Radar point cloud acquisition method and device based on guide rail with scales

Abstract

The invention provides a radar point cloud acquisition method and device based on a guide rail with scales, and the method comprises the steps: enabling a radar to be connected to the guide rail with the scales through a fixed connection device, determining the relative position relation between the radar and the guide rail, sequentially collecting point cloud data at different scale positions, and fusing the multiple groups of point cloud data into a global coordinate system, and a complete point cloud set is formed. Scale position information and external reference calibration are output in real time through the guide rail, accurate alignment and efficient fusion of point cloud data are achieved, the problems of data distortion and spatial drift caused by radar movement are avoided, the integrity and collection precision of the point cloud data are improved, the radar sensing range and the point cloud data density are effectively improved, cost is reduced, and the method is suitable for popularization and application. Meanwhile, for some working face scenes, the radar can be automatically controlled to collect data around the working face back and forth, and the data precision and quality are improved.

Description

Technical Field

[0001] The present application relates to the technical field of radar data acquisition, and in particular to a radar point cloud acquisition method and device based on a graduated guide rail. Background Art

[0002] Existing radar equipment usually has a small scanning field of view. If a larger field of view coverage is required, the traditional approach is usually to use multiple radars for stitching.

[0003] However, this approach has many problems, such as the complexity and high cost of external parameter calibration between multiple radars, the difficulty in avoiding stitching errors, and the greatly increased complexity of the system. In addition, due to the asynchrony of multiple radar devices in space and time, overlapping errors or holes may occur when fusion of point cloud data, reducing the integrity and accuracy of the point cloud. Summary of the invention

[0004] The present application aims to solve one of the technical problems in the related art at least to some extent.

[0005] To this end, the first purpose of the present application is to propose a radar point cloud acquisition method based on a graduated guide rail.

[0006] The second objective of the present application is to propose a radar point cloud acquisition device based on a graduated guide rail.

[0007] The third objective of the present application is to provide an electronic device.

[0008] A fourth objective of the present application is to provide a computer-readable storage medium.

[0009] A fifth object of the present application is to provide a computer program product.

[0010] To achieve the above-mentioned purpose, the first embodiment of the present application proposes a radar point cloud acquisition method based on a graduated guide rail, comprising:

[0011] S1. Connect the radar to the guide rail through a fixing device, wherein the fixing device can move along the guide rail, and the guide rail can output the scale position information of the fixing device in real time;

[0012] S2. Determine the relative position relationship between the guide rail and the radar through external parameter calibration, wherein the calibration result includes the angle between the guide rail and the horizontal plane of the radar;

[0013] S3, collecting point cloud data at the initial scale position on the guide rail by the radar, recording the collected point cloud data as a first set of point cloud data, and using the current radar coordinate system as a global coordinate system;

[0014] S4, moving the fastening device along the guide rail for a certain scale, collecting point cloud data by the radar, and recording the collected point cloud data as a second set of point cloud data;

[0015] S5, projecting the second set of point cloud data into a global coordinate system corresponding to the first set of point cloud data, and adjusting the coordinates of each point in the second set of point cloud data;

[0016] S6. Repeat steps S4 and S5 to collect and fuse point cloud data at multiple scale positions to form a complete point cloud set.

[0017] Optionally, the relative position relationship between the guide rail and the radar is determined by external parameter calibration, and the calibration result includes an angle between the guide rail and the horizontal plane of the radar, including:

[0018] A vertical plane is arranged in front of the radar as a calibration target;

[0019] Adjusting the radar so that the radar horizontal plane is perpendicular to the calibration target plane;

[0020] Move the radar to the minimum position and the maximum position of the rail scale respectively, and record the distances d0 and d1 from the radar to the calibration target and the moving distance s of the rail respectively;

[0021] The angle between the guide rail and the radar horizontal plane is calculated according to the following formula:

[0022]

[0023] Wherein, α is the angle between the guide rail and the radar horizontal plane.

[0024] Optionally, adjusting the radar so that the radar horizontal plane is perpendicular to the calibration target plane includes:

[0025] For single-line radar, by collecting calibration target cloud data and fitting a straight line, the radar angle is adjusted until the straight line fitting parameters meet the vertical condition;

[0026] For multi-line or three-dimensional radars, by collecting calibration target cloud data and fitting the plane, the radar angle is adjusted until the plane fitting parameters meet the vertical conditions.

[0027] Optionally, projecting the second set of point cloud data into a global coordinate system corresponding to the first set of point cloud data to adjust the coordinates of each point in the second set of point cloud data includes:

[0028] Adjust the y coordinate of each point in the second set of point cloud data to y new =yL*cosα;

[0029] Adjust the z coordinate of each point in the second set of point cloud data to z new =z+L*sinα.

[0030] To achieve the above-mentioned purpose, the second embodiment of the present application proposes a radar point cloud acquisition device based on a graduated guide rail, comprising:

[0031] Radar, used to collect point cloud data;

[0032] A fixing device, used to fix the radar on the guide rail and enable it to move along the guide rail;

[0033] A guide rail, which is a linear guide rail with a scale, supports the movement of the fixing device and outputs the scale position information of the fixing device in real time;

[0034] A processing unit is connected to the radar and the guide rail, and is used to process point cloud data, including external parameter calibration, coordinate conversion and point cloud fusion.

[0035] Optionally, the processing unit is a consumer-grade computer, an industrial control computer or other terminal equipment supporting data processing.

[0036] Optionally, the radar is a single-line radar, a multi-line radar, or a radar device capable of collecting three-dimensional point cloud data.

[0037] To achieve the above-mentioned purpose, the third aspect of the present application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0038] The memory stores computer-executable instructions;

[0039] The processor executes the computer-executable instructions stored in the memory to implement the method as described in any one of the first aspects.

[0040] To achieve the above-mentioned purpose, the fourth aspect embodiment of the present application proposes a computer-readable storage medium, in which computer-readable storage medium is stored computer execution instructions, and when the computer execution instructions are executed by a processor, they are used to implement the method as described in any one of the first aspects.

[0041] To achieve the above-mentioned purpose, the fifth aspect of the present application proposes a computer program product, which implements any method in the first aspect when executed by a processor.

[0042] The technical solution provided by the embodiments of the present application brings at least the following beneficial effects:

[0043] By moving a single radar along a scaled guide rail to collect point cloud data, and using external parameter calibration and data fusion methods to accurately align data from multiple collection positions to generate a complete point cloud, this method effectively avoids the complexity and errors caused by multi-radar stitching, while reducing equipment costs. It provides a new idea for achieving large field of view and high-precision point cloud acquisition, and can effectively improve the radar perception range and point cloud data density, reducing costs. At the same time, for certain work surface scenarios, the radar can be automatically controlled to collect data back and forth near the work surface to improve data accuracy and quality.

[0044] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0046] Figure 1 A schematic diagram of a flow chart of a radar point cloud acquisition method based on a graduated guide rail provided in an embodiment of the present application;

[0047] Figure 2 A schematic structural diagram of a radar point cloud acquisition device based on a graduated guide rail provided in an embodiment of the present application. DETAILED DESCRIPTION

[0048] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0049] In response to the problems existing in the prior art, the present application proposes a radar point cloud acquisition method and device based on a graduated guide rail. Point cloud data is collected by moving a single radar along a graduated guide rail, and external parameter calibration and data fusion methods are used to accurately align data from multiple acquisition positions to generate a complete point cloud. This method effectively avoids the complexity and errors caused by multi-radar stitching, while reducing equipment costs, providing a new idea for achieving large field of view and high-precision point cloud acquisition.

[0050] Figure 1 A schematic diagram of a flow chart of a radar point cloud acquisition method based on a graduated guide rail provided in an embodiment of the present application, which is applied to Figure 2 A radar point cloud acquisition device based on a graduated guide rail is shown in the embodiment.

[0051] like Figure 2 As shown, the device includes a radar, a fixing device, a guide rail and a processing unit. During design, it is necessary to ensure that the radar and the fixing device are fixedly connected, the Y-axis of the radar and the forward axis of the fixing device are consistent, and the plane formed by the Z-axis of the fixing device and the movement direction of the guide rail just intersects with the Y-axis of the radar.

[0052] Among them, the radar is used to collect point cloud data; the fixing device is used to fix the radar on the guide rail and can move along the guide rail; the guide rail is a linear guide rail with a scale, which supports the movement of the fixing device and outputs the scale position information of the fixing device in real time; the processing unit is connected to the radar and the guide rail, and is used to process point cloud data, including external parameter calibration, coordinate transformation and point cloud fusion.

[0053] In addition, in the embodiment of the present application, the processing unit is a consumer-grade computer, an industrial control computer or other terminal device that supports data processing; the radar is a single-line radar, a multi-line radar or a radar device that can collect three-dimensional point cloud data. This application does not make specific restrictions on this.

[0054] The radar point cloud acquisition method based on the graduated guide rail shown in the embodiment of the present application is described in detail below. Figure 1 As shown, the method comprises the following steps:

[0055] S1. Connect the radar to the guide rail through a fixed connection device. The fixed connection device can move along the guide rail, and the guide rail can output the scale position information of the fixed connection device in real time.

[0056] In the embodiment of the present application, as shown in the above-mentioned device, the fixing device is a component used to fix the radar to the guide rail. Through the fixing device, the radar can be firmly attached to the guide rail to ensure the stability of its position and direction during movement.

[0057] In addition, the connecting device is designed to slide or move along the guide rail. This movement allows the radar to collect point cloud data at different positions, providing a basis for subsequent multi-position point cloud fusion.

[0058] There are scale marks on the rails, which can record and output the current position of the fixed device in real time. These scale position information is transmitted to the processing unit to determine the precise position when the radar collects the point cloud, ensuring the relevance and accuracy of the data at different positions.

[0059] Through this step, the controllable movement and position tracking of the radar on the guide rail are realized, which provides the basic conditions for multi-location point cloud data collection, and lays a precise positioning basis for subsequent coordinate transformation and point cloud fusion.

[0060] S2. Determine the relative position relationship between the guide rail and the radar through external parameter calibration. The calibration result includes the angle between the guide rail and the horizontal plane of the radar.

[0061] It should be noted that before collecting radar point cloud data, the relative position relationship between the guide rail and the radar needs to be determined, especially the angle between the guide rail and the radar horizontal plane. This calibration process is to ensure that in the subsequent point cloud data collection, the point cloud data at different positions can be accurately projected into the global coordinate system.

[0062] The specific calibration process is as follows:

[0063] Step 1: Set a vertical plane in front of the radar as a calibration target.

[0064] First, a vertical plane is set in front of the radar as a calibration target to provide a known standard plane to help determine the radar's attitude.

[0065] It should be noted that the calibration target can be a plane substantially perpendicular to the ground, or a wall, and this application does not make any specific limitation on this.

[0066] Step 2: Adjust the radar so that the radar horizontal plane is perpendicular to the calibration target plane.

[0067] First, determine whether the radar horizontal plane is perpendicular to the calibration target. If not, adjust the radar direction. The specific operations are as follows:

[0068] For a single-line radar, a set of one-dimensional point cloud data is obtained on the calibration target, and the straight line parameter ax+by+c=0 is calculated using the straight line fitting method. If a is extremely small, it means that the radar is perpendicular to the scanning line. The radar is controlled to rotate a certain angle along the radar horizontal plane, and the data is re-collected to fit the plane. If the rotated radar is perpendicular to the scanning line, it means that the current radar horizontal plane is perpendicular to the target plane. Otherwise, adjust the radar position until the radar is perpendicular to the horizontal plane.

[0069] For multi-line or three-dimensional radars, plane fitting is performed directly after acquiring point cloud data on the calibration target. The fitting equation is ax+by+cz+d=0. If c is extremely small, it is considered that the current radar horizontal plane is perpendicular to the target plane, and the next step can be started. Otherwise, the radar position can be adjusted until the two are perpendicular.

[0070] Step 3: Move the radar to the minimum and maximum positions of the rail scale, and record the distances d0 and d1 from the radar to the calibration target, as well as the moving distance s of the rail.

[0071] After ensuring that the radar horizontal plane is perpendicular to the target plane, move the radar to the minimum and maximum positions of the rail scale respectively, and record the distances d0 and d1 from the radar to the calibration target and the moving distance s of the rail respectively.

[0072] Step 4: Calculate the angle between the guide rail and the radar horizontal plane using trigonometric relationship based on the above measurement data. The expression is:

[0073]

[0074] Among them, α is the angle between the guide rail and the radar horizontal plane.

[0075] Step 5: After calculating the angle, complete the calibration.

[0076] It should be noted that through external parameter calibration, the relative position relationship between the radar and the guide rail can be accurately measured and corrected to ensure that the point cloud data will not be wrong due to the radar angle deviation when the guide rail moves. This step provides an accurate geometric basis for the subsequent point cloud coordinate conversion and fusion.

[0077] S3. The radar collects point cloud data at the initial scale position on the guide rail, records the collected point cloud data as the first set of point cloud data, and uses the current radar coordinate system as the global coordinate system.

[0078] Before collecting radar point cloud data, you need to select a scale position on the guide rail as the starting point. Usually, the scale position at the far left of the guide rail (marked as 0) is used as the default initial position. Figure 2 The black rectangle on one side of the image marks this starting point.

[0079] In actual operation, the initial acquisition position is not fixed, and data can be collected from any position of the guide rail as needed. However, in the embodiments of the present application, the guide rail scale 0 is used as the starting point for illustration.

[0080] That is, in the embodiment of the present application, point cloud data is collected by radar at the starting scale position, and this set of collected point cloud data is recorded as the first set of point cloud data P0. In addition, the coordinate system where the radar is located is defined as the global coordinate system during the initial collection, which means that all subsequent point cloud data will be aligned and fused based on this coordinate system.

[0081] It should be noted that by using the radar coordinate system at the initial position as the global coordinate system, subsequent point cloud data can be unified into a reference frame, which can avoid the difficulty of aligning multiple sets of data due to inconsistent coordinate systems.

[0082] S4. Move the fastening device along the guide rail by a certain scale, collect point cloud data by radar, and record the collected point cloud data as the second set of point cloud data.

[0083] In the embodiment of the present application, the fixed device is controlled to move along the guide rail to a new scale position. The distance of this movement is calibrated by the scale on the guide rail to ensure that the distance of each movement is known and controllable. The purpose of the movement is to collect data by moving the radar to a new position, gradually cover the target area, and obtain more complete point cloud information.

[0084] Then, at the new scale position, point cloud data is collected again by radar, and this set of data is recorded as the second set of point cloud data P1. Compared with the first set of point cloud data P0 at the initial position, the second set of point cloud data P1 contains environmental information at different positions.

[0085] S5. Project the second set of point cloud data into the global coordinate system corresponding to the first set of point cloud data, and adjust the coordinates of each point in the second set of point cloud data.

[0086] In an embodiment of the present application, the second set of point cloud data is projected into the global coordinate system where the first set of point cloud data is located, so that the two sets of data can be accurately aligned in the same coordinate system.

[0087] In addition, the new position of the point cloud data in the global coordinate system is calculated using the scale distance L of the rail movement and the angle α between the rail and the radar horizontal plane, that is, the y coordinate of each point in the second set of point cloud data is adjusted to y new =yL*cosα, adjust the z coordinate of each point in the second set of point cloud data to z new =z+L*sinα.

[0088] In this coordinate transformation formula, L*cosα is the projection of the guide rail movement in the y direction. After subtracting this value, the y coordinate of the adjustment point is converted to the global coordinate system; L*sinα is the projection of the guide rail movement in the z direction. After adding this value, the z coordinate of the adjustment point is converted to the global coordinate system.

[0089] After the coordinates are adjusted, the data of all acquisition locations can be unified in one coordinate system, thereby eliminating the coordinate deviation caused by radar movement and providing an accurate data basis for generating a complete point cloud model.

[0090] S6. Repeat steps S4 and S5 to collect and fuse point cloud data at multiple scale positions to form a complete point cloud set.

[0091] According to the needs, the fixed device continues to move along the guide rail, and the radar collects new point cloud data at each scale position. These new point cloud data are recorded as corresponding groups (such as the third group, the fourth group of point cloud data, etc.). And each time a new group of point cloud data is collected, it is projected into the global coordinate system according to the method of S5 and fused with the previously collected data.

[0092] Finally, through repeated acquisition and fusion, all scale positions within the guide rail motion range are gradually covered, and the point cloud data of multiple scale positions within the guide rail range are spliced ​​together to generate a complete point cloud set covering the target area.

[0093] It can be understood that the fused point cloud set includes data collected from different perspectives and positions, which makes up for the defects caused by the limited field of view of a single radar and realizes high-precision point cloud collection over a large range. In addition, each projection and fusion is performed in the global coordinate system, which can ensure the accuracy of data alignment and reduce stitching errors.

[0094] In order to implement the above embodiments, the present application also proposes an electronic device, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the method provided by the above embodiments.

[0095] In order to implement the above embodiments, the present application also proposes a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the methods provided by the above embodiments.

[0096] In order to implement the above embodiments, the present application also proposes a computer program product, including a computer program, which implements the methods provided by the above embodiments when executed by a processor.

[0097] The collection, storage, use, processing, transmission, provision and disclosure of user personal information involved in this application are in compliance with relevant laws and regulations and do not violate public order and good morals.

[0098] It should be noted that personal information from users should be collected for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. In addition, such collection / sharing should be carried out after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign the agreement / authorization including authorization of relevant user information before the user uses the function. In addition, any necessary steps should be taken to protect and safeguard access to such personal information data and ensure that others who have access to personal information data comply with its privacy policy and procedures.

[0099] The present application is expected to provide an implementation scheme for users to selectively block the use or access of personal information data. That is, the present disclosure is expected to provide hardware and / or software to prevent or block access to such personal information data. Once the personal information data is no longer needed, the risk can be minimized by limiting data collection and deleting the data. In addition, when applicable, such personal information is de-identified to protect the privacy of the user.

[0100] In the description of the aforementioned embodiments, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0101] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0102] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0103] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute the instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0104] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0105] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0106] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0107] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

[0108] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution of this application can be achieved, and this document is not limited here.

[0109] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art 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 principles of this application should be included in the protection scope of this application.

Claims

1. A radar point cloud acquisition method based on a graduated guide rail, characterized in that: The following steps are involved: S1. Connect the radar to the guide rail through a fixing device, wherein the fixing device can move along the guide rail, and the guide rail can output the scale position information of the fixing device in real time; S2. Determine the relative position relationship between the guide rail and the radar through external parameter calibration, wherein the calibration result includes the angle between the guide rail and the horizontal plane of the radar; S3, collecting point cloud data at the initial scale position on the guide rail by the radar, recording the collected point cloud data as a first set of point cloud data, and using the current radar coordinate system as a global coordinate system; S4, moving the fastening device along the guide rail for a certain scale, collecting point cloud data by the radar, and recording the collected point cloud data as a second set of point cloud data; S5, projecting the second set of point cloud data into a global coordinate system corresponding to the first set of point cloud data, and adjusting the coordinates of each point in the second set of point cloud data; S6. Repeat steps S4 and S5 to collect and fuse point cloud data at multiple scale positions to form a complete point cloud set.

2. The method according to claim 1, characterized in that The relative position relationship between the guide rail and the radar is determined by external parameter calibration, and the calibration result includes the angle between the guide rail and the horizontal plane of the radar, including: A vertical plane is arranged in front of the radar as a calibration target; Adjusting the radar so that the radar horizontal plane is perpendicular to the calibration target plane; Move the radar to the minimum position and the maximum position of the rail scale respectively, and record the distances d0 and d1 from the radar to the calibration target and the moving distance s of the rail respectively; The angle between the guide rail and the radar horizontal plane is calculated according to the following formula: Wherein, α is the angle between the guide rail and the radar horizontal plane.

3. The method according to claim 2, characterized in that The step of adjusting the radar so that the radar horizontal plane is perpendicular to the calibration target plane includes: For single-line radar, by collecting calibration target cloud data and fitting a straight line, the radar angle is adjusted until the straight line fitting parameters meet the vertical condition; For multi-line or three-dimensional radars, by collecting calibration target cloud data and fitting the plane, the radar angle is adjusted until the plane fitting parameters meet the vertical conditions.

4. The method according to claim 3, characterized in that The projecting the second set of point cloud data into the global coordinate system corresponding to the first set of point cloud data to adjust the coordinates of each point in the second set of point cloud data includes: Adjust the y coordinate of each point in the second set of point cloud data to y new =yL*cosα; Adjust the z coordinate of each point in the second set of point cloud data to z new =z+L*sinα.

5. A radar point cloud acquisition device based on a graduated guide rail, characterized in that: include: Radar, used to collect point cloud data; A fixing device, used to fix the radar on the guide rail and enable it to move along the guide rail; A guide rail, which is a linear guide rail with a scale, supports the movement of the fixing device and outputs the scale position information of the fixing device in real time; A processing unit is connected to the radar and the guide rail, and is used to process point cloud data, including external parameter calibration, coordinate conversion and point cloud fusion.

6. The device according to claim 5, characterized in that The processing unit is a consumer-grade computer, an industrial control computer or other terminal equipment supporting data processing.

7. The device according to claim 5, characterized in that The radar is a single-line radar, a multi-line radar or a radar device capable of collecting three-dimensional point cloud data.

8. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 4.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 4 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 4 when being executed by a processor.

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