3D camera calibration method, point cloud image acquisition method and camera calibration system

Abstract

This disclosure provides a 3D camera calibration method, a point cloud image acquisition method, and a camera calibration system, relating to the field of industrial camera technology. The 3D camera calibration method includes: acquiring the measured position coordinates of multiple points on a calibration plate in the camera coordinate system; determining the initial pose of the 3D camera in the translation stage coordinate system; and determining a compensation matrix for the 3D camera based on the measured position coordinates of the multiple points in the camera coordinate system and the initial pose of the 3D camera in the translation stage coordinate system. This disclosure can determine a compensation matrix specific to the camera itself, thereby enabling more accurate acquisition of the position coordinates of surface points of a target object in the camera coordinate system based on the compensation matrix, and ultimately obtaining a more accurate 3D point cloud image.

Description

Technical Field

[0001] This disclosure relates to the field of industrial camera technology, and in particular to a 3D camera calibration method, a point cloud image acquisition method, and a camera calibration system. Background Technology

[0002] In recent years, machine vision technology has been increasingly widely used. For example, machine vision technology can be used to identify objects to be detected. Before identifying an object, it is necessary to first acquire a three-dimensional (3D) point cloud image of the object, and then identify the object based on the 3D point cloud image.

[0003] In related technologies, the 3D point cloud image of the object to be detected is obtained in the following way: the position coordinates of multiple points on the surface of the object to be detected in the camera coordinate system are acquired by a 3D camera with fixed parameters, and a 3D point cloud image of the object to be detected is generated based on the position coordinates.

[0004] However, the position coordinates of multiple points on the surface of the object to be detected acquired by the above method in the camera coordinate system are not accurate enough, which leads to low accuracy of the generated 3D point cloud image. Summary of the Invention

[0005] This disclosure provides a 3D camera calibration method, a point cloud image acquisition method, and a camera calibration system to solve the problem that the position coordinates of multiple points on the surface of the object to be detected acquired by related technologies are not accurate enough in the camera coordinate system, which leads to the low accuracy of the generated 3D point cloud image.

[0006] Firstly, this disclosure provides a 3D camera calibration method, including:

[0007] Obtain the measured position coordinates of multiple points on the calibration plate in the camera coordinate system;

[0008] Determine the initial pose of the 3D camera in the coordinate system of the translation stage;

[0009] Based on the measured position coordinates of multiple locations in the camera coordinate system and the initial pose of the 3D camera in the displacement stage coordinate system, the compensation matrix for the 3D camera is determined.

[0010] Secondly, this disclosure provides a method for acquiring point cloud images, including:

[0011] Obtain the position coordinates of the target object's surface point in the camera coordinate system;

[0012] The position coordinates are compensated according to the compensation matrix to obtain the compensated position coordinates. The compensation matrix is ​​determined based on the measured position coordinates of multiple positions of the calibration plate in the camera coordinate system and the initial pose of the 3D camera in the displacement stage coordinate system.

[0013] Based on the compensated position coordinates, a point cloud image corresponding to the target object is generated.

[0014] Thirdly, this disclosure provides a camera calibration system, including a 3D camera and a displacement stage;

[0015] A 3D camera is used to acquire the measured position coordinates of multiple points on the calibration plate in the camera coordinate system; determine the initial pose of the 3D camera in the translation stage coordinate system; and determine the compensation matrix for the 3D camera based on the measured position coordinates of multiple points in the camera coordinate system and the initial pose of the 3D camera in the translation stage coordinate system.

[0016] A displacement stage is used to move a calibration plate via a base.

[0017] Fourthly, this disclosure provides a 3D camera calibration device, comprising:

[0018] The acquisition module is used to acquire the measured position coordinates of multiple position points on the calibration board in the camera coordinate system;

[0019] The first determining module is used to determine the initial pose of the 3D camera in the translation stage coordinate system;

[0020] The second determining module is used to determine the compensation matrix for the 3D camera based on the measured position coordinates of multiple position points in the camera coordinate system and the initial pose of the 3D camera in the displacement stage coordinate system.

[0021] Fifthly, this disclosure provides a point cloud image acquisition device, comprising:

[0022] The acquisition module is used to acquire the position coordinates of the surface position points of the target object in the camera coordinate system;

[0023] The processing module is used to compensate the position coordinates according to the compensation matrix to obtain the compensated position coordinates. The compensation matrix is ​​determined based on the measured position coordinates of multiple position points of the calibration plate in the camera coordinate system and the initial pose of the 3D camera in the displacement stage coordinate system.

[0024] The generation module is used to generate a point cloud image corresponding to the target object based on the compensated position coordinates.

[0025] In a sixth aspect, this disclosure provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0026] The memory stores instructions that the computer executes;

[0027] The processor executes computer execution instructions stored in memory to implement the 3D camera calibration method as described in the first aspect of this disclosure or the point cloud image acquisition method as described in the second aspect of this disclosure.

[0028] In a seventh aspect, this disclosure provides a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the 3D camera calibration method as described in the first aspect of this disclosure or the point cloud image acquisition method as described in the second aspect of this disclosure.

[0029] Eighthly, this disclosure provides a computer program product, including a computer program that, when executed by a processor, implements the 3D camera calibration method as described in the first aspect of this disclosure or the point cloud image acquisition method as described in the second aspect of this disclosure.

[0030] The 3D camera calibration method, point cloud image acquisition method, and camera calibration system disclosed herein acquire the measured position coordinates of multiple points on a calibration plate in the camera coordinate system; determine the initial pose of the 3D camera in the translation stage coordinate system; and determine a compensation matrix for the 3D camera based on the measured position coordinates of the multiple points in the camera coordinate system and the initial pose of the 3D camera in the translation stage coordinate system. Since this disclosure uses a translation stage when determining the compensation matrix for the 3D camera, and the translation stage is a high-precision device, errors caused by the translation stage itself can be disregarded. Therefore, a compensation matrix for the 3D camera itself can be determined, thereby improving the accuracy of the 3D camera. Specifically, the position coordinates of the surface points of the target object in the camera coordinate system can be obtained more accurately based on the compensation matrix, resulting in a more accurate 3D point cloud image. Furthermore, since the position coordinates of the surface points of the target object in the camera coordinate system can be compensated using the compensation matrix, the accuracy requirements for the initial position coordinates of the surface points of the target object captured by the 3D camera in the camera coordinate system can be reduced, thus lowering the accuracy requirements for the 3D camera. Attached Figure Description

[0031] 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 some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a schematic diagram illustrating an application scenario of the 3D camera calibration method provided in an embodiment of this disclosure;

[0033] Figure 2 A flowchart illustrating a 3D camera calibration method provided in an embodiment of this disclosure;

[0034] Figure 3A flowchart illustrating a 3D camera calibration method provided in another embodiment of this disclosure;

[0035] Figure 4 A schematic diagram illustrating the principle of spatial region segmentation provided in an embodiment of this disclosure;

[0036] Figure 5 A flowchart for point cloud image acquisition provided in an embodiment of this disclosure;

[0037] Figure 6 This is a schematic diagram of the structure of a 3D camera calibration device provided in an embodiment of the present disclosure;

[0038] Figure 7 This is a schematic diagram of the structure of a point cloud image acquisition device provided in an embodiment of the present disclosure;

[0039] Figure 8 This is a schematic diagram of a camera calibration system provided in an embodiment of the present disclosure;

[0040] Figure 9 This is a schematic diagram of an electronic device structure provided in this disclosure. Detailed Implementation

[0041] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0042] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data and other information involved in the technical solutions disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0043] First, some of the technical terms used in this disclosure will be explained:

[0044] In camera parameters, image measurement processes, and machine vision applications, to determine the relationship between the three-dimensional geometric position of a point on the surface of a spatial object and its corresponding point in the image, a geometric model of camera imaging must be established. These geometric model parameters are called camera parameters; camera parameters include the camera's intrinsic and extrinsic parameters.

[0045] Camera calibration is the process of determining the geometric model parameters (camera parameters) for camera imaging.

[0046] Calibration plates can be used to correct lens distortion in applications such as machine vision, image measurement, photogrammetry, and 3D reconstruction.

[0047] In some application scenarios, when a 3D camera is used to acquire the position coordinates of multiple points on the surface of an object in the camera coordinate system, the intrinsic and extrinsic parameters of the 3D camera will directly affect the accuracy of the position coordinates of the multiple points on the surface of the object in the camera coordinate system.

[0048] In related technologies, multiple location points in a 3D point cloud image are obtained using the same camera intrinsic parameters. However, after calibrating the 3D camera, the universality of location points at different depths from the 3D camera on the object's surface varies—meaning the intrinsic parameters are not applicable to all location points in the entire space. Therefore, 3D point cloud images obtained using the same camera intrinsic parameters will have different accuracy within and outside a preset range, resulting in lower precision. Thus, determining the accurate position coordinates of multiple location points on an object's surface in the camera coordinate system is the technical problem this disclosure aims to solve.

[0049] To address the aforementioned issues, this disclosure provides a 3D camera calibration method, a point cloud image acquisition method, and a camera calibration system. By determining a compensation matrix based on the measured position coordinates of multiple points on the calibration plate in the camera coordinate system and the initial pose of the 3D camera in the displacement stage coordinate system, the position coordinates of the surface points of the target object in the camera coordinate system are compensated. This allows for more accurate acquisition of the position coordinates of the surface points of the target object in the camera coordinate system, thereby obtaining a more precise 3D point cloud image.

[0050] The following section provides examples illustrating the application scenarios of the solutions provided in this disclosure.

[0051] Figure 1 This is a schematic diagram illustrating an application scenario of the 3D camera calibration method provided in an embodiment of this disclosure. For example... Figure 1 As shown, in this application scenario, the calibration plate is fixed to the front end of the displacement stage via a base. The displacement stage can move the calibration plate to change its position via the base, while the 3D camera is fixed at a preset position. There are various ways to position the 3D camera in practice. For example, the 3D camera can be fixed to a support outside the displacement stage or to the displacement stage itself. This embodiment uses the example of the 3D camera being fixed to a support outside the displacement stage. Specifically, the displacement stage moves the calibration plate to multiple spatial positions via the base. The 3D camera acquires multiple images of the calibration plate at different spatial positions, multiple first poses of the base in the displacement stage coordinate system, and multiple second poses of the calibration plate in the camera coordinate system. Based on these multiple images, multiple first poses, and multiple second poses, the 3D camera determines a compensation matrix for itself.

[0052] It should be noted that during the process of the displacement stage moving the calibration plate to multiple spatial positions via the base, the calibration plate can be moved along the z-axis of the camera coordinate system, or moved in the plane formed by the x-axis and y-axis of the camera coordinate system, or rotated along the x-axis or y-axis, etc. Here, the origin of the camera coordinate system is the optical center of the 3D camera, the x-axis and y-axis of the camera coordinate system are parallel to the x-axis and y-axis of the calibration plate image, respectively, and the z-axis of the camera coordinate system is the optical axis of the 3D camera.

[0053] Furthermore, the embodiments disclosed herein can be applied to scenarios involving point cloud image acquisition.

[0054] It should be noted that, Figure 1 This is merely a schematic diagram illustrating one application scenario provided by an embodiment of this disclosure; this embodiment does not necessarily represent an application scenario. Figure 1 The included equipment is not limited, nor is it restricted. Figure 1 The positional relationships between the devices are defined.

[0055] The technical solutions of this disclosure will now be described in detail through specific embodiments. It should be noted that the following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0056] Figure 2 This is a flowchart illustrating a 3D camera calibration method provided in an embodiment of this disclosure. Figure 2 As shown, the method in this embodiment includes:

[0057] S201. Obtain the measured position coordinates of multiple positions on the calibration plate in the camera coordinate system.

[0058] For example, refer to Figure 1 When the displacement stage moves the calibration plate to a certain spatial position through the base, the calibration plate image can be directly captured by a 3D camera. Then, based on the calibration plate image, the measurement position coordinates of multiple position points of the calibration plate in the camera coordinate system can be obtained.

[0059] S202. Determine the initial pose of the 3D camera in the coordinate system of the displacement stage.

[0060] In this step, for example, refer to Figure 1 The pose of the base station in the translation stage coordinate system and the pose of the position point on the calibration plate in the camera coordinate system can be acquired multiple times to determine the initial pose of the 3D camera in the translation stage coordinate system. For details on how to determine the initial pose of the 3D camera in the translation stage coordinate system, please refer to subsequent embodiments; details will not be elaborated here.

[0061] S203. Based on the measured position coordinates of multiple location points in the camera coordinate system and the initial pose of the 3D camera in the displacement stage coordinate system, determine the compensation matrix for the 3D camera.

[0062] In this step, after obtaining the measured position coordinates of multiple points on the calibration board in the camera coordinate system and the initial pose of the 3D camera in the translation stage coordinate system, a compensation matrix for the 3D camera can be determined based on these coordinates. The compensation matrix includes compensation for the intrinsic parameters of the 3D camera, which affect the accuracy of the position coordinates of the points acquired by the 3D camera. The compensation matrix can compensate for errors caused by deviations in the 3D camera's intrinsic parameters. For example, the multiple points on the calibration board can be divided into multiple groups of points in the camera coordinate system according to different spatial regions. The compensation matrix for the 3D camera is then determined based on the measured position coordinates of each group of points in the camera coordinate system and the initial pose of the 3D camera in the translation stage coordinate system. By dividing the points in the camera coordinate system according to spatial regions, different spatial depths and regions can have corresponding compensation matrices, thereby improving the accuracy of the position coordinates in the camera coordinate system. For details on how to determine the compensation matrix for the 3D camera, please refer to the subsequent embodiments, which will not be repeated here.

[0063] After determining the compensation matrix for the 3D camera, the position coordinates of the points in the camera coordinate system can be compensated according to the compensation matrix to obtain more accurate position coordinates, thereby obtaining a point cloud image with higher precision.

[0064] The 3D camera calibration method provided in this disclosure involves: acquiring the measured position coordinates of multiple points on a calibration plate in the camera coordinate system; determining the initial pose of the 3D camera in the translation stage coordinate system; and determining a compensation matrix for the 3D camera based on the measured position coordinates of the multiple points in the camera coordinate system and the initial pose of the 3D camera in the translation stage coordinate system. Since this disclosure uses a translation stage to determine the compensation matrix for the 3D camera, and the translation stage is a high-precision device, errors caused by the translation stage itself can be disregarded. Therefore, a compensation matrix for the 3D camera itself can be determined, thereby improving the accuracy of the 3D camera. This means that the position coordinates of the surface points of the target object in the camera coordinate system can be obtained more accurately based on the compensation matrix, resulting in a more accurate 3D point cloud image. Furthermore, since the position coordinates of the surface points of the target object in the camera coordinate system can be compensated using the compensation matrix, the accuracy requirement for the initial position coordinates of the surface points of the target object captured by the 3D camera in the camera coordinate system can be reduced, thus lowering the accuracy requirements for the 3D camera.

[0065] Figure 3 This is a flowchart illustrating a 3D camera calibration method according to another embodiment of this disclosure. Based on the above embodiments, this disclosure further describes the 3D camera calibration method. Figure 3 As shown, the method in this embodiment of the disclosure may include:

[0066] In this embodiment of the disclosure, Figure 2 Step S201 can further include the following two steps: S301 and S302:

[0067] S301. In response to the calibration board moving to multiple spatial positions, acquire multiple calibration board images.

[0068] For example, the displacement stage moves the calibration plate via the base to adjust its position, allowing the calibration plate to be moved to multiple spatial locations. It is understood that since the intrinsic parameters of a 3D camera have varying universality across the entire spatial region, the calibration plate can be controlled to change positions at multiple locations during adjustment, enabling the 3D camera to acquire the measurement coordinates of multiple points at different spatial locations in the camera coordinate system. In this step, in response to the calibration plate moving to multiple spatial locations, the 3D camera can directly capture multiple images of the calibration plate.

[0069] S302. Based on multiple calibration plate images, obtain the measured position coordinates of multiple position points of the calibration plate in the camera coordinate system.

[0070] In this step, after obtaining multiple calibration plate images, the measurement position coordinates of multiple position points of the calibration plate in the camera coordinate system can be obtained based on the multiple calibration plate images.

[0071] In this embodiment of the disclosure, Figure 2 Step S202 can further include the following two steps, S303 and S304:

[0072] S303. During the process of the displacement stage moving the calibration plate through the base, the first pose of multiple bases in the displacement stage coordinate system and the second pose of multiple calibration plates in the camera coordinate system are obtained.

[0073] In this step, the base is, for example, an axis. It can be understood that when the translation stage moves the calibration plate to multiple spatial positions via the base, the first pose of the base in the translation stage coordinate system and the second pose of the calibration plate in the camera coordinate system can be obtained each time the calibration plate moves to a certain spatial position. The first pose of the base in the translation stage coordinate system is a known quantity, which can be measured and determined by sensors on the translation stage; the second pose of the calibration plate in the camera coordinate system can be acquired by a 3D camera.

[0074] S304. Based on any set of first and second poses, determine the initial pose of the 3D camera in the translation stage coordinate system.

[0075] For example, after obtaining the first pose of multiple pedestals in the translation stage coordinate system and the second pose of multiple calibration plates in the camera coordinate system, any one of the first and second poses can be selected, and the initial pose of the 3D camera in the translation stage coordinate system can be obtained according to the following formula:

[0076]

[0077] in, This represents the initial pose of the 3D camera in the translation stage coordinate system; This indicates the pose of the platform in the displacement stage coordinate system; This indicates the pose of the calibration plate relative to the base. When acquiring the initial pose of the 3D camera in the translation stage coordinate system, As a known quantity, use The initial value; This indicates the pose of the calibration board relative to the camera coordinate system.

[0078] In this embodiment of the disclosure, Figure 2 Step S203 can further include the following two steps, S305 and S306:

[0079] S305. In the camera coordinate system, the multiple position points of the calibration plate are divided into multiple groups of position points according to the different spatial regions.

[0080] It is understandable that dividing position points according to spatial regions in the camera coordinate system allows for the creation of corresponding compensation matrices for different spatial depths and regions, thereby improving the accuracy of position point coordinates in the camera coordinate system. For example, multiple position points can be divided in the camera coordinate system according to different spatial divisions. Specifically, they can be divided into multiple layers based on height, and then each layer can be further divided into multiple partitions according to a defined region, resulting in multiple sets of position points.

[0081] Further, optionally, in the camera coordinate system, multiple location points are divided into multiple groups of location points according to different spatial regions, including: dividing the space into multiple layers according to different heights in the camera coordinate system, with each layer divided into multiple partitions; and dividing location points in the same partition within the same layer into a group of location points according to the position coordinates of multiple location points in the camera coordinate system.

[0082] For example, Figure 4 This is a schematic diagram illustrating the principle of spatial region segmentation provided in an embodiment of the present disclosure, as shown below. Figure 4 As shown, in the camera coordinate system according to the spatial region (i.e. Figure 4In the region represented by 401, different segmentation regions are used to divide multiple location points. Specifically, the region can be divided into multiple layers according to height, and then each layer can be divided into multiple partitions according to the set region to obtain multiple sets of location points.

[0083] Optionally, the pose of the calibration plate relative to the base can be determined based on the initial pose of the 3D camera in the translation stage coordinate system, the measured position coordinates of the position points in the camera coordinate system, and the pose of the base in the translation stage coordinate system. Furthermore, based on the position coordinates of each position point on the calibration plate in the calibration plate coordinate system, the pose of the calibration plate relative to the base, and the pose of the base in the translation stage coordinate system, the theoretical position coordinates of multiple position points on the calibration plate in the camera coordinate system can be determined. in, This represents a matrix consisting of the theoretical position coordinates of multiple points on the calibration plate in the camera coordinate system, i.e., the theoretical coordinates corresponding to n points. It can be represented as:

[0084] S306. Based on the measured position coordinates of each group of position points in the camera coordinate system and the initial pose of the 3D camera in the displacement stage coordinate system, determine the compensation matrix for the 3D camera.

[0085] In this step, after dividing the multiple position points of the calibration plate into multiple groups of position points, the compensation matrix for the 3D camera can be determined based on the measured position coordinates of each group of position points in the camera coordinate system and the initial pose of the 3D camera in the displacement stage coordinate system.

[0086] Further, optionally, based on the measured position coordinates and initial pose of each group of position points in the camera coordinate system, a compensation matrix for the 3D camera is determined, including: determining the initial theoretical position coordinates of each group of position points in the camera coordinate system based on the initial pose of the 3D camera in the translation stage coordinate system; fitting the measured position coordinates and initial theoretical position coordinates of each group of position points in the camera coordinate system to determine the initial compensation matrix; obtaining the adjusted poses of multiple 3D cameras in the translation stage coordinate system and determining the adjusted theoretical position coordinates of multiple groups of position points in the 3D camera; adjusting the initial compensation matrix based on the current poses returned after adjustment of multiple 3D cameras in the translation stage coordinate system, the measured position coordinates of multiple groups of position points in the camera coordinate system, and the adjusted current position coordinates of the 3D camera, until the Euclidean distance between the measured position coordinates and the current theoretical position coordinates of each group of position points in the camera coordinate system is less than a preset threshold and / or the adjustment reaches a preset number of times, and determining the compensation matrix as the current compensation matrix.

[0087] For example, based on the above embodiments, there are multiple implementation schemes for determining the initial theoretical position coordinates of each set of position points in the camera coordinate system according to the initial pose of the 3D camera in the displacement stage coordinate system. For example, the position points in the calibration plate can be imported into the camera coordinate system by finding an intermediate medium between each set of position points and the camera coordinate system, thereby determining the initial theoretical position coordinates of each set of position points in the camera coordinate system. Determining the initial theoretical position coordinates of each set of position points in the camera coordinate system based on the initial pose of the 3D camera in the translation stage coordinate system can further include: obtaining the pose of the base in the translation stage coordinate system and the pose of the calibration plate in the camera coordinate system corresponding to each set of position points; determining the pose of the calibration plate relative to the base based on the initial pose of the 3D camera in the translation stage coordinate system, the pose of the base in the translation stage coordinate system, and the pose of the calibration plate in the camera coordinate system; determining the position coordinates of each set of position points in the translation stage coordinate system based on the position coordinates of each set of position points in the calibration plate coordinate system, the pose of the calibration plate relative to the base, and the pose of the base in the translation stage coordinate system; and determining the initial theoretical position coordinates of each set of position points in the camera coordinate system based on the position coordinates of each set of position points in the translation stage coordinate system and the initial pose.

[0088] In this embodiment, after determining the initial theoretical position coordinates of each group of position points in the camera coordinate system, the measured position coordinates and initial theoretical position coordinates of each group of position points in the camera coordinate system can be fitted to determine the initial compensation matrix for the 3D camera. There are multiple implementation schemes for this, for example, the least squares method can be used for fitting. Specifically, the measured position coordinates and initial theoretical position coordinates of each group of position points in the camera coordinate system can be fitted according to the following Formula 2 to determine the initial compensation matrix for the 3D camera:

[0089]

[0090] in, This indicates the error in the camera's position coordinates in the displacement stage coordinate system; Indicates the theoretical position coordinates; M represents the coordinates of the measurement location, which can be obtained directly by a 3D camera; M represents the compensation matrix.

[0091] In practice, based on the measured position coordinates, theoretical position coordinates, and Formula 2 above, the pose of the 3D camera in the displacement stage coordinate system is adjusted multiple times. The compensation matrix M for the 3D camera is obtained when the Euclidean distance between the measured coordinates and the current theoretical coordinates of each set of positions in the camera coordinate system is less than a preset threshold and / or the adjustment reaches a preset number of times. The compensation matrix contains the compensation amount for each position region in each layer.

[0092] The 3D camera calibration method provided in this disclosure involves: acquiring multiple calibration plate images in response to the calibration plate moving to multiple spatial positions; acquiring the measured position coordinates of multiple position points of the calibration plate in the camera coordinate system based on the multiple calibration plate images; acquiring the measured position coordinates of multiple position points of the calibration plate in the camera coordinate system based on the multiple calibration plate images; acquiring the first pose of multiple bases in the translation stage coordinate system and the second pose of multiple calibration plates in the camera coordinate system during the movement of the calibration plate driven by the base via the displacement stage; determining the initial pose of the 3D camera in the translation stage coordinate system based on any set of first and second poses; dividing the multiple position points of the calibration plate into multiple sets of position points in the camera coordinate system according to different spatial regions; and determining a compensation matrix for the 3D camera based on the measured position coordinates of each set of position points in the camera coordinate system and the initial pose of the 3D camera in the translation stage coordinate system. Since this embodiment uses a displacement stage to determine the compensation matrix for the 3D camera, and the displacement stage is a high-precision device, the error caused by the displacement stage itself does not need to be considered. Therefore, a compensation matrix for the 3D camera itself can be determined to improve the accuracy of the 3D camera. In particular, dividing the multiple position points of the calibration plate according to the spatial region in the camera coordinate system can make different spatial depths and regions have corresponding compensation matrices, which can effectively improve the accuracy of the position point coordinates in the camera coordinate system. Thus, the position coordinates of the surface position points of the target object in the camera coordinate system can be obtained more accurately according to the compensation matrix, thereby obtaining a more accurate three-dimensional point cloud image.

[0093] Based on the above embodiments, Figure 5 This is a flowchart illustrating point cloud image acquisition according to an embodiment of the present disclosure. Figure 5 As shown, the method in this embodiment includes:

[0094] S501. Obtain the position coordinates of the surface position point of the target object in the camera coordinate system.

[0095] For example, an image of the target object can be directly captured by a 3D camera, and then the position coordinates of the surface position points of the target object in the camera coordinate system can be obtained based on the image of the target object.

[0096] S502. The position coordinates are compensated according to the compensation matrix to obtain the compensated position coordinates. The compensation matrix is ​​determined based on the measured position coordinates of multiple positions of the calibration plate in the camera coordinate system and the initial pose of the 3D camera in the displacement stage coordinate system.

[0097] For example, the position coordinates can be compensated according to the following formula three:

[0098] M·A=A′ Formula 3

[0099] Where M represents the compensation matrix; A represents the position coordinates of the target object's surface point in the camera coordinate system; and A′ represents the position coordinates of the target object's surface point in the camera coordinate system after compensation. Furthermore, A′ can be normalized to obtain the compensated position coordinates.

[0100] Further, optionally, the position coordinates are compensated according to the compensation matrix to obtain the compensated position coordinates, including: determining the layer and partition where the surface position point is located based on the position coordinates of the surface position point in the camera coordinate system and the intrinsic parameters of the 3D camera; and compensating the position coordinates of the surface position point in the camera coordinate system according to the compensation matrix corresponding to the layer and partition where the surface position point is located to obtain the compensated position coordinates.

[0101] For example, the z-axis, x-axis, and y-axis coordinates of the surface location points of the target object in the camera coordinate system can be extracted respectively to determine the layer and zone in which the surface location points of the target object are located. Further, optionally, determining the layer and zone in which the surface location points are located based on the position coordinates of the surface location points of the target object in the camera coordinate system and the intrinsic parameters of the 3D camera may include: determining the layer in which the surface location points are located based on the z-axis coordinates of the surface location points in the camera coordinate system; determining the pixel coordinates corresponding to the surface location points based on the x-axis and y-axis coordinates of the surface location points in the camera coordinate system and the intrinsic parameters of the 3D camera; and determining the zone in which the surface location points are located based on the pixel coordinates.

[0102] Since different layers and partitions correspond to different compensation matrices, the position coordinates of the surface position point in the camera coordinate system can be compensated according to the compensation matrix corresponding to the layer and partition where the surface position point is located, using Formula 3 above, to obtain the compensated position coordinates.

[0103] Since the surface position points of the target object may actually lie between different layers or partitions, and not entirely within any arbitrary layer or partition, in order to improve the compensation accuracy of surface position points that cross layers or partitions, the compensation of the position coordinates of the target object's surface position points in the camera coordinate system can further include the following: when the surface position points of the target object are across layers and / or partitions, determine the weight of each layer and / or partition across based on the distance between the surface position point and the layer and / or partition across; compensate the position coordinates of the surface position points in the camera coordinate system according to the compensation matrix of each layer and / or partition across, to obtain multiple compensated position coordinates of the surface position points; and perform a weighted summation of the multiple compensated position coordinates of the surface position points according to the weight of each layer and / or partition across, to obtain the compensated position coordinates as the weighted summation compensated position coordinates.

[0104] For example, when surface location points belong to different partitions within the same layer, the compensation results for each surface location point are weighted and summed according to the distances between the surface location point and each partition, based on the weights corresponding to these distances, to obtain the weighted summed compensated location coordinates for that surface location point. When surface location points belong to different layers, the compensation results are weighted and summed according to the distances between the surface location point and each layer, based on the weights corresponding to these distances, to obtain the weighted summed compensated location coordinates for that surface location point. When surface location points belong to both different layers and different partitions, the compensation results are weighted and summed according to both the distances between the surface location point and each layer and each partition, to obtain the weighted summed compensated location coordinates for that surface location point. Therefore, by weighting and summing the compensation results for surface location points located in boundary regions, the accuracy of the obtained coordinates of the boundary surface location points can be improved.

[0105] S503. Generate a point cloud image corresponding to the target object based on the compensated position coordinates.

[0106] In this step, after obtaining the compensated position coordinates, a more accurate point cloud image of the target object can be generated based on the compensated position coordinates.

[0107] The point cloud image acquisition method provided in this disclosure acquires the position coordinates of surface points of a target object in the camera coordinate system; compensates the position coordinates according to a compensation matrix to obtain compensated position coordinates. The compensation matrix is ​​determined based on the measured position coordinates of multiple points of the calibration plate in the camera coordinate system and the initial pose of the 3D camera in the translation stage coordinate system; and generates a point cloud image corresponding to the target object based on the compensated position coordinates. Since this disclosure embodiment compensates the position coordinates according to the compensation matrix, which is determined based on the measured position coordinates of multiple points of the calibration plate in the camera coordinate system and the initial pose of the 3D camera in the translation stage coordinate system, and since the translation stage is a high-precision device, errors caused by the translation stage itself do not need to be considered. Therefore, a compensation matrix specific to the 3D camera itself can be determined to improve the accuracy of the 3D camera. This means that the position coordinates of the surface points of the target object in the camera coordinate system can be obtained more accurately based on the compensation matrix, resulting in a more accurate 3D point cloud image.

[0108] Based on the above embodiments, optionally, the compensation matrix in the above point cloud image acquisition method is obtained by the 3D camera calibration method as described in any of the above method embodiments.

[0109] The following are embodiments of the apparatus disclosed herein, which can be used to execute embodiments of the method disclosed herein. For details not disclosed in the apparatus embodiments of this disclosure, please refer to the embodiments of the method disclosed herein.

[0110] Figure 6 This is a schematic diagram of the structure of a 3D camera calibration device provided in an embodiment of the present disclosure, as shown below. Figure 6 As shown, the 3D camera calibration device 600 of this embodiment includes: an acquisition module 601, a first determination module 602, and a second determination module 603. Wherein:

[0111] The acquisition module 601 is used to acquire the measured position coordinates of multiple position points of the calibration plate in the camera coordinate system.

[0112] The first determining module 602 is used to determine the initial pose of the 3D camera in the displacement stage coordinate system.

[0113] The second determining module 603 is used to determine the compensation matrix for the 3D camera based on the measured position coordinates of multiple position points in the camera coordinate system and the initial pose of the 3D camera in the displacement stage coordinate system.

[0114] In some embodiments, the second determining module 603 may be specifically used to: divide multiple location points into multiple groups of location points according to different spatial regions in the camera coordinate system; and determine a compensation matrix for the 3D camera based on the measured position coordinates and initial pose of each group of location points in the camera coordinate system.

[0115] Optionally, when the second determining module 603 is used to divide multiple location points into multiple groups of location points according to different spatial regions in the camera coordinate system, it can be specifically used to: divide the space into multiple layers according to different heights in the camera coordinate system, and each layer is divided into multiple partitions; and divide the location points in the same partition within the same layer into a group of location points according to the position coordinates of multiple location points in the camera coordinate system.

[0116] Optionally, when the second determining module 603 determines the compensation matrix for the 3D camera based on the measured position coordinates and initial pose of each group of position points in the camera coordinate system, it can specifically be used to: determine the initial theoretical position coordinates of each group of position points in the camera coordinate system based on the initial pose; fit the measured position coordinates and initial theoretical position coordinates of each group of position points in the camera coordinate system to determine the initial compensation matrix; obtain the adjusted poses of multiple 3D cameras in the translation stage coordinate system and determine the adjusted theoretical position coordinates of multiple groups of position points in the 3D camera; adjust the initial compensation matrix based on the current pose returned by multiple 3D cameras after adjustment in the translation stage coordinate system, the measured position coordinates of multiple groups of position points in the camera coordinate system, and the adjusted current position coordinates of the 3D camera, until the Euclidean distance between the measured position coordinates and the current theoretical position coordinates of each group of position points in the camera coordinate system is less than a preset threshold and / or the adjustment reaches a preset number of times, and determine the compensation matrix as the current compensation matrix.

[0117] Optionally, when the second determining module 603 is used to determine the initial theoretical position coordinates of each group of position points in the camera coordinate system based on the initial pose, it can specifically be used to: obtain the pose of the base in the displacement stage coordinate system and the pose of the calibration plate in the camera coordinate system corresponding to each group of position points; determine the pose of the calibration plate relative to the base based on the initial pose of the 3D camera in the displacement stage coordinate system, the pose of the base in the displacement stage coordinate system, and the pose of the calibration plate in the camera coordinate system; determine the position coordinates of each group of position points in the displacement stage coordinate system based on the position coordinates of each group of position points in the calibration plate coordinate system, the pose of the calibration plate relative to the base, and the pose of the base in the displacement stage coordinate system; and determine the initial theoretical position coordinates of each group of position points in the camera coordinate system based on the position coordinates of each group of position points in the displacement stage coordinate system and the initial pose.

[0118] In some embodiments, the acquisition module 601 may be specifically used to: acquire multiple calibration board images in response to the calibration board moving to multiple spatial positions; and acquire the measurement position coordinates of multiple position points in the camera coordinate system based on the multiple calibration board images.

[0119] In some embodiments, the first determining module 602 may be specifically used to: during the process of the displacement stage driving the calibration plate to move through the base, acquire the first pose of multiple bases in the displacement stage coordinate system and the second pose of multiple calibration plates in the camera coordinate system; and determine the initial pose of the 3D camera in the displacement stage coordinate system based on any set of first poses and second poses.

[0120] The apparatus of this embodiment can be used to execute the technical solution of the 3D camera calibration method in any of the above method embodiments. Its implementation principle and technical effect are similar, and will not be described again here.

[0121] Figure 7 This is a schematic diagram of the structure of a point cloud image acquisition device provided in an embodiment of the present disclosure, as shown below. Figure 7 As shown, the point cloud image acquisition device 700 of this embodiment includes: an acquisition module 701, a processing module 702, and a generation module 703. Wherein:

[0122] The acquisition module 701 is used to acquire the position coordinates of the surface position points of the target object in the camera coordinate system.

[0123] The processing module 702 is used to perform compensation processing on the position coordinates according to the compensation matrix to obtain the compensated position coordinates. The compensation matrix is ​​determined based on the measured position coordinates of multiple position points of the calibration plate in the camera coordinate system and the initial pose of the 3D camera in the displacement stage coordinate system.

[0124] The generation module 703 is used to generate a point cloud image corresponding to the target object based on the compensated position coordinates.

[0125] Optionally, the compensation matrix is ​​obtained through the 3D camera calibration method in any of the above method embodiments.

[0126] In some embodiments, the processing module 702 may be specifically used to: determine the layer and partition where the surface position point is located based on the position coordinates of the surface position point of the target object in the camera coordinate system and the intrinsic parameters of the 3D camera; and perform compensation processing on the position coordinates of the surface position point in the camera coordinate system based on the compensation matrix corresponding to the layer and partition where the surface position point is located, to obtain the compensated position coordinates.

[0127] Optionally, when the processing module 702 determines the layer and partition where the surface position point is located based on the position coordinates of the surface position point of the target object in the camera coordinate system and the intrinsic parameters of the 3D camera, it can specifically be used to: determine the layer where the surface position point is located based on the z-axis coordinate of the surface position point in the camera coordinate system; determine the pixel coordinates corresponding to the surface position point based on the x-axis and y-axis coordinates of the surface position point in the camera coordinate system and the intrinsic parameters of the 3D camera; and determine the partition where the surface position point is located based on the pixel coordinates.

[0128] Optionally, when processing module 702 performs compensation processing on the position coordinates of the surface position point in the camera coordinate system according to the compensation matrix corresponding to the layer and partition where the surface position point is located, to obtain the compensated position coordinates, it can specifically perform the following: when the surface position point is in a cross-layer and / or cross-partition situation, determine the weight of each cross-layer and / or partition according to the distance between the surface position point and the cross-layer and / or partition; perform compensation processing on the position coordinates of the surface position point in the camera coordinate system according to the compensation matrix of each cross-layer and / or partition, to obtain multiple compensated position coordinates of the surface position point; and perform weighted summation on the multiple compensated position coordinates of the surface position point according to the weight of each cross-layer and / or partition to obtain the compensated position coordinates after weighted summation.

[0129] The apparatus of this embodiment can be used to execute the technical solution of the point cloud image acquisition method in any of the above method embodiments. Its implementation principle and technical effect are similar, and will not be described again here.

[0130] Based on the above embodiments, Figure 8 This is a schematic diagram of a camera calibration system provided in an embodiment of the present disclosure, as shown below. Figure 8 As shown, the camera calibration system 800 of this embodiment includes a 3D camera 801 and a displacement stage 802.

[0131] The 3D camera 801 is used to acquire the measured position coordinates of multiple positions of the calibration plate in the camera coordinate system; determine the initial pose of the 3D camera in the displacement stage coordinate system; and determine the compensation matrix for the 3D camera based on the measured position coordinates of multiple positions in the camera coordinate system and the initial pose of the 3D camera in the displacement stage coordinate system.

[0132] The displacement stage 802 is used to move the calibration plate via the base.

[0133] Optionally, the 3D camera 801 can be used to execute the 3D camera calibration method as described in any of the above method embodiments, and correspondingly, it can employ... Figure 6 The structure of the device embodiment is similar in principle and technical effect, and will not be described again here.

[0134] Figure 9 This is a schematic diagram of an electronic device structure provided in this disclosure. Figure 9 As shown, the electronic device 900 may include at least one processor 901 and a memory 902.

[0135] The memory 902 is used to store programs. Specifically, the program may include program code, which includes computer-executable instructions.

[0136] The memory 902 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.

[0137] The processor 901 executes computer execution instructions stored in the memory 902 to implement the 3D camera calibration method or point cloud image acquisition method described in the foregoing method embodiments. The processor 901 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this disclosure. Specifically, when implementing the 3D camera calibration method or point cloud image acquisition method described in the foregoing method embodiments, the electronic device may be, for example, a 3D camera or other electronic device with processing capabilities.

[0138] Optionally, the electronic device 900 may also include a communication interface 903. In specific implementations, if the communication interface 903, memory 902, and processor 901 are implemented independently, they can be interconnected via a bus to complete communication. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc., but this does not imply that there is only one bus or one type of bus.

[0139] Optionally, in a specific implementation, if the communication interface 903, memory 902, and processor 901 are integrated on a single chip, then the communication interface 903, memory 902, and processor 901 can communicate through an internal interface.

[0140] This disclosure also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described 3D camera calibration method or point cloud image acquisition method.

[0141] This disclosure also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described 3D camera calibration method or point cloud image acquisition method.

[0142] The aforementioned computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0143] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Alternatively, the readable storage medium can be an integral part of the processor. The processor and the readable storage medium can reside in an application-specific integrated circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in a 3D camera calibration device or a point cloud image acquisition device.

[0144] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit them. Although this disclosure 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 or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this disclosure.

Claims

1. A 3D camera calibration method, characterized in that, include: Obtain the measured position coordinates of multiple points on the calibration plate in the camera coordinate system; Determine the initial pose of the 3D camera in the coordinate system of the translation stage; In the camera coordinate system, the space is divided into multiple layers according to different heights, and each layer is further divided into multiple partitions. Based on the position coordinates of the multiple position points in the camera coordinate system, the position points in the same layer and the same partition are divided into a group of position points; Based on the measured position coordinates of each group of position points in the camera coordinate system and the initial pose, a compensation matrix for the 3D camera is determined; wherein, each group of position points corresponds to a different compensation matrix, and each group of position points corresponds to a different layer and partition.

2. The 3D camera calibration method according to claim 1, characterized in that, The step of determining the compensation matrix for the 3D camera based on the measured position coordinates of each set of position points in the camera coordinate system and the initial pose includes: Determine the initial theoretical position coordinates of each group of position points in the camera coordinate system based on the initial pose. The measured position coordinates and initial theoretical position coordinates of each group of position points in the camera coordinate system are fitted to determine the initial compensation matrix; The poses of multiple 3D cameras after adjustment in the coordinate system of the displacement stage are obtained, and the theoretical position coordinates of multiple sets of position points after the adjustment of the 3D cameras are determined. Based on the current pose returned by the multiple 3D cameras after adjustment in the coordinate system of the displacement stage, the measured position coordinates of each group of position points in the camera coordinate system, and the current position coordinates after adjustment by the 3D cameras, the initial compensation matrix is ​​adjusted until the Euclidean distance between the measured position coordinates and the current theoretical position coordinates of each group of position points in the camera coordinate system is less than a preset threshold and / or the adjustment reaches a preset number of times, and the compensation matrix is ​​determined as the current compensation matrix.

3. The 3D camera calibration method according to claim 2, characterized in that, Determining the initial theoretical position coordinates of each group of position points in the camera coordinate system based on the initial pose includes: Obtain the pose of the base corresponding to each set of position points in the displacement stage coordinate system and the pose of the calibration plate in the camera coordinate system; Based on the initial pose of the 3D camera in the coordinate system of the displacement stage, the pose of the base in the coordinate system of the displacement stage, and the pose of the calibration plate in the coordinate system of the camera, the pose of the calibration plate relative to the base is determined. Based on the position coordinates of each group of position points in the calibration plate coordinate system, the pose of the calibration plate relative to the base, and the pose of the base in the displacement stage coordinate system, determine the position coordinates of each group of position points in the displacement stage coordinate system. Based on the position coordinates of each group of position points in the displacement stage coordinate system and the initial pose, determine the initial theoretical position coordinates of each group of position points in the camera coordinate system.

4. The 3D camera calibration method according to any one of claims 1 to 3, characterized in that, The process of obtaining the measured position coordinates of multiple position points of the calibration board in the camera coordinate system includes: In response to the calibration board moving to multiple spatial positions, multiple calibration board images are acquired; Based on the multiple calibration plate images, the measured position coordinates of the multiple location points in the camera coordinate system are obtained.

5. The 3D camera calibration method according to any one of claims 1 to 3, characterized in that, Determining the initial pose of the 3D camera in the translation stage coordinate system includes: During the process of the displacement stage moving the calibration plate through the base, the first pose of multiple bases in the displacement stage coordinate system and the second pose of multiple calibration plates in the camera coordinate system are acquired. The initial pose of the 3D camera in the displacement stage coordinate system is determined based on any one set of the first pose and the second pose.

6. A method for acquiring point cloud images, characterized in that, include: Obtain the position coordinates of the target object's surface point in the camera coordinate system; The position coordinates are compensated according to the compensation matrix to obtain the compensated position coordinates. The compensation matrix is ​​obtained by the 3D camera calibration method as described in any one of claims 1 to 5. Based on the compensated position coordinates, a point cloud image corresponding to the target object is generated.

7. The point cloud image acquisition method according to claim 6, characterized in that, The step of compensating the position coordinates according to the compensation matrix to obtain the compensated position coordinates includes: Based on the position coordinates of the surface location points of the target object in the camera coordinate system and the intrinsic parameters of the 3D camera, determine the layer and partition where the surface location points are located; Based on the compensation matrix corresponding to the layer and partition where the surface location point is located, the position coordinates of the surface location point in the camera coordinate system are compensated to obtain the compensated position coordinates.

8. The point cloud image acquisition method according to claim 7, characterized in that, The step of determining the layer and partition where the surface position point is located based on the position coordinates of the surface position point of the target object in the camera coordinate system and the intrinsic parameters of the 3D camera includes: The layer in which the surface location point is located is determined based on the z-axis coordinate of the surface location point in the camera coordinate system; The pixel coordinates corresponding to the surface position point are determined based on the x-axis and y-axis coordinates of the surface position point in the camera coordinate system and the intrinsic parameters of the 3D camera. Based on the pixel coordinates, the partition in which the surface location point is located is determined.

9. The point cloud image acquisition method according to claim 7, characterized in that, The step of compensating the position coordinates of the surface location point in the camera coordinate system according to the compensation matrix corresponding to the layer and partition where the surface location point is located, to obtain the compensated position coordinates, includes: When the surface location point is across layers and / or across partitions, the weight of each layer and / or partition is determined based on the distance between the surface location point and the layer and / or partition it is across; The position coordinates of the surface position points in the camera coordinate system are compensated according to the compensation matrix of each layer and / or partition, so as to obtain multiple compensated position coordinates of the surface position points. The multiple compensated position coordinates of the surface position points are weighted and summed according to the weights of each layer and / or partition, and the compensated position coordinates are obtained by weighted summation.

10. A camera calibration system, characterized in that, Includes 3D cameras and displacement stages; The 3D camera is used to acquire the measured position coordinates of multiple points on the calibration plate in the camera coordinate system; and to determine the initial pose of the 3D camera in the displacement stage coordinate system. In the camera coordinate system, the space is divided into multiple layers according to different heights, and each layer is further divided into multiple partitions; based on the position coordinates of the multiple position points in the camera coordinate system, position points in the same partition within the same layer are grouped into a set of position points; Based on the measured position coordinates of each group of position points in the camera coordinate system and the initial pose, a compensation matrix for the 3D camera is determined; wherein, each group of position points corresponds to a different compensation matrix, and each group of position points corresponds to a different layer and partition. The displacement stage is used to move the calibration plate via the base.

11. The camera calibration system according to claim 10, characterized in that, The 3D camera is used to perform the 3D camera calibration method as described in any one of claims 1 to 5.

12. A 3D camera calibration device, characterized in that, include: The acquisition module is used to acquire the measured position coordinates of multiple position points on the calibration board in the camera coordinate system; The first determining module is used to determine the initial pose of the 3D camera in the translation stage coordinate system; The second determining module is used to divide the space into multiple layers according to different heights in the camera coordinate system, and each layer is divided into multiple partitions. Based on the position coordinates of the multiple position points in the camera coordinate system, the position points in the same layer and the same partition are divided into a group of position points; Based on the measured position coordinates of each group of position points in the camera coordinate system and the initial pose, a compensation matrix for the 3D camera is determined; wherein, each group of position points corresponds to a different compensation matrix, and each group of position points corresponds to a different layer and partition.

13. A point cloud image acquisition device, characterized in that, include: The acquisition module is used to acquire the position coordinates of the surface position points of the target object in the camera coordinate system; The processing module is used to perform compensation processing on the position coordinates according to the compensation matrix to obtain the compensated position coordinates, wherein the compensation matrix is ​​obtained by the 3D camera calibration method as described in any one of claims 1 to 5; The generation module is used to generate a point cloud image corresponding to the target object based on the compensated position coordinates.

14. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in any one of claims 1 to 9.

15. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the method as described in any one of claims 1 to 9.

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