Calibration method and device for integrated navigation equipment and vehicle

Abstract

The invention provides a calibration method and device for integrated navigation equipment and a vehicle, and relates to the field of smart mines, automatic driving and unmanned vehicles. The method comprises the following steps: determining first ground point cloud data under a coordinate system of point cloud acquisition equipment; an error equation is determined, the error equation comprises a known constant term and a to-be-optimized variable, the known constant term comprises a first transformation matrix for converting the coordinate system of the point cloud acquisition equipment into the coordinate system of the combined navigation equipment, and the to-be-optimized variable comprises a second transformation matrix for converting the coordinate system of the combined navigation equipment into the coordinate system of the vehicle; and adjusting the parameters of the second transformation matrix until the measurement of the error equation meets the convergence condition, and obtaining the second transformation matrix meeting the optimization target. The method does not depend on high-precision point cloud map information, so that the calibration process is quicker and easier to deploy, the calibration deviation between the integrated navigation equipment and the vehicle can be reduced to the maximum extent, and the accuracy of vehicle positioning is improved.

Description

Technical Field

[0001] The present application relates to the fields of smart mines, autonomous driving, and unmanned vehicle technology, and specifically to a calibration method and apparatus for a combined navigation device and vehicle. Background Art

[0002] At present, the external parameters of the integrated navigation device and the vehicle are generally obtained through measurement. However, due to the installation error of the integrated navigation device, there may be a position deviation between it and the reference point in the vehicle (the reference point represents the origin of the vehicle coordinate system). If the vehicle body is long, the error will be large, which will lead to inaccurate positioning of the vehicle, which will also affect the subsequent control planning. For example, the precise parking function fails and the vehicle is always not parked in place. Therefore, it is necessary to accurately calibrate the integrated navigation device and the vehicle. Summary of the invention

[0003] In view of this, an embodiment of the present application provides a method and apparatus for calibrating a combined navigation device and a vehicle.

[0004] In a first aspect, an embodiment of the present application provides a calibration method for a combined navigation device and a vehicle, comprising: determining first ground point cloud data in a point cloud acquisition device coordinate system; determining an error equation, the error equation being used to quantify the distances from multiple points in the first ground point cloud data to an ideal ground model in a vehicle coordinate system, the error equation comprising known constant terms and variables to be optimized, the known constant terms comprising a first transformation matrix for converting the point cloud acquisition device coordinate system to the combined navigation device coordinate system, the variables to be optimized comprising a second transformation matrix for converting the combined navigation device coordinate system to the vehicle coordinate system; adjusting the parameters of the second transformation matrix until the measure of the error equation satisfies a convergence condition, thereby obtaining a second transformation matrix that meets the optimization objective.

[0005] In combination with the first aspect, in certain implementations of the first aspect, determining the error equation includes: using a first transformation matrix and a second transformation matrix to perform a coordinate transformation on the first ground point cloud data to obtain second ground point cloud data in a vehicle coordinate system; calculating the deviation vectors of multiple points in the second ground point cloud data relative to the center point of the ideal ground model; performing a dot product operation on each deviation vector and a normal vector of the ideal ground model to obtain the distances of each of the multiple points to the ideal ground model; summing the distances of each of the multiple points to the ideal ground model to obtain the sum of the distances, and taking the minimum value of the sum of the distances as the first measure of the error equation, thereby determining the error equation.

[0006] In combination with the first aspect, in certain implementations of the first aspect, the method also includes: dividing the total distance by the number of multiple points to obtain an average distance, and taking the minimum value of the average distance as a second measure of the error equation; determining the error equation also includes: determining the error equation based on the first measure and the second measure.

[0007] In combination with the first aspect, in certain implementations of the first aspect, the ideal ground model is parallel to the XY plane of the vehicle coordinate system, and when the height of the origin of the vehicle coordinate system above the ground is h, the expression of the ideal ground model is Z=h, or Z=-h; and / or, the center of the rear axle of the vehicle is the origin of the vehicle coordinate system; and / or, the first ground point cloud data is ground point cloud data belonging to the same frame.

[0008] In combination with the first aspect, in certain implementations of the first aspect, the parameters of the second transformation matrix are adjusted until the measure of the error equation meets the convergence condition, including: using the parameters of the current second transformation matrix to calculate the measure of the error equation; adjusting the parameters of the current second transformation matrix based on the measure of the error equation; after each parameter adjustment, recalculating the measure of the error equation until the measure of the error equation reaches a preset accuracy threshold.

[0009] In combination with the first aspect, in certain implementations of the first aspect, the method also includes: if the measure of the error equation does not meet the convergence condition, redetermining the first ground point cloud data in the coordinate system of the point cloud acquisition device; using the first ground point cloud data in the redetermined point cloud acquisition device coordinate system, continuing to adjust the parameters of the second transformation matrix until the measure of the error equation meets the convergence condition.

[0010] In combination with the first aspect, in certain implementations of the first aspect, the method also includes: detecting whether the second transformation matrix that meets the optimization target meets the target accuracy condition; if the second transformation matrix that meets the optimization target does not meet the target accuracy condition, redetermining the second transformation matrix that meets the optimization target, and / or sending a prompt message to the control end for the operator to perform manual verification.

[0011] In combination with the first aspect, in certain implementations of the first aspect, detecting whether the second transformation matrix that meets the optimization objective meets the target accuracy condition includes: for each point in the first ground point cloud data, based on the second transformation matrix that meets the optimization objective, calculating the distance from each point to the ideal ground model; based on the distance from each point to the ideal ground model, calculating the average distance from the points in the first ground point cloud data to the ideal ground model; if the average distance is greater than a preset distance threshold, determining that the second transformation matrix that meets the optimization objective does not meet the target accuracy condition.

[0012] In combination with the first aspect, in certain implementations of the first aspect, determining first ground point cloud data in the coordinate system of a point cloud acquisition device includes: acquiring point cloud data around the vehicle by means of a point cloud acquisition device on the vehicle; determining the first ground point cloud data in the coordinate system of the point cloud acquisition device from the point cloud data around the vehicle based on geometric features of the ground point cloud data; or, performing ground segmentation on the point cloud data around the vehicle to obtain the first ground point cloud data in the coordinate system of the point cloud acquisition device.

[0013] In combination with the first aspect, in certain implementations of the first aspect, the geometric features of the ground point cloud data include at least one of ground point cloud height, ground point cloud density, ground point cloud curvature, and ground point cloud connectivity.

[0014] In the second aspect, an embodiment of the present application provides a calibration device for a combined navigation device and a vehicle, including: a first determination module, used to determine the first ground point cloud data in the coordinate system of the point cloud acquisition device; a second determination module, used to determine the error equation, the error equation is used to quantify the distances from multiple points in the first ground point cloud data to the ideal ground model in the vehicle coordinate system, the error equation includes known constant terms and variables to be optimized, the known constant terms include a first transformation matrix that converts the coordinate system of the point cloud acquisition device to the coordinate system of the combined navigation device, and the variables to be optimized include a second transformation matrix that converts the coordinate system of the combined navigation device to the vehicle coordinate system; an adjustment module, used to adjust the parameters of the second transformation matrix until the measure of the error equation meets the convergence condition, and a second transformation matrix that meets the optimization objective is obtained.

[0015] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program for executing the calibration method of the combined navigation device and vehicle described in the first aspect.

[0016] In a fourth aspect, an embodiment of the present application provides an electronic device, comprising: a processor; a memory for storing instructions executable by the processor; the processor is used to execute the calibration method of the combined navigation device and vehicle described in the first aspect.

[0017] In the present application, by acquiring the first ground point cloud data in the coordinate system of the point cloud acquisition device, the accurate information of the ground under the vehicle can be determined. Then, an error equation for quantifying the distance from the point in the first ground point cloud data to the ideal ground model in the vehicle coordinate system is determined. The equation provides a framework to evaluate and optimize the spatial relationship between the combined navigation device coordinate system and the vehicle coordinate system. This method does not rely on high-precision point cloud map information, making the calibration process faster and easier to deploy. Then, by continuously iteratively adjusting the parameters of the second transformation matrix until the measure of the error equation meets the convergence condition, this process can provide real-time feedback to guide the adjustment direction of the parameters of the second transformation matrix, and finally ensure that the optimal parameters of the second transformation matrix are found, thereby minimizing the calibration deviation between the combined navigation device and the vehicle and improving the accuracy of vehicle positioning. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0019] Figure 1 Shown is a flow chart of a calibration method for a combined navigation device and a vehicle provided in one embodiment of the present application.

[0020] Figure 2 The figure is a schematic diagram of a process for determining first ground point cloud data provided by an embodiment of the present application.

[0021] Figure 3 The figure is a schematic diagram of a flow chart of determining an error equation provided in an embodiment of the present application.

[0022] Figure 4 FIG. 1 is a schematic diagram of a flow chart of adjusting parameters of a second transformation matrix provided in an embodiment of the present application.

[0023] Figure 5 FIG. 4 is a schematic diagram of a flow chart of adjusting parameters of a second transformation matrix provided in another embodiment of the present application.

[0024] Figure 6 The figure is a schematic diagram of a flow chart for detecting whether a target accuracy condition is met according to an embodiment of the present application.

[0025] Figure 7 Shown is a schematic structural diagram of a combined navigation device and a vehicle calibration apparatus provided in one embodiment of the present application.

[0026] Figure 8Shown is a schematic structural diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0028] Figure 1 FIG. 1 is a flow chart of a combined navigation device and a vehicle calibration method provided by an embodiment of the present application. Figure 1 As shown, the method includes the following steps.

[0029] Step S110, determining first ground point cloud data in the coordinate system of the point cloud acquisition device.

[0030] The first ground point cloud data refers to a collection of ground point clouds acquired by a point cloud acquisition device (such as a laser radar), representing the three-dimensional shape and features of the ground detected at a specific moment. Optionally, the first ground point cloud data is ground point cloud data belonging to the same frame, that is, these ground point cloud data are obtained in a continuous scanning process and represent the state of the ground at the same moment or within a short period of time.

[0031] Optionally, the point cloud acquisition device coordinate system refers to the local reference coordinate system of the point cloud data acquisition device, in which the position of the point cloud acquisition device itself is defined as the origin (0, 0, 0), and all first ground point cloud data collected by the point cloud acquisition device are relative to the position of this origin.

[0032] Step S120, determining an error equation.

[0033] Specifically, the error equation is used to quantify the distances of multiple points in the first ground point cloud data to the ideal ground model in the vehicle coordinate system. The ideal ground model is a mathematical model that represents the geometric shape of the ground, and its purpose is to provide a constant, reliable and completely flat reference plane. The vehicle coordinate system is a reference coordinate system used to describe the position and posture of the vehicle in space. It is usually fixed to the vehicle and defined according to the geometric characteristics of the vehicle.

[0034] Optionally, the error equation includes a known constant term and a variable to be optimized, the known constant term includes a first transformation matrix that transforms the coordinate system of the point cloud acquisition device into the coordinate system of the integrated navigation device, and the variable to be optimized includes a second transformation matrix that transforms the coordinate system of the integrated navigation device into the vehicle coordinate system. Exemplarily, the optimal second transformation matrix can be found by minimizing the error equation.

[0035] In some embodiments, the ideal ground model is parallel to the XY plane of the vehicle coordinate system, and when the height of the origin of the vehicle coordinate system from the ground is h, the expression of the ideal ground model is Z=h, or Z=-h. The rear axle center of the vehicle is the origin of the vehicle coordinate system.

[0036] It can be understood that the XY plane of the vehicle coordinate system is a horizontal plane in the vehicle coordinate system, and the height (Z-axis coordinate) of any point in this plane is 0. The ideal ground model is parallel to the XY plane of the vehicle coordinate system, indicating that the plane of the ideal ground model and the XY plane of the vehicle coordinate system are at the same horizontal height, without any difference in angle or height. In some embodiments, if the positive direction of the Z axis of the vehicle coordinate system is the direction away from the ground, the expression of the ideal ground model is Z=-h; if the positive direction of the Z axis of the vehicle coordinate system is the direction close to the ground, the expression of the ideal ground model is Z=h.

[0037] In this embodiment, the ideal ground model is set to be parallel to the XY plane of the vehicle coordinate system and to take the center of the rear axle of the vehicle as the origin, which can simplify the recognition and processing of the ground and provide a fixed reference plane for the vehicle to facilitate accurate positioning and navigation. The mathematical expression of the ideal ground model is set to Z = h, or Z = -h, which can simplify the calculation of the position of the vehicle relative to the ideal ground model, thereby reducing the complexity of the calculation.

[0038] Step S130, adjusting the parameters of the second transformation matrix until the measure of the error equation meets the convergence condition, thereby obtaining a second transformation matrix that meets the optimization objective.

[0039] The parameters of the second transformation matrix represent the spatial relationship between the combined navigation device coordinate system and the vehicle coordinate system, including rotation, translation and possible scale changes. These parameters are variables that need to be optimized during the calibration process.

[0040] The measure of the error equation is an indicator of the deviation between the predicted value and the actual value. In this embodiment, it represents the magnitude of the value of the error equation caused by the currently estimated second transformation matrix, that is, the distance deviation from the point in the first ground point cloud data to the ideal ground model. In some embodiments, the measure of the error equation changes with the adjustment of the parameters of the second transformation matrix. The smaller the measure of the error equation, the closer the predicted value is to the actual value, that is, the more accurate the second transformation matrix is.

[0041] The convergence condition refers to a threshold value set in the process of optimizing the second transformation matrix. When the measure of the error equation decreases to below the threshold value, it is considered that a satisfactory solution has been found and further iterative optimization can be stopped. Exemplarily, numerical optimization methods such as gradient descent or Newton's method are used to gradually optimize the parameters of the second transformation matrix through iterative calculations.

[0042] In this embodiment, by acquiring the first ground point cloud data in the coordinate system of the point cloud acquisition device, the accurate information of the ground under the vehicle can be determined. Then, an error equation for quantifying the distance from the point in the first ground point cloud data to the ideal ground model in the vehicle coordinate system is determined. The equation provides a framework to evaluate and optimize the spatial relationship between the coordinate system of the combined navigation device and the vehicle coordinate system. This method does not rely on high-precision point cloud map information, making the calibration process faster and easier to deploy. Then, by continuously iteratively adjusting the parameters of the second transformation matrix until the measure of the error equation meets the convergence condition, this process can provide real-time feedback to guide the adjustment direction of the parameters of the second transformation matrix, and finally ensure that the optimal parameters of the second transformation matrix are found, thereby minimizing the calibration deviation between the combined navigation device and the vehicle and improving the accuracy of vehicle positioning.

[0043] In some embodiments of the present application, if the measure of the error equation does not meet the convergence condition, the first ground point cloud data in the point cloud acquisition device coordinate system is re-determined; using the first ground point cloud data in the re-determined point cloud acquisition device coordinate system, the parameters of the second transformation matrix are continued to be adjusted until the measure of the error equation meets the convergence condition.

[0044] For example, if the measure of the error equation does not meet the convergence condition, the point cloud acquisition device is used to collect data again under the same environmental conditions to obtain new first ground point cloud data. Then, using the new first ground point cloud data, according to Figure 1 The method in the illustrated embodiment adjusts the parameters of the second transformation matrix until the measure of the error equation satisfies the convergence condition. This process is to obtain more accurate or more reliable first ground point cloud data so as to more accurately determine the conversion relationship between the combined navigation device coordinate system and the vehicle coordinate system, thereby improving the accuracy of vehicle positioning.

[0045] Figure 2 The figure shows a schematic diagram of a process for determining first ground point cloud data provided by an embodiment of the present application. Figure 1 Based on the embodiment shown, Figure 2 The embodiment shown is described below in detail. Figure 2 The embodiment shown and Figure 1 The differences and similarities of the illustrated embodiments are not described in detail.

[0046] like Figure 2 As shown, in this embodiment, determining the first ground point cloud data in the point cloud acquisition device coordinate system includes the following steps.

[0047] Step S210, collecting point cloud data around the vehicle by means of a point cloud collection device on the vehicle; and determining first ground point cloud data in a coordinate system of the point cloud collection device from the point cloud data around the vehicle based on geometric features of the ground point cloud data.

[0048] Geometric features refer to geometric attributes extracted from point cloud data, which can describe information such as the shape, structure and spatial distribution of point cloud data. In some embodiments, the geometric features of ground point cloud data include at least one of ground point cloud height, ground point cloud density, ground point cloud curvature and ground point cloud connectivity. Among them, the ground point cloud height refers to the vertical distance of each point in the point cloud relative to a reference plane (usually the ground). In the processing of point cloud data around the vehicle, the ground point cloud height can be used to distinguish ground points from other non-ground points. The ground point cloud density refers to the distribution density of the ground point cloud in a certain area. The higher the density, the denser the point cloud in the area, and vice versa. The curvature of the ground point cloud reflects the density of the point cloud and the relative direction between adjacent points. Among them, high curvature indicates that the density of the point cloud is high or the direction changes greatly, and low curvature indicates that the distribution of the point cloud is relatively sparse or the direction changes little. Ground point cloud connectivity refers to the spatial connection relationship between adjacent points in ground point cloud data in three-dimensional space. If two points are close enough in space, they can be considered to be connected.

[0049] It should be noted that the geometric features of at least two types of ground point cloud data may be combined to continuously determine the first ground point cloud data in the point cloud data around the vehicle.

[0050] Exemplarily, taking the height of the ground point cloud as an example, the first ground point cloud data is screened in the point cloud data around the vehicle. The specific process is: set a height threshold, such as the height of the ground point cloud determined based on statistical analysis plus a standard deviation to obtain a height threshold, which can ensure that most ground points are selected while excluding some outliers. Then, by comparing the height of each point in the point cloud data around the vehicle with this height threshold, points above the threshold are considered to be non-ground points, such as trees, buildings, etc.; points below the threshold are considered to be ground points. Finally, the first ground point cloud data in the coordinate system of the point cloud acquisition device is accurately determined from the point cloud data around the vehicle.

[0051] Exemplarily, taking the curvature of the ground point cloud as an example, the first ground point cloud data is screened in the point cloud data around the vehicle. The specific process is: calculate the normal vector of each point in the point cloud data around the vehicle, and then calculate the curvature of each point, which describes the degree of curvature of the point. It can be understood that for ground points, since the ground is usually relatively flat, its curvature value is relatively small. Then, a curvature threshold is set, and points with smaller curvature can be identified as ground points. In this way, the first ground point cloud data in the coordinate system of the point cloud acquisition device can be determined.

[0052] Step S220, performing ground segmentation on the point cloud data around the vehicle to obtain first ground point cloud data in the coordinate system of the point cloud acquisition device.

[0053] Exemplarily, in this embodiment, the point cloud data around the vehicle is ground segmented through a deep learning model to obtain the first ground point cloud data in the coordinate system of the point cloud acquisition device.

[0054] In this embodiment, according to the geometric features of the ground point cloud data, it is possible to distinguish between ground point clouds and non-ground point clouds by analyzing the distribution characteristics of the point cloud data around the vehicle based on the intrinsic properties of the point cloud data around the vehicle. Secondly, the calculation method of geometric features usually only involves simple threshold operations and geometric calculations. This method has high computational efficiency and can meet the needs of real-time processing. In addition, the use of a deep learning model to perform ground segmentation on the point cloud data around the vehicle can improve the automation and accuracy of ground segmentation. Moreover, the deep learning model masters the laws of ground segmentation by learning the data itself. This method reduces the reliance on parameter adjustment and improves the adaptability of the ground segmentation algorithm. The two methods in this embodiment are used in combination to make full use of their respective advantages to achieve more accurate and robust extraction of the first ground point cloud data.

[0055] Figure 3 FIG. 1 is a flow chart of determining an error equation according to an embodiment of the present application. Figure 1 Based on the embodiment shown, Figure 3 The embodiment shown is described below in detail. Figure 3 The embodiment shown and Figure 1 The differences and similarities of the illustrated embodiments are not described in detail.

[0056] like Figure 3 As shown, in this embodiment, determining the error equation includes the following steps.

[0057] Step S310: Use the first transformation matrix and the second transformation matrix to perform coordinate transformation on the first ground point cloud data to obtain second ground point cloud data in a vehicle coordinate system.

[0058] In this step, the position of each point in the first ground point cloud data is updated by the first transformation matrix and the second transformation matrix during the coordinate transformation process. For example, if the coordinates of point A in the first ground point cloud data are (x 1 ,y 1 ,z 1 ), through the first transformation matrix M 1 After the transformation, the coordinates of point A become (x 2 ,y 2 ,z 2 ). Then, use the second transformation matrix M 2 Further transformation is performed to obtain the coordinates of point A in the vehicle coordinate system (x 3 ,y 3 ,z 3 ).

[0059] Exemplarily, the implementation formula of step S310 is: c =M 2 ×M 1 ×p l Among them, p l Represents the first ground point cloud data in the coordinate system of the point cloud acquisition device, p c Represents the second ground point cloud data in the vehicle coordinate system.

[0060] Step S320, calculating deviation vectors of a plurality of points in the second ground point cloud data relative to the center point of the ideal ground model.

[0061] Continuing with the above example, the deviation vector of the point in the second ground point cloud data relative to the center point of the ideal ground model can be expressed as in, Represents the coordinates of the center point of the ideal ground model.

[0062] For example, it is assumed that the ideal ground model is parallel to the XY plane of the vehicle coordinate system, and the height of the origin of the vehicle coordinate system from the ground is h. Then, when the positive direction of the Z axis of the vehicle coordinate system is the direction away from the ground, the center point of the ideal ground model is The coordinates are (0, 0, -h); when the positive direction of the Z axis of the vehicle coordinate system is close to the ground, the center point of the ideal ground model The coordinates are (0, 0, h).

[0063] Step S330 , performing a dot product operation on each deviation vector and the normal vector of the ideal ground model to obtain the distances of each of the multiple points to the ideal ground model.

[0064] The deviation vector represents the vector offset from the point in the second ground point cloud data to the ideal ground model, and the normal vector of the ideal ground model is a vector perpendicular to the plane of the ideal ground model. It can be understood that when two vectors are dot-producted, the result is a scalar whose value is equal to the length of the two vectors multiplied by the cosine value of the angle between them. When the two vectors are perpendicular, the angle between them is 90 degrees, the cosine value is 0, and the dot product result is 0.

[0065] In this embodiment, the normal vector of the ideal ground model is perpendicular to the ideal ground model, and the projection of the deviation vector on the normal vector of the ideal ground model is the vertical distance from the point in the second ground point cloud data to the ideal ground model. Therefore, by calculating the dot product of the deviation vector and the normal vector of the ideal ground model, a projection length can be obtained, and the projection length is the vertical distance from the point in the second ground point cloud data to the ideal ground model.

[0066] Step S340, summing up the distances from each of the multiple points to the ideal ground model to obtain a distance sum, and taking the minimum value of the distance sum as the first measure of the error equation, thereby determining the error equation.

[0067] It can be understood that the distance calculated in step S330 is a separate error measurement, each error measurement represents the degree of deviation of a point, and in order to obtain a comprehensive error index, it is necessary to add up these separate distances to obtain a distance sum, which can be used to evaluate the degree of fit of the current second transformation matrix to the second ground point cloud data.

[0068] It is understandable that the goal of the optimization process is to minimize the deviation between the second ground point cloud data and the ideal ground model, so an indicator is needed to reflect the degree of achievement of this minimization goal. In this embodiment, the minimum value of the sum of distances is the most intuitive and reasonable choice as the first measure of the error equation, which directly represents the minimum deviation that can be achieved between the second ground point cloud data and the ideal ground model under the current second transformation matrix.

[0069] Exemplarily, the mathematical expression of the error equation in this embodiment is:

[0070] in, Represents the normal vector of the ideal ground model.

[0071] In this embodiment, the first ground point cloud data is transformed by using the first transformation matrix and the second transformation matrix to transform the coordinates of the first point cloud data from the point cloud acquisition device coordinate system to the second ground point cloud data in the vehicle coordinate system, which provides a reference for the subsequent deviation calculation. Next, the deviation vector of the point in the second ground point cloud data relative to the center point of the ideal ground model is calculated, and the deviation vector provides a direct measurement of the point to the ideal ground model. Then, the distances from multiple points in the second point cloud data to the ideal ground model are summed to obtain the sum of the distances. The sum of the distances is used as a comprehensive error indicator to evaluate the performance of the current second transformation matrix. Finally, the minimum value of the sum of the distances is used as the first measure of the error equation to determine the error equation, which can better implement the optimization process of the second transformation matrix and find the optimal parameters of the second transformation matrix.

[0072] Combination Figure 3 In the illustrated embodiment, in other embodiments of the present application, the method further includes: dividing the sum of the distances by the number of the plurality of points to obtain an average distance, and using the minimum value of the average distance as a second measure of the error equation.

[0073] By calculating the average distance, it is helpful to understand how close the second ground point cloud data is to the ideal ground model as a whole. Secondly, in some cases, there may be some abnormal points in the second point cloud data that are far away from other points. If only the sum of the distances is considered, these abnormal points will have a greater impact on the overall error measurement. The average distance reduces this impact by dividing by the number of points, making the second measure of the error equation more stable and reliable. Furthermore, taking the minimum value of the average distance as the second measure of the error equation indicates that the present application not only focuses on the overall deviation, but also on the uniformity of the deviation distribution, which helps to find an optimal second transformation matrix during the calibration process.

[0074] Exemplarily, in this embodiment, the mathematical expression of the error equation is:

[0075] Here, n is the number of multiple points in the second ground point cloud data mentioned in step S320.

[0076] In some embodiments, determining the error equation further comprises: determining the error equation based on the first metric and the second metric.

[0077] That is, in this embodiment, when determining the error equation, the first metric and the second metric are considered simultaneously to obtain a more comprehensive and accurate error assessment.

[0078] Exemplarily, the error equation combines the two metrics in a specific way, for example, finding an optimal second transformation matrix so that both the first metric and the second metric reach a minimum; or in some cases, different weights are given to the two metrics to reflect their importance in the error assessment.

[0079] Specifically, the error equation is an optimization problem, the goal is to minimize one or more error metrics. This optimization problem is formalized as a mathematical model that includes the first metric and the second metric, and the parameters of the optimal second transformation matrix are determined by solving this mathematical model. In this way, the error equation can take into account both the total deviation represented by the first metric and the average deviation represented by the second metric, thereby providing more precise control during the calibration process.

[0080] Figure 4 FIG. 1 is a flow chart of adjusting the parameters of the second transformation matrix provided by an embodiment of the present application. Figure 1 Based on the embodiment shown, Figure 4 The embodiment shown is described below in detail. Figure 4 The embodiment shown and Figure 1 The differences and similarities of the illustrated embodiments are not described in detail.

[0081] like Figure 4 As shown, in this embodiment, adjusting the parameters of the second transformation matrix until the measure of the error equation meets the convergence condition includes the following steps.

[0082] Step S410, using the parameters of the current second transformation matrix, calculate the measure of the error equation.

[0083] Exemplarily, first, the first ground point cloud data in the point cloud acquisition device coordinate system is converted to the vehicle coordinate system using the currently known second transformation matrix parameters to obtain the second ground point cloud data. Then, the distance between the point in the second ground point cloud data and the ideal ground model in the vehicle coordinate system is calculated. Next, the distances corresponding to the multiple points are accumulated to obtain the sum of the distances (first measure). At the same time, the average of the sum of the distances is calculated to obtain the average distance (second measure). Finally, the measure of the error equation is determined based on the sum of the distances and the average distance.

[0084] Step S420: adjusting the parameters of the current second transformation matrix based on the measure of the error equation.

[0085] The magnitude of the error equation metric calculated in step S410 is analyzed to determine whether the parameters of the second transformation matrix need to be adjusted. For example, if the error equation metric exceeds a preset accuracy threshold, the parameters of the current second transformation matrix need to be further adjusted.

[0086] In this embodiment, the direction and magnitude of the parameter adjustment of the current second transformation matrix are evaluated according to the measure of the error equation. For example, in the gradient descent method, the gradient of the measure of the error equation with respect to the parameters of the second transformation matrix is ​​calculated. Then, along the direction of the gradient descent, the parameters of the second transformation matrix are updated to reduce the measure of the error equation.

[0087] Step S430, after each parameter adjustment, recalculate the measure of the error equation until the measure of the error equation reaches a preset accuracy threshold.

[0088] Specifically, each time the parameters of the second transformation matrix are updated, step S410 is performed again to calculate the metric of the new error equation to evaluate the effect of the parameter adjustment.

[0089] That is, this process is iterative, and each iteration adjusts the parameters of the current second transformation matrix based on the measure of the current error equation, and then recalculates the measure of the error equation to evaluate the adjustment effect, until the measure of the error equation meets the preset accuracy threshold or reaches other stop conditions (such as the number of iterations limit). In this way, the parameters of the second transformation matrix can be gradually optimized so that the second ground point cloud data in the vehicle coordinate system is aligned with the ideal ground model as much as possible, thereby improving the calibration accuracy of the combined navigation device and the vehicle.

[0090] Figure 5 FIG. 2 is a flow chart of adjusting the parameters of the second transformation matrix provided by another embodiment of the present application. Figure 1 Based on the embodiment shown, Figure 5 The embodiment shown is described below in detail. Figure 5 The embodiment shown and Figure 1 The differences and similarities of the illustrated embodiments are not described in detail.

[0091] like Figure 5 As shown, in this embodiment, the method also includes the following steps.

[0092] Step S510, detecting whether the second transformation matrix that meets the optimization target satisfies the target accuracy condition.

[0093] The target accuracy condition usually refers to an accuracy standard set during the calibration process of the combined navigation device and the vehicle in order to ensure that the distance between the first ground point cloud data and the ideal ground model is within an acceptable range. This standard is used to evaluate whether the parameters of the currently estimated second transformation matrix are accurate enough to accurately align the first ground point cloud data to the ideal ground model.

[0094] Exemplarily, a maximum allowable deviation distance is set as the target accuracy condition. Only when the distances from all points in the first ground point cloud data to the ideal ground model are less than the maximum allowable deviation distance, the second transformation matrix that meets the optimization target is considered acceptable.

[0095] Step S520: if the second transformation matrix that meets the optimization target does not meet the target accuracy condition, then re-determine the second transformation matrix that meets the optimization target.

[0096] Exemplarily, in step S520, the process of re-determining the second transformation matrix that meets the optimization target involves adjusting the parameters of the second transformation matrix based on the measure of the current error equation to better meet the target accuracy condition. The method of adjusting the parameters of the second transformation matrix based on the measure of the current error equation can be referred to in the above embodiment, and will not be repeated here.

[0097] Step S530: If the second transformation matrix that meets the optimization target does not meet the target accuracy condition, a prompt message is sent to the control end so that the operator can perform manual verification.

[0098] It is understandable that although the automated calibration process can give relatively accurate results, there may still be some special circumstances, such as quality problems of the point cloud data itself, interference from environmental factors, sensor anomalies, or limitations of the calibration algorithm itself, which make it difficult to automatically find the second transformation matrix that meets the target accuracy conditions and meets the optimization goals using the solution in this application, resulting in the calibration process of the combined navigation device and vehicle being unable to achieve the expected accuracy.

[0099] In this case, a prompt message will be sent to the control end. After receiving such a prompt, the operator can perform manual verification, including checking the quality of the point cloud data, confirming the installation and calibration status of the sensor, and evaluating whether the environmental conditions are suitable for calibration.

[0100] In this embodiment, if the second transformation matrix that meets the optimization target does not meet the target accuracy condition, the parameters of the second transformation matrix that meets the optimization target will be adjusted, and the measurement analysis of the error equation will be re-performed. This process is iterative until the second transformation matrix that meets the target accuracy condition and meets the optimization target is found. During the iterative optimization process, if the second transformation matrix that meets the optimization target does not meet the target accuracy condition, a prompt message will be sent to the control end to notify the operator to perform manual verification. This method provides additional safety protection to ensure that manual intervention can be carried out in time when the iterative optimization process cannot solve the problem. Through this solution, the efficiency of the calibration process is improved, thereby improving the accuracy of vehicle positioning.

[0101] Figure 6The figure shows a flow chart of detecting whether the target accuracy condition is met according to an embodiment of the present application. Figure 5 Based on the embodiment shown, Figure 6 The embodiment shown is described below in detail. Figure 6 The embodiment shown and Figure 5 The differences and similarities of the illustrated embodiments are not described in detail.

[0102] like Figure 6 As shown, in this embodiment, detecting whether the second transformation matrix that meets the optimization target meets the target accuracy condition includes the following steps.

[0103] Step S610: For each point in the first ground point cloud data, based on a second transformation matrix that meets the optimization objective, calculate the distance between each point and the ideal ground model.

[0104] In some embodiments, a second transformation matrix that meets the optimization goal and a known first transformation matrix are used to transform the coordinates of each point in the first ground point cloud data to obtain the second ground point cloud data in the vehicle coordinate system. For each point in the second ground point cloud data, a deviation vector from the ideal ground model is calculated. A dot product operation is performed on each deviation vector and a normal vector of the ideal ground model to obtain a vertical distance from each point to the ideal ground model.

[0105] Step S620: Calculate the average distance between the points in the first ground point cloud data and the ideal ground model based on the distance between each point and the ideal ground model.

[0106] Specifically, for the vertical distance from each point to the ideal ground model calculated in step S610, these vertical distances are accumulated and then divided by the total number of points to obtain the average distance from the points in the first ground point cloud data to the ideal ground model. This average distance reflects the overall closeness of the first ground point cloud data to the ideal ground model. It can be understood that if the second transformation matrix that meets the optimization goal is already very close to the optimal solution, then this average distance will be very small, which means that the first ground point cloud data set can be well aligned with the ideal ground model.

[0107] Step S630: If the average distance is greater than a preset distance threshold, it is determined that the second transformation matrix that meets the optimization target does not meet the target accuracy condition.

[0108] If the calculated average distance is greater than the preset distance threshold, it means that the second transformation matrix that meets the optimization goal has not yet achieved the expected accuracy and needs further adjustment.

[0109] In this embodiment, by calculating the distance from each point in the first ground point cloud data to the ideal ground model, the accuracy of the first ground point cloud data is evaluated one by one. Then, the average distance from all points in the first ground point cloud data to the ideal ground model is calculated, wherein the average distance is used as a statistic to reduce the influence of outliers, which provides a comprehensive and stable evaluation index to reflect the overall accuracy of the first ground point cloud data.

[0110] Combination of the above Figures 1 to 6 , describes in detail the combined navigation device and vehicle calibration method embodiment of the present application, and the following is combined with Figure 7 , the combined navigation device and the vehicle calibration device embodiment of the present application are described in detail. It should be understood that the description of the combined navigation device and the vehicle calibration method embodiment corresponds to the description of the combined navigation device and the vehicle calibration device embodiment, so the parts not described in detail can refer to the previous method embodiment.

[0111] Figure 7 FIG. 1 is a schematic diagram of the structure of a combined navigation device and a vehicle calibration device provided in an embodiment of the present application. Figure 7 As shown, the combined navigation device and vehicle calibration device provided in the embodiment of the present application include:

[0112] A first determination module 710 is used to determine first ground point cloud data in a point cloud acquisition device coordinate system;

[0113] A second determination module 720 is used to determine an error equation, where the error equation is used to quantify the distances from a plurality of points in the first ground point cloud data to the ideal ground model in the vehicle coordinate system, respectively. The error equation includes a known constant term and a variable to be optimized, where the known constant term includes a first transformation matrix that transforms the coordinate system of the point cloud acquisition device into the coordinate system of the integrated navigation device, and the variable to be optimized includes a second transformation matrix that transforms the coordinate system of the integrated navigation device into the vehicle coordinate system;

[0114] The adjustment module 730 is used to adjust the parameters of the second transformation matrix until the measure of the error equation meets the convergence condition, thereby obtaining the second transformation matrix that meets the optimization target.

[0115] In one embodiment of the present application, the second determination module 720 is also used to use the first transformation matrix and the second transformation matrix to perform coordinate transformation on the first ground point cloud data to obtain second ground point cloud data in the vehicle coordinate system; calculate the deviation vectors of multiple points in the second ground point cloud data relative to the center point of the ideal ground model; perform a dot product operation on each deviation vector and the normal vector of the ideal ground model to obtain the distances of each of the multiple points to the ideal ground model; sum the distances of each of the multiple points to the ideal ground model to obtain the sum of the distances, and use the minimum value of the sum of the distances as the first measure of the error equation to determine the error equation.

[0116] In one embodiment of the present application, the second determination module 720 is further used to divide the total distance by the number of multiple points to obtain an average distance, and use the minimum value of the average distance as the second measure of the error equation; and determine the error equation based on the first measure and the second measure.

[0117] In one embodiment of the present application, the ideal ground model is parallel to the XY plane of the vehicle coordinate system, and when the height of the origin of the vehicle coordinate system above the ground is h, the expression of the ideal ground model is Z=h, or Z=-h; and / or, the center of the rear axle of the vehicle is the origin of the vehicle coordinate system; and / or, the first ground point cloud data is ground point cloud data belonging to the same frame.

[0118] In one embodiment of the present application, the adjustment module 730 is also used to calculate the measurement of the error equation using the parameters of the current second transformation matrix; adjust the parameters of the current second transformation matrix based on the measurement of the error equation; and recalculate the measurement of the error equation after each parameter adjustment until the measurement of the error equation reaches a preset accuracy threshold.

[0119] In one embodiment of the present application, the adjustment module 730 is also used to, if the measurement of the error equation does not meet the convergence condition, redetermine the first ground point cloud data in the point cloud acquisition device coordinate system; and use the redetermined first ground point cloud data in the point cloud acquisition device coordinate system to continue adjusting the parameters of the second transformation matrix until the measurement of the error equation meets the convergence condition.

[0120] In one embodiment of the present application, the device also includes a third determination module, which is used to detect whether the second transformation matrix that meets the optimization target meets the target accuracy condition; if the second transformation matrix that meets the optimization target does not meet the target accuracy condition, the second transformation matrix that meets the optimization target is re-determined, and / or a prompt message is sent to the control end for the operator to perform manual verification.

[0121] In one embodiment of the present application, the third determination module is also used to, for each point in the first ground point cloud data, calculate the distance from each point to the ideal ground model based on the second transformation matrix that meets the optimization objective; based on the distance from each point to the ideal ground model, calculate the average distance from the points in the first ground point cloud data to the ideal ground model; if the average distance is greater than a preset distance threshold, it is determined that the second transformation matrix that meets the optimization objective does not meet the target accuracy condition.

[0122] In one embodiment of the present application, the first determination module 710 is also used to collect point cloud data around the vehicle through a point cloud acquisition device on the vehicle; based on the geometric features of the ground point cloud data, determine the first ground point cloud data in the coordinate system of the point cloud acquisition device from the point cloud data around the vehicle; or, perform ground segmentation on the point cloud data around the vehicle to obtain the first ground point cloud data in the coordinate system of the point cloud acquisition device.

[0123] In one embodiment of the present application, the geometric features of the ground point cloud data include at least one of ground point cloud height, ground point cloud density, ground point cloud curvature, and ground point cloud connectivity.

[0124] Below, reference Figure 8 To describe an electronic device according to an embodiment of the present application. Figure 8 Shown is a schematic structural diagram of an electronic device provided by an exemplary embodiment of the present application.

[0125] like Figure 8 As shown, the electronic device 80 includes one or more processors 801 and a memory 802 .

[0126] The processor 801 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 80 to perform desired functions.

[0127] The memory 802 may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 801 may run the program instructions to implement the calibration method of the combined navigation device and vehicle of each embodiment of the present application described above and / or other desired functions. Various contents such as first ground point cloud data, error equations, first transformation matrix, second transformation matrix, etc. may also be stored in the computer-readable storage medium.

[0128] In one example, the electronic device 80 may further include: an input device 803 and an output device 804 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0129] The input device 803 may include, for example, a keyboard, a mouse, etc.

[0130] The output device 804 can output various information to the outside, including the first ground point cloud data, the error equation, the first transformation matrix, the second transformation matrix, etc. The output device 804 can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, etc.

[0131] Of course, to simplify, Figure 8 Only some of the components related to the present application in the electronic device 80 are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application situations, the electronic device 80 may also include any other appropriate components.

[0132] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, which, when executed by a processor, enable the processor to execute the steps of the calibration method of the combined navigation device and vehicle according to various embodiments of the present application described above in this specification.

[0133] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages, such as Java, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0134] In addition, an embodiment of the present application may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps of the calibration method of the combined navigation device and the vehicle according to various embodiments of the present application described above in this specification.

[0135] The computer readable storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0136] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be noted that the advantages, strengths, effects, etc. mentioned in the present application are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required by each embodiment of the present application. In addition, the specific details disclosed above are only for the purpose of illustration and ease of understanding, not for limitation, and the above details do not limit the present application to being implemented by adopting the above specific details.

[0137] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably with them. The words "or" and "and" used here refer to the words "and / or" and can be used interchangeably with them, unless the context clearly indicates otherwise. The words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably with them.

[0138] It should also be noted that in the apparatus, device and method of the present application, each component or each step can be decomposed and / or recombined. Such decomposition and / or recombination should be regarded as equivalent solutions of the present application.

[0139] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0140] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for calibrating a combined navigation device and a vehicle, characterized in that: include: Determine first ground point cloud data in the coordinate system of the point cloud acquisition device; Determine an error equation, the error equation is used to quantify the distances of multiple points in the first ground point cloud data to the ideal ground model in the vehicle coordinate system, the error equation includes a known constant term and a variable to be optimized, the known constant term includes a first transformation matrix that transforms the point cloud acquisition device coordinate system to the integrated navigation device coordinate system, and the variable to be optimized includes a second transformation matrix that transforms the integrated navigation device coordinate system to the vehicle coordinate system; The parameters of the second transformation matrix are adjusted until the measure of the error equation meets the convergence condition, thereby obtaining a second transformation matrix that meets the optimization objective.

2. The combined navigation device and vehicle calibration method according to claim 1, characterized in that: The determining error equation comprises: Performing coordinate transformation on the first ground point cloud data using the first transformation matrix and the second transformation matrix to obtain second ground point cloud data in the vehicle coordinate system; Calculating deviation vectors of a plurality of points in the second ground point cloud data relative to a center point of the ideal ground model; Performing a dot product operation on each of the deviation vectors and a normal vector of the ideal ground model to obtain a distance from each of the plurality of points to the ideal ground model; The distances from each of the multiple points to the ideal ground model are summed to obtain a distance sum, and the minimum value of the distance sum is used as the first measure of the error equation, thereby determining the error equation.

3. The combined navigation device and vehicle calibration method according to claim 2, characterized in that: The method further comprises: Dividing the sum of the distances by the number of the plurality of points to obtain an average distance, and using the minimum value of the average distance as a second measure of the error equation; Determining the error equation further includes: The error equation is determined based on the first metric and the second metric.

4. The combined navigation device and vehicle calibration method according to claim 1, characterized in that: The ideal ground model is parallel to the XY plane of the vehicle coordinate system, and when the height of the origin of the vehicle coordinate system from the ground is h, the expression of the ideal ground model is Z=h, or Z=-h; and / or, The center of the rear axle of the vehicle is the origin of the vehicle coordinate system; and / or, The first ground point cloud data are ground point cloud data belonging to the same frame.

5. The combined navigation device and vehicle calibration method according to any one of claims 1 to 4, characterized in that: The step of adjusting the parameters of the second transformation matrix until the measure of the error equation satisfies a convergence condition comprises: Calculating a measure of the error equation using parameters of the current second transformation matrix; Based on the measure of the error equation, adjusting the parameters of the current second transformation matrix; After each parameter adjustment, the measure of the error equation is recalculated until the measure of the error equation reaches a preset accuracy threshold.

6. The combined navigation device and vehicle calibration method according to any one of claims 1 to 4, characterized in that: Also includes: If the measure of the error equation does not satisfy the convergence condition, re-determining the first ground point cloud data in the coordinate system of the point cloud acquisition device; Using the re-determined first ground point cloud data in the point cloud acquisition device coordinate system, continue to adjust the parameters of the second transformation matrix until the measure of the error equation meets the convergence condition.

7. The combined navigation device and vehicle calibration method according to any one of claims 1 to 4, characterized in that: Also includes: Detecting whether the second transformation matrix that meets the optimization target meets the target accuracy condition; If the second transformation matrix that meets the optimization target does not meet the target accuracy condition, the second transformation matrix that meets the optimization target is re-determined, and / or a prompt message is sent to the control end for the operator to perform manual verification.

8. The combined navigation device and vehicle calibration method according to claim 7, characterized in that: The detecting whether the second transformation matrix meeting the optimization target meets the target accuracy condition includes: For each point in the first ground point cloud data, based on the second transformation matrix that meets the optimization objective, calculate the distance between each point and the ideal ground model; Based on the distance from each point to the ideal ground model, calculating the average distance from the points in the first ground point cloud data to the ideal ground model; If the average distance is greater than a preset distance threshold, it is determined that the second transformation matrix that meets the optimization target does not meet the target accuracy condition.

9. The combined navigation device and vehicle calibration method according to any one of claims 1 to 8, characterized in that: The determining of the first ground point cloud data in the coordinate system of the point cloud acquisition device includes: Collecting point cloud data around the vehicle by using a point cloud acquisition device on the vehicle; Based on the geometric features of the ground point cloud data, determining first ground point cloud data in the coordinate system of the point cloud acquisition device from the point cloud data around the vehicle; Alternatively, ground segmentation is performed on the point cloud data around the vehicle to obtain first ground point cloud data in the coordinate system of the point cloud acquisition device.

10. The combined navigation device and vehicle calibration method according to claim 9, characterized in that: The geometric features of the ground point cloud data include at least one of ground point cloud height, ground point cloud density, ground point cloud curvature, and ground point cloud connectivity.

11. A calibration device for a combined navigation device and a vehicle, characterized in that: include: A first determination module, used to determine first ground point cloud data in a point cloud acquisition device coordinate system; A second determination module is used to determine an error equation, wherein the error equation is used to quantify the distances of multiple points in the first ground point cloud data to an ideal ground model in a vehicle coordinate system, wherein the error equation includes a known constant term and a variable to be optimized, wherein the known constant term includes a first transformation matrix that transforms the coordinate system of the point cloud acquisition device into the coordinate system of the integrated navigation device, and the variable to be optimized includes a second transformation matrix that transforms the coordinate system of the integrated navigation device into the vehicle coordinate system; The adjustment module is used to adjust the parameters of the second transformation matrix until the measure of the error equation meets the convergence condition, thereby obtaining the second transformation matrix that meets the optimization target.

12. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the calibration method according to any one of claims 1 to 10 above.

13. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is used to execute the calibration method described in any one of claims 1 to 10 above.

Images