Point cloud data processing method and device

Abstract

The invention provides a point cloud data processing method and device, and relates to the field of automatic driving and the field of data processing. The method comprises the specific steps that first point cloud data and second point cloud data are acquired, and the second point cloud data are sensitive to high-reflectivity noise; comparing the first point cloud data with the second point cloud data to determine a target area; and performing statistics on the second point cloud data in the target area to determine target point cloud data. According to the invention, the target point cloud data is determined by comparing the first point cloud data with the second point cloud data, so that the data belonging to the high-inverse noise in the point cloud data are screened, the interference of the high-inverse noise on the point cloud data is avoided, and the accuracy of the point cloud data is improved.

Description

Technical Field

[0001] The present disclosure relates to the fields of autonomous driving and data processing, and in particular to a point cloud data processing method and device. Background Art

[0002] In related technologies, high-reflectivity noise in laser point clouds refers to the diffused point cloud emitted by a LiDAR sensor on a surface or object with high reflectivity. In this case, the point cloud scanned by the LiDAR is often larger than the actual object, reducing the credibility of the point cloud data and the accuracy of object recognition using this data. However, there is currently a lack of solutions for mining high-reflectivity noise data. Summary of the Invention

[0003] The present disclosure provides a point cloud data processing method, device, and system to at least address the problem of high-intensity anti-noise interference with point cloud data in related technologies. The technical solutions of the present disclosure are as follows:

[0004] According to a first aspect of an embodiment of the present disclosure, a point cloud data processing method is provided, comprising:

[0005] Acquire first point cloud data and second point cloud data, wherein the second point cloud data is more sensitive to high-reflection noise;

[0006] Comparing the first point cloud data with the second point cloud data to determine a target area;

[0007] Statistics are performed on the second point cloud data in the target area to determine target point cloud data.

[0008] Optionally, comparing the first point cloud data with the second point cloud data to determine the target area includes:

[0009] Aligning the first point cloud data and the second point cloud data according to acquisition time;

[0010] The target area is determined according to the quantity of the first point cloud data and the quantity of the second point cloud data corresponding to the same time point.

[0011] Optionally, the method further includes:

[0012] Point cloud data belonging to a dynamic object in the first point cloud data and the second point cloud data is determined, and the point cloud data of the dynamic object is deleted.

[0013] Optionally, the step of determining the target area according to the number of the first point cloud data and the number of the second point cloud data corresponding to the same time point includes:

[0014] Divide the space into sub-areas;

[0015] comparing the quantity of the first point cloud data and the second point cloud data in the sub-region;

[0016] A subregion in which the amount of the second point cloud data is greater than the amount of the first point cloud data is determined as the target region.

[0017] Optionally, performing statistics on the second point cloud data in the target area to determine the target point cloud data includes:

[0018] Clustering the second point cloud data in the target area to obtain at least one point cloud type and a corresponding number of second point cloud data;

[0019] The target point cloud data is determined according to the amount of second point cloud data corresponding to the point cloud type.

[0020] Optionally, determining the target point cloud data according to the amount of second point cloud data corresponding to the point cloud type includes:

[0021] If the number of the second point cloud data corresponding to the point cloud type is greater than a preset number threshold, determining the second point cloud data corresponding to the point cloud type as the target point cloud data;

[0022] If the amount of the second point cloud data corresponding to the point cloud type is less than or equal to a preset amount threshold, it is determined that the second point cloud data corresponding to the point cloud type is not the target point cloud data.

[0023] According to a second aspect of an embodiment of the present disclosure, there is provided a point cloud data processing apparatus, comprising:

[0024] A data acquisition module, configured to acquire first point cloud data and second point cloud data;

[0025] a comparison module, configured to compare the first point cloud data with the second point cloud data to determine a target area;

[0026] The data processing module is used to perform statistics on the second point cloud data in the target area to determine target point cloud data.

[0027] According to a third aspect of an embodiment of the present disclosure, there is provided an electronic device, including:

[0028] processor;

[0029] a memory for storing instructions executable by the processor;

[0030] The processor is configured to execute the instructions to implement the method as described in any one of the first aspects.

[0031] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided. When instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method as described in any one of the first aspects.

[0032] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program implements the method according to any one of the first aspects when executed by a processor.

[0033] The technical solutions provided by the embodiments of the present disclosure bring at least the following beneficial effects:

[0034] The present disclosure determines the target point cloud data by comparing the first point cloud data with the second point cloud data, thereby screening the data belonging to high-reflection noise in the point cloud data, avoiding the interference of high-reflection noise on the point cloud data, and improving the accuracy of the point cloud data.

[0035] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute an improper limitation of the present disclosure.

[0037] Figure 1 The figure is a schematic diagram of point cloud data according to an exemplary embodiment.

[0038] Figure 2 The figure is a flowchart of a method for processing point cloud data according to an exemplary embodiment.

[0039] Figure 3 The figure is a flowchart of a method for processing point cloud data according to an exemplary embodiment.

[0040] Figure 4 The figure is a flowchart of a method for processing point cloud data according to an exemplary embodiment.

[0041] Figure 5 FIG. 1 is a schematic diagram of a sub-region according to an exemplary embodiment.

[0042] Figure 6 FIG. 1 is a schematic diagram of point cloud data according to an exemplary embodiment.

[0043] Figure 7 The figure is a block diagram of a point cloud data processing device according to an exemplary embodiment.

[0044] Figure 8It is a block diagram of a device according to an exemplary embodiment.

[0045] Figure 9 It is a block diagram of a device according to an exemplary embodiment. DETAILED DESCRIPTION

[0046] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0047] It should be noted that the terms "first," "second," and the like 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 precedence. It should be understood that the numbers used in this manner are 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. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0048] It should be noted that the user information involved in this disclosure (including but not limited to user device information, user personal information, etc.) is all information authorized by the user or fully authorized by all parties.

[0049] LiDAR stands for Light Detection and Ranging, also known as optical radar. LiDAR operates by emitting an infrared beam that reflects off surfaces. The reflected beam is then received by the LiDAR and used to detect the object. The data obtained includes, but is not limited to, the distance between the vehicle and a specific object, whether during the day or at night. In one embodiment, lane markings and the road surface can even be distinguished due to differences in reflectivity. It should be noted that the LiDAR beam cannot detect obscured objects.

[0050] LiDAR is mainly composed of laser transmitter, receiver, scanner, lens antenna and signal processing circuit. According to the scanning method, LiDAR can be divided into MEMS LiDAR, Flash LiDAR, phased array LiDAR and mechanical rotating LiDAR.

[0051] Automotive LiDAR operates on the principle of time of flight (TOF) measurement. The distance from a point to the LiDAR is calculated by the time between emitting laser light and receiving the reflected laser light. The LiDAR analyzes information such as the reflected energy from the target object's surface, the amplitude, frequency, and phase of the reflected wave spectrum, and outputs point cloud data, thereby presenting precise three-dimensional structural information about the target object. LiDAR can provide high-resolution maps, enabling vehicles to achieve high-precision positioning and path planning. Furthermore, LiDAR has a large field of view and can detect targets from multiple angles. In complex environments, LiDAR can also provide more accurate target location information, enabling autonomous vehicles to avoid collisions.

[0052] When lasers emitted by a LiDAR reflect off highly reflective objects or surfaces, they "expand" as they reflect back. This phenomenon occurs when the laser scans a highly reflective object, causing the output point cloud to spread outward, giving it the appearance of expansion. This is why it's called "high-reflectivity expansion." In these cases, the point cloud captured by the LiDAR scan is often larger than the actual object.

[0053] Figure 1 is a schematic diagram of point cloud data according to an exemplary embodiment. For example, Figure 1 (a) and (b) are point cloud data images. The point cloud in the box is the point cloud data corresponding to the road sign. Figure 1 (c) shows the RGB image corresponding to the point cloud data, with the box showing the RGB image corresponding to the road sign. It can be seen that the point clouds scanned by the LiDAR in (a) and (b) are much larger than the actual road sign in (c). Therefore, when identifying objects around a vehicle based on point cloud data, high-reflection noise can affect the accuracy of object size recognition, leading to misjudgment of the vehicle's surroundings. Due to this characteristic of LiDAR, it is necessary to mine data containing this high-reflection noise when training object recognition models. This allows the model to discriminate against the high-reflection noise and minimize its impact on recognition accuracy.

[0054] Figure 2 is a flow chart of a point cloud data processing method according to an exemplary embodiment. Figure 2 As shown, the method includes the following steps.

[0055] Step 101: Acquire first point cloud data and second point cloud data.

[0056] In this embodiment, point cloud data is collected using two different LiDAR sensors. The second LiDAR has lower power than the first, resulting in a more stable point cloud generated by the first LiDAR. This difference is more pronounced in rainy conditions. However, when encountering highly reflective objects like road signs and road studs, the two LiDAR sensors exhibit a completely different behavior: the second LiDAR point cloud will have more points than the first. The diffused point cloud in the second LiDAR sensor represents noise data.

[0057] In a possible embodiment, the model of the first laser radar is Pandar128, and the model of the second laser radar is AT128.

[0058] Step 102: Compare the first point cloud data and the second point cloud data to determine a target area;

[0059] In this embodiment, based on the characteristics of the first and second point cloud data, it can be analyzed that if the number of second point cloud data points in a certain area of ​​the point cloud data acquired at the same time is greater than the number of first point cloud data points, the data in that area may contain high-reflective noise, and the object corresponding to that area may be a highly reflective object. Therefore, the target area likely to contain high-reflective noise can be determined based on the number of point cloud data points.

[0060] Step 103: performing statistics on the second point cloud data in the target area to determine target point cloud data.

[0061] In this embodiment, after determining the target area, it is necessary to further analyze the second point cloud data in the target area to determine which second point cloud data belong to the same category or the same object, and then analyze the number of second point cloud data of the same type, and determine the second point cloud data with a larger number as high anti-noise, that is, the target point cloud data.

[0062] This embodiment determines the target point cloud data by comparing the first point cloud data with the second point cloud data, thereby filtering out the data with high anti-noise in the point cloud data, avoiding the interference of high anti-noise on the point cloud data, and improving the accuracy of the point cloud data.

[0063] Figure 3 is a flow chart of a point cloud data processing method according to an exemplary embodiment. Figure 3 As shown, the step 102 compares the first point cloud data and the second point cloud data to determine that the target area includes:

[0064] Step 201: align the first point cloud data and the second point cloud data according to acquisition time.

[0065] Step 202 : determining a target area according to the number of the first point cloud data and the number of the second point cloud data corresponding to the same time point.

[0066] In this embodiment, in order to compare and analyze the quantity of the first point cloud data and the second point cloud data, the first point cloud data and the second point cloud data must first be aligned according to the acquisition time to facilitate the analysis of the first point cloud data and the second point cloud data at the same time.

[0067] Optionally, the method further includes:

[0068] Point cloud data belonging to a dynamic object in the first point cloud data and the second point cloud data is determined, and the point cloud data of the dynamic object is deleted.

[0069] In this embodiment, the main target of analysis is the point cloud data corresponding to static objects (such as road signs, road studs, etc.), so it is necessary to remove the data belonging to dynamic objects from the first point cloud data and the second point cloud data.

[0070] In one possible embodiment, based on the point cloud data of two adjacent frames, the first motion information of the laser radar in the reference coordinate system and the second motion information of the target object to be detected relative to the laser radar are determined; based on the first motion information and the second motion information, the target motion information of the target object in the reference coordinate system is determined. The motion information includes at least one of speed information, acceleration information, and direction information. The first motion information is the motion information of the laser radar, and the second motion information is the motion information of the target object in the point cloud data. If the first motion information and the second motion information are close, it can be determined that the target object is a moving dynamic object, and its corresponding point cloud data should be deleted.

[0071] Figure 4 is a flow chart of a point cloud data processing method according to an exemplary embodiment. Figure 4 As shown, the step 202 of determining the target area according to the number of the first point cloud data and the number of the second point cloud data corresponding to the same time point includes:

[0072] Step 301: Divide the space into multiple sub-areas.

[0073] Figure 5 FIG. 1 is a schematic diagram of a sub-area according to an exemplary embodiment. Figure 5 As shown in FIG, each certain space size in the three-dimensional space is divided into a grid, that is, a sub-region, and these sub-regions are called voxels.

[0074] During feature extraction, a pointnet network can be used to extract features from the point cloud data in this subregion. The extracted features are used to represent the subregion and then placed back into 3D space. This converts the disordered point cloud data into high-dimensional feature data and makes the data ordered in 3D space, making it easier to extract the 3D voxel data using 3D convolution.

[0075] Step 302: Compare the quantity of the first point cloud data and the quantity of the second point cloud data in the sub-region;

[0076] Step 303: Determine a subregion where the amount of the second point cloud data is greater than the amount of the first point cloud data as the target region.

[0077] Figure 6 FIG. 1 is a schematic diagram of point cloud data according to an exemplary embodiment. Figure 6 As shown, Figure 6 (a) is the second point cloud data, Figure 6 (b) in the figure shows the first point cloud data. The area within the box represents the point cloud data corresponding to the road sign. It is clear that for highly reflective objects, the amount of second point cloud data is far greater than the amount of first point cloud data. Therefore, we only need to compare the amount of second point cloud data and the amount of first point cloud data corresponding to the same object. The sub-area where the amount of second point cloud data is greater than the amount of first point cloud data is the target area.

[0078] Optionally, the step 103 of performing statistics on the second point cloud data in the target area to determine the target point cloud data includes:

[0079] Clustering the second point cloud data in the target area to obtain at least one point cloud type and a corresponding number of second point cloud data;

[0080] The target point cloud data is determined according to the amount of second point cloud data corresponding to the point cloud type.

[0081] In this embodiment, the second point cloud data are clustered by a clustering algorithm to generate types of the second point cloud data. Second point cloud data of the same type belong to the same object.

[0082] In a possible embodiment, a density-based spatial clustering of applications with noise (DBSCAN) algorithm is used for clustering. The DBSCAN algorithm can find all dense areas of sample points and treat these dense areas as clusters.

[0083] Optionally, determining the target point cloud data according to the amount of second point cloud data corresponding to the point cloud type includes:

[0084] If the number of the second point cloud data corresponding to the point cloud type is greater than a preset number threshold, determining the second point cloud data corresponding to the point cloud type as the target point cloud data;

[0085] If the amount of the second point cloud data corresponding to the point cloud type is less than or equal to a preset amount threshold, it is determined that the second point cloud data corresponding to the point cloud type is not the target point cloud data.

[0086] In this embodiment, the filtered target point cloud data can be used to train the object recognition model. Through the training of these data, the object recognition model can filter out high-reflective noise and remove it to more accurately identify the object corresponding to the point cloud data, and avoid the automatic navigation decision-making error caused by the identified object being larger than the actual size of the object.

[0087] Figure 7 FIG. 1 is a block diagram of a point cloud data processing device according to an exemplary embodiment. Figure 7 , the apparatus 700 comprises:

[0088] A data acquisition module 710 is used to acquire first point cloud data and second point cloud data;

[0089] a comparison module 720, configured to compare the first point cloud data with the second point cloud data to determine a target area;

[0090] The data processing module 730 is configured to perform statistics on the second point cloud data in the target area to determine target point cloud data.

[0091] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0092] Figure 8 FIG8 is a block diagram of an apparatus 800 according to an exemplary embodiment. For example, the apparatus 800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0093] Reference Figure 8 , the apparatus 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .

[0094] The processing component 802 generally controls the overall operation of the device 800, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 802 may include one or more modules to facilitate interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

[0095] The memory 804 is configured to store various types of data to support operations on the device 800. Examples of such data include instructions for any application or method operating on the device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0096] The power supply component 806 provides power to the various components of the device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device 800.

[0097] The multimedia component 808 includes a screen that provides an output interface between the device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor can not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have a focal length and optical zoom capability.

[0098] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), which is configured to receive external audio signals when the device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0099] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include but are not limited to: a home button, volume buttons, a start button, and a lock button.

[0100] The sensor assembly 814 includes one or more sensors for providing various aspects of the status assessment of the device 800. For example, the sensor assembly 814 can detect the open / closed state of the device 800, the relative positioning of components, such as the display and keypad of the device 800. The sensor assembly 814 can also detect changes in the position of the device 800 or a component of the device 800, the presence or absence of user contact with the device 800, the orientation or acceleration / deceleration of the device 800, and temperature changes of the device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0101] The communication component 816 is configured to facilitate wired or wireless communication between the device 800 and other devices. The device 800 can access a wireless network based on a communication standard, such as WiFi, an operator network (such as 2G, 3G, 4G or 5G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0102] In an exemplary embodiment, the apparatus 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0103] In an exemplary embodiment, a storage medium including instructions is further provided, such as a memory 804 including instructions, and the instructions can be executed by the processor 820 of the apparatus 800 to perform the above method. Alternatively, the storage medium can be a non-transitory computer-readable storage medium, such as a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0104] Figure 9 900 is a block diagram of an apparatus 900 according to an exemplary embodiment. For example, the apparatus 900 may be provided as a server. Figure 9 The apparatus 900 includes a processing component 922, which further includes one or more processors, and memory resources represented by a memory 932 for storing instructions, such as applications, that can be executed by the processing component 922. The application stored in the memory 932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 922 is configured to execute the instructions to perform the above-described method.

[0105] The device 900 may also include a power supply component 926 configured to perform power management of the device 900, a wired or wireless network interface 950 configured to connect the device 900 to a network, and an input / output (I / O) interface 958. The device 900 may operate based on an operating system stored in the memory 932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or the like.

[0106] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the following claims.

[0107] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A point cloud data processing method, characterized in that: include: Acquire first point cloud data and second point cloud data; Comparing the first point cloud data with the second point cloud data to determine a target area; Statistics are performed on the second point cloud data in the target area to determine target point cloud data.

2. The method according to claim 1, characterized in that: The comparing the first point cloud data with the second point cloud data to determine the target area includes: Aligning the first point cloud data and the second point cloud data according to acquisition time; The target area is determined according to the quantity of the first point cloud data and the quantity of the second point cloud data corresponding to the same time point.

3. The method according to claim 2, characterized in that The method further comprises: Point cloud data belonging to a dynamic object in the first point cloud data and the second point cloud data is determined, and the point cloud data of the dynamic object is deleted.

4. The method according to claim 2 or 3, characterized in that: The step of determining the target area according to the number of the first point cloud data and the number of the second point cloud data corresponding to the same time point comprises: Divide the space into sub-areas; Comparing the quantity of the first point cloud data and the second point cloud data in the sub-area; A subregion in which the amount of the second point cloud data is greater than the amount of the first point cloud data is determined as the target region.

5. The method according to claim 1, characterized in that The performing statistics on the second point cloud data in the target area to determine the target point cloud data comprises: Clustering the second point cloud data in the target area to obtain at least one point cloud type and a corresponding amount of second point cloud data; The target point cloud data is determined according to the amount of second point cloud data corresponding to the point cloud type.

6. The method according to claim 5, characterized in that The determining the target point cloud data according to the amount of second point cloud data corresponding to the point cloud type includes: If the amount of the second point cloud data corresponding to the point cloud type is greater than a preset amount threshold, determining the second point cloud data corresponding to the point cloud type as the target point cloud data; If the amount of the second point cloud data corresponding to the point cloud type is less than or equal to a preset amount threshold, it is determined that the second point cloud data corresponding to the point cloud type is not the target point cloud data.

7. A point cloud data processing device, characterized in that: include: A data acquisition module, used for acquiring first point cloud data and second point cloud data; A comparison module, used for comparing the first point cloud data with the second point cloud data to determine a target area; The data processing module is used to perform statistics on the second point cloud data in the target area to determine the target point cloud data.

8. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the method according to any one of claims 1 to 6. 9 . A computer-readable storage medium, when instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the method according to any one of claims 1 to 6.

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