a memory, a transceiver, and a communication interface, where the memory and the communication interface are connected to the processor and the transceiver and perform communication therebetween, the memory is used to store a computer program, the communication interface is used to perform communication, and the processor and the transceiver are used to run the computer program, so that the electronic device performs each step of the above method.
In this embodiment, the memory may include a random access memory (Random Access Memory, abbreviated as RAM), and may further include a non-volatile memory (non-volatile memory), such as at least one disk memory.
The processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, abbreviated as CPU), a graphics processor (Graphics Processing Unit, abbreviated as GPU), a network processor (Network Processor, abbreviated as NP), etc.; but may also be a digital signal processor (DIGITAL SIGNAL Processing, DSP), application Specific Integrated Circuit (ASIC), field-Programmable gate array (FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware components.
The above description and the drawings illustrate embodiments of the disclosure sufficiently to enable those skilled in the art to practice them. Other embodiments may involve structural, logical, electrical, process, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and sub-samples of some embodiments may be included in or substituted for portions and sub-samples of other embodiments. Moreover, the terminology used in the present application is for the purpose of describing embodiments only and is not intended to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a," "an," and "the" (the) are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term "and/or" as used in this disclosure is meant to encompass any and all possible combinations of one or more of the associated listed. In addition, when used in this disclosure, the terms "comprises," "comprising," and/or variations thereof mean the presence of the stated sub-sample, integer, step, operation, element, and/or component, but do not exclude the presence or addition of one or more other sub-samples, integers, steps, operations, elements, components, and/or groups of these. Without further limitation, an element defined by the phrase "comprising one …" does not exclude the presence of other like elements in a process, method or apparatus that includes the element. In this context, each embodiment may be described with emphasis on the differences from the other embodiments, and the same similar parts between the various embodiments may be referred to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method sections disclosed in the embodiments, the description of the method sections may be referred to for relevance.
Those of skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. The skilled artisan may use different methods for each particular application to achieve the described functionality, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure. It will be clearly understood by those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the embodiments disclosed herein, the disclosed methods, articles of manufacture (including but not limited to devices, apparatuses, etc.) may be practiced in other ways. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the units may be merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple units or components may be combined or integrated into another system, or some sub-samples may be omitted, or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form. The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to implement the present embodiment. In addition, each functional unit in the embodiments of the present disclosure may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than that disclosed in the description, and sometimes no specific order exists between different operations or steps. For example, two consecutive operations or steps may actually be performed substantially in parallel, they may sometimes be performed in reverse order, which may be dependent on the functions involved. Each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Claims (10)
1. A method for identifying a rod, comprising:
Acquiring contour point cloud data corresponding to two ends in a target bar bundle;
determining arc characteristic parameters corresponding to the contour point cloud data, and performing contour segmentation on the contour point cloud data based on the parameter change period of the arc characteristic parameters to obtain arc segment point cloud data corresponding to each end;
determining vertex coordinate data corresponding to each end according to the arc segment point cloud data, and determining vector feature data corresponding to the target bar bundle according to the vertex coordinate data corresponding to each end;
The method comprises the steps that arc characteristic parameters of the end of a target bar bundle are used for representing the curvature of a clustered arc section of the end of the target bar bundle, the parameter change period of the arc characteristic parameters is used for representing the arc scale or the outer diameter of the bar bundle, and segmentation point cloud data larger than a preset period threshold are filtered through the parameter change period of the arc characteristic parameters to obtain arc section point cloud data.
2. The method of claim 1, wherein obtaining contour point cloud data corresponding to two ends in a target bundle of bars comprises:
along with the movement of a mobile device, the outlines of two ends in the target bar bundle are scanned by scanning equipment arranged on the mobile device respectively, so that device displacement data corresponding to the mobile device and original point cloud data corresponding to the ends are obtained;
Determining a real-time displacement vector according to the device displacement data, and converting the original point cloud data from a sensor coordinate system to a system coordinate system based on the real-time displacement vector to obtain conversion point cloud data corresponding to each end;
and extracting contour point cloud data corresponding to each end from the conversion point cloud data through a straight-through filtering algorithm.
3. The method of claim 2, wherein the raw point cloud data is converted from a sensor coordinate system to a system coordinate system by the following formula:
In the method, in the process of the invention, For converting point cloud data based on a system coordinate system,/>For the original point cloud data based on the sensor coordinate system, R is a rotation matrix, T 1 is a relative displacement vector between the mobile device and the scanning device, and T 2 is a real-time displacement vector corresponding to the mobile device.
4. The method of claim 2, wherein the scanning device scans either end of the target bundle of bars in a direction perpendicular to a line connecting the ends of the target bundle of bars.
5. The method according to claim 1, wherein performing contour segmentation on the contour point cloud data based on a parameter variation period of the arc characteristic parameter to obtain arc segment point cloud data corresponding to each end includes:
Performing contour segmentation on the contour point cloud data according to an arc segmentation model to obtain segmentation point cloud data corresponding to the contour point cloud data, wherein the arc segmentation model is obtained by training a preset neural network model through contour sample data with segmentation position labels;
And determining arc segment point cloud data from the segmentation point cloud data based on a comparison result between the parameter change period of the arc characteristic parameter and a preset period threshold.
6. The method of claim 1, wherein determining vertex coordinate data corresponding to each of the tips from the arc segment point cloud data comprises:
determining whether the arc segment point cloud data meets an expected standard according to a preset evaluation algorithm;
If the arc segment point cloud data meets the expected standard, establishing an end head circular model corresponding to each end head according to the arc segment point cloud data;
and extracting arc radius characteristic information corresponding to each end from the end circular model, and determining vertex coordinate data corresponding to each end according to the arc radius characteristic information.
7. The method according to any one of claims 1 to 6, further comprising:
Acquiring a bar bundle set to be tested, wherein the bar bundle set to be tested comprises a plurality of bar bundles to be tested which are stacked according to a groined shape;
Determining a bar bundle to be detected which is positioned at the topmost layer in the bar bundle set to be detected as a top bar bundle, and acquiring contour point cloud data, vertex coordinate data and vector feature data corresponding to each top bar bundle;
Performing data fitting based on contour point cloud data, vertex coordinate data and vector feature data of each top bar bundle, and establishing a bar bundle stacking model corresponding to the top bar bundles;
Determining an abnormal stacking state between the top bundles of bars based on the bundle stacking model;
Uploading at least one of contour point cloud data, vertex coordinate data, vector feature data, a bar bundle stacking model and an abnormal stacking state to a server side.
8. An identification system for a rod, comprising:
the acquisition module is used for acquiring contour point cloud data corresponding to two ends in the target bar bundle;
The segmentation module is used for determining arc characteristic parameters corresponding to the contour point cloud data, and carrying out contour segmentation on the contour point cloud data based on the parameter change period of the arc characteristic parameters to obtain arc segment point cloud data corresponding to each end;
the determining module is used for determining vertex coordinate data corresponding to each end according to the arc segment point cloud data and determining vector feature data corresponding to the target bar bundle according to the vertex coordinate data corresponding to each end;
The method comprises the steps that arc characteristic parameters of the end of a target bar bundle are used for representing the curvature of a clustered arc section of the end of the target bar bundle, the parameter change period of the arc characteristic parameters is used for representing the arc scale or the outer diameter of the bar bundle, and segmentation point cloud data larger than a preset period threshold are filtered through the parameter change period of the arc characteristic parameters to obtain arc section point cloud data.
9. An electronic device, comprising: a processor and a memory;
The memory is configured to store a computer program, and the processor is configured to execute the computer program stored in the memory, to cause the electronic device to perform the method according to any one of claims 1 to 7.
10. A computer-readable storage medium having stored thereon a computer program, characterized by:
the computer program implementing the method of any of claims 1 to 7 when executed by a processor.
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