A point cloud projection method and apparatus for vehicle-mounted lidar

Abstract

This disclosure provides a point cloud projection method, apparatus, storage medium, and electronic device for vehicle-mounted LiDAR. The method includes: setting a forward projection map for projection based on a predetermined size; setting the position values ​​of points to be scanned in a two-dimensional spherical grid according to the predetermined size and a predetermined scanning method; scanning the field of view based on the position values ​​of the points to be scanned; and realizing spherical projection of the point cloud on the forward projection map. This disclosure achieves rapid and regularized projection of high-frequency point clouds by fusing prior features of point cloud data collected by vehicle-mounted LiDAR, reducing the time and resource consumption of point cloud data preprocessing, and improving data integrity and utilization.

Description

Technical Field

[0001] This disclosure relates to the technical field of point cloud data processing, and more specifically to a point cloud projection method, apparatus, storage medium, and electronic device for vehicle-mounted lidar. Background Technology

[0002] As more and more smart cars are equipped with onboard LiDAR, the demands on point cloud data processing are increasing. This requires minimizing time and resource consumption while maintaining accuracy. Because past technologies primarily focused on accuracy and paid less attention to reducing time and resource consumption, there is a need for point cloud data processing technologies that offer high performance and low resource consumption and can be widely used in the industry.

[0003] Point cloud spherical projection, as an important preprocessing method in point cloud semantic segmentation, is facing increasingly serious problems related to time consumption, resource consumption, and data integrity as the point cloud frequency continues to increase. Specifically, the point cloud spherical projection method, a crucial preprocessing method in point cloud semantic segmentation using current automotive LiDAR perception algorithms, pays little attention to time and resource consumption. However, with the development of the automotive LiDAR market, the point cloud frequency and data volume are increasing, making the impact of time and resource consumption increasingly significant. This is because point cloud spherical projection requires performing two arcsine calculations for each point to obtain the index in the range view. Furthermore, due to perturbations in the point cloud, overlay of the projection calculation data can occur, leading to data loss. Summary of the Invention

[0004] The purpose of this disclosure is to provide a point cloud projection method, apparatus, storage medium, and electronic device for vehicle-mounted lidar, in order to solve the problems existing in the prior art.

[0005] To solve the above-mentioned technical problems, the embodiments of this disclosure adopt the following technical solutions:

[0006] A point cloud projection method for vehicle-mounted lidar includes: setting a forward projection map for projection based on a predetermined size; setting the position values ​​of the points to be scanned in a two-dimensional spherical grid according to the predetermined size and a predetermined scanning method; scanning the field of view based on the position values ​​of the points to be scanned, and realizing spherical projection of the point cloud on the forward projection map.

[0007] In some embodiments, the predetermined scanning method is determined based on the prior characteristics of the vehicle-mounted lidar.

[0008] In some embodiments, the predetermined scanning method includes at least the scanning order between fields of view, the scanning order within each field of view, and the scanning interval within each field of view.

[0009] In some embodiments, the scanning order within each field of view is from top to bottom and from left to right.

[0010] In some embodiments, the scanning interval within each field of view is equivalent to 120 points horizontally and 125 lines vertically.

[0011] In some embodiments, the position value is obtained through The numbers represent the pitch angle and yaw angle of each of the points to be scanned.

[0012] In some embodiments, the predetermined size is 128 × 600.

[0013] This disclosure also provides a point cloud projection device for vehicle-mounted LiDAR, comprising: a first setting module for setting a forward projection map for projection based on a predetermined size; a second setting module for setting the position values ​​of the points to be scanned in a two-dimensional spherical grid according to a predetermined scanning method based on the predetermined size; and a projection module for scanning the field of view based on the position values ​​of the points to be scanned, thereby realizing spherical projection of the point cloud on the forward projection map.

[0014] This disclosure also provides a storage medium storing a computer program that, when executed by a processor, implements the steps of any of the methods described above.

[0015] This disclosure also provides an electronic device, including at least a memory and a processor, wherein the memory stores a computer program, and the processor, when executing the computer program in the memory, implements the steps of any of the methods described above.

[0016] This embodiment of the present disclosure achieves rapid regularization projection of high-frequency point clouds by fusing prior features of point cloud data collected by vehicle-mounted LiDAR, thereby reducing the time and resource consumption of point cloud data preprocessing and improving data integrity and utilization.

[0017] The embodiments disclosed herein are particularly applicable to the preprocessing stage of vehicle-mounted LiDAR perception algorithms, thereby reducing the processing time and resource consumption of the entire intelligent assisted driving system, reducing the data loss rate in the point cloud data projection stage, and improving data integrity. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of point cloud projection according to an embodiment of the present disclosure;

[0020] Figure 2 This is a schematic diagram illustrating the steps of the point cloud projection method according to an embodiment of the present disclosure;

[0021] Figures 3(a) and 3(b) are schematic diagrams comparing the point cloud projection effect of the present disclosure embodiment with the prior art;

[0022] Figure 4 This is a schematic diagram of a predetermined scanning mode in the point cloud projection method of this disclosure. Detailed Implementation

[0023] Various embodiments and features of this disclosure are described herein with reference to the accompanying drawings.

[0024] It should be understood that various modifications can be made to the embodiments described herein. Therefore, the above description should not be considered as limiting, but merely as an example of embodiments. Other modifications within the scope and spirit of this disclosure will be apparent to those skilled in the art.

[0025] The accompanying drawings, which are included in and form part of this specification, illustrate embodiments of the present disclosure and, together with the general description of the disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the disclosure.

[0026] These and other features of this disclosure will become apparent from the following description of preferred forms of embodiments given as non-limiting examples, with reference to the accompanying drawings.

[0027] It should also be understood that although this disclosure has been described with reference to some specific examples, those skilled in the art can certainly implement many other equivalent forms of this disclosure, which have the features described in the claims and are therefore all within the scope of protection defined herein.

[0028] The above and other aspects, features and advantages of this disclosure will become more apparent when taken in conjunction with the accompanying drawings and in view of the following detailed description.

[0029] Specific embodiments of this disclosure are described thereafter with reference to the accompanying drawings; however, it should be understood that the claimed embodiments are merely examples of this disclosure, which may be implemented in various ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that could obscure this disclosure. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely to serve as the basis and representative basis for the claims to teach those skilled in the art to use this disclosure in a variety of substantially any suitable detailed structures.

[0030] This specification may use the phrases “in one embodiment,” “in another embodiment,” “in yet another embodiment,” or “in still another embodiment,” all of which may refer to one or more of the same or different embodiments according to this disclosure.

[0031] This disclosure provides a point cloud projection method for vehicle-mounted LiDAR, also known as a vehicle-mounted 3D laser scanner, which is a mobile 3D laser scanning system. The vehicle-mounted 3D laser scanner utilizes the high propagation speed and good linearity of laser light to emit laser light and receive the returned information, thereby describing the surface morphology of the measured object. Considering the different reflectivities of the measured object, the received returned information varies in strength. The "3D" aspect refers to using the horizontal rotation feature of the vehicle-mounted 3D laser scanner to scan and cover an entire area to obtain raw point cloud data, which is then processed through semantic segmentation to ultimately form a 3D model. The object of semantic segmentation is the point cloud data obtained by scanning based on point cloud projection.

[0032] like Figure 1 As shown, in the process of semantic segmentation of point cloud data, a preprocessing operation of point cloud projection is required. Point cloud projection here refers to transforming the original point cloud image acquired by the vehicle-mounted LiDAR through, for example, spherical projection transformation to obtain a feature map. This feature map includes, for example, the x, y, z coordinates of the scanned points, and may also include a point cloud density map and a point cloud range map. The focus of this embodiment is on obtaining a point cloud range map from the original point cloud data.

[0033] In the process of semantic segmentation of point cloud data, since existing technologies mostly use mechanical LiDAR, the preprocessing method for point cloud spherical projection is generally calculated according to the following formula:

[0034]

[0035]

[0036] Where θ and φ represent the pitch angle and yaw angle, respectively, as shown below. Figure 1 As shown; Δθ and Δφ are the discretization resolutions. This indicates the position of a point on a two-dimensional spherical grid.

[0037] However, considering that current smart cars are equipped with vehicle-mounted LiDAR, which is quite different from mechanical LiDAR and has different application scenarios, using the above-mentioned point cloud projection preprocessing method will lead to a series of problems, such as time consumption, resource consumption, data integrity and utilization. The embodiments disclosed in this disclosure can solve the above problems on this basis.

[0038] This disclosure provides a point cloud projection method for vehicle-mounted LiDAR, applicable to vehicle-mounted LiDAR, and particularly suitable for acquiring point cloud extent maps based on point cloud data through point cloud projection. Current vehicle-mounted LiDAR employs repetitive scanning, generally including single-field-of-view and multi-field-of-view stitching methods. This disclosure enables preprocessing of point cloud projection. Its principle is based on the original prior characteristics of the vehicle-mounted LiDAR and the characteristics of repetitive scanning, namely, the relative position and scanning order of each point in the point cloud data are determined. By incorporating this positional information into the point cloud projection preprocessing, it eliminates the need to calculate θ and φ using the aforementioned formulas, and instead directly uses the relative order and position between points to quickly obtain the point cloud extent map. This reduces time and resource consumption, and improves data integrity and utilization, such as Figure 2 As shown, it includes the following steps:

[0039] S101, a front projection diagram for projection is set based on a predetermined size.

[0040] In this step, a forward projection map for projection is set based on a predetermined size. Here, for example, the size of the forward projection map for point cloud projection can be set to a predetermined size based on the prior characteristics, data acquisition range, and display size requirements of different vehicle-mounted LiDARs used on the vehicle. The forward projection map here can refer to the aforementioned point cloud range map.

[0041] Furthermore, the predetermined size here can be, for example, a format of 128×600, where the vertical position range is 0-127 and the horizontal position range is 0-599. In this way, the point cloud data acquired within different fields of view can be projected onto the forward-looking projection map using the vehicle-mounted LiDAR.

[0042] S102, based on the predetermined size, set the position value of the point to be scanned in the two-dimensional spherical grid according to the predetermined scanning method.

[0043] After setting the forward projection map for projection based on a predetermined size in step S101, this step sets the position value of the point to be scanned in the two-dimensional spherical grid according to the predetermined scanning size and a predetermined scanning method. The predetermined scanning method here is related to the prior characteristics of different vehicle-mounted LiDARs used on the vehicle. These prior characteristics differ for different vehicle-mounted LiDARs. For example, for a multi-field-of-view stitched vehicle-mounted LiDAR, the prior characteristics include at least the number of fields of view and the order between fields of view. For instance, the field of view of the vehicle-mounted LiDAR can be divided into multiple sequentially connected fields of view based on the prior characteristics. These multiple fields of view are independent of each other, and the point to be scanned in each of the multiple fields of view needs to be mapped onto a forward projection map with a predetermined size. For example, in this embodiment, the field of view of the vehicle-mounted LiDAR can be divided into 5 sequentially connected fields of view, which correspond to a predetermined size format of 128×600.

[0044] Specifically, based on different fields of view and the relative order between fields of view, the position value of the point to be scanned is set according to a predetermined scanning method. This position value can be determined by... This indicates the position of each point on a two-dimensional spherical grid. Multiple fields of view can be scanned according to a predetermined scanning method. This predetermined scanning method includes at least the scanning order between fields of view, the scanning order within each field of view, and the scanning interval within each field of view. These methods can be determined according to actual needs. For example, scanning can be performed sequentially from the first field of view to the fifth field of view, with independent and synchronous scanning within each field of view. Specifically, scanning is performed in a Z-shaped pattern from top to bottom and from left to right, with a scanning interval equivalent to 120 points horizontally and 125 lines vertically. The position value of each point to be scanned is set based on the above predetermined scanning method.

[0045] Specifically, in this embodiment, the forward projection image is set to a predetermined size of 128×600 as an example. The value is an integer in the range [0, 127]. The value is an integer in the range [0, 599].

[0046] Among them, such as Figure 4 As shown, the position values ​​can be set for different fields of view as follows:

[0047] a. First field of view The value is an integer in the range [0, 127]. The values ​​are integers within the range [0, 119], and each point is assigned a value in sequence as (0, 0)...(0, 119)(1, 119)...(1, 0)(2, 0)...(2, 119)...(126, 0)...(126, 119)(127, 119)...(127, 0);

[0048] b. Second field of view The value is an integer in the range [0, 127]. The values ​​are integers within the range [120, 239], and each point is assigned the value (0, 120)...(0, 239)(1, 239)...(1, 120)(2, 120)...(2, 239),,,(126, 120)...(126, 239)(127, 239)...(127, 120) in sequence;

[0049] c. Third field of view The value is an integer in the range [0, 127]. The values ​​are integers within the range [240, 359], and each point is assigned the value (0, 240)...(0, 359)(1, 359)...(1, 240)(2, 240)...(2, 359),,,(126, 240)...(126, 359)(127, 359)...(127, 240) in sequence;

[0050] d. Fourth field of view The value is an integer in the range [0, 127]. The values ​​are integers within the range [360, 479], and each point is assigned the value (0, 360)...(0, 479)(1, 479)...(1, 360)(2, 360)...(2, 479),,,(126, 360)...(126, 479)(127, 479)...(127, 360) in sequence;

[0051] e. Fifth field of view The value is an integer in the range [0, 127]. The values ​​are integers within the range [480, 599], and each point is assigned a value in the following order: (0, 480)...(0, 599)(1, 599)...(1, 480)(2, 480)...(2, 599),,,(126, 480)...(126, 599)(127, 599)...(127, 480).

[0052] In this way, the position values ​​of the points to be scanned in multiple fields of view are set in the above manner to facilitate subsequent scanning.

[0053] S103, the field of view is scanned based on the position value of the point to be scanned, and the point cloud spherical projection is realized on the forward projection map.

[0054] After setting the position values ​​of the points to be scanned according to the predetermined size and scanning method in step S102, this step involves scanning the field of view based on the position values ​​of the points to be scanned, and projecting the point cloud onto the forward projection map. Specifically, projection is performed based on the position values ​​set for each point to be scanned in the point cloud data on the forward projection map.

[0055] As shown in Figures 3(a) and 3(b), Figure 3(a) is a point cloud range feature map after point cloud spherical projection processing in the prior art, and Figure 3(b) is a point cloud range feature map after point cloud spherical projection preprocessing operation according to the present disclosure. In Figure 3(a), the white area is the effective projection data pixel area, the black dots in the white area represent the overlapping data of spherical projection caused by point cloud disturbance, and the black area is the invalid projection pixel area, that is, the area where spherical projection data is lost. It can be clearly seen that Figure 3(b) can quickly complete the projection and ensure the integrity of the point cloud data by fusing the point cloud repetitive scanning of the vehicle-mounted LiDAR of the present disclosure embodiment.

[0056] This embodiment of the present disclosure achieves rapid regularization projection of high-frequency point clouds by fusing prior features of point cloud data collected by vehicle-mounted LiDAR, thereby reducing the time and resource consumption of point cloud data preprocessing and improving data integrity and utilization.

[0057] The embodiments disclosed herein are particularly applicable to the preprocessing stage of vehicle-mounted LiDAR perception algorithms, thereby reducing the processing time and resource consumption of the entire intelligent assisted driving system, reducing the data loss rate in the point cloud data projection stage, and improving data integrity.

[0058] A second embodiment of this disclosure provides a point cloud projection device for vehicle-mounted LiDAR, comprising a first setting module, a second setting module, and a projection module coupled together, wherein:

[0059] The first setting module is used to set a front projection image for projection based on a predetermined size;

[0060] The second setting module is used to set the position value of the point to be scanned in the two-dimensional spherical grid according to the predetermined size and a predetermined scanning method;

[0061] The projection module is used to scan the field of view based on the position value of the point to be scanned, and to realize the point cloud spherical projection on the forward projection map.

[0062] Furthermore, the predetermined scanning method is determined based on the prior characteristics of the vehicle-mounted lidar.

[0063] Furthermore, the predetermined scanning method includes at least the scanning order between fields of view, the scanning order within each field of view, and the scanning interval within each field of view.

[0064] Furthermore, the scanning order within each field of view is from top to bottom and from left to right.

[0065] Furthermore, the scanning interval within each field of view is equivalent to 120 points horizontally and 125 lines vertically.

[0066] Furthermore, the position value is obtained through... The numbers represent the pitch angle and yaw angle of each of the points to be scanned.

[0067] Furthermore, the predetermined size is 128×600.

[0068] This embodiment of the present disclosure achieves rapid regularization projection of high-frequency point clouds by fusing prior features of point cloud data collected by vehicle-mounted LiDAR, thereby reducing the time and resource consumption of point cloud data preprocessing and improving data integrity and utilization.

[0069] The embodiments disclosed herein are particularly applicable to the preprocessing stage of vehicle-mounted LiDAR perception algorithms, thereby reducing the processing time and resource consumption of the entire intelligent assisted driving system, reducing the data loss rate in the point cloud data projection stage, and improving data integrity.

[0070] The third embodiment of this disclosure provides a storage medium, which is a computer-readable medium storing a computer program. When executed by a processor, the computer program implements the method provided in the first embodiment of this disclosure, including the following steps S11 to S13:

[0071] S11, Set the front projection diagram for projection based on a predetermined size;

[0072] S12, Based on the predetermined size, set the position value of the point to be scanned in the two-dimensional spherical grid according to the predetermined scanning method;

[0073] S13, the field of view is scanned based on the position value of the point to be scanned, and the point cloud spherical projection is realized on the forward projection map.

[0074] Furthermore, when the computer program is executed by the processor, it implements other methods provided in the first embodiment of this disclosure.

[0075] This embodiment of the present disclosure achieves rapid regularization projection of high-frequency point clouds by fusing prior features of point cloud data collected by vehicle-mounted LiDAR, thereby reducing the time and resource consumption of point cloud data preprocessing and improving data integrity and utilization.

[0076] The embodiments disclosed herein are particularly applicable to the preprocessing stage of vehicle-mounted LiDAR perception algorithms, thereby reducing the processing time and resource consumption of the entire intelligent assisted driving system, reducing the data loss rate in the point cloud data projection stage, and improving data integrity.

[0077] A fourth embodiment of this disclosure provides an electronic device, which includes at least a memory and a processor. The memory stores a computer program, and the processor, when executing the computer program in the memory, implements the method provided in any embodiment of this disclosure. Exemplarily, the computer program steps of the electronic device are as follows: S21 to S23:

[0078] S21, Set the front projection diagram for projection based on a predetermined size;

[0079] S22, Based on the predetermined size, set the position value of the point to be scanned in the two-dimensional spherical grid according to the predetermined scanning method;

[0080] S23, the field of view is scanned based on the position value of the point to be scanned, and the point cloud spherical projection is realized on the forward projection map.

[0081] Furthermore, the computer program may also be other programs provided in the third embodiment of this disclosure.

[0082] This embodiment of the present disclosure achieves rapid regularization projection of high-frequency point clouds by fusing prior features of point cloud data collected by vehicle-mounted LiDAR, thereby reducing the time and resource consumption of point cloud data preprocessing and improving data integrity and utilization.

[0083] The embodiments disclosed herein are particularly applicable to the preprocessing stage of vehicle-mounted LiDAR perception algorithms, thereby reducing the processing time and resource consumption of the entire intelligent assisted driving system, reducing the data loss rate in the point cloud data projection stage, and improving data integrity.

[0084] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0085] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0086] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0087] In the embodiments provided in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0088] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0089] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0090] If the integrated module is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-described embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various motor torque control method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0091] Furthermore, the features of the embodiments shown in the accompanying drawings or the various embodiments mentioned in this specification should not be construed as independent embodiments. Rather, each feature described in one example of an embodiment can be combined with one or more other desired features from other embodiments to produce other embodiments not described in words or with reference to the accompanying drawings.

[0092] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A point cloud projection method for vehicle-mounted laser radar, characterized in that, The method comprises: setting a front-view projection map for projection based on a predetermined size; setting a position value of a to-be-scanned point in a two-dimensional spherical grid according to a predetermined scanning mode based on the predetermined size; scanning a field of view based on the position value of the to-be-scanned point, and realizing point cloud spherical projection on the front-view projection map.

2. The point cloud projection method of claim 1, wherein, The predetermined scanning mode is determined based on prior characteristics of the vehicle-mounted laser radar.

3. The point cloud projection method of claim 2, wherein, The predetermined scanning mode at least comprises a scanning sequence between fields of view, a scanning sequence within each field of view, and a scanning interval within each field of view.

4. The point cloud projection method of claim 3, wherein, The scanning sequence within each field of view is from top to bottom and from left to right.

5. The point cloud projection method of claim 3, wherein, The scanning interval within each field of view is equivalent to 120 points horizontally and 125 lines vertically.

6. The method of claim 1, wherein, The position value is represented by an elevation angle and a yaw angle of each to-be-scanned point.

7. The method of claim 1, wherein, The predetermined size is 128x600.

8. A point cloud projection device for a vehicular lidar, comprising: The method comprises: a first setting module for setting a front-view projection map for projection based on a predetermined size; a second setting module for setting a position value of a to-be-scanned point in a two-dimensional spherical grid according to a predetermined scanning mode based on the predetermined size; a projection module for scanning a field of view based on the position value of the to-be-scanned point, and realizing point cloud spherical projection on the front-view projection map.

9. A storage medium storing a computer program, characterized by The computer program, when executed by a processor, implements the steps of the method of any one of claims 1 to 7.

10. An electronic device comprising at least a memory, a processor, said memory having stored thereon a computer program, characterized in that, The processor, when executing the computer program on the memory, implements the steps of the method of any one of claims 1 to 7.

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