A data processing method, device and storage medium

Abstract

The application provides a data processing method and device and a storage medium, wherein the method comprises: obtaining a point cloud frame; wherein the point cloud frame comprises point clouds of at least two collection periods, and each point cloud of each collection period comprises point clouds corresponding to a target entity; determining the point cloud corresponding to the target entity in the point cloud frame as a first point cloud; adjusting the coordinates of the first point cloud according to a first speed to obtain a second point cloud; wherein the first speed represents the speed of the target entity; and in response to the second point cloud satisfying a preset condition, taking the first speed as a labeled speed. The data processing scheme provided by the application adjusts the coordinates of the point clouds of the target entity in multiple collection periods according to the assumed speed of the target entity, labels the speed of the target entity by using the relationship between the target entity and the point cloud corresponding to the target entity, can simply and efficiently construct a point cloud data labeling set containing more labeling parameters, and can be used for subsequent algorithm training.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to a data processing method, device and storage medium. BACKGROUND

[0002] In the related art, in order to realize safer automatic driving, the surrounding environment and other moving objects (vehicles, pedestrians, etc.) need to be perceived, and the perceived data is used to predict the behavior intention of the moving objects in the future period of time. Among them, the speed prediction of other moving objects is of great significance to realize the behavior intention prediction of other moving objects, but the algorithm model often needs to have an original data set for training to realize model optimization and achieve better prediction results.

[0003] In the prior art, the labeling of point cloud is often the position labeling of the environment or other moving objects, therefore, how to construct a more richly labeled data set including the speed of moving objects for algorithm training also becomes a problem to be solved. SUMMARY

[0004] Embodiments of the present application provide a data processing scheme to solve the problem of constructing a data labeling set including the speed of a target object in the prior art.

[0005] To achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0006] According to one aspect of the present disclosure, a data processing method comprises:

[0007] Obtaining a point cloud frame; wherein the point cloud frame includes point clouds of at least two acquisition periods, and each point cloud of each acquisition period has a point cloud corresponding to a target entity;

[0008] Determining the point cloud corresponding to the target entity in the point cloud frame as a first point cloud;

[0009] Adjusting the coordinates of each point in the first point cloud according to the first speed to obtain a second point cloud; and

[0010] In response to the second point cloud satisfying a preset condition, taking the first speed as a labeled speed; wherein the labeled speed represents the moving speed of the target entity.

[0011] According to another aspect of the present disclosure, a data processing device comprises a processor and at least one memory, at least one memory having at least one machine executable instruction stored therein, and the processor executes the at least one machine executable instruction to perform the method as described above.

[0012] According to still another aspect of the present disclosure, a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method as described above.

[0013] The data processing scheme provided by the embodiments of the present application adjusts the coordinates of the point cloud of the target entity in multiple acquisition cycles according to the assumed speed of the target entity, and annotates the speed of the target entity by using the relationship between the target entity and the corresponding point cloud, so that a point cloud data annotation set containing more annotation parameters can be simply and efficiently constructed, thereby providing subsequent algorithm training. BRIEF DESCRIPTION OF DRAWINGS

[0014] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain exemplary implementations of the application. It is readily apparent to one skilled in the art that the accompanying drawings typically illustrate only some embodiments of the present application and therefore should not be considered as limiting the scope of the application, for which other embodiments can be used according to these drawings without inventive labor. In all the drawings, the same reference numerals refer to like but not necessarily identical elements.

[0015] Figure 1 is a structural block diagram of a data processing apparatus according to an exemplary embodiment;

[0016] Figure 2 is an architectural schematic diagram of a data processing apparatus according to an exemplary embodiment;

[0017] Figure 3 is one of flowcharts of a data processing method according to an exemplary embodiment;

[0018] Figure 4 is another one of flowcharts of a data processing method according to an exemplary embodiment;

[0019] Figures 5a-5c is one of schematic diagrams of actual application scenarios according to an exemplary embodiment, which respectively show three cases of speed value;

[0020] Figure 6 is another one of schematic diagrams of actual application scenarios according to an exemplary embodiment;

[0021] Figure 7 is still another one of schematic diagrams of actual application scenarios according to an exemplary embodiment. DETAILED DESCRIPTION

[0022] With reference to the drawings of the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0023] In the present disclosure, the term "multiple" refers to two or more, unless otherwise specified. In the present disclosure, the term "and / or" describes the association relationship of the associated objects, and covers any one and all possible combinations of the listed objects. The character " / " generally represents an "or" relationship between the associated objects before and after it.

[0024] In the present disclosure, unless otherwise specified, the terms "first", "second", and the like are used to distinguish similar objects, and are not intended to limit the positional relationship, the time sequence relationship or the importance relationship. It should be understood that the terms thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in ways other than those illustrated or described herein.

[0025] In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to only those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, systems, products or devices.

[0026] The "target entity" in the present application can generally be understood as other vehicles or other moving objects traveling on the road, which are not limited herein.

[0027] A point cloud is a set of points of each sampling point on the surface of an object obtained by a measuring instrument. Specifically, a point cloud obtained according to a laser measurement principle includes three-dimensional coordinates (XYZ) and laser reflection intensity (Intensity); a point cloud obtained according to a photographic measurement principle includes three-dimensional coordinates (XYZ) and color information (RGB); a point cloud obtained by combining laser measurement and photographic measurement principles includes three-dimensional coordinates (XYZ), laser reflection intensity (Intensity) and color information (RGB).

[0028] In the related art, an autonomous vehicle inevitably encounters various environments during actual travel, wherein the perception and behavior prediction of other dynamic vehicles during travel are crucial for the safe travel of the autonomous vehicle, and according to the development of the current autonomous driving system, the training of the prediction algorithm needs to rely on a large amount of data sets.

[0029] Some embodiments of the present application provide a data processing scheme. Figure 1 The structure of the data processing apparatus provided by the embodiments of the present application is shown. The apparatus 1 comprises a processor 11 and a memory 12.

[0030] In some embodiments, the memory 12 can be a storage device in various forms, such as a temporary or non-temporary storage medium. At least one machine executable instruction can be stored in the memory 12, and the at least one machine executable instruction is executed by the processor 11 to implement the data processing method provided by the embodiments of the present application.

[0031] In some embodiments, the data processing apparatus 1 can be located on the server side. In other embodiments, the data processing apparatus 1 can also be located in the cloud server. In other embodiments, the data processing apparatus 1 can also be located in the client side.

[0032] As shown in Figure 2 The data processing provided by the embodiments of the present application can include front-end processing 13 and back-end processing 14. The relevant three-dimensional point cloud frame and / or image are displayed through the front-end processing 13, and the relevant data or information input by the annotator is received. For example, the front-end processing 13 can be a processing realized through a web page, or a processing realized through a separate application interface. The back-end processing 14 performs corresponding data processing according to the relevant data and information received by the front-end processing 13. After the data processing is completed, the data processing apparatus 1 can further provide the annotation result to other processing or application on the client side, server side, or cloud server side.

[0033] Among them, when displaying the three-dimensional point cloud, the three-dimensional point cloud can be displayed according to the specified display direction. The specified display direction can be a preset display direction, or a display direction input by the annotator. For example, in some embodiments, after the data processing apparatus reads a frame of three-dimensional point cloud, the frame of three-dimensional point cloud can be displayed according to the preset display direction. For another example, in some embodiments, when the annotator needs to carefully observe the scene or object expressed by the three-dimensional point cloud, the required display direction can be selected and input, and the data processing apparatus displays the three-dimensional point cloud according to the received display direction, so as to facilitate the observation and identification of the annotator. The point cloud frame can be understood as a three-dimensional point cloud displayed from a certain direction. For example, in order to facilitate the understanding of the scheme, Figures 5a-5c 、 Figures 6-7 The top view is adopted in the above two examples.

[0034] The data processing method implemented by the data processing apparatus 1 executing the at least one machine executable instruction will be described below.

[0035] Figure 3The data processing method provided by the embodiment of the application is shown in the figure, that is, the flow of data processing by the data processing apparatus, comprising:

[0036] S301 obtains a point cloud frame; wherein the point cloud frame comprises point clouds of at least two acquisition periods.

[0037] Specifically, the point cloud frame can be displayed by the data processing apparatus, and the point cloud frame comprises point clouds of at least two acquisition periods. The point cloud can be collected by a laser radar, and the acquisition period is the time for one scan of the laser radar. In the at least two acquisition periods, the target entity is located in the field of view of the laser radar, so the laser radar can collect the corresponding point clouds of the target entity in the at least two acquisition periods.

[0038] At the same time, in order to realize the present scheme, the point clouds of at least two acquisition periods are generally selected, and the point clouds of the at least two acquisition periods can be displayed in the same point cloud frame or in different point cloud frames.

[0039] S303 determines the corresponding point cloud of the target entity in the point cloud frame as a first point cloud.

[0040] Specifically, the first point cloud can be input by a labeler through a human-computer interaction interface provided by the data processing apparatus. For example, the specific parameter value is directly input in the data input box in the human-computer interaction interface, a button or a key on the human-computer interface is clicked, the button or the key has a corresponding preset instruction or data, or a corresponding option is selected in the drop-down menu provided by the human-computer interface, the drop-down menu can include one or more submenus, and each submenu can include one or more options. The data processing apparatus receives the labeling data input by the labeler through the human-computer interface to determine the corresponding point cloud of the target entity in the point cloud frame, that is, the first point cloud.

[0041] Alternatively, the position coordinates of the corresponding point cloud of the target entity in the image can be calculated by using a target detection algorithm, and in some application scenarios, the result recognized by the target detection algorithm can also be manually verified and calibrated by the labeler, and the result after verification and calibration is taken as the first point cloud.

[0042] Alternatively, the algorithm model trained in advance can be used to process the point cloud frame to obtain a three-dimensional labeling box of the target entity, and then the point cloud located in the three-dimensional labeling box can be determined as the corresponding point cloud of the target entity. At the same time, the result recognized by the algorithm model can still be manually verified and calibrated by the labeler, and the result after verification and calibration is taken as the first point cloud.

[0043] S305 adjusts the coordinates of each point in the first point cloud according to the first speed to obtain a second point cloud.

[0044] Specifically, the first speed can be input by a human-computer interaction interface provided by the data processing apparatus by a labeler, for example, directly inputting a specific parameter value in a data input box in the human-computer interaction interface, clicking a preset button, key, button or key on the human-computer interface, the button or key having a corresponding preset instruction or data, or selecting a corresponding option in a drop-down menu provided by the human-computer interface, the drop-down menu can include one or more submenus, each submenu can include one or more options, and the data processing apparatus receives the first speed input by the labeler through the human-computer interface. Alternatively, a preset parameter value can be used as the first speed.

[0045] Meanwhile, since the timestamps corresponding to each point cloud are different, and even the timestamps corresponding to each point in the point cloud are different, by giving a hypothetical speed value, for example, the first speed, the coordinates of the points in the first point cloud corresponding to the target entity are adjusted to the position at a specified time according to the first speed.

[0046] S307, in response to the second point cloud satisfying the preset condition, taking the first speed as the labeling speed, the labeling speed representing the moving speed of the target entity.

[0047] Specifically, the preset condition can be set in advance, and when the second point cloud obtained after adjusting the coordinates satisfies the preset condition, the first speed is taken as the labeling speed. The preset condition can be set according to actual needs or historical experience, which is not limited herein.

[0048] According to Figure 3 The method shown in the figure, the data processing apparatus adjusts the coordinates of each point in the first point cloud corresponding to the target entity in different collection periods to obtain the second point cloud, and further determines the labeling speed of the target entity by judging whether the second point cloud satisfies the preset condition. Through the above scheme, more abundant labeling data including the speed information of the target entity can be obtained for the point cloud set, which can be used for algorithm training or other purposes.

[0049] Further, as Figure 4 shown, in some embodiments, if the second point cloud does not satisfy the preset condition, the scheme further includes:

[0050] S306, in response to the second point cloud not satisfying the preset condition, adjusting the first speed to obtain a second speed;

[0051] S308, according to the second speed, adjusting each point in the first point cloud to a target time to obtain a third point cloud;

[0052] S309, until the third point cloud satisfies the preset condition, taking the second speed as the labeling speed.

[0053] This is because the second point cloud obtained by adjusting the first point cloud according to the first speed in S305 may not satisfy the preset condition, and at this time, the first speed needs to be adjusted so that the point cloud adjusted according to the second speed satisfies the preset condition.

[0054] Specifically, in S306, when adjusting the first speed, the first speed can be adjusted as the second speed according to a preset adjustment step, or a speed value input by the annotator through a human-computer interaction interface is received as the second speed. At the same time, in practice, it is often difficult to adjust the annotation speed at one time, and at this time, the speed needs to be adjusted for multiple times to obtain the annotation speed that can make the adjusted point cloud satisfy the preset condition.

[0055] In some embodiments, S305 adjusts the coordinates of the first point cloud according to the first speed to obtain the second point cloud, including: adjusting the coordinates of the first point cloud according to an adjustment function; wherein the adjustment function is a coordinate transformation and a relationship function between the first speed, the target time, and the collection time of the point cloud.

[0056] Specifically, since the first speed represents the assumed speed of the target entity, and the first point cloud is the point cloud corresponding to the target entity, when the speed of the target entity is set as the first speed, it can be considered that the first point cloud corresponding to the target entity also has the first speed. According to the driving state of the target entity, the adjustment mode can be divided into the following categories:

[0057] a1 Assuming that the target entity is uniformly straight driving, when adjusting the first point cloud, since all the point clouds in the first point cloud have their collection times, when traveling at a given first speed, the point cloud corresponding to the target entity will have different positions at different collection times, so the adjustment function is a coordinate transformation and a relationship function between the first speed, the target time, and the collection time of the point cloud. At this time, the point cloud corresponding to the target entity can be coordinate-transformed according to the determined driving direction of the target entity, the first speed, and the specified target time to obtain the second point cloud.

[0058] a2 Assuming that the target entity is non-uniformly straight driving, at this time, the target entity has a certain acceleration in addition to the first speed, so when adjusting the coordinates of the first point cloud, the adjustment function is a coordinate transformation and a relationship function between the first speed, the acceleration, the target time, and the collection time of the point cloud. The driving direction of the target entity, the first speed, the acceleration, and the specified target time need to be considered to adjust the coordinates of the point cloud from the collection time to the coordinates corresponding to the target time, thereby obtaining the second point cloud.

[0059] a3, assuming that the target entity is moving at a constant speed in a non-straight line, the target entity may have an initial angle and an angular velocity in addition to the first speed, so the adjustment function is a coordinate transformation and a relationship function between the first speed, the angular velocity, the initial angle, the target time, and the collection time of the point cloud. At this time, the second point cloud can be obtained by performing coordinate transformation on the point cloud corresponding to the target entity according to the determined moving direction of the target entity, the first speed, the initial angle, the angular velocity, and the specified target time.

[0060] a4, assuming that the target entity is moving at a non-constant speed in a non-straight line, the target entity may have an initial angle and an angular velocity in addition to the first speed, so the adjustment function is a coordinate transformation and a relationship function between the first speed, the acceleration, the angular velocity, the initial angle, the target time, and the collection time of the point cloud. At this time, the second point cloud can be obtained by performing coordinate transformation on the point cloud corresponding to the target entity according to the determined moving direction of the target entity, the first speed, the acceleration, the initial angle, the angular velocity, and the specified target time.

[0061] wherein the acceleration, the initial angle, and the angular velocity can be input by the annotator through the human-computer interaction interface of the data processing device, or set in other ways, and the moving direction of the target entity can be determined through the image corresponding to the point cloud frame displayed by the front-end processing 13, which is not limited herein. It should be noted that the values of the acceleration, the initial angle, and the angular velocity can also be adjusted according to actual needs.

[0062] Similarly, when the third point cloud is obtained by performing coordinate adjustment on each point in the first point cloud according to the second speed, the above-mentioned a1-a4 four ways can also be used for processing, which will not be repeated here.

[0063] In some embodiments, the way of judging whether the second point cloud satisfies the preset condition comprises:

[0064] b1, accepting an input confirmation instruction, the confirmation instruction indicating that the second point cloud satisfies the preset condition.

[0065] Specifically, the annotator can determine whether the second point cloud meets the requirements according to experience, and if so, the annotator inputs a confirmation instruction through the human-computer interaction interface.

[0066] b2, generating a three-dimensional annotation box of the second point cloud; the three-dimensional annotation box satisfies the preset size requirement.

[0067] Specifically, the labeling data of the second point cloud can be input by the labeler through the human-computer interaction interface, and the data processing apparatus generates a three-dimensional labeling box of the second point cloud according to the input labeling data, and determines whether the second point cloud satisfies the preset condition by judging whether the size of the three-dimensional labeling box satisfies the preset size requirement; or the smallest three-dimensional labeling box surrounding the second point cloud can be automatically generated by an algorithm, and then whether the second point cloud satisfies the preset condition is determined by further judging whether the size of the three-dimensional labeling box satisfies the preset size requirement.

[0068] The specific size requirement can be a length-width-height value range or a maximum length-width-height value of the set three-dimensional labeling box, and how to set the size requirement can be adjusted according to actual needs, which is not limited here.

[0069] Similarly, when judging whether the third point cloud satisfies the preset condition, the above two methods b1 and b2 can also be used, which will not be repeated here.

[0070] Figures 5a-5c and Figure 6 respectively show instances of the application of the present scheme in specific scenarios, wherein, Figures 5a-5c shows the diagram before and after coordinate transformation when three adjacent collection periods are selected, Figure 6 shows the diagram before and after coordinate transformation when three collection periods far apart are selected, for the convenience of understanding, the diagrams are all in the perspective of top view. However, it should be understood that the selection of the perspective does not constitute a limitation of the present application.

[0071] As shown in Figures 5a-5c , the target entity travels at a constant speed along the road, and the point clouds of the target entity corresponding to the three collection periods (T1, T2, T3) are shown in the same point cloud frame. In order to facilitate the understanding of the present scheme, three kinds of frames are used to identify the point cloud of each collection period. It can be seen that the point clouds of the target entity corresponding to different collection periods will overlap, making the presented point cloud spread over a long range in the direction of travel. In specific operation, the first speed can be input by the labeler through the human-computer interaction interface, and the point clouds of the three collection periods are respectively subjected to coordinate transformation in the direction of travel according to the first speed and a specified target time to obtain the second point cloud. Assuming that other conditions remain unchanged, for the target entity, when it is in a stationary state, the three-dimensional point cloud corresponding to its collection should have a certain size: L, W, H. As shown in Figure 5a , if the given first speed is close to the actual speed of the target entity, the length of the second point cloud formed after coordinate transformation in the direction of travel will be close to the length L of the corresponding point cloud when the target entity is in a stationary state; as shown in Figure 5bAs shown, if the given first velocity is too small and differs significantly from the actual velocity of the target entity, the length of the second point cloud formed after coordinate transformation along the direction of travel will be much greater than the length L of the point cloud corresponding to the target entity when it is stationary; for example... Figure 5c As shown, if the given first velocity is too large, the length of the second point cloud formed after coordinate transformation along the direction of travel will be much smaller than the length L of the corresponding point cloud when the target entity is stationary.

[0072] After coordinate transformation, the annotator can view the transformation results through a human-computer interaction interface and input corresponding confirmation or adjustment commands. Alternatively, the data processing device can generate a minimum 3D annotation box for the second point cloud and then compare its size with a preset 3D annotation box size. The preset 3D annotation box size can be set with reference to the size (L, W, H) of the point cloud corresponding to the target entity when it is stationary. If the given first velocity is close to the actual velocity of the target entity, the size of the minimum 3D annotation box should be close to the size of the point cloud corresponding to the target entity when it is stationary. This allows it to be determined whether the first velocity can be used as the annotation velocity or whether further adjustment is needed.

[0073] like Figure 6 As shown, the target entity travels at a constant speed along the road. The point clouds of the target entity corresponding to the three acquisition cycles (T1, T2, T3) are displayed in the same point cloud frame or in different point cloud frames, and are marked with three different borders for easy viewing. It can be seen that, due to the large interval between the selected cycles, the point clouds of the target entity corresponding to different acquisition cycles do not overlap. In practice, the annotator can still input the first speed through the human-computer interaction interface, and then perform coordinate transformations on the point clouds of the three acquisition cycles according to the first speed and the specified target time in the direction of travel to obtain the second point cloud. If the given first velocity is close to the actual velocity of the target entity, the length of the second point cloud formed after coordinate transformation along the direction of travel will be close to the length of the corresponding point cloud when the target entity is stationary. At this time, the annotator can view the transformation result through the human-computer interaction interface and input the corresponding confirmation or adjustment command; or, the data processing device can generate the minimum three-dimensional annotation box surrounding the second point cloud, and then compare its size with the preset three-dimensional annotation box size. If the given first velocity is close to the actual velocity of the target entity, the size of the minimum three-dimensional annotation box should be close to the size of the corresponding point cloud when the target entity is stationary, so as to determine whether the first velocity can be used as the annotation velocity or whether further adjustment is needed.

[0074] Furthermore, such as Figure 7As shown, the target entity is uniformly turning at an initial angle, and the point clouds of the target entity corresponding to three acquisition periods (T1, T2, and T3) are displayed in the same point cloud frame or different point cloud frames. For convenience of viewing, the point clouds are marked with three kinds of frames in the figure. At this time, the target entity has a first speed in the forward direction and also moves at an angular speed. In a specific operation, a labeler can input the first speed, the initial angle, and the angular speed through a human-computer interaction interface. The first point cloud corresponding to the target entity in the three periods is subjected to coordinate transformation through a relationship function between the first speed, the angular speed, the initial angle, a target time, and an acquisition time of the point cloud, to obtain a second point cloud. If the given first speed, the initial angle, and the angular speed are close to the actual speed of the target entity, the size of the second point cloud formed after the coordinate transformation will be close to the size of the corresponding point cloud when the target entity is in a stationary state. At this time, the labeler can view the transformation result through the human-computer interaction interface and input a corresponding confirmation instruction or an adjustment instruction. Alternatively, the data processing apparatus can generate a minimum three-dimensional labeling frame of the second point cloud, and then compare the size of the three-dimensional labeling frame with a preset three-dimensional labeling frame size. If the given first speed is close to the actual speed of the target entity, the three-dimensional labeling frame of the second point cloud formed after the coordinate transformation should be close to the size of the corresponding point cloud when the target entity is in a stationary state, so that it can be determined whether the first speed can be used as a labeling speed or needs to be further adjusted, and whether the initial angle and the angular speed need to be adjusted.

[0075] It should be noted that if the angular speed also changes, an angular acceleration parameter can be further added for adjustment to realize coordinate transformation of the first point cloud. Details are not described herein.

[0076] Through the above scheme, the characteristics of the point cloud and the correlation between the target entity and the corresponding point cloud can be effectively utilized. The point cloud data set containing the speed parameter and the like can be simply and quickly constructed by performing coordinate transformation on the point clouds of the same target entity acquired at different times according to certain speed and travel direction parameters, so as to be used for subsequent algorithm training, thereby improving the performance of the automatic driving system.

[0077] Those skilled in the art will understand that embodiments of the present application can be provided as methods, systems, or computer program products. 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, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0078] The computer program instructions can also be loaded onto 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 Figure 1 one or more functions specified in the flowchart or multiple flows and / or blocks. Figure 1 one or more functions specified in the flowchart or multiple flows and / or blocks.

[0079] 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 Figure 1 one or more functions specified in the flowchart or multiple flows and / or blocks. Figure 1 one or more functions specified in the flowchart or multiple flows and / or blocks.

[0080] These computer program instructions can also be loaded onto 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 Figure 1 one or more functions specified in the flowchart or multiple flows and / or blocks. Figure 1 one or more functions specified in the flowchart or multiple flows and / or blocks.

[0081] The principles and implementation manners of the present application are described in the embodiments, and the above embodiment descriptions are only used to help understand the method and core idea of the present application; meanwhile, for the ordinary skilled in the art, the specific implementation manners and application range can be changed according to the idea of the present application, and the above description should not be understood as limitation of the present application.

Claims

1. A data processing method, comprising: obtaining a point cloud frame; wherein the point cloud frame comprises point clouds of at least two acquisition periods; determining a point cloud corresponding to a target entity in the point cloud frame as a first point cloud; adjusting coordinates of each point in the first point cloud according to a first speed to obtain a second point cloud; and in response to the second point cloud satisfying a preset condition, taking the first speed as a labeling speed; wherein the labeling speed represents a moving speed of the target entity.

2. The data processing method of claim 1, wherein the determining a point cloud corresponding to the target entity in the point cloud frame as a first point cloud specifically comprises: processing the point cloud frame through a preset algorithm model to obtain a three-dimensional labeling box of the target entity; and determining the point cloud in the three-dimensional labeling box as the first point cloud.

3. The data processing method of claim 2, wherein the determining a point cloud corresponding to the target entity in the point cloud frame as a first point cloud specifically comprises: receiving labeling data of the target entity; generating a three-dimensional labeling box of the target entity according to the labeling data; and determining the point cloud in the three-dimensional labeling box as the first point cloud.

4. The data processing method of claim 1, wherein the adjusting coordinates of each point in the first point cloud according to a first speed to obtain a second point cloud comprises: adjusting coordinates of each point in the first point cloud according to an adjustment function; wherein the adjustment function is a coordinate transformation and a relationship function between the first speed, a target time, and an acquisition time of the point cloud.

5. The data processing method of claim 1, further comprising: in response to the second point cloud not satisfying the preset condition, adjusting the first speed to obtain a second speed; adjusting each point in the first point cloud to a target time according to the second speed to obtain a third point cloud; and in response to the third point cloud satisfying a preset condition, taking the second speed as a labeling speed.

6. The data processing method of claim 5, wherein the adjusting the first speed to obtain the second speed comprises: adjusting the first speed according to a preset adjustment step; or receiving an input speed value as the second speed.

7. The data processing method of claim 1, wherein the second point cloud satisfies a preset condition, specifically comprising: accepting an input confirmation instruction, the confirmation instruction indicating that the second point cloud satisfies a preset condition.

8. The data processing method of claim 1, wherein the second point cloud satisfies a preset condition, specifically comprising: generating a three-dimensional labeling box enclosing the second point cloud; and the three-dimensional labeling box satisfying a preset size requirement. A device comprising a processor and a memory, the memory storing at least one machine executable instruction, the processor executing the at least one machine executable instruction to perform the method of any one of claims 1-8.

10. A computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the method of any one of claims 1-8. ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 9. A data processing apparatus, characterized by, ​ ​

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