Road surface point cloud data processing method and device, equipment and storage medium

Abstract

The invention discloses a road surface point cloud data processing method and device, equipment and a storage medium. Comprising the steps of obtaining pavement point cloud data; wherein the road surface point cloud data comprises reflectivity data; grouping the road surface point cloud data to obtain multiple groups of road surface point cloud data; determining statistical parameters of the reflectivity data in each group of road surface point cloud data; and adjusting the reflectivity data based on the statistical parameters to obtain adjusted road surface point cloud data. According to the road surface point cloud data processing method provided by the embodiment of the invention, after the road surface point cloud data are grouped, the reflectivity of the road surface point cloud data in each group is adjusted based on the statistical parameters of the reflectivity, so that the reflectivity data of fuzzy point cloud can be accurately processed, and the precision and reliability of road surface point cloud data processing are improved; therefore, the subsequent recognition of the road surface information based on the later road surface point cloud data is facilitated.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of vehicles, and in particular, to a method, device, equipment and storage medium for processing road surface point cloud data. Background Art

[0002] In the prior art, when identifying a road surface, the following two methods are adopted: One is an image processing algorithm; this method is achieved by enhancing the image contrast of point clouds with low reflectivity. This method has problems such as high resource consumption, long cycle, and uncertain effect. The other method is: an extreme value processing algorithm for point cloud data; this method first calculates the maximum value of the lane line and takes the maximum value as the center point. Then, points with reflectivity in a set interval within a set angular interval are removed, and a small amount of point clouds around the maximum value are retained. This method is theoretically feasible, but the judgment operation is relatively complex, and many points may be erroneously deleted, affecting subsequent analysis and processing. Summary of the Invention

[0003] Embodiments of the present invention provide a method, device, equipment and storage medium for processing road surface point cloud data, which can accurately process the reflectivity data of fuzzy point clouds and improve the accuracy and reliability of road surface point cloud data processing.

[0004] In a first aspect, embodiments of the present invention provide a method for processing road surface point cloud data, including:

[0005] Obtain road surface point cloud data; wherein, the road surface point cloud data includes reflectivity data;

[0006] Group the road surface point cloud data to obtain multiple groups of road surface point cloud data;

[0007] Determine the statistical parameters of the reflectivity data in each group of road surface point cloud data;

[0008] Adjust the reflectivity data based on the statistical parameters to obtain adjusted road surface point cloud data.

[0009] In a second aspect, embodiments of the present invention further provide a device for processing road surface point cloud data, including:

[0010] A road surface point cloud data acquisition module, configured to obtain road surface point cloud data; wherein, the road surface point cloud data includes reflectivity data;

[0011] A road surface point cloud data grouping module, configured to group the road surface point cloud data to obtain multiple groups of road surface point cloud data;

[0012] A statistical parameter determination module, configured to determine the statistical parameters of the reflectivity data in each group of road surface point cloud data;

[0013] A road surface point cloud data adjustment module, configured to adjust the reflectivity data based on the statistical parameters to obtain adjusted road surface point cloud data.

[0014] In a third aspect, an embodiment of the present invention further provides an electronic device, which includes:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor can execute the processing method of the road surface point cloud data according to the embodiment of the present invention.

[0018] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer instructions, and when the computer instructions are executed by a processor, the processing method of the road surface point cloud data according to the embodiment of the present invention is implemented.

[0019] An embodiment of the present invention discloses a processing method, device, equipment and storage medium for road surface point cloud data. Obtain road surface point cloud data; wherein, the road surface point cloud data includes reflectivity data; group the road surface point cloud data to obtain multiple groups of road surface point cloud data; determine the statistical parameters of the reflectivity data in each group of road surface point cloud data; adjust the reflectivity data based on the statistical parameters to obtain adjusted road surface point cloud data. In the processing method of the road surface point cloud data provided in this embodiment, after grouping the road surface point cloud data, the reflectivity of the road surface point cloud data in each group is adjusted based on the statistical parameters of the reflectivity, and the reflectivity data of the fuzzy point cloud can be accurately processed, improving the accuracy and reliability of the road surface point cloud data processing, thereby facilitating the subsequent identification of road surface information based on the adjusted road surface point cloud data. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a flowchart of a processing method for road surface point cloud data in Embodiment 1 of the present invention;

[0021] Figure 2 is a schematic structural diagram of a processing device for road surface point cloud data in Embodiment 2 of the present invention;

[0022] Figure 3 is a schematic structural diagram of an electronic device in Embodiment 3 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present invention, rather than limiting the present invention. Additionally, it should be noted that for ease of description, only parts related to the present invention rather than all structures are shown in the accompanying drawings.

[0024] Embodiment 1

[0025] Figure 1 FIG. is a flowchart of a method for processing road surface point cloud data provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation of processing road surface point cloud data. The method can be executed by a processing device for road surface point cloud data, and the device can be implemented in the form of software and / or hardware. Optionally, it is implemented by an electronic device, and the electronic device can be a mobile terminal, a PC, or a server, etc. The specific steps are as follows:

[0026] S110. Obtain road surface point cloud data.

[0027] Among them, the road surface point cloud data includes reflectivity data. The road surface point cloud data can be obtained by irradiating the road surface with an on-vehicle lidar and receiving the lidar data returned by the road surface. There can be multiple beams of the on-vehicle lidar, so the road surface point cloud data can include multiple channels, such as 32 channels. The road surface point cloud data can also include information such as channel numbers, angle values, and distance values. In this embodiment, only the reflectivity data among them is processed.

[0028] S120. Group the road surface point cloud data to obtain multiple groups of road surface point cloud data.

[0029] Among them, since the road surface point cloud data includes multiple channels, when grouping the road surface point cloud data, the road surface point cloud data of each channel is grouped separately, that is, the road surface point cloud data between channels is independent of each other and does not affect each other.

[0030] Optionally, the method of grouping the road surface point cloud data to obtain multiple groups of road surface point cloud data can be: extract the reflectivity data from the road surface point cloud data and cache the extracted reflectivity data in a set memory; group the cached reflectivity data to obtain multiple groups of reflectivity data.

[0031] Among them, the set memory can be a Random Access Memory (RAM). The method of extracting the reflectivity data from the road surface point cloud data can be to extract the data corresponding to the reflectivity field in the road surface point cloud data. After caching the extracted reflectivity data in the RAM module, group the cached reflectivity data to obtain multiple groups of reflectivity data.

[0032] Specifically, the method of grouping the cached reflectivity data to obtain multiple groups of reflectivity data can be: respectively grouping the reflectivity data of each channel in the cache according to the reflection of a set number of reflectivity data in each group to obtain multiple groups of reflectivity data.

[0033] Among them, the set number can be set in advance. For example, it can be any integer between 15 and 20. In this embodiment, the reflectivity data can be processed by a Field Programmable Gate Array (FPGA). To facilitate the FPGA to process the point cloud data, the set number can be set to 16, that is, every 16 reflectivity data are divided into a group.

[0034] S130. Determine the statistical parameters of the reflectivity data in each group of road surface point cloud data.

[0035] Among them, the statistical parameters can include the mean value and the variance. Among them, the method of calculating the mean value and variance of the set number of reflectivity data refers to the existing statistical principles and will not be elaborated here.

[0036] S140. Adjust the reflectivity data based on the statistical parameters to obtain the adjusted road surface point cloud data.

[0037] In this embodiment, through the mean value and variance of the reflectivity data, it can be judged whether the road surface point cloud in this group is a fuzzy point cloud. If it is a fuzzy point cloud, the reflectivity needs to be adjusted. If it is not a fuzzy point cloud, no adjustment is required.

[0038] In this embodiment, the method of adjusting the reflectivity data based on the statistical parameters can be: if the average value is less than the first set value and the variance is less than the second set value, then adjust the reflectivity data of this group to the set reflectivity. If the average value is greater than or equal to the first set value and / or the variance is greater than or equal to the second set value, then keep the reflectivity data of this group unchanged.

[0039] Among them, the first set value and the second set value can be pre-set values. For example: the first set value is set to 30, and the second set value is set to 20. The set reflectivity can also be pre-set. For example: it can be 5. Specifically, if the average value of a set of reflectivity data is less than the first set value and the variance is less than the second set value, it indicates that the road surface point cloud in this group is a fuzzy point cloud, and then the reflectivity data in this group are all adjusted to the set reflectivity (such as: 5). If the average value of a set of reflectivity data is greater than or equal to the first set value or the variance is greater than or equal to the second set value, it indicates that the road surface point cloud in this group is a non-fuzzy point cloud, and no adjustment is required, that is, the reflectivity data of this group remains unchanged. In this embodiment, by adjusting the reflectivity of the road surface point cloud based on the mean and variance, the fuzzy point cloud can be accurately eliminated, ensuring that the lidar can effectively identify the road surface point cloud information in practical applications. In this way, not only can misidentification be avoided, but also the application range of the lidar can be effectively expanded. It makes the lidar have higher accuracy and practicability in lane line detection.

[0040] The technical solution of this embodiment is to obtain road surface point cloud data; among them, the road surface point cloud data includes reflectivity data; group the road surface point cloud data to obtain multiple groups of road surface point cloud data; determine the statistical parameters of the reflectivity data in each group of road surface point cloud data; and adjust the reflectivity data based on the statistical parameters to obtain the adjusted road surface point cloud data. The method for processing road surface point cloud data provided in this embodiment, after grouping the road surface point cloud data, adjusts the reflectivity of the road surface point cloud data in each group based on the statistical parameters of the reflectivity, can accurately process the reflectivity data of the fuzzy point cloud, improve the accuracy and reliability of the road surface point cloud data processing, and thus facilitate the subsequent identification of road surface information based on the adjusted road surface point cloud data.

[0041] Embodiment 2

[0042] Figure 2 is a schematic structural diagram of a device for processing road surface point cloud data provided by Embodiment 2 of the present invention, as Figure 2 shown, the device includes:

[0043] A road surface point cloud data acquisition module 210, configured to acquire road surface point cloud data; among them, the road surface point cloud data includes reflectivity data;

[0044] A road surface point cloud data grouping module 220, configured to group the road surface point cloud data to obtain multiple groups of road surface point cloud data;

[0045] A statistical parameter determination module 230, configured to determine the statistical parameters of the reflectivity data in each group of road surface point cloud data;

[0046] The road surface point cloud data adjustment module 240 is configured to adjust the reflectivity data based on statistical parameters to obtain the adjusted road surface point cloud data.

[0047] Optionally, the road surface point cloud data grouping module 220 is further configured to:

[0048] Extract the reflectivity data from the road surface point cloud data and cache the extracted reflectivity data in a set memory;

[0049] Group the cached reflectivity data to obtain multiple groups of reflectivity data.

[0050] Optionally, the road surface point cloud data includes multiple channels; the road surface point cloud data grouping module 220 is further configured to:

[0051] Group the reflectivity data of each channel in the cache according to the reflection of a set number of reflectivity data in each group to obtain multiple groups of reflectivity data.

[0052] Optionally, the statistical parameter determination module 230 is further configured to:

[0053] Determine the average value and variance of each group of reflectivity data.

[0054] Optionally, the road surface point cloud data adjustment module 240 is further configured to:

[0055] If the average value is less than the first set value and the variance is less than the second set value, then adjust the reflectivity data of this group to a set reflectivity.

[0056] Optionally, the road surface point cloud data adjustment module 240 is further configured to:

[0057] If the average value is greater than or equal to the first set value and / or the variance is greater than or equal to the second set value, then keep the reflectivity data of this group unchanged.

[0058] The above device can execute the methods provided in all the foregoing embodiments of the present invention, and has corresponding functional modules and beneficial effects for executing the above methods. For technical details not described in detail in this embodiment, reference can be made to the methods provided in all the foregoing embodiments of the present invention.

[0059] Embodiment III

[0060] Figure 3FIG. 0 shows a schematic structural diagram of an electronic device 10 that can be used to implement an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as, for example, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, for example, personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0061] As Figure 3 shown, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. The memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0062] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0063] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the method for processing road surface point cloud data.

[0064] In some embodiments, the method for processing road surface point cloud data can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for processing road surface point cloud data described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the method for processing road surface point cloud data in any other suitable manner (e.g., by means of firmware).

[0065] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems on a chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0066] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer programs are executed by the processor, the functions / operations specified in the flowchart and / or block diagram are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0067] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0068] To provide for interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0069] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0070] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The relationship between the client and the server is created by computer programs that run on respective computers and have a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, and solves the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0071] It should be understood that various forms of processes shown above can be used, steps can be reordered, added or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is made herein.

[0072] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for processing pavement point cloud data, characterized in that, Including: Obtain road surface point cloud data; wherein, the road surface point cloud data includes reflectivity data; Group the road surface point cloud data to obtain multiple groups of road surface point cloud data; Determine the statistical parameters of the reflectivity data in each group of road surface point cloud data; Adjust the reflectivity data based on the statistical parameters to obtain the adjusted road surface point cloud data.

2. The method according to claim 1, wherein Grouping the road surface point cloud data to obtain multiple groups of road surface point cloud data includes: Extract the reflectivity data from the road surface point cloud data and cache the extracted reflectivity data into a set memory; Group the cached reflectivity data to obtain multiple groups of reflectivity data.

3. The method according to claim 2, wherein The road surface point cloud data includes multiple channels; grouping the cached reflectivity data to obtain multiple groups of reflectivity data includes: Group the reflectivity data of each channel in the cache according to the reflection of a set number of reflectivity data in each group to obtain multiple groups of reflectivity data.

4. The method according to claim 1 or 2, characterized in that, Determining the statistical parameters of the reflectivity data in each group of road surface point cloud data includes: Determine the average value and variance of each group of reflectivity data.

5. The method according to claim 4, characterized in that, Adjusting the reflectivity data based on the statistical parameters includes: If the average value is less than a first set value and the variance is less than a second set value, then adjust the reflectivity data of this group to a set reflectivity.

6. The method according to claim 4, wherein Adjusting the reflectivity data based on the statistical parameters includes: If the average value is greater than or equal to the first set value and / or the variance is greater than or equal to the second set value, then keep the reflectivity data of this group unchanged.

7. A processing device for pavement point cloud data, characterized in that, Including: A road surface point cloud data acquisition module for acquiring road surface point cloud data; wherein, the road surface point cloud data includes reflectivity data; A road surface point cloud data grouping module for grouping the road surface point cloud data to obtain multiple groups of road surface point cloud data; A statistical parameter determination module for determining the statistical parameters of the reflectivity data in each group of road surface point cloud data; A road surface point cloud data adjustment module for adjusting the reflectivity data based on the statistical parameters to obtain the adjusted road surface point cloud data.

8. The device according to claim 7, characterized in that, The road surface point cloud data grouping module is further configured to: Extract the reflectivity data from the road surface point cloud data and cache the extracted reflectivity data into a set memory; Group the cached reflectivity data to obtain multiple groups of reflectivity data.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for processing road surface point cloud data according to any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a processor to execute the method for processing road surface point cloud data according to any one of claims 1-6 when executed.

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