Point cloud acquisition equipment calibration method, equipment and storage medium, program product

Abstract

The present application provides a point cloud acquisition device calibration method, device, storage medium, and program product. In addition to calibrating each sub-point cloud acquisition device through the main point cloud acquisition device, the method also combines point cloud acquisition devices in pairs to obtain multiple point cloud acquisition device combinations, and determines a set of matching point pairs through the registration of point cloud data of two point cloud acquisition devices in each point cloud acquisition device combination. Based on the set of matching point pairs, a joint calibration loss function is constructed, and the conversion parameters of the point cloud acquisition devices in the multiple point cloud acquisition device combinations are optimized using the joint calibration loss function as the objective function, thereby realizing mutual calibration between point cloud acquisition devices. When calibrating, the embodiment of the present application realizes high-precision global calibration of point cloud acquisition devices, improves the accuracy of multi-point cloud acquisition device systems in practical applications, and ensures the accuracy of positioning data.

Description

Technical Field

[0001] The present application belongs to the field of point cloud acquisition equipment calibration, and in particular, relates to a point cloud acquisition equipment calibration method, equipment, storage medium, and program product. Background Art

[0002] In the field of modern large-scale equipment construction, accurate real-time positioning technology is essential to ensure construction efficiency and safety. Point cloud acquisition equipment has become an indispensable positioning tool in the construction process due to its high-precision ranging capability. However, when the construction equipment is large, the coverage limitations of a single point cloud acquisition device are obvious, and it is impossible to achieve accurate monitoring of the entire range of the equipment. For this reason, a solution in which multiple point cloud acquisition devices work together must be adopted to meet the needs of all-round positioning.

[0003] Since each point cloud acquisition device has its own coordinate system, in order to achieve the collaborative operation of multiple point cloud acquisition devices, it is necessary to accurately calibrate each point cloud acquisition device. However, the point cloud acquisition device calibration technology in the current related technology mostly focuses on the calibration of each sub-point cloud acquisition device to the main point cloud acquisition device, while ignoring the necessity of high-precision calibration of multiple point cloud acquisition devices from a global perspective. This limitation leads to the fact that in actual applications, the collaborative efficiency and positioning accuracy of multiple point cloud acquisition devices are often difficult to reach the ideal state, which in turn affects construction efficiency and safety. Summary of the invention

[0004] The embodiments of the present application provide a point cloud acquisition device calibration method, device, storage medium, and program product, which can achieve global calibration of multiple sub-point cloud acquisition devices while calibrating through the main point cloud acquisition device.

[0005] In a first aspect, an embodiment of the present application provides a method for calibrating a point cloud acquisition device, the method comprising:

[0006] Acquire point cloud data measured by each point cloud acquisition device for a target area among a plurality of point cloud acquisition devices; the plurality of point cloud acquisition devices include a main point cloud acquisition device and a plurality of sub-point cloud acquisition devices;

[0007] For each sub-point cloud acquisition device, based on the registration between the point cloud data of the main point cloud acquisition device and the point cloud data of the sub-point cloud acquisition device, determining an initial value of a conversion parameter calibrated by the sub-point cloud acquisition device to the main point cloud acquisition device;

[0008] For each point cloud acquisition device combination in a plurality of point cloud acquisition device combinations, determining a set of matching point pairs between the point cloud data of a first point cloud acquisition device and the point cloud data of a second point cloud acquisition device in the point cloud acquisition device combination based on the registration between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device, wherein the first point cloud acquisition device and the second point cloud acquisition device in the point cloud acquisition device combination are two different point cloud acquisition devices in the plurality of point cloud acquisition devices;

[0009] Constructing a joint calibration loss function based on the conversion parameters of the first point cloud acquisition device and the second point cloud acquisition device in each point cloud acquisition device combination and a set of matching point pairs between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device;

[0010] The conversion parameters of the sub-point cloud acquisition devices in the combination of multiple point cloud acquisition devices are optimized using the joint calibration loss function as the objective function to obtain optimized values ​​of the conversion parameters of the sub-point cloud acquisition devices in the combination of multiple point cloud acquisition devices.

[0011] In a second aspect, an embodiment of the present application provides an electronic device, comprising: a processor and a memory storing computer program instructions, wherein the processor is coupled to the memory; when the processor executes the computer program instructions, the point cloud acquisition device calibration method as described in the first aspect is implemented.

[0012] In a third aspect, an embodiment of the present application provides a computer storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, the point cloud acquisition device calibration method described in the first aspect is implemented.

[0013] In a fourth aspect, an embodiment of the present application provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the point cloud acquisition device calibration method as described in the first aspect.

[0014] The point cloud acquisition device calibration method, device, storage medium, and program product of the embodiment of the present application, in addition to calibrating each sub-point cloud acquisition device based on the main point cloud acquisition device, also combines the point cloud acquisition devices in pairs to obtain multiple point cloud acquisition device combinations, and determines a set of matching point pairs through the registration of the point cloud data of two point cloud acquisition devices in each point cloud acquisition device combination. Based on the set of matching point pairs, a joint calibration loss function is constructed, and the conversion parameters of the point cloud acquisition devices in the multiple point cloud acquisition device combinations are optimized using the joint calibration loss function as the objective function, thereby realizing mutual calibration between the point cloud acquisition devices. Therefore, when performing calibration, the embodiment of the present application not only considers the main point cloud acquisition device, but also fully considers the calibration between each point cloud acquisition device, realizes high-precision global calibration of the point cloud acquisition device, improves the accuracy of the multi-point cloud acquisition device system in practical applications, and ensures the accuracy of the positioning data. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0016] Figure 1 It is a flowchart of a method for calibrating a point cloud acquisition device provided by an embodiment of the present application;

[0017] Figure 2 It is a flowchart of another point cloud acquisition device calibration method provided by an embodiment of the present application;

[0018] Figure 3 It is a structural schematic diagram of a point cloud acquisition device calibration device provided by an embodiment of the present application;

[0019] Figure 4 It is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0020] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0021] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0022] As described in the background technology, the current point cloud acquisition equipment calibration technology in related technologies mostly focuses on the calibration of each sub-point cloud acquisition device to the main point cloud acquisition device, but ignores the necessity of high-precision calibration of the multi-point cloud acquisition device system from a global perspective. This limitation leads to the fact that in actual applications, the collaborative efficiency and positioning accuracy of multi-point cloud acquisition devices are often difficult to reach an ideal state, which in turn affects construction efficiency and safety.

[0023] In order to solve the problems of the prior art, the embodiments of the present application provide a point cloud acquisition device calibration method, device, computer storage medium, computer program product and photovoltaic array semi-finished product installation device. The point cloud acquisition device calibration method provided in the embodiments of the present application is first introduced below.

[0024] Figure 1 FIG. 1 is a flow chart showing a first method for calibrating a point cloud acquisition device provided by an embodiment of the present application. Figure 1 As shown, the method comprises the following steps:

[0025] S101, acquiring point cloud data measured by each point cloud acquisition device for a target area among a plurality of point cloud acquisition devices; the plurality of point cloud acquisition devices include a main point cloud acquisition device and a plurality of sub-point cloud acquisition devices.

[0026] In specific implementation, the multiple point cloud acquisition devices in the embodiments of the present application may be multiple point cloud acquisition devices that collaborate to perform detection or data collection in a certain application scenario. A point cloud acquisition device is a device for collecting point cloud data. In one example, the point cloud acquisition device includes a laser radar, a laser scanner, a depth camera, a three-dimensional scanner and other devices. In one example, the above-mentioned multiple point cloud acquisition devices may be multiple laser radars fixedly installed on large equipment in the project. The point cloud acquisition device calibration is performed to ensure the uniformity of the point cloud data collected by multiple point cloud acquisition devices in terms of the coordinate system, so that the point cloud data of each point cloud acquisition device can be used for subsequent calculations, fusion and other processing.

[0027] The point cloud data of the target area measured by each point cloud acquisition device is obtained. The target area is the area within the measurement range of multiple point cloud acquisition devices at the same time. This allows the point cloud data measured by each point cloud acquisition device for the target area to include the point cloud data of the same measurement object in the target area, ensuring that the registration between the point cloud data can be effectively performed. The specific target area can be set as needed. Optionally, after obtaining the original point cloud data measured by each point cloud acquisition device for the target area, the point cloud data of the same measurement object in the target area can be determined by technical means such as point cloud preprocessing and point cloud segmentation, and then the registration is performed based on the point cloud data of the same measurement object, wherein the point cloud preprocessing includes abnormal point filtering and point cloud downsampling.

[0028] In order to calibrate each point cloud acquisition device, multiple point cloud acquisition devices can be divided into a main point cloud acquisition device and multiple sub-point cloud acquisition devices, wherein the coordinate system of the main point cloud acquisition device is used as the calibration standard, and the coordinate system of each sub-point cloud acquisition device needs to be calibrated to the coordinate system of the main point cloud acquisition device to determine the conversion relationship of the coordinate system. In some embodiments, the main point cloud acquisition device can be determined as needed. In one example, a main point cloud acquisition device can be randomly determined from multiple point cloud acquisition devices, or the main point cloud acquisition device can be determined according to the position of each point cloud acquisition device. For example, the point cloud acquisition device located in the middle position can be determined as the main point cloud acquisition device.

[0029] In one embodiment, a plurality of point cloud acquisition devices may be represented as: , where the first point cloud acquisition device ( ) is determined as the main point cloud acquisition device, and other point cloud acquisition devices are determined as sub-point cloud acquisition devices. n represents the number of multiple point cloud acquisition devices, and the specific value of n can be set as needed and is not limited to this.

[0030] In some embodiments, after acquiring the point cloud data of each point cloud acquisition device in the target area among the multiple point cloud acquisition devices, the point cloud data of each point cloud acquisition device can be preprocessed, and the specific preprocessing process includes but is not limited to abnormal point filtering and point cloud downsampling. In one example, the point cloud data corresponding to the above n point cloud acquisition devices can be expressed as: .

[0031] S102: For each sub-point cloud acquisition device, based on the registration between the point cloud data of the main point cloud acquisition device and the point cloud data of the sub-point cloud acquisition device, determine the initial value of the conversion parameter calibrated from the sub-point cloud acquisition device to the main point cloud acquisition device.

[0032] In specific implementation, when calibrating the point cloud acquisition device, the initial value of the conversion parameter of each sub-point cloud acquisition device to the main point cloud acquisition device can be determined based on the registration between the point cloud data of each sub-point cloud acquisition device and the point cloud data of the main point cloud acquisition device. Point cloud registration is to determine the conversion relationship between two sets of point cloud data to unify them into the same coordinate system. In the embodiment of the present application, the conversion relationship is represented by conversion parameters.

[0033] In some embodiments, the point cloud data registration is performed by performing certain rotation and translation transformations on each point cloud data to unify two or more groups of point cloud data in different coordinate systems into the same reference coordinate system, that is, a rigid transformation without considering deformation factors. In this embodiment, the transformation parameters may include rotation parameters and translation parameters. It is understandable that the registration between point cloud data may consider other forms of transformation, such as affine transformation, perspective transformation, etc., and the transformation parameters may also have other parameter expressions, which are not specifically limited here, and are only explained below using rigid transformation as an example.

[0034] Considering point cloud A and point cloud B, the rigid transformation of the point cloud can be expressed by the following formula:

[0035] ;

[0036] in, , , Respectively represent the rotation angles around the x, y, and z axes, represents the rotation parameter, Represents the translation parameter. When point cloud A and point cloud B are represented by coordinate vectors, It can be in the form of a parameter matrix, This can be in the form of a parameter vector.

[0037] As a non-limiting example, the external parameter matrix To unify the transformation parameters, that is, to characterize the rotation parameters and translation parameters at the same time, the external parameter matrix The specific representation is as follows:

[0038] ;

[0039] Among them, the 3*3 part in the upper left corner of the above matrix represents the rotation parameter , , , Respectively represent the translation parameters in the x, y, and z axes .

[0040] Corresponding to the number of the above-mentioned multiple point cloud acquisition devices, the external parameter matrix corresponding to each point cloud acquisition device can be expressed as: By registering the point cloud data, the initial value of the external parameter matrix corresponding to each sub-point cloud acquisition device can be determined, and the matching point pairs in the two sets of point cloud data can also be determined. Represents the extrinsic parameter matrix of the main point cloud acquisition device. Since all sub-point cloud acquisition devices are calibrated to the main point cloud acquisition device, the extrinsic parameter matrix of the main point cloud acquisition device is the unit matrix.

[0041] In some embodiments, when registering the point cloud data of the main point cloud acquisition device with the point cloud data of each of the sub-point cloud acquisition devices, a suitable point cloud data matching algorithm may be selected as needed, without limitation. For example, in one example, the point cloud data matching algorithm includes: iterative closest point (ICP) algorithm, iterative dual correspondence (IDC) algorithm, probabilistic iterative correspondence (PIC) algorithm, normal distribution transformation (NDT) algorithm, etc.

[0042] S103, for each point cloud acquisition device combination in a plurality of point cloud acquisition device combinations, determine a set of matching point pairs between the point cloud data of a first point cloud acquisition device and the point cloud data of a second point cloud acquisition device in the point cloud acquisition device combination, wherein the first point cloud acquisition device and the second point cloud acquisition device in the point cloud acquisition device combination are two different point cloud acquisition devices among the plurality of point cloud acquisition devices.

[0043] In the specific implementation, in order to avoid simply considering the calibration of each sub-point cloud acquisition device to the main point cloud acquisition device, it is impossible to perform high-precision calibration on the multi-point cloud acquisition device system from a global perspective. In the embodiment of the present application, multiple point cloud acquisition devices are combined in pairs to obtain multiple point cloud acquisition device combinations, and each point cloud acquisition device combination is composed of a first point cloud acquisition device and a second point cloud acquisition device, that is, not only the calibration between the sub-point cloud acquisition device and the main point cloud acquisition device is considered, but also the calibration between the sub-point cloud acquisition devices is considered. Then, the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device in each point cloud acquisition device combination are respectively aligned to determine the set of matching point pairs between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device. It should be noted that the alignment between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device in each point cloud acquisition device combination can refer to the configuration process between the point cloud data of the sub-point cloud acquisition device and the point cloud data of the main point cloud acquisition device in the above embodiment, which will not be repeated.

[0044] In some embodiments, based on the registration between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device in the point cloud acquisition device combination, determining a set of matching point pairs between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device specifically includes:

[0045] Matching the point cloud data of the second point cloud acquisition device to the point cloud data of the first point cloud acquisition device to obtain a first point cloud pair set; and / or

[0046] Matching the point cloud data of the first point cloud acquisition device to the point cloud data of the second point cloud acquisition device to obtain a second point cloud pair set;

[0047] The matching point pair set is determined based on the first point cloud pair set and / or the second point cloud pair set.

[0048] In specific implementation, in one example, for each point cloud acquisition device combination, the calibration between the first point cloud acquisition device and the second point cloud acquisition device may only consider one-way calibration, that is, matching the point cloud data of the second point cloud acquisition device to the point cloud data of the first point cloud acquisition device to obtain a first point cloud pair set, or matching the point cloud data of the first point cloud acquisition device to the point cloud data of the second point cloud acquisition device to obtain a second point cloud pair set. In this case, the first point cloud pair set or the second point cloud pair set is used as the matching point pair set. In another example, for each point cloud acquisition device combination, the calibration between the first point cloud acquisition device and the second point cloud acquisition device may consider two-way calibration. After obtaining the first point cloud pair set and the second point cloud pair set, the intersection of the two sets may be taken as the matching point pair set, or both the first point cloud pair set and the second point cloud pair set may be taken as the matching point pair set, for example, the first point cloud pair set is taken as the first matching point cloud pair set, and the second point cloud pair set is taken as the second matching point cloud pair set. Preferably, bidirectional calibration between the first point cloud acquisition device and the second point cloud acquisition device is considered, and the intersection of the first point cloud pair set and the second point cloud pair set is used as the matching point pair set between the two, so that the determination of the matching point pair set is more accurate and more robust.

[0049] In some embodiments, the C i and C j Represents the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device in the point cloud acquisition device combination. Then the point cloud data of the second point cloud acquisition device is matched to the point cloud data of the first point cloud acquisition device, and the obtained first point cloud pair set can be expressed as:

[0050] ;

[0051] in, represents a point in the point cloud data of the first point cloud acquisition device, Represents a point in the point cloud data of the second point cloud acquisition device. Each point in the point cloud data can be represented by its coordinates in a three-dimensional coordinate system, that is, (x, y, z).

[0052] Similarly, the point cloud data of the first point cloud acquisition device is matched to the point cloud data of the second point cloud acquisition device, and the obtained second point cloud pair set can be expressed as:

[0053] ;

[0054] in, represents a point in the point cloud data of the second point cloud acquisition device, Represents a point in the point cloud data of the first point cloud acquisition device.

[0055] It can be seen that the first point cloud pair set and the second point cloud pair set obtained by the above two matching methods are not the same. In order to accurately obtain the matching point pair set between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device, in some embodiments, the matching point pair set is the intersection of the first point cloud pair set and the second point cloud pair set. and The set of matching point pairs obtained by the intersection of can be expressed as:

[0056] ;

[0057] or,

[0058] ;

[0059] It should be noted that since the above matching point pair set and Both and The content of the two is the same. Therefore, in practical applications, you can choose one of the two matching point pair sets.

[0060] Preferably, the plurality of point cloud acquisition device combinations may involve all combinations of two different point cloud acquisition devices among the plurality of point cloud acquisition devices, thereby globally considering the calibration between any two point cloud acquisition devices among the plurality of point cloud acquisition devices, and improving the joint calibration accuracy as much as possible. It is understandable that the plurality of point cloud acquisition device combinations may also involve partial combinations of two different point cloud acquisition devices among the plurality of point cloud acquisition devices, and this is not limited.

[0061] S104, constructing a joint calibration loss function based on the conversion parameters of the first point cloud acquisition device and the second point cloud acquisition device in each point cloud acquisition device combination, and a set of matching point pairs between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device.

[0062] In a specific implementation, after obtaining a set of matching point pairs between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device in each point cloud acquisition device combination, a joint calibration loss function can be constructed according to the conversion parameters and matching point pair sets corresponding to the first point cloud acquisition device and the second point cloud acquisition device in each point cloud acquisition device combination. The joint calibration loss function is used to determine the matching loss of the matching point pairs in the matching point pair set after conversion to the main point cloud acquisition device coordinate system. Since the joint calibration loss function takes into account the calibration between different point cloud acquisition device combinations, such as the calibration between the sub-point cloud acquisition device and the main point cloud acquisition device, and between the sub-point cloud acquisition devices, the calibration of the point cloud acquisition device can be quantified from a global perspective.

[0063] In some embodiments, the above-mentioned matching loss can be expressed by the distance between the mapping point pairs of the matching point pairs in the coordinate system of the main point cloud acquisition device, that is, the joint calibration loss function is related to the distance between the two mapping points of each matching point pair in the matching point pair set of each point cloud acquisition device combination in the coordinate system of the main point cloud acquisition device.

[0064] In specific implementation, in order to accurately construct a joint calibration loss function, the distance between the two mapping points of the two matching points of each matching point pair in the matching point pair set of each point cloud acquisition device combination in the main point cloud acquisition device coordinate system can be calculated first, and then the total number of point pairs in the matching point pair sets of all the point cloud acquisition device combinations can be counted, and a joint calibration loss function can be constructed based on the distance between the two mapping points of the two matching points of each matching point pair in the main point cloud acquisition device coordinate system and the total number of point pairs in the matching point pair sets of each point cloud acquisition device combination.

[0065] It should be noted that, when calculating the distance between two mapping points of two matching points of each matching point pair in the matching point pair set of each point cloud acquisition device combination in the coordinate system of the main point cloud acquisition device, the two matching points in the matching point pair set of the point cloud acquisition device combination can be converted to the coordinate system of the main point cloud acquisition device to obtain two mapping points according to the conversion parameters of the first point cloud acquisition device and the second point cloud acquisition device in each point cloud acquisition device combination, such as the external parameter matrix, and then the distance between the two mapping points is calculated. In some embodiments, the distance between two mapping points can be determined by any distance between two points in the relevant technology, such as Euclidean distance, Manhattan distance, Chebyshev distance, etc.

[0066] In some embodiments, based on the external parameter matrix of the first point cloud acquisition device and the second point cloud acquisition device in each point cloud acquisition device combination, and the set of matching point pairs between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device, a joint calibration loss function is constructed, specifically including:

[0067] For each of the point cloud acquisition device combinations, constructing a sub-loss function for each of the point cloud acquisition device combinations based on the distance between two points of each matching point pair in the matching point pair set in the main point cloud acquisition device coordinate system;

[0068] The joint calibration loss function is constructed based on the sub-loss functions of each of the point cloud acquisition device combinations and the number of matching point pairs included in the matching point pair sets of each of the point cloud acquisition device combinations.

[0069] In specific implementation, the sub-loss function of each point cloud acquisition device combination can be constructed first, and then based on the sub-loss function of each point cloud acquisition device combination and the number of matching point pairs included in the matching point pair set of each point cloud acquisition device combination, a joint calibration loss function is constructed. Among them, the sub-loss function of each point cloud acquisition device combination also reflects the matching loss of the matching point pairs in the matching point pair set after being converted to the main point cloud acquisition device coordinate system. Specifically, it can be constructed based on the distance between two matching points of each matching point pair in the matching point pair set of each point cloud acquisition device combination in the main point cloud acquisition device coordinate system. For example, the sub-loss function of each point cloud acquisition device combination is constructed as the sum of the distances between each mapping point pair of each matching point pair in the main point cloud acquisition device coordinate system.

[0070] In some embodiments, the joint calibration loss function can be constructed by the following formula:

[0071] ;

[0072] ;

[0073] ;

[0074] Among them, E represents the constructed joint calibration loss function, A sub-loss function representing each point cloud acquisition device combination, where the sub-loss function is determined by the sum of the distances between each mapping point pair in the main point cloud acquisition device coordinate system of each matching point pair set; Indicates the point in the matching point pair that belongs to the point cloud data of the first point cloud acquisition device, Indicates the point in the matching point pair that belongs to the point cloud data of the second point cloud acquisition device. represents the conversion parameters for mapping points in the point cloud data of the first point cloud acquisition device to the coordinate system of the main point cloud acquisition device, Represents the conversion parameters for mapping points in the point cloud data of the second point cloud acquisition device to the coordinate system of the main point cloud acquisition device. represents the Euclidean distance between two points. In this embodiment, the distance between the mapping points can be determined by the Euclidean distance. Indicates the total number of point pairs in the matching point pair set corresponding to each point cloud acquisition device combination. represents the total number of point pairs in the matching point pair set of all the point cloud acquisition device combinations, It should be noted that for each point cloud acquisition device combination, if the first point cloud pair set or the second point cloud pair set or the intersection of the two sets is used as the matching point cloud pair set, when performing the point cloud acquisition device combination, the point cloud acquisition device combination , combined with point cloud acquisition equipment Considered as the same point cloud acquisition device combination, in order to avoid repeated calculation twice during calculation, in the above formula for constructing the joint calibration loss function, let ; For each point cloud acquisition device combination, if the first point cloud pair set is used as the first matching point cloud set and the second point cloud pair set is used as the second matching point cloud pair set, then .

[0075] In the above example, the sub-loss functions of each point cloud acquisition device combination are first added together to obtain a total loss function, and then the number of matching point pairs included in the matching point pair sets of each point cloud acquisition device combination is added together to obtain the total number of point pairs, and then the total loss function is divided by the total number of point pairs to obtain a joint calibration loss function. In another example, when constructing a joint calibration loss function, the sub-loss function of each point cloud acquisition device combination can be first divided by the number of matching point pairs corresponding to it, and then the results of each division are added together to obtain the final joint calibration loss function.

[0076] S105, optimizing the conversion parameters of the sub-point cloud acquisition devices in the combination of multiple point cloud acquisition devices by taking the joint calibration loss function as the objective function, and obtaining the optimized values ​​of the conversion parameters of the sub-point cloud acquisition devices in the combination of multiple point cloud acquisition devices.

[0077] In specific implementation, after obtaining the joint calibration loss function, the conversion parameters such as the extrinsic parameter matrix of the sub-point cloud acquisition device in the combination of the multiple point cloud acquisition devices are optimized using the joint calibration loss function as the objective function. In specific optimization, a variety of optimization methods can be used, which are not specifically limited here. For example, the least squares method, gradient descent method, Newton method, conjugate gradient method and other optimization methods can be used. In the optimization process, for example, the conversion parameters of the sub-point cloud acquisition device, such as the parameter values ​​of the extrinsic parameter matrix, can be continuously adjusted to obtain the values ​​of the adjusted multiple objective functions, and then the minimum value of the objective function is determined therefrom, and the value of the extrinsic parameter matrix of each sub-point cloud acquisition device when the value of the objective function is the minimum is determined as the optimized value of the conversion parameter such as the extrinsic parameter matrix. In one example, the extrinsic parameter matrix of each point cloud acquisition device after optimization can be expressed as: .

[0078] In some embodiments, the point cloud acquisition device calibration of the embodiment of the present application further includes:

[0079] Determine an initial value of the joint calibration loss function based on the initial values ​​of the conversion parameters of the first point cloud acquisition device and the second point cloud acquisition device in each point cloud acquisition device combination, and a set of matching point pairs between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device;

[0080] Determine the optimized value of the joint calibration loss function based on the optimized values ​​of the conversion parameters of the first point cloud acquisition device and the second point cloud acquisition device in each point cloud acquisition device combination, and a set of matching point pairs between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device;

[0081] When the optimized value of the joint calibration loss function is greater than or equal to the initial value of the joint calibration loss function, determining the initial value of the conversion parameter of each of the sub-point cloud acquisition devices as the final value of the conversion parameter of each of the sub-point cloud acquisition devices;

[0082] When the optimized value of the joint calibration loss function is less than the initial value of the joint calibration loss function, the optimized value of the conversion parameter of each of the sub-point cloud acquisition devices is determined as the final value of the conversion parameter of each of the sub-point cloud acquisition devices.

[0083] In a specific implementation, in order to verify the optimization effect of the extrinsic parameter matrix, after obtaining the optimized values ​​of the conversion parameters of the sub-point cloud acquisition devices in the multiple point cloud acquisition device combinations, the coordinate values ​​of each matching point in the matching point pair set between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device in each point cloud acquisition device combination and the conversion parameters of the first point cloud acquisition device and the second point cloud acquisition device, such as the initial value of the extrinsic parameter matrix, can be substituted into the expression of the joint calibration loss function to obtain the initial value of the joint calibration loss function. At the same time, the coordinate values ​​of each matching point in the matching point pair set between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device in each point cloud acquisition device combination and the conversion parameters of the first point cloud acquisition device and the second point cloud acquisition device, such as the optimized values ​​of the extrinsic parameter matrix, can be substituted into the expression of the joint calibration loss function to obtain the optimized value of the joint calibration loss function. After obtaining the optimized value and initial value of the joint calibration loss function, compare the two. When the optimized value of the joint calibration loss function is greater than or equal to the initial value of the joint calibration loss function, it means that the global calibration effect of considering multiple point cloud acquisition devices is inferior to the effect of calibration only through the main point cloud acquisition device. At this time, the initial value of the conversion parameter of each sub-point cloud acquisition device is determined as the final value of the conversion parameter of each sub-point cloud acquisition device. When the optimized value of the joint calibration loss function is less than the initial value of the joint calibration loss function, it means that the global calibration effect of considering multiple point cloud acquisition devices is better than the effect of calibration through the main point cloud acquisition device. At this time, the optimized value of the conversion parameter of each sub-point cloud acquisition device is determined as the final value of the conversion parameter of each sub-point cloud acquisition device.

[0084] In some embodiments, the point cloud acquisition device calibration method of the embodiment of the present application further includes:

[0085] For each of the point cloud acquisition device combinations, determining an initial value of a sub-loss function of each of the point cloud acquisition device combinations based on a distance between mapping point pairs of each matching point pair in the matching point pair set in the main point cloud acquisition device coordinate system and based on initial values ​​of conversion parameters of the first point cloud acquisition device and the second point cloud acquisition device;

[0086] For each of the point cloud acquisition device combinations, determining an optimized value of a sub-loss function of each of the point cloud acquisition device combinations based on a distance between mapping point pairs of each matching point pair in the matching point pair set in the main point cloud acquisition device coordinate system and based on optimized values ​​of conversion parameters of the first point cloud acquisition device and the second point cloud acquisition device;

[0087] When the optimized values ​​of the sub-loss functions of each of the point cloud acquisition device combinations are less than or equal to the initial values ​​of the sub-loss functions, the optimized values ​​of the conversion parameters of each of the sub-point cloud acquisition devices are determined as the final values ​​of the conversion parameters of each of the sub-point cloud acquisition devices;

[0088] When the optimized value of the sub-loss function of at least one of the point cloud acquisition device combinations is greater than the optimized value of the sub-loss function, the initial value of the conversion parameter of each of the sub-point cloud acquisition devices is determined as the final value of the conversion parameter of each of the sub-point cloud acquisition devices.

[0089] In specific implementation, in the embodiment of the present application, the initial value and optimized value of the sub-loss function of each of the point cloud acquisition device combinations can be determined first, and then when the optimized values ​​of the sub-loss functions of each of the point cloud acquisition device combinations are less than or equal to the initial value of the sub-loss function, the optimized value of the conversion parameter of each of the sub-point cloud acquisition devices is determined as the final value of the conversion parameter of each of the sub-point cloud acquisition devices. When the optimized value of the sub-loss function of at least one of the point cloud acquisition device combinations is greater than the optimized value of the sub-loss function, the initial value of the conversion parameter of each of the sub-point cloud acquisition devices is determined as the final value of the conversion parameter of each of the sub-point cloud acquisition devices.

[0090] When determining the initial value of the sub-loss function of each of the point cloud acquisition device combinations, the coordinates of each matching point pair in the matching point pair set of the first point cloud acquisition device and the second point cloud acquisition device in each of the point cloud acquisition device combinations, as well as the initial value of the conversion parameters of the first point cloud acquisition device and the second point cloud acquisition device, such as the external parameter matrix, can be first substituted into the expression of the sub-loss function to obtain the initial value of the sub-loss function of each of the point cloud acquisition device combinations. At the same time, the coordinates of each matching point pair in the matching point pair set of the first point cloud acquisition device and the second point cloud acquisition device in each of the point cloud acquisition device combinations and the optimized values ​​of the conversion parameters of the first point cloud acquisition device and the second point cloud acquisition device are substituted into the expression of the sub-loss function to obtain the optimized value of the sub-loss function of each of the point cloud acquisition device combinations.

[0091] In some embodiments, after obtaining the optimized value of the conversion parameter of the sub-point cloud acquisition device in the plurality of point cloud acquisition device combinations, the point cloud acquisition device calibration method of the embodiment of the present application further includes:

[0092] For each of the sub-point cloud acquisition devices, determining an offset index between an initial value and an optimized value of the conversion parameter;

[0093] When the offset index satisfies a preset offset condition, target prompt information is output; the target prompt information is used to prompt adjustment of the position and posture of the sub-point cloud acquisition device.

[0094] In the specific implementation, considering that when the installation position and installation posture of a sub-point cloud acquisition device are inappropriate, simply improving the accuracy of the point cloud data collected by the point cloud acquisition device through global point cloud acquisition device calibration has certain limitations. Therefore, when it is determined that the offset index between the initial value and the optimized value of the conversion parameter of a sub-point cloud acquisition device meets the preset offset condition, the target prompt information is output. The target prompt information is used to prompt the adjustment of the position and posture of the sub-point cloud acquisition device, so as to timely adjust the position and posture of the sub-point cloud acquisition device, thereby improving the overall accuracy of multiple point cloud acquisition devices when working. It should be noted that the preset offset condition can be that the offset index is greater than the preset offset threshold, or is within the preset offset range, etc., and there is no limitation on this.

[0095] In some embodiments, the conversion parameters include rotation parameters and translation parameters, and determining the offset index between the initial value and the optimized value of the conversion parameters specifically includes:

[0096] Determining a first offset index between an initial value and an optimized value of the rotation parameter;

[0097] Determining a second offset index of an initial value and an optimized value of the translation parameter;

[0098] Accordingly, the offset indicator satisfies a preset offset condition, including at least one of the following:

[0099] The first deviation index is greater than a first preset threshold;

[0100] The second deviation indicator is greater than a second preset threshold.

[0101] In specific implementation, taking into account that the rotation parameters and translation parameters in the conversion parameters correspond to different quantization units, in order to accurately reflect the offset index between the initial value and the optimized value of the conversion parameter, in the embodiment of the present application, the offsets of the rotation parameters and the translation parameters are compared respectively.

[0102] In some embodiments, the conversion parameter includes multiple parameters; determining the offset index between the initial value and the optimized value of the extrinsic parameter matrix specifically includes:

[0103] Normalizing the initial values ​​and optimized values ​​of the multiple parameters respectively;

[0104] Summing the normalized initial values ​​of the plurality of parameters to obtain a first target value;

[0105] The normalized optimized values ​​of the plurality of parameters are summed to obtain a second target value;

[0106] A deviation index between an initial value and an optimized value of the conversion parameter is determined based on the first target value and the second target value.

[0107] In specific implementation, in order to unify the quantization standards of multiple parameters in the conversion parameters, the initial values ​​and optimized values ​​of the multiple parameters of the conversion parameters can be normalized respectively, and then the normalized initial values ​​of the multiple parameters are summed to obtain a first target value, and the normalized optimized values ​​of the multiple parameters are summed to obtain a second target value. Finally, the offset index between the initial value and the optimized value of the conversion parameter is determined by the difference between the first target value and the second target value.

[0108] The point cloud acquisition device calibration method of the embodiment of the present application, in addition to calibrating each sub-point cloud acquisition device with the main point cloud acquisition device as the calibration reference to obtain the initial value of the conversion parameter, also combines the point cloud acquisition devices in pairs to obtain multiple point cloud acquisition device combinations, and determines the matching point pair set through the registration of the point cloud data of the two point cloud acquisition devices in each point cloud acquisition device combination, and constructs a joint calibration loss function based on the matching point pair set. The conversion parameters of the point cloud acquisition devices in the multiple point cloud acquisition device combinations are optimized using the joint calibration loss function as the objective function, thereby realizing mutual calibration between the point cloud acquisition devices. Therefore, when performing calibration, the embodiment of the present application not only considers the main point cloud acquisition device, but also fully considers the calibration between each point cloud acquisition device, realizes high-precision global calibration of the point cloud acquisition device, improves the accuracy of the multi-point cloud acquisition device system in practical applications, and ensures the accuracy of the positioning data. At the same time, the point cloud acquisition device calibration method of the embodiment of the present application, from a global perspective, significantly improves the calibration accuracy of the multi-point cloud acquisition device system by accurately calibrating the relative positions and postures of multiple point cloud acquisition devices. In addition, the point cloud acquisition device calibration method of the embodiment of the present application not only provides high-precision, comprehensive real-time positioning information for the construction of large-scale equipment, but also lays a solid foundation for the high-precision information representation and data processing of the point cloud acquisition device system.

[0109] refer to Figure 2 , is a flow chart of another method for calibrating a point cloud acquisition device provided by an embodiment of the present application, wherein Figure 2 In the method for calibrating a point cloud acquisition device provided, the point cloud acquisition device includes a laser radar. Taking the calibration of the laser radar as an example, the method includes the following steps:

[0110] S201, obtaining point cloud data of multiple laser radars in the same static scene.

[0111] S202, obtaining conversion parameters for sub-radar to main radar calibration through ICP matching method.

[0112] S203, construct matching point pairs between each pair of point clouds.

[0113] S204, constructing a point cloud joint calibration loss function.

[0114] S205, optimizing the conversion parameters of the sub-radar point cloud.

[0115] S206, verifying the performance of the joint calibration result.

[0116] During specific implementation, in this embodiment, the point cloud data of multiple laser radars in the same static scene are first obtained. The static scene can be selected as needed and is not limited to this. The multiple laser radars include a main radar and multiple sub-radars. After obtaining the point cloud data of multiple laser radars, the conversion parameters of the sub-radar to the main radar calibration are obtained by the ICP matching method. At the same time, based on the point cloud data of multiple laser radars, matching point pairs between two point clouds are constructed. The matching point pairs of each group of point clouds are the matching point pair sets of each radar combination in the above embodiment. Then, the point cloud joint calibration loss function is constructed according to the matching point pairs and conversion parameters between two point clouds. It should be noted that the specific construction of the joint calibration loss function can refer to any process of constructing the joint calibration loss function in the above embodiment, which will not be repeated. After obtaining the joint calibration loss function, the conversion parameters corresponding to each sub-radar point cloud are optimized using the joint calibration loss function as the objective function to obtain the optimized value of the conversion parameter. After optimization, the performance of the joint calibration result is verified by the initial value and optimized value of the joint calibration loss function.

[0117] The point cloud acquisition device calibration method of the embodiment of the present application optimizes the relative position and posture between multiple point cloud acquisition devices from a global perspective, rather than focusing only on the calibration of a single point cloud acquisition device, thereby improving the consistency and accuracy of the entire system. By accurately calibrating multiple point cloud acquisition devices, high-precision, all-round positioning information is provided for the construction of large-scale equipment, ensuring the efficiency and safety of construction. By constructing matching point pairs between point clouds and jointly optimizing the loss function, the accuracy and reliability of point cloud matching are improved. By comprehensively considering the errors of all point cloud acquisition devices, the problem of error accumulation in the multi-point cloud acquisition device system is reduced, and the overall performance of the system is improved. By using a nonlinear optimization method, the method can effectively handle complex calibration problems, and can handle abnormal situations during the optimization process, ensuring the robustness of the algorithm. Therefore, the point cloud acquisition device calibration method of the embodiment of the present application not only solves the shortcomings of the calibration accuracy of the multi-point cloud acquisition device system in the prior art, but also improves the accuracy and reliability of information processing during the entire construction process, which is of great significance for promoting the development of large-scale equipment construction technology.

[0118] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a point cloud acquisition equipment calibration device.

[0119] refer to Figure 3 , the point cloud acquisition equipment calibration device comprises:

[0120] An acquisition module 301 acquires point cloud data measured by each point cloud acquisition device for a target area from among a plurality of point cloud acquisition devices; the plurality of point cloud acquisition devices include a main point cloud acquisition device and a plurality of sub-point cloud acquisition devices;

[0121] A first determination module 302 determines, for each sub-point cloud acquisition device, an initial value of a conversion parameter calibrated from the sub-point cloud acquisition device to the main point cloud acquisition device based on the registration between the point cloud data of the main point cloud acquisition device and the point cloud data of the sub-point cloud acquisition device;

[0122] The second determination module 303 determines, for each point cloud acquisition device combination in the plurality of point cloud acquisition device combinations, a set of matching point pairs between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device in the point cloud acquisition device combination, based on the registration between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device in the point cloud acquisition device combination, wherein the first point cloud acquisition device and the second point cloud acquisition device in the point cloud acquisition device combination are two different point cloud acquisition devices in the plurality of point cloud acquisition devices;

[0123] The function construction module 304 constructs a joint calibration loss function based on conversion parameters of the first point cloud acquisition device and the second point cloud acquisition device in each point cloud acquisition device combination, and a set of matching point pairs between point cloud data of the first point cloud acquisition device and point cloud data of the second point cloud acquisition device;

[0124] The optimization module 305 optimizes the conversion parameters of the sub-point cloud acquisition devices in the combination of multiple point cloud acquisition devices by using the joint calibration loss function as the objective function, and obtains the optimized values ​​of the conversion parameters of the sub-point cloud acquisition devices in the combination of multiple point cloud acquisition devices.

[0125] In some embodiments, the plurality of point cloud acquisition device combinations involve all combinations of two different point cloud acquisition devices among the plurality of point cloud acquisition devices.

[0126] In some embodiments, the second determining module is specifically configured to:

[0127] Matching the point cloud data of the second point cloud acquisition device to the point cloud data of the first point cloud acquisition device to obtain a first point cloud pair set; and / or

[0128] Matching the point cloud data of the first point cloud acquisition device to the point cloud data of the second point cloud acquisition device to obtain a second point cloud pair set;

[0129] The matching point pair set is determined based on the first point cloud pair set and / or the second point cloud pair set.

[0130] In some embodiments, the matching point pair set is an intersection of the first point cloud pair set and the second point cloud pair set.

[0131] In some embodiments, the joint calibration loss function is related to the distance between two corresponding mapping points of two matching points of each matching point pair in the set of matching point pairs of each point cloud acquisition device combination in the main point cloud acquisition device coordinate system.

[0132] In some embodiments, the function building module is specifically used to:

[0133] For each of the point cloud acquisition device combinations, constructing a sub-loss function for each of the point cloud acquisition device combinations based on the distance between the mapping point pairs of each matching point pair in the matching point pair set in the main point cloud acquisition device coordinate system;

[0134] The joint calibration loss function is constructed based on the sub-loss functions of each of the point cloud acquisition device combinations and the number of matching point pairs included in the matching point pair sets of each of the point cloud acquisition device combinations.

[0135] In some embodiments, the device further comprises a verification module, configured to:

[0136] Determine an initial value of the joint calibration loss function based on the initial values ​​of the conversion parameters of the first point cloud acquisition device and the second point cloud acquisition device in each point cloud acquisition device combination, and a set of matching point pairs between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device;

[0137] Determine the optimized value of the joint calibration loss function based on the optimized values ​​of the conversion parameters of the first point cloud acquisition device and the second point cloud acquisition device in each point cloud acquisition device combination, and a set of matching point pairs between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device;

[0138] When the optimized value of the joint calibration loss function is greater than or equal to the initial value of the joint calibration loss function, determining the initial value of the conversion parameter of each of the sub-point cloud acquisition devices as the final value of the conversion parameter of each of the sub-point cloud acquisition devices;

[0139] When the optimized value of the joint calibration loss function is less than the initial value of the joint calibration loss function, the optimized value of the conversion parameter of each of the sub-point cloud acquisition devices is determined as the final value of the conversion parameter of each of the sub-point cloud acquisition devices.

[0140] In some embodiments, the verification module is further configured to:

[0141] For each of the point cloud acquisition device combinations, determining an initial value of a sub-loss function of each of the point cloud acquisition device combinations based on a distance between mapping point pairs of each matching point pair in the matching point pair set in the main point cloud acquisition device coordinate system and based on initial values ​​of conversion parameters of the first point cloud acquisition device and the second point cloud acquisition device;

[0142] For each of the point cloud acquisition device combinations, determining an optimized value of a sub-loss function of each of the point cloud acquisition device combinations based on a distance between two points of each matching point pair in the matching point pair set in the main point cloud acquisition device coordinate system and based on optimized values ​​of conversion parameters of the first point cloud acquisition device and the second point cloud acquisition device;

[0143] When the optimized values ​​of the sub-loss functions of each of the point cloud acquisition device combinations are less than or equal to the initial values ​​of the sub-loss functions, the optimized values ​​of the conversion parameters of each of the sub-point cloud acquisition devices are determined as the final values ​​of the conversion parameters of each of the sub-point cloud acquisition devices;

[0144] When the optimized value of the sub-loss function of at least one of the point cloud acquisition device combinations is greater than the optimized value of the sub-loss function, the initial value of the conversion parameter of each of the sub-point cloud acquisition devices is determined as the final value of the conversion parameter of each of the sub-point cloud acquisition devices.

[0145] In some embodiments, the device further comprises a prompt module, configured to:

[0146] For each of the sub-point cloud acquisition devices, determining an offset index between an initial value and an optimized value of the conversion parameter;

[0147] When the offset index satisfies a preset offset condition, target prompt information is output; the target prompt information is used to prompt adjustment of the position and posture of the sub-point cloud acquisition device.

[0148] For the convenience of description, the above device is described in terms of functions divided into various modules. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0149] The point cloud acquisition device calibration apparatus of the above-mentioned embodiment is used to implement the corresponding point cloud acquisition device calibration method in any of the above-mentioned embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0150] Figure 4 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown.

[0151] In some embodiments the electronic device may include a processor 401 and a memory 402 storing computer program instructions.

[0152] Specifically, the processor 401 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0153] The memory 402 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 402 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 402 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 402 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 402 is a non-volatile solid-state memory.

[0154] In certain embodiments, memory 402 includes a read-only memory (ROM). The ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM), or flash memory, or a combination of two or more of the above, where appropriate.

[0155] The memory may include read-only memory (ROM), random access memory (RAM), magnetic disk storage media devices, optical storage media devices, flash memory devices, electrical, optical or other physical / tangible memory storage devices. Thus, typically, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0156] The processor 401 implements any one of the point cloud acquisition device calibration methods in the above embodiments by reading and executing computer program instructions stored in the memory 402 .

[0157] In one example, the electronic device may further include a communication interface 403 and a bus 410. Figure 4 As shown, the processor 401, the memory 402, and the communication interface 403 are connected via a bus 410 and communicate with each other.

[0158] The communication interface 403 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0159] Bus 410 includes hardware, software or both, and couples the components of online data traffic billing equipment to each other. For example, but not limitation, the bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnect (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. Where appropriate, bus 410 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0160] The electronic device of the above embodiment is used to implement the corresponding point cloud acquisition device calibration method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0161] In addition, in combination with the method for calibrating a point cloud acquisition device in the above embodiment, the embodiment of the present application may provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any one of the methods for calibrating a point cloud acquisition device in the above embodiment is implemented.

[0162] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the point cloud acquisition device calibration method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0163] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed, implements any one of the point cloud acquisition device calibration methods in the above embodiments.

[0164] In some embodiments, the computer program instructions may be executed by one or more processors of a computer so that the computer and / or the processor execute the point cloud acquisition device calibration method described in the above embodiments. Corresponding to the execution subject corresponding to each step in each embodiment of the point cloud acquisition device calibration method, the processor executing the corresponding step may belong to the corresponding execution subject.

[0165] The computer program product of the above embodiment is used to enable the computer and / or the processor to execute the point cloud acquisition device calibration method described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0166] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0167] The functional blocks shown in the structural block diagram described above can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier. "Machine-readable medium" may include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0168] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0169] Aspects of the present disclosure are described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the functions / actions specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0170] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A point cloud acquisition device calibration method, characterized in that: include: Acquire point cloud data measured by each point cloud acquisition device for a target area among a plurality of point cloud acquisition devices; The multiple point cloud acquisition devices include a main point cloud acquisition device and multiple sub-point cloud acquisition devices; For each sub-point cloud acquisition device, based on the registration between the point cloud data of the main point cloud acquisition device and the point cloud data of the sub-point cloud acquisition device, determining an initial value of a conversion parameter calibrated by the sub-point cloud acquisition device to the main point cloud acquisition device; For each point cloud acquisition device combination in a plurality of point cloud acquisition device combinations, determining a set of matching point pairs between the point cloud data of a first point cloud acquisition device and the point cloud data of a second point cloud acquisition device in the point cloud acquisition device combination based on the registration between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device, wherein the first point cloud acquisition device and the second point cloud acquisition device in the point cloud acquisition device combination are two different point cloud acquisition devices in the plurality of point cloud acquisition devices; Constructing a joint calibration loss function based on the conversion parameters of the first point cloud acquisition device and the second point cloud acquisition device in each point cloud acquisition device combination and a set of matching point pairs between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device; The conversion parameters of the sub-point cloud acquisition devices in the combination of multiple point cloud acquisition devices are optimized using the joint calibration loss function as the objective function to obtain optimized values ​​of the conversion parameters of the sub-point cloud acquisition devices in the combination of multiple point cloud acquisition devices.

2. The point cloud acquisition device calibration method according to claim 1, characterized in that: The multiple point cloud acquisition device combinations refer to all combinations of two different point cloud acquisition devices among the multiple point cloud acquisition devices.

3. The point cloud acquisition device calibration method according to claim 1, characterized in that: Based on the registration between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device in the point cloud acquisition device combination, determining a set of matching point pairs between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device specifically includes: Matching the point cloud data of the second point cloud acquisition device to the point cloud data of the first point cloud acquisition device to obtain a first point cloud pair set; and / or Matching the point cloud data of the first point cloud acquisition device to the point cloud data of the second point cloud acquisition device to obtain a second point cloud pair set; The matching point pair set is determined based on the first point cloud pair set and / or the second point cloud pair set.

4. The point cloud acquisition device calibration method according to claim 3, characterized in that: The matching point pair set is the intersection of the first point cloud pair set and the second point cloud pair set.

5. The point cloud acquisition device calibration method according to claim 1, characterized in that: The joint calibration loss function is related to the distance between two corresponding mapping points of two matching points of each matching point pair in the matching point pair set of each point cloud acquisition device combination in the main point cloud acquisition device coordinate system.

6. The point cloud acquisition device calibration method according to claim 5, characterized in that: Based on the conversion parameters of the first point cloud acquisition device and the second point cloud acquisition device in each point cloud acquisition device combination, and a set of matching point pairs between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device, a joint calibration loss function is constructed, specifically including: For each of the point cloud acquisition device combinations, constructing a sub-loss function for each of the point cloud acquisition device combinations based on the distance between the mapping point pairs of each matching point pair in the matching point pair set in the main point cloud acquisition device coordinate system; The joint calibration loss function is constructed based on the sub-loss functions of each of the point cloud acquisition device combinations and the number of matching point pairs included in the matching point pair sets of each of the point cloud acquisition device combinations.

7. The point cloud acquisition device calibration method according to any one of claims 1 to 6, characterized in that: The method further comprises: Determine an initial value of the joint calibration loss function based on the initial values ​​of the conversion parameters of the first point cloud acquisition device and the second point cloud acquisition device in each point cloud acquisition device combination, and a set of matching point pairs between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device; Determine the optimized value of the joint calibration loss function based on the optimized values ​​of the conversion parameters of the first point cloud acquisition device and the second point cloud acquisition device in each point cloud acquisition device combination, and a set of matching point pairs between the point cloud data of the first point cloud acquisition device and the point cloud data of the second point cloud acquisition device; When the optimized value of the joint calibration loss function is greater than or equal to the initial value of the joint calibration loss function, determining the initial value of the conversion parameter of each of the sub-point cloud acquisition devices as the final value of the conversion parameter of each of the sub-point cloud acquisition devices; When the optimized value of the joint calibration loss function is less than the initial value of the joint calibration loss function, the optimized value of the conversion parameter of each of the sub-point cloud acquisition devices is determined as the final value of the conversion parameter of each of the sub-point cloud acquisition devices.

8. The point cloud acquisition device calibration method according to claim 6, characterized in that: The method further comprises: For each of the point cloud acquisition device combinations, determining an initial value of a sub-loss function of each of the point cloud acquisition device combinations based on a distance between mapping point pairs of each matching point pair in the matching point pair set in the main point cloud acquisition device coordinate system and based on initial values ​​of conversion parameters of the first point cloud acquisition device and the second point cloud acquisition device; For each of the point cloud acquisition device combinations, determining an optimized value of a sub-loss function of each of the point cloud acquisition device combinations based on a distance between mapping point pairs of each matching point pair in the matching point pair set in the main point cloud acquisition device coordinate system and based on optimized values ​​of conversion parameters of the first point cloud acquisition device and the second point cloud acquisition device; When the optimized values ​​of the sub-loss functions of each of the point cloud acquisition device combinations are less than or equal to the initial values ​​of the sub-loss functions, the optimized values ​​of the conversion parameters of each of the sub-point cloud acquisition devices are determined as the final values ​​of the conversion parameters of each of the sub-point cloud acquisition devices; When the optimized value of the sub-loss function of at least one of the point cloud acquisition device combinations is greater than the optimized value of the sub-loss function, the initial value of the conversion parameter of each of the sub-point cloud acquisition devices is determined as the final value of the conversion parameter of each of the sub-point cloud acquisition devices.

9. The point cloud acquisition device calibration method according to any one of claims 1 to 6, characterized in that: After obtaining the optimized value of the conversion parameter of the sub-point cloud acquisition device in the plurality of point cloud acquisition device combinations, the method further includes: For each of the sub-point cloud acquisition devices, determining an offset index between an initial value and an optimized value of the conversion parameter; When the offset index satisfies a preset offset condition, target prompt information is output; the target prompt information is used to prompt adjustment of the position and posture of the sub-point cloud acquisition device.

10. An electronic device, characterized in that: The device includes: a processor and a memory storing computer program instructions, the processor is coupled to the memory; when the processor executes the computer program instructions, the point cloud acquisition device calibration method according to any one of claims 1 to 9 is implemented.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by the processor, the point cloud acquisition device calibration method according to any one of claims 1 to 9 is implemented.

12. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the point cloud acquisition device calibration method as described in any one of claims 1 to 9.

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