Point cloud data differential loading method, system, medium and device

Abstract

The application discloses a point cloud data differentiation loading method, system, medium and equipment, and the method comprises the following steps: obtaining to-be-tested point cloud data, and storing the to-be-tested point cloud data in a dynamic library and a static library respectively, and obtaining first edition dynamic library data and original static library data correspondingly; loading differentiation data according to the first edition dynamic library data and the original static library data, and obtaining differentiation data of the to-be-tested point cloud data; processing and comparing through a partition database, accurately distinguishing the differentiation data, avoiding a large amount of manual correction, and efficiently displaying the differentiation data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of high-precision maps, and in particular to a point cloud data differentiation loading method, system, medium and device. BACKGROUND

[0002] Under the background of the rapid development of current automatic driving technology, high-precision maps for automatic driving emerge as the times require, and play an indispensable role in the safety of ADAS (Advanced Driving Assistance System). They play an important role in vehicle positioning, decision-making and real scene simulation verification.

[0003] In the production of point cloud data of high-precision maps, differentiation data loading is required. However, the current differentiation data loading of high-precision maps is generally manually compared, which is low in efficiency and accuracy. Therefore, how to quickly and accurately find differentiation data is a problem to be solved. SUMMARY

[0004] The present application provides a point cloud data differentiation loading method, system, medium and device, which processes and compares through a partition database, accurately distinguishes difference data, avoids a large amount of manual correction, and efficiently displays differentiation data.

[0005] In a first aspect, a point cloud data differentiation loading method is provided, comprising the following steps:

[0006] Obtain the point cloud data to be tested, and store the point cloud data to be tested in a dynamic library and a static library respectively, to obtain first version dynamic library data and original static library data;

[0007] According to the first version dynamic library data and the original static library data, the differentiation data is loaded, and the differentiation data of the point cloud data to be tested is obtained.

[0008] According to the first aspect, in a first possible implementation manner of the first aspect, the step of "loading differentiation data according to the first version dynamic library data and the original static library data, and obtaining the differentiation data of the point cloud data to be tested" specifically comprises the following steps:

[0009] The first version dynamic library data is sent to the static library to replace the original static library data to generate first version static library data. When it is detected that there is a data difference in the first version static library data in the static library, the first version static library data is sent to the dynamic library for data difference modification to generate second version dynamic library data.

[0010] The second edition dynamic library data is sent to the static library to generate second edition static library data, when detecting that there is data difference in the second edition static library data in the static library, the second edition static library data is sent to the dynamic library to modify the data difference and generate third edition dynamic library data, and when it is detected that there is no data difference in the static library, the data in the dynamic library is stopped from being sent to the static library.

[0011] The adjacent two versions of static library data are compared, and the comparison result is the differentiated data of the to-be-tested point cloud data.

[0012] According to the first possible implementation manner of the first aspect, in a second possible implementation manner of the first aspect, before the step of comparing the adjacent two versions of static library data, the following steps are specifically included:

[0013] The static library data before the adjacent two versions of static library data to be compared is deleted.

[0014] According to the first aspect, in a third possible implementation manner of the first aspect, after the step of loading the differentiated data according to the first edition dynamic library data and the original static library data to obtain the differentiated data of the to-be-tested point cloud data, the following steps are specifically included:

[0015] The differentiated data of the to-be-tested point cloud data is marked.

[0016] The second aspect provides a point cloud data differentiation loading system, which comprises:

[0017] A dynamic static data acquisition module is configured to acquire to-be-tested point cloud data, store the to-be-tested point cloud data in a dynamic library and a static library respectively, and acquire first edition dynamic library data and original static library data.

[0018] A differentiated data loading module is in communication connection with the dynamic static data acquisition module and is configured to load differentiated data according to the first edition dynamic library data and the original static library data, and acquire differentiated data of the to-be-tested point cloud data.

[0019] According to a second aspect, in a first possible implementation manner of the second aspect, the differential data loading module is configured to: send the first version dynamic library data to the static library to replace the original static library data to generate first version static library data; when detecting that the first version static library data in the static library has a data difference, send the first version static library data to the dynamic library to modify the data difference and generate second version dynamic library data; send the second version dynamic library data to the static library to generate second version static library data; when detecting that the second version static library data in the static library has a data difference, send the second version static library data to the dynamic library to modify the data difference and generate third version dynamic library data; and when no data difference is detected in the static library, terminate sending the data in the dynamic library to the static library; and compare the adjacent two version static library data, and obtain a comparison result as the differential data of the to-be-tested point cloud data.

[0020] According to the first possible implementation manner of the second aspect, in a second possible implementation manner of the second aspect, the differential data loading module is configured to delete the static library data before the adjacent two version static library data for comparison.

[0021] According to the second aspect, in a third possible implementation manner of the second aspect, the differential data loading module is further connected with a marking module in communication, and the marking module is configured to mark the differential data of the to-be-tested point cloud data.

[0022] A third aspect provides a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the point cloud data differential loading method according to any one of the above aspects.

[0023] A fourth aspect provides an electronic device, which includes a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor. The processor executes the computer program to implement the point cloud data differential loading method according to the above aspects.

[0024] Compared with the prior art, the advantages of the present application are as follows: firstly, the to-be-tested point cloud data is acquired and stored in a dynamic library and a static library respectively, corresponding to the acquisition of the first version of dynamic library data and the original static library data; then, according to the first version of dynamic library data and the original static library data, the differentiated data is loaded to acquire the differentiated data of the to-be-tested point cloud data; therefore, the point cloud data requirements are comprehensively considered, a fast differentiation method is provided for the point cloud data of the high-precision map, a large amount of data needs to be updated in the traditional high-precision map drawing, the data quality inspection is time-consuming, in order to facilitate the data self-inspection and quality inspection, the partition database is processed and compared to accurately distinguish the differential data, and then the detailed differential change details are effectively identified, a large amount of manual correction is avoided, and the differentiated data is efficiently displayed. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 is a flowchart of an embodiment of the point cloud data differentiation loading method of the present application;

[0026] Figure 2 is a flowchart of another embodiment of the point cloud data differentiation loading method of the present application;

[0027] Figure 3 is a structural diagram of a point cloud data differentiation loading system of the present application. BRIEF DESCRIPTION OF DRAWINGS:

[0029] 100, point cloud data differentiation loading system; 110, dynamic and static data acquisition module; 120, differentiated data loading module; 130, marking module. DETAILED DESCRIPTION

[0030] Reference will now be made in detail to the present application, examples of which are illustrated in the accompanying drawings. While the present application will be described in conjunction with the specific embodiments, it will be understood that the present application is not intended to be limited to the specific embodiments. On the contrary, the present application is intended to cover modifications, alterations, and equivalents, which are included within the spirit and scope of the present application, as defined by the appended claims. It should be noted that the method steps described herein can all be implemented by any functional block or functional arrangement, and any functional block or functional arrangement can be implemented as a physical entity or a logical entity, or a combination of both.

[0031] In order for those skilled in the art to better understand the present application, the present application will be further described in detail below in conjunction with the drawings and specific embodiments.

[0032] Note: the examples to be introduced next are only specific examples, and are not intended to limit the embodiments of the present application to the specific steps, values, conditions, data, sequences, etc. Those skilled in the art can use the concept of the present application to construct more embodiments not mentioned in the present specification by reading the present specification.

[0033] Referring to Figure 1 As shown in the figure, the embodiment of the present application provides a test method for constructing a fusion positioning test scene library, characterized by comprising the following steps:

[0034] S100, acquiring point cloud data to be tested, and storing the point cloud data to be tested in a dynamic library and a static library respectively, corresponding to acquiring first version dynamic library data and original static library data;

[0035] S200, loading differentiated data according to the first version dynamic library data and the original static library data, and acquiring differentiated data of the point cloud data to be tested;

[0036] Specifically, in the present embodiment,

[0037] Dynamic library creation: first step, write source file, second step, compile to generate dynamic library, third step, compile and link dynamic library to generate executable file, dynamic library data supports modification, fourth step, run executable file, dynamic library will be dynamically loaded into memory; that is, build dynamic library, usually with.so as suffix, execute g++-c-fpic test.cpp, display result test.o, generate.o file from.c; execute g++-shared test.o-o libmymath.so, encapsulate into dynamic library

[0038] Static library creation: first step, create source file, second step, compile source file into target file, third step, package target file into static link library, fourth step, run file, static library data does not support modification; that is, build static library, usually with.a as suffix, execute g++-ctest.cpp, display result test.o, generate.o file; execute ar rcs libmymath.atest.o, package and encapsulate into static library.

[0039] The present application comprehensively considers the point cloud data requirements, and provides a quick differentiation method for point cloud data of high-precision maps. Traditional high-precision map drawing has a large amount of data to be updated, and data quality inspection takes a long time. In order to facilitate data self-inspection and quality inspection, the data is processed by partitioning the database and comparing, the differentiated data is accurately differentiated, and the detailed differentiated change details are effectively identified, thereby avoiding a large amount of manual correction and efficiently displaying differentiated data.

[0040] Preferably, in another embodiment of the present application, the "S200, loading the differentiated data according to the first version of dynamic library data and the original static library data, to obtain the differentiated data of the to-be-tested point cloud data" step specifically comprises the following steps:

[0041] S210, sending the first version of dynamic library data to the static library to replace the original static library data to generate first version of static library data, and when detecting that the first version of static library data in the static library has a data difference, sending the first version of static library data to the dynamic library to modify the data difference and generate second version of dynamic library data;

[0042] S220, sending the second version of dynamic library data to the static library to generate second version of static library data, and when detecting that the second version of static library data in the static library has a data difference, sending the second version of static library data to the dynamic library to modify the data difference and generate third version of dynamic library data, and when no data difference is detected in the static library, terminating the sending of the data in the dynamic library to the static library;

[0043] S230, comparing the adjacent two versions of static library data, and obtaining the comparison result as the differentiated data of the to-be-tested point cloud data.

[0044] Specifically, in the present embodiment, as shown in Figure 2 , the first version of dynamic library data A1 is submitted to generate the first version of static library data B1, and when the first version of static library data B1 has a data difference and needs to be modified, it is returned to the dynamic library to form the second version of dynamic library data A2. After the modification of the second version of dynamic library data A2 is completed, the static library is re-submitted to form the second version of static library data B2, and the data can be modified and submitted in turn. Therefore, the current static library B1 and B2 data will have differences, and therefore the differentiated data is loaded to compare the latest submitted two versions of data. When there are more than three versions of data (B1, B2, B3), the last two versions of data (B2, B3) are compared.

[0045] Preferably, in another embodiment of the present application, before the "S230, comparing the adjacent two versions of static library data" step, the following steps are specifically included:

[0046] Deleting the static library data before the adjacent two versions of static library data for comparison.

[0047] Specifically, in the present embodiment, the third version of dynamic library data is sent to the static library to generate the third version of static library data, the first version of static library data is deleted, and the second version of static library data is compared with the third version of static library data; as shown in Figure 2As shown, when there are more than three versions of data (B1, B2, B3), the first version B1 is deleted, and the last two versions (B2, B3) of data are compared.

[0048] Preferably, in another embodiment of the application, after the step of "S200, loading the differential data according to the first version of dynamic library data and the original static library data, obtaining the differential data of the to-be-tested point cloud data", the application specifically comprises the following steps:

[0049] S300, marking the differential data of the to-be-tested point cloud data.

[0050] Specifically, in this embodiment, after the comparison is completed, the differential data is displayed, and specific element information and change types can be displayed. The change type is defined as addition, deletion, attribute modification, shape modification, and attribute & shape modification. The change content can be quickly distinguished, and the differential data is marked with an addition state to locate the changed element.

[0051] Meanwhile, referring to Figure 3 As shown, the embodiment of the application further provides a point cloud data differential loading system 100, which comprises a dynamic and static data acquisition module 110, a differential data loading module 120, and a marking module 130.

[0052] The dynamic and static data acquisition module 110 is used to acquire to-be-tested point cloud data and store the to-be-tested point cloud data in a dynamic library and a static library respectively, so as to acquire first version dynamic library data and original static library data.

[0053] The differential data loading module 120 is in communication connection with the dynamic and static data acquisition module 110 and is used to load differential data according to the first version dynamic library data and the original static library data, so as to acquire the differential data of the to-be-tested point cloud data.

[0054] The differential data loading module 120 is used to send the first version dynamic library data to the static library to replace the original static library data and generate first version static library data. When it is detected that there is a data difference in the first version static library data in the static library, the first version static library data is sent to the dynamic library for data difference modification and to generate second version dynamic library data. The second version dynamic library data is sent to the static library to generate second version static library data. When it is detected that there is a data difference in the second version static library data in the static library, the second version static library data is sent to the dynamic library for data difference modification and to generate third version dynamic library data. When it is detected that there is no data difference in the static library, the sending of the data in the dynamic library to the static library is terminated. The adjacent two versions of static library data are compared, and the comparison result is the differential data of the to-be-tested point cloud data.

[0055] The differential data loading module 120 is configured to delete the static library data before the static library data of the adjacent two versions to be compared.

[0056] The marking module 130 in communication connection with the differential data loading module 120 is configured to mark the differential data of the point cloud data to be tested.

[0057] Therefore, the present application comprehensively considers the point cloud data requirements and provides a fast distinguishing manner for the point cloud data of the high-precision map. In the traditional high-precision map drawing, a large amount of data needs to be updated, and the data quality inspection is time-consuming. In order to facilitate data self-inspection and quality inspection, the data is processed and compared through the partition database, the differential data is accurately distinguished, the detailed differential change details are effectively identified, a large amount of manual correction is avoided, and the differential data is efficiently displayed.

[0058] Specifically, the present embodiment corresponds to the above method embodiment one by one, and the functions of each module have been described in detail in the corresponding method embodiment, and thus will not be described one by one.

[0059] Based on the same inventive concept, the present application also provides a computer readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement all method steps or part of method steps of the above method.

[0060] The present application implements all or part of the above method, and can also be completed by a computer program to instruct related hardware. The computer program can be stored in a computer readable storage medium. When the computer program is executed by a processor, the steps of each method embodiment can be implemented. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms. The computer readable medium can include any entity or device that can carry computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content of the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0061] Based on the same inventive concept, the embodiments of the present application further provide an electronic device, comprising a memory and a processor, the memory storing a computer program running on the processor, and the processor implements all the method steps or part of the method steps in the above method when executing the computer program.

[0062] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor and the like, and the processor is a control center of the computer device, and connects all parts of the computer device through various interfaces and lines.

[0063] The memory can be used to store computer programs and / or modules, and the processor realizes various functions of the computer device by running or executing the computer programs and / or modules stored in the memory, and calling data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, etc.); and the data storage area can store data created according to the use of the mobile phone (such as audio data, video data, etc.). In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one magnetic disk storage device, a flash memory device, or other volatile solid-state memory devices.

[0064] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, a server or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0065] The present application is described in reference to the appended drawings figures and / or block diagrams of methods, apparatus (systems), servers, and computer program products according to embodiments of the application. It will be understood that each block of the flowchart and / or block diagrams, and combinations of blocks in the flowchart and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0066] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0067] The computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0068] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A method for differentiating loading of point cloud data, characterized in that, The method comprises the following steps: acquiring point cloud data to be tested and storing the point cloud data to be tested in a dynamic library and a static library respectively, so as to acquire first dynamic library data and original static library data; loading differential data according to the first dynamic library data and the original static library data to acquire differential data of the point cloud data to be tested; the step of loading differential data according to the first dynamic library data and the original static library data to acquire differential data of the point cloud data to be tested comprises the following steps: sending the first dynamic library data to the static library to replace the original static library data to generate first static library data, sending the first static library data to the dynamic library to modify data differences and generate second dynamic library data when detecting that the first static library data in the static library has data difference; sending the second dynamic library data to the static library to generate second static library data, sending the second static library data to the dynamic library to modify data differences and generate third dynamic library data when detecting that the second static library data in the static library has data difference, and terminating the sending of data in the dynamic library to the static library when no data difference is detected in the static library; comparing adjacent two versions of static library data to acquire comparison results as differential data of the point cloud data to be tested.

2. The point cloud data differencing loading method of claim 1, wherein, Before the step of comparing adjacent two versions of static library data, the method comprises the following steps: deleting static library data before the adjacent two versions of static library data to be compared.

3. The point cloud data differencing loading method of claim 1, wherein, After the step of loading differential data according to the first dynamic library data and the original static library data to acquire differential data of the point cloud data to be tested, the method comprises the following steps: labeling differential data of the point cloud data to be tested.

4. A point cloud data differential loading system, characterized by, The method comprises the following steps: a dynamic and static data acquisition module for acquiring point cloud data to be tested and storing the point cloud data to be tested in a dynamic library and a static library respectively, so as to acquire first dynamic library data and original static library data; a differential data loading module in communication connection with the dynamic and static data acquisition module, for loading differential data according to the first dynamic library data and the original static library data to acquire differential data of the point cloud data to be tested; and The differential data loading module is configured to send the first version dynamic library data to the static library to replace the original static library data to generate first version static library data, and when detecting that the first version static library data in the static library has a data difference, send the first version static library data to the dynamic library to modify the data difference and generate second version dynamic library data; send the second version dynamic library data to the static library to generate second version static library data, and when detecting that the second version static library data in the static library has a data difference, send the second version static library data to the dynamic library to modify the data difference and generate third version dynamic library data, until no data difference is detected in the static library, and the data in the dynamic library is stopped from being sent to the static library; and compare the adjacent two version static library data, and the comparison result is the differential data of the to-be-tested point cloud data.

5. The point cloud data differencing loading system of claim 4, wherein, The differential data loading module is configured to delete the static library data before the adjacent two version static library data for comparison.

6. The point cloud data differencing loading system of claim 4, wherein, Further comprising a marking module in communication connection with the differential data loading module, configured to mark the differential data of the to-be-tested point cloud data.

7. A computer readable storage medium having stored thereon a computer program, characterized in that The computer program, when executed by a processor, implements the point cloud data differential loading method of any one of claims 1 to 3.

8. An electronic device comprising a storage medium, a processor, and a computer program stored in the storage medium and operable on the processor, characterized in that, The processor, when running the computer program, implements the point cloud data differential loading method of any one of claims 1 to 3.

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