Point cloud data processing method and device, equipment, storage medium and product

Abstract

The invention relates to a point cloud data processing method and device, equipment, a storage medium and a product. In the present disclosure, laser radar scanning is performed on a target area to obtain first point cloud data and second point cloud data of the target area, and the second point cloud data is point cloud data generated at a current moment and is point cloud data of a next frame of the first point cloud data. And performing data comparison based on the first point cloud data and the second point cloud data, and determining a data comparison result. And under the condition that the data comparison result is that the data of part of the wire harnesses are different, calculation is carried out based on the first point cloud data and the second point cloud data, and distinguishing point cloud data are determined. And updating the original point cloud data based on the distinguishing point cloud data to obtain target point cloud data of the target area, the original point cloud data being first frame point cloud data generated after the laser radar is started. The original point cloud data is updated by distinguishing the point cloud data, so that the data processing amount can be reduced, repeated data processing is avoided, and the processing efficiency of the point cloud data is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a point cloud data processing method, device, equipment, storage medium and product. Background Art

[0002] LiDAR has unique capabilities in 3D perception and measurement, and has become a key means of obtaining high-resolution, high-precision 3D point cloud data. This data usually includes a large number of points, each of which carries spatial position information, making it suitable for a variety of applications. However, laser point cloud data is usually very large, and there may be large delays in the transmission and processing of point cloud data. Therefore, the processing efficiency of point cloud data will be reduced. Summary of the invention

[0003] The present disclosure provides a point cloud data processing method, device, equipment, storage medium and product, which can improve the processing efficiency of point cloud data.

[0004] In a first aspect, the present disclosure provides a method for processing point cloud data, comprising:

[0005] Performing a laser radar scan on a target area to obtain first point cloud data and second point cloud data of the target area, wherein the second point cloud data is point cloud data generated at a current moment and is point cloud data of a next frame of the first point cloud data;

[0006] Performing data comparison based on the first point cloud data and the second point cloud data, and determining a data comparison result;

[0007] When the data comparison result shows that there is a difference in the data of the partial wire harness, performing calculation based on the first point cloud data and the second point cloud data to determine the difference point cloud data;

[0008] The original point cloud data is updated based on the difference point cloud data to obtain the target point cloud data of the target area, and the original point cloud data is the first frame of point cloud data generated after the laser radar is started.

[0009] Optionally, performing data comparison based on the first point cloud data and the second point cloud data to determine a data comparison result includes:

[0010] The data of each line bundle of the first point cloud data is compared with the data of the corresponding line bundle of the second point cloud data in sequence to determine the data comparison result, and the data comparison result is used to characterize the similarity between the first point cloud data and the second point cloud data.

[0011] Optionally, when the data comparison result indicates that there is a difference in the data of a part of the wire harness, performing calculation based on the first point cloud data and the second point cloud data to determine the difference point cloud data includes:

[0012] According to the data comparison result, performing difference calculation on the data of the wire harness having differences between the first point cloud data and the second point cloud data to obtain a difference result;

[0013] The difference result is used as the difference point cloud data.

[0014] Optionally, after performing data comparison based on the first point cloud data and the second point cloud data and determining a data comparison result, the method further includes:

[0015] When it is determined that the data comparison result shows that all the wire harnesses are different, the data comparison result is filtered according to a preset comparison fuzziness rule, and the data comparison result is updated.

[0016] Optionally, when the data comparison result indicates that there is a difference in the data of a partial wire harness, before performing calculation based on the first point cloud data and the second point cloud data to determine the difference point cloud data, the method further includes:

[0017] If the third point cloud data of the target area is obtained, the third point cloud data is transmitted to the buffer area, and the third point cloud data is the next frame of point cloud data of the second point cloud data.

[0018] Optionally, when the data comparison result is that there is a difference in the data of a partial wire harness, after performing calculation based on the first point cloud data and the second point cloud data to determine the difference point cloud data, the method further includes:

[0019] The third point cloud data is used as the second point cloud data, and the step of performing data comparison based on the first point cloud data and the second point cloud data to determine the data comparison result is repeatedly performed.

[0020] Optionally, after performing data comparison based on the first point cloud data and the second point cloud data and determining a data comparison result, the method further includes:

[0021] According to the data comparison result, the emission range of the laser radar beam is adjusted.

[0022] In a second aspect, the present disclosure provides a point cloud data processing device, comprising:

[0023] An acquisition module, used to perform a laser radar scan on a target area to acquire first point cloud data and second point cloud data of the target area, wherein the second point cloud data is point cloud data generated at a current moment and is point cloud data of a next frame of the first point cloud data;

[0024] A first determination module, configured to perform data comparison based on the first point cloud data and the second point cloud data, and determine a data comparison result;

[0025] A second determination module is used to determine different point cloud data by performing calculation based on the first point cloud data and the second point cloud data when the data comparison result shows that there is a difference in the data of the partial wire harness;

[0026] An updating module is used to update the original point cloud data based on the difference point cloud data to obtain the target point cloud data of the target area, wherein the original point cloud data is the first frame of point cloud data generated after the laser radar is started.

[0027] In a third aspect, the present disclosure provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the point cloud data processing method described in the above aspects.

[0028] In a fourth aspect, the present disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for processing point cloud data described in the above aspects.

[0029] In a fifth aspect, the present disclosure provides a computer program product, including a computer program / instructions, which, when executed by a processor, implements the steps of the point cloud data processing method described in the above aspects.

[0030] In the present disclosure, a laser radar scan is performed on the target area to obtain the first point cloud data and the second point cloud data of the target area, wherein the second point cloud data is the point cloud data generated at the current moment and is the next frame of point cloud data of the first point cloud data. Data comparison is performed based on the first point cloud data and the second point cloud data to determine the data comparison result. When the data comparison result shows that there is a difference in the data of a part of the wire harness, calculation is performed based on the first point cloud data and the second point cloud data to determine the difference point cloud data. The original point cloud data is updated based on the difference point cloud data to obtain the target point cloud data of the target area, wherein the original point cloud data is the first frame of point cloud data generated after the laser radar is started. By updating the original point cloud data with the difference point cloud data, the processing amount of the point cloud data corresponding to each frame can be reduced. For the point cloud data corresponding to each frame after the first frame of point cloud data, it is sufficient to process the part different from the previous frame of point cloud data, thereby avoiding repeated processing of a large amount of identical point cloud data. Therefore, the processing efficiency of the point cloud data can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0032] Figure 1 A flowchart of a point cloud data processing method provided by the present invention.

[0033] Figure 2 A schematic structural diagram of a point cloud data processing device provided by the present invention.

[0034] Figure 3 A flowchart of the point cloud data processing method provided in the present disclosure being applied to the device 200 .

[0035] Figure 4 A schematic diagram of the structure of a point cloud data processing device provided by the present invention. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the application scenario of the solution of the present application is first described below.

[0037] LiDAR has unique capabilities in 3D perception and measurement, and has become a key means of obtaining high-resolution, high-precision 3D point cloud data. This data usually includes a large number of points, each of which carries spatial position information, making it suitable for a variety of applications. By emitting laser pulses and measuring their reflection time, LiDAR can generate accurate point clouds in real time, with much higher resolution and accuracy than many other sensors. However, laser point cloud data is usually very large, and in the process of transmitting point cloud data, a lot of storage space and computing resources are required to process and analyze it.

[0038] At present, the point cloud data generated by LiDAR with more than 200 laser beams is usually very large. The complexity of transmitting, processing and analyzing data and the need for a large amount of storage and processing resources lead to particularly large delays in the final practical application process. However, in some specific application scenarios, the delay requirements are more stringent. For example, complex data processing and analysis of point cloud data are performed to extract useful information. If there is a large delay, it may affect the effects of object recognition, terrain modeling, etc. Therefore, the efficiency of point cloud data processing will be reduced.

[0039] In order to solve the above-mentioned technical problems, the present disclosure provides a method, device, equipment, storage medium and product for processing point cloud data. In the present disclosure, a laser radar is scanned on a target area to obtain first point cloud data and second point cloud data of the target area, wherein the second point cloud data is point cloud data generated at the current moment and is the next frame of point cloud data of the first point cloud data. Data comparison is performed based on the first point cloud data and the second point cloud data to determine the data comparison result. When the data comparison result shows that there is a difference in the data of a part of the wire harness, calculation is performed based on the first point cloud data and the second point cloud data to determine the difference point cloud data. The original point cloud data is updated based on the difference point cloud data to obtain the target point cloud data of the target area, wherein the original point cloud data is the first frame of point cloud data generated after the laser radar is started. By updating the original point cloud data with the difference point cloud data, the processing amount of the point cloud data corresponding to each frame can be reduced. For the point cloud data corresponding to each frame after the first frame of point cloud data, the part different from the previous frame of point cloud data can be processed, thereby avoiding repeated processing of a large amount of the same point cloud data. Therefore, the processing efficiency of the point cloud data can be improved.

[0040] In order to enable those skilled in the art to better understand the technical solution of the present disclosure, and to fully understand and implement how the present disclosure applies technical means to solve technical problems and achieve the corresponding technical effects, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only embodiments of a part of the present disclosure, not all of the embodiments. The embodiments of the present disclosure and the various features in the embodiments can be combined with each other without conflict, and the technical solutions formed are all within the scope of protection of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present disclosure.

[0041] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0042] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0043] Embodiment 1

[0044] Figure 1 A flowchart of a method for processing point cloud data provided by the present disclosure. Figure 1 As shown, the method includes:

[0045] S101: Perform a laser radar scan on a target area to obtain first point cloud data and second point cloud data of the target area.

[0046] Specifically, this embodiment is applied in the logistics transportation scenario of coal sample transfer, in which the target area is the coal yard. By performing laser radar scanning on the coal yard, the coal pile in the coal yard can be modeled to form a three-dimensional stockpile model. The coal yard will perform random sampling at preset intervals. Random sampling can use the Monte Carlo algorithm to screen the coordinates of coal samples to ensure the fairness of sampling. After each sampling, the local area of ​​the coal pile will change, and the point cloud data obtained by the laser radar scanning will also change. Therefore, it is necessary to continuously perform laser radar scanning on the target area to update the three-dimensional stockpile model in real time.

[0047] By scanning the coal pile using a laser radar, three-dimensional point cloud data of the target area can be obtained, wherein the second point cloud data is the point cloud data generated at the current moment and is the next frame of point cloud data of the first point cloud data.

[0048] S102: Perform data comparison based on the first point cloud data and the second point cloud data to determine a data comparison result.

[0049] Specifically, the three-dimensional point cloud data formed by the laser radar scanning may be affected by unstable light sources. At the same time, if the coal pile is sampled to collect coal, it will also cause changes in the coal pile in the target area. Therefore, there may be data differences between the first point cloud data and the second point cloud data collected in two adjacent frames, or the target area may not change when the point cloud data is collected in two adjacent frames, and the first point cloud data and the second point cloud data are the same. If the point cloud data of the two frames are different, it is necessary to form a three-dimensional stockpile model based on the point cloud data of the latest frame. Therefore, it is necessary to perform data comparison based on the first point cloud data and the second point cloud data to determine the data comparison result, and determine the subsequent steps based on the result.

[0050] S103: When the data comparison result shows that there are differences in the data of the partial wire harness, calculation is performed based on the first point cloud data and the second point cloud data to determine the difference point cloud data.

[0051] Specifically, if the data comparison result shows that there are differences in the data of some wire harnesses, it means that the target area may have changed locally at this time, and the difference point cloud data between the first point cloud data and the second point cloud data is calculated, and the difference point cloud data is the point cloud data of the area where there are differences between the first point cloud data and the second point cloud data. In this way, when the three-dimensional stockpile model is subsequently formed, repeated calculation of the point cloud data that is the same as the first point cloud data and the second point cloud data can be avoided, which reduces the amount of data and improves the processing efficiency of the point cloud data.

[0052] S104: updating the original point cloud data based on the difference point cloud data to obtain target point cloud data of the target area.

[0053] Specifically, after the laser radar is started, it will generate original point cloud data, that is, the original point cloud data is the first frame of point cloud data generated after the laser radar is started. The point cloud data corresponding to each subsequent frame is compared and calculated with the point cloud data of the previous frame to determine the difference point cloud data. The original point cloud data can be updated with the difference point cloud data that differs from the previous frame. In other words, only the point cloud data of the area where the target area has changed locally is updated each time. This can greatly reduce the amount of point cloud data processing and improve the efficiency of point cloud data processing to obtain the target point cloud data of the target area.

[0054] In the present disclosure, a laser radar scan is performed on the target area to obtain the first point cloud data and the second point cloud data of the target area, wherein the second point cloud data is the point cloud data generated at the current moment and is the next frame of point cloud data of the first point cloud data. Data comparison is performed based on the first point cloud data and the second point cloud data to determine the data comparison result. When the data comparison result shows that there is a difference in the data of a part of the wire harness, calculation is performed based on the first point cloud data and the second point cloud data to determine the difference point cloud data. The original point cloud data is updated based on the difference point cloud data to obtain the target point cloud data of the target area, wherein the original point cloud data is the first frame of point cloud data generated after the laser radar is started. By updating the original point cloud data with the difference point cloud data, the processing amount of the point cloud data corresponding to each frame can be reduced. For the point cloud data corresponding to each frame after the first frame of point cloud data, it is sufficient to process the part different from the previous frame of point cloud data, thereby avoiding repeated processing of a large amount of identical point cloud data. Therefore, the processing efficiency of the point cloud data can be improved.

[0055] Embodiment 2

[0056] On the basis of the above embodiment, an exemplary method for performing data comparison based on the first point cloud data and the second point cloud data and determining the data comparison result includes:

[0057] The data of each line bundle of the first point cloud data is sequentially compared with the data of the corresponding line bundle of the second point cloud data to determine the data comparison result.

[0058] Specifically, the point cloud data obtained by the laser radar is measured by the beam emitted by the laser radar. The laser line emitted by the laser radar in each frame includes multiple beams. When the laser of each beam contacts the object in the target area, it will return the received laser ranging value, the reflectivity value of the echo, the horizontal rotation angle value and the timestamp information. The above data constitutes the three-dimensional point cloud data. In other words, the point cloud data is measured by multiple beams emitted by the laser radar transmitter at the same frame time.

[0059] When using a laser radar to scan a target area, the position and placement of the laser radar usually do not change. Therefore, the area measured by each laser beam emitted at the current moment is the same position as the laser beam emitted in the previous frame. After acquiring the second point cloud data, the data obtained by measuring each beam of the second point cloud data is compared with the data obtained by measuring each beam of the first point cloud data to obtain a data comparison result. The data comparison result is used to characterize the similarity between the first point cloud data and the second point cloud data, that is, whether the two point cloud data are consistent. If they are inconsistent, it means that the local area of ​​the target area has changed.

[0060] Embodiment 3

[0061] On the basis of the above embodiment, when the data comparison result shows that there is a difference in the data of a part of the wire harness, an exemplary method for determining the difference point cloud data by calculation based on the first point cloud data and the second point cloud data includes:

[0062] According to the data comparison result, the difference calculation is performed on the data of the wire harness where there is a difference between the first point cloud data and the second point cloud data to obtain the difference result; and the difference result is used as the difference point cloud data.

[0063] Specifically, a difference result is calculated based on the difference between the first point cloud data and the second point cloud data, and the difference result is used as the difference point cloud data. The difference point cloud data can be packaged and forwarded to the processor. After parsing the difference point cloud data, the processor can update the difference part of the original point cloud data to reduce the amount of data transmission and data processing.

[0064] For example, assuming that the laser radar emits a total of 400 lines of light beams, after the second point cloud data is generated, the first point cloud data is compared with the second point cloud data, and the point cloud data within the 1st line to the 350th line are the same, and the point cloud data of the 351st line to the 400th line are different. At this time, the point cloud data of the 351st line to the 400th line of the first point cloud data and the second point cloud data can be extracted respectively, and the difference result of the 351st line to the 400th line can be calculated.

[0065] Embodiment 4

[0066] On the basis of the above embodiment, after performing data comparison based on the first point cloud data and the second point cloud data and determining the data comparison result, the method further includes:

[0067] When it is determined that the data comparison result shows that all the wire harnesses are different, the data comparison result is filtered according to a preset comparison fuzziness rule, and the data comparison result is updated.

[0068] Specifically, as mentioned above, when the LiDAR is scanning, the light source may be unstable in the target area, which will affect the scanning results of the LiDAR and cause all the line bundles in the determined data comparison results to differ from the previous frame.

[0069] The interference caused by the above situation can be filtered through the preset contrast fuzziness rule. In this embodiment, the target area is a coal yard. As mentioned above, it is necessary to randomly sample coal from the coal pile according to the preset sampling time. According to the Monte Carlo algorithm of random sampling, the change area of ​​the target area can be predicted. When it is determined that there are differences in all the beams in the data comparison results, the misidentified interference beams can be filtered according to the predicted change area, and the filtered data comparison results can be updated. The following formula is the contrast fuzziness rule formula:

[0070] S=M(A,T)

[0071] Among them, S is the contrast ambiguity, M is the function of the three-dimensional point cloud data of the target area scanned by the lidar, A is the prediction parameter of the local change area, and the value range is determined according to the horizontal projection area of ​​the target area volume, and T is the sampling time for sampling coal.

[0072] Embodiment 5

[0073] On the basis of the above embodiment, when the data comparison result shows that there is a difference in the data of the partial wire harness, before calculating based on the first point cloud data and the second point cloud data to determine the difference point cloud data, the method further includes:

[0074] If the third point cloud data of the target area is obtained, the third point cloud data is transmitted to the buffer area.

[0075] Specifically, before determining the difference point cloud data, that is, before completing the difference calculation between the first point cloud data and the second point cloud data, or before determining the data comparison result, if the laser radar scans and obtains the third point cloud data of the target area, the third point cloud data needs to be temporarily stored in the cache area. The third point cloud data is the next frame of point cloud data of the second point cloud data.

[0076] Furthermore, when acquiring each frame of point cloud data, the point cloud data at the current moment can be temporarily stored in the cache area, and the progress of the current data comparison can be fed back to the cache area through the status bit register. If the data comparison progress fed back by the status bit register is being processed, the third point cloud data obtained by the new scan will be temporarily stored in the cache area.

[0077] Embodiment 6

[0078] On the basis of the above embodiment, when the data comparison result shows that there is a difference in the data of the partial wire harness, after calculating based on the first point cloud data and the second point cloud data to determine the difference point cloud data, the method further includes:

[0079] The third point cloud data is used as the second point cloud data, and the steps of performing data comparison based on the first point cloud data and the second point cloud data to determine the data comparison result are repeatedly performed.

[0080] Specifically, after determining the difference point cloud data, if there is a third point cloud data in the cache area, the third point cloud data is used as the second point cloud data, and the steps of performing data comparison based on the first point cloud data and the second point cloud data are repeated to determine the data comparison result and restart the data comparison.

[0081] Embodiment 7

[0082] On the basis of the above embodiment, after performing data comparison based on the first point cloud data and the second point cloud data and determining the data comparison result, the method further includes:

[0083] According to the data comparison results, the emission range of the laser radar beam is adjusted.

[0084] Specifically, after the laser radar has scanned multiple times, the range of the laser beam that needs to be used can be predicted based on the historical local change areas, thereby adjusting the emission range of the laser radar beam and reducing the amount of scanned data. In other words, by predicting the local change area, areas with a small possibility of change and a large possibility of change can be obtained. For areas with a small possibility of change, after multiple scans, valid point cloud data has been obtained, and there is no need for repeated scans. The laser radar beam can be adjusted to target areas with a high possibility of change, focusing on scanning valid point cloud data, which can improve the efficiency of point cloud data processing.

[0085] Similarly, the processing efficiency of point cloud data can be improved by adjusting the scanning frequency of the lidar.

[0086] Embodiment 8

[0087] Based on the above embodiments, this embodiment provides an application example.

[0088] Figure 2 A schematic diagram of a point cloud data processing device provided by the present disclosure. Figure 2 As shown, the device 200 includes: a laser radar 210, an internal storage space 211 of the laser radar, a host computer main processor 220, a difference comparison processing module 212 and a status bit register 213.

[0089] The laser radar 210 divides the internal storage space 211 into three storage areas F1, F2 and F3 and a status register 213. The functions of the three storage areas are as follows: the F1 storage area is used to store the last sent point cloud data, that is, the first point cloud data; the F2 storage area is used to store the currently acquired point cloud data, that is, the second point cloud data; and the F3 storage area is used to cache or transmit newly generated point cloud data. The difference comparison processing module 212 is used to calculate the point cloud difference, and the status register 213 is used to feedback the progress of the difference comparison between the first point cloud data extracted from the F1 storage area and the second point cloud data extracted from the F2 storage area.

[0090] Figure 3 Flowchart of the point cloud data processing method provided by the present disclosure applied to the device 200. Figure 3 As shown, the method includes:

[0091] S31: The laser radar is turned on and the laser radar transmitter forms the main data stream of the first frame of point cloud data based on the 400-beam laser radar, and the main data stream completes the measurement of the three-dimensional point cloud data. Among them, the three-dimensional point cloud data contains the laser ranging value, the reflectivity value of the echo, the horizontal rotation angle value and the timestamp information of each frame.

[0092] S32: After the first frame of data is generated after startup, the first frame of data will pass through the F3 storage area, the F2 storage area and the F1 storage area in sequence, and finally be sent to the host computer main processor 220. The host computer main processor 220 drives the reception of the main data stream, performs protocol analysis on the main data stream, and simultaneously separately copies a copy of the main data stream data to the F1 storage area for storage and then processes it.

[0093] S33: When the 400-beam-based laser radar generates a second frame of point cloud data, the second frame of point cloud data will first enter the F3 storage area and then enter the F2 storage area. At this time, the difference comparison processing module 212 will compare the main data stream of the second frame of point cloud data newly arrived in the F2 storage area with the main data stream of the previous frame stored in the F1 storage area.

[0094] S34: Pack the differences obtained by the difference comparison processing module 212, encapsulate the new data protocol, and send it to the host processor 220 of the upper computer.

[0095] The host computer main processor 220 parses the newly encapsulated data protocol and updates the corresponding position of the point cloud data stored in the host computer main processor 220. The host computer main processor 220 can perform actual business data processing or local business data processing, such as image recognition corner recognition function, based on the changing data area and local data scene of the stored point cloud data.

[0096] Furthermore, the device 200 also includes a status bit register 213, which is used to feedback the progress of the difference comparison between the first point cloud data extracted from the F1 storage area and the second point cloud data extracted from the F2 storage area. When the first point cloud data and the second point cloud data are being processed, the newly scanned data will be temporarily stored in the F3 storage area. When the difference calculation of the first point cloud data and the second point cloud data is completed, the difference will be sent to the host computer main processor 220, the point cloud data in the F3 storage area will be moved to the F2 storage area, and the point cloud data in the F2 storage area will be moved to the F1 storage area, and then the difference comparison will be restarted.

[0097] In addition, except for the first frame of point cloud data generated when the laser radar is turned on and running, the complete data is compressed and packaged and sent to the host computer main processor 220, the point cloud data corresponding to all subsequent frames only need to send the difference point cloud data of the changed area, which can greatly reduce the amount of transmitted point cloud data.

[0098] Embodiment 9

[0099] Figure 4 The structure diagram of a point cloud data processing device provided by the present disclosure is shown in FIG. Figure 4As shown, the device 400 includes: an acquisition module 410 , a first determination module 420 , a second determination module 430 and an update module 440 .

[0100] An acquisition module 410 is used to perform a laser radar scan on a target area to acquire first point cloud data and second point cloud data of the target area, where the second point cloud data is point cloud data generated at a current moment and is point cloud data of a next frame of the first point cloud data;

[0101] A first determination module 420, configured to perform data comparison based on the first point cloud data and the second point cloud data, and determine a data comparison result;

[0102] A second determination module 430 is used to determine difference point cloud data by performing calculation based on the first point cloud data and the second point cloud data when the data comparison result shows that there is a difference in the data of the partial wire harness;

[0103] The updating module 440 is used to update the original point cloud data based on the difference point cloud data to obtain the target point cloud data of the target area. The original point cloud data is the first frame of point cloud data generated after the laser radar is started.

[0104] Optionally, the first determining module is used to:

[0105] The data of each line bundle of the first point cloud data is compared with the data of the corresponding line bundle of the second point cloud data in sequence to determine the data comparison result, and the data comparison result is used to characterize the similarity between the first point cloud data and the second point cloud data.

[0106] Optionally, the second determining module is used to:

[0107] According to the data comparison result, performing difference calculation on the data of the wire harness having differences between the first point cloud data and the second point cloud data to obtain a difference result;

[0108] The difference result is used as the difference point cloud data.

[0109] Optionally, the device further comprises:

[0110] After performing data comparison based on the first point cloud data and the second point cloud data and determining the data comparison result, if it is determined that the data comparison result shows that all the wire harnesses are different, the data comparison result is filtered according to a preset comparison fuzziness rule and the data comparison result is updated.

[0111] Optionally, the device further comprises:

[0112] When the data comparison result shows that there are differences in the data of part of the wire harness, calculation is performed based on the first point cloud data and the second point cloud data, and before determining the difference point cloud data, if third point cloud data of the target area is obtained, the third point cloud data is transferred to the cache area, and the third point cloud data is the next frame of point cloud data of the second point cloud data.

[0113] Optionally, the device further comprises:

[0114] When the data comparison result shows that there are differences in the data of part of the wiring harness, calculation is performed based on the first point cloud data and the second point cloud data to determine the difference point cloud data, and then the third point cloud data is used as the second point cloud data, and the step of performing data comparison based on the first point cloud data and the second point cloud data to determine the data comparison result is repeated.

[0115] Optionally, the device further comprises:

[0116] The method comprises performing data comparison based on the first point cloud data and the second point cloud data, and after determining the data comparison result, adjusting the emission range of the laser radar beam according to the data comparison result.

[0117] On the basis of the above embodiments, this embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method described in the above embodiments.

[0118] In some implementations of this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in the above embodiment are implemented.

[0119] In some implementations of this embodiment, a computer program product is provided, including a computer program / instructions, and when the computer program is executed by a processor, the steps of the method described in the above embodiment are implemented.

[0120] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components to execute the method in the above embodiments.

[0121] The computer-readable storage medium may be implemented by any type of volatile or non-volatile storage device or a combination thereof, and the computer-readable storage medium may include but is not limited to, for example, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (e.g., hard disk, floppy disk, solid-state drive, removable disk, CD-ROM, DVD-ROM, Blu-ray disc, etc.).

[0122] The computer-readable storage medium may also store at least one computer executable program / instruction, which may be, for example, a computer-readable instruction. The computer-readable storage medium includes, but is not limited to, for example, a volatile memory and / or a non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The computer-readable storage medium may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device runs the computer-readable instructions stored on the computer-readable storage medium, the various methods described above may be performed.

[0123] In addition, the computer device may also include (but not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (eg, keyboard, mouse, speaker, etc.), etc.

[0124] The processor may communicate with external devices via an I / O bus via a wired or wireless network.

[0125] In one embodiment, the at least one computer executable instruction may also be compiled into or constitute a software product / computer program product, wherein one or more computer executable instructions are executed by a processor to perform the various functions and / or method steps in the embodiments described in the present technology.

[0126] In the embodiments provided in the present disclosure, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the above-mentioned module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0127] It should be noted that in the present disclosure, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element limited by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0128] Although the embodiments disclosed in the present disclosure are as above, the above contents are only embodiments adopted for facilitating the understanding of the present disclosure and are not intended to limit the present disclosure. Any technician in the technical field to which the present disclosure belongs can make any modifications and changes in the form and details of the implementation without departing from the spirit and scope disclosed in the present disclosure, but the scope of patent protection of the present disclosure shall still be subject to the scope defined in the attached claims.

Claims

1. A method for processing point cloud data, characterized in that: include: Performing a laser radar scan on a target area to obtain first point cloud data and second point cloud data of the target area, wherein the second point cloud data is point cloud data generated at a current moment and is point cloud data of a next frame of the first point cloud data; Performing data comparison based on the first point cloud data and the second point cloud data, and determining a data comparison result; When the data comparison result shows that there is a difference in the data of the partial wire harness, performing calculation based on the first point cloud data and the second point cloud data to determine the difference point cloud data; The original point cloud data is updated based on the difference point cloud data to obtain the target point cloud data of the target area, and the original point cloud data is the first frame of point cloud data generated after the laser radar is started.

2. The method according to claim 1, characterized in that The performing data comparison based on the first point cloud data and the second point cloud data to determine the data comparison result includes: The data of each line bundle of the first point cloud data is compared with the data of the corresponding line bundle of the second point cloud data in sequence to determine the data comparison result, and the data comparison result is used to characterize the similarity between the first point cloud data and the second point cloud data.

3. The method according to claim 1, characterized in that When the data comparison result shows that there is a difference in the data of the partial wire harness, performing calculation based on the first point cloud data and the second point cloud data to determine the difference point cloud data includes: According to the data comparison result, performing difference calculation on the data of the wire harness having differences between the first point cloud data and the second point cloud data to obtain a difference result; The difference result is used as the difference point cloud data.

4. The method according to claim 1, characterized in that: After performing data comparison based on the first point cloud data and the second point cloud data and determining a data comparison result, the method further includes: When it is determined that the data comparison result shows that all the wire harnesses are different, the data comparison result is filtered according to a preset comparison fuzziness rule, and the data comparison result is updated.

5. The method according to claim 1, characterized in that When the data comparison result shows that there is a difference in the data of the partial wire harness, before performing calculation based on the first point cloud data and the second point cloud data to determine the difference point cloud data, the method further includes: If the third point cloud data of the target area is obtained, the third point cloud data is transmitted to the buffer area, and the third point cloud data is the next frame of point cloud data of the second point cloud data.

6. The method according to claim 5, characterized in that When the data comparison result shows that there is a difference in the data of the partial wire harness, after performing calculation based on the first point cloud data and the second point cloud data to determine the difference point cloud data, the method further includes: The third point cloud data is used as the second point cloud data, and the step of performing data comparison based on the first point cloud data and the second point cloud data to determine the data comparison result is repeatedly performed.

7. A point cloud data processing device, characterized in that: include: An acquisition module, used to perform a laser radar scan on a target area to acquire first point cloud data and second point cloud data of the target area, wherein the second point cloud data is point cloud data generated at a current moment and is point cloud data of a next frame of the first point cloud data; A first determination module, configured to perform data comparison based on the first point cloud data and the second point cloud data, and determine a data comparison result; A second determination module is used to determine different point cloud data by performing calculation based on the first point cloud data and the second point cloud data when the data comparison result shows that there is a difference in the data of the partial wire harness; An updating module is used to update the original point cloud data based on the difference point cloud data to obtain the target point cloud data of the target area, wherein the original point cloud data is the first frame of point cloud data generated after the laser radar is started.

8. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images