Calibration Method, Device, Medium, Equipment and Vehicle of Sensor

Abstract

The present disclosure relates to a calibration method, apparatus, medium, device, and vehicle for a sensor. The calibration method includes: obtaining a reference pose and a motion speed of a vehicle coordinate system, a pose of a sensor to be calibrated, and a pose of an inertial measurement unit; performing coupled integration based on the reference pose, the motion speed, and the pose of the inertial measurement unit to determine a pose of an odometer corresponding to the vehicle coordinate system; and determining rotation and translation parameters of the sensor to be calibrated relative to the vehicle coordinate system based on the pose of the odometer corresponding to the vehicle coordinate system and the pose of the sensor to be calibrated. Thus, by performing coupled integration in combination with the pose of the inertial measurement unit, the pose of the odometer corresponding to the vehicle coordinate system is obtained, and then the sensor to be calibrated is calibrated, so that the external parameter calibration of the in-vehicle sensor to be calibrated can be conveniently implemented in a three-dimensional space, including calibrating the height, and the calibration accuracy is relatively high, and there is no need for a calibration board assistance or to establish a high-precision three-dimensional map of a calibration site in advance.

Description

Technical Field

[0001] The present disclosure relates to the technical field of vehicles, and particularly to a calibration method, device, medium, equipment and vehicle for a sensor. Background Art

[0002] In vehicles, especially autonomous vehicles, it is usually necessary to be equipped with multiple sensors, such as image sensors, lidars, etc. When using multiple sensors, generally, external parameter calibration is required, that is, obtaining the conversion relationship between sensor coordinate systems, such as rotation and translation, etc., to unify the coordinate systems of the data collected by multiple sensors.

[0003] For in-vehicle sensors, they are usually calibrated with the rear axle coordinate system of the vehicle, and usually need to be calibrated before the vehicle leaves the factory. However, with the increase in the types and quantities of in-vehicle sensors, as well as the increase in autonomous vehicles, the factory calibration method is difficult to efficiently calibrate the external parameters of in-vehicle sensors; moreover, after leaving the factory, during the actual operation of the vehicle, the external parameters of the sensors will change due to vibration loosening or accessory damage, and recalibration is required.

[0004] In related technologies, there are mainly three implementation methods for external parameter calibration. The first is to first establish a high-precision three-dimensional map of the calibration site in advance. During the driving process of the vehicle in this map, the in-vehicle sensors obtain their poses in the map through the method of relocating in this map, and perform non-linear optimization of the poses and external parameters to achieve multi-sensor external parameter calibration without a common view area. This requires establishing a high-precision three-dimensional map in advance and depends on a fixed scene. The second is to use a calibration auxiliary board, such as a checkerboard calibration board combined with a wheel odometer for external parameter calibration. In this process, it is necessary to rely on the calibration auxiliary board and requires manual intervention, and full automation calibration cannot be achieved. The third is to discard the checkerboard calibration board on the basis of the second method, thereby reducing manual intervention, but this will cause errors in the visual odometry scale and affect the calibration accuracy. In addition, both the second and third methods assume that the vehicle is moving on a plane, but this assumption often does not really hold, and on the premise of this assumption, the height of the in-vehicle sensors cannot be calibrated. Summary of the Invention

[0005] In order to solve the above technical problems or at least partially solve the above technical problems, the present disclosure provides a calibration method, device, medium, equipment and vehicle for a sensor.

[0006] The present disclosure provides a calibration method for a sensor loaded on a moving object, including:

[0007] Obtaining a reference pose and a moving speed of a vehicle coordinate system, a pose of a sensor to be calibrated, and a pose of an inertial measurement unit;

[0008] Perform coupled integration based on the reference pose, the motion speed, and the pose of the inertial measurement unit to determine the pose of the odometer corresponding to the vehicle coordinate system;

[0009] Based on the pose of the odometer corresponding to the vehicle coordinate system and the pose of the sensor to be calibrated, determine the rotation and translation parameters of the sensor to be calibrated relative to the vehicle coordinate system.

[0010] The present disclosure also provides a calibration device for a sensor loaded on a moving body, including:

[0011] An acquisition module, configured to acquire the reference pose and motion speed of the vehicle coordinate system, the pose of the sensor to be calibrated, and the pose of the inertial measurement unit;

[0012] A first determination module, configured to perform coupled integration based on the reference pose, the motion speed, and the pose of the inertial measurement unit to determine the pose of the odometer corresponding to the vehicle coordinate system;

[0013] A second determination module, configured to determine the rotation and translation parameters of the sensor to be calibrated relative to the vehicle coordinate system based on the pose of the odometer corresponding to the vehicle coordinate system and the pose of the sensor to be calibrated.

[0014] The present disclosure also provides a computer-readable storage medium storing a computer program for executing the steps of any of the above methods.

[0015] The present disclosure also provides a vehicle device, including: a processor; a memory for storing executable instructions of the processor; the processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the steps of any of the above methods.

[0016] The present disclosure also provides a vehicle including any of the above vehicle devices.

[0017] The technical solution provided by the present disclosure has the following advantages compared with the prior art:

[0018] The calibration method for the sensor provided by the present disclosure includes obtaining the reference pose and motion speed of the vehicle coordinate system, the pose of the sensor to be calibrated, and the pose of the inertial measurement unit; performing coupled integration based on the reference pose, motion speed, and the pose of the inertial measurement unit to determine the pose of the odometer corresponding to the vehicle coordinate system; and determining the rotation and translation parameters of the sensor to be calibrated relative to the vehicle coordinate system based on the pose of the odometer corresponding to the vehicle coordinate system and the pose of the sensor to be calibrated. Thus, by performing coupled integration in combination with the pose of the inertial measurement unit, the pose of the odometer corresponding to the vehicle coordinate system is obtained, and then the sensor to be calibrated is calibrated, enabling convenient external parameter calibration of the in-vehicle sensor to be calibrated in three-dimensional space, including calibrating the height, with high calibration accuracy, and without the need for a calibration board assistance or the prior establishment of a high-precision three-dimensional map of the calibration site. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings herein are incorporated into and constitute a part of this specification, showing embodiments consistent with the present disclosure and, together with the specification, are used to explain the principles of the present disclosure.

[0020] To more clearly illustrate the technical solutions in the embodiments of the present disclosure or in the prior art, the following briefly introduces the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0021] Figure 1 It is a schematic flowchart of a calibration method for a sensor provided by an embodiment of the present disclosure;

[0022] Figure 2 For Figure 1 shown in the method, it is a detailed flowchart of S120;

[0023] Figure 3 It is a comparison schematic diagram of three different integrations provided by an embodiment of the present disclosure;

[0024] Figure 4 For Figure 1 shown in the method, it is a detailed flowchart of S130;

[0025] Figure 5 It is a technical roadmap of a calibration method for a sensor provided by an embodiment of the present disclosure;

[0026] Figure 6 It is a schematic structural diagram of a calibration device for a sensor provided by an embodiment of the present disclosure;

[0027] Figure 7 It is a schematic structural diagram of a vehicle device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] To more clearly understand the above objects, features, and advantages of the present disclosure, the solutions of the present disclosure will be further described below. It should be noted that, without conflict, the embodiments of the present disclosure and the features in the embodiments may be combined with each other.

[0029] In the following description, many specific details are set forth to facilitate a thorough understanding of the present disclosure, but the present disclosure may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only a part of the embodiments of the present disclosure, rather than all of the embodiments.

[0030] The technical solutions provided by the embodiments of the present disclosure are applicable to the scenario of multi-sensor data fusion. Before using the sensors for fusion positioning / sensing, the sensors are calibrated, that is, the position conversion relationship between different sensors is obtained, so as to be able to unify the data of multiple sensors under the same perspective / coordinate system. The sensor calibration method provided by the embodiments of the present disclosure can conveniently achieve the automatic calibration of vehicle-mounted sensors in three-dimensional space without the assistance of the environment and manual labor.

[0031] The following will exemplarily describe the calibration method, device, equipment, medium, and vehicle of the sensors provided by the embodiments of the present disclosure with reference to the accompanying drawings.

[0032] Exemplarily, Figure 1 is a schematic flowchart of a sensor calibration method provided by an embodiment of the present disclosure, which is applied to the calibration of sensors loaded on a moving object. The moving object may be a vehicle, and the sensor correspondingly is a vehicle-mounted sensor; or the moving object may be a drone flying in the air or operating in water, and the sensor correspondingly is a sensor loaded on the drone.

[0033] Referring to Figure 1 , the method may include the following steps:

[0034] S110. Obtain the reference pose and motion speed of the vehicle coordinate system, the pose of the sensor to be calibrated, and the pose of the inertial measurement unit.

[0035] Among them, the pose of the sensor to be calibrated is calibrated based on the reference pose of the vehicle coordinate system, that is, with the vehicle coordinate system as the reference, the external parameters of the sensor to be calibrated are determined, that is, the rotation amount and translation amount of the sensor to be calibrated transformed into the vehicle coordinate system are determined, so as to unify the data collected by the sensor to be calibrated into the vehicle coordinate system.

[0036] Among them, in the process of odometer integration based on the reference pose and motion speed, the pose of the Inertial Measurement Unit (IMU) is used to obtain the angular velocity integral rotation increment, corresponding to perform trajectory deduction to obtain the pose of the odometer corresponding to the vehicle coordinate system in three-dimensional space, which will be described in detail later. This is to facilitate the external parameter calibration in three-dimensional space including height.

[0037] Among them, the motion speed can be obtained based on the data collected by the wheel speed sensor. The reference pose and motion speed of the vehicle coordinate system, the pose of the sensor to be calibrated, and the pose of the inertial measurement unit can also be obtained by any method known to those skilled in the art, which will not be elaborated or limited here.

[0038] S120. Perform coupled integration based on the reference pose, motion speed, and the pose of the inertial measurement unit to determine the pose of the odometer corresponding to the vehicle coordinate system.

[0039] Among them, in the related art, odometer integration is performed using the reference pose and motion speed to obtain the pose of the odometer corresponding to the vehicle coordinate system. In the embodiments of the present disclosure, the pose of the inertial measurement unit is used to provide the angular velocity integral rotation increment, so that the motion speed corresponding to the inertial measurement unit and the reference pose can be coupled for three-dimensional space integration to obtain the pose of the odometer corresponding to the vehicle coordinate system in three-dimensional space, which is convenient for calibrating the external parameters of the sensor to be calibrated in three-dimensional space.

[0040] The specific implementation manner of this step will be exemplarily described later.

[0041] S130. Based on the pose of the odometer corresponding to the vehicle coordinate system and the pose of the sensor to be calibrated, determine the rotation and translation parameters of the sensor to be calibrated relative to the vehicle coordinate system.

[0042] Among them, based on the pose of the odometer corresponding to the vehicle coordinate system and the pose of the sensor to be calibrated, a transformation is performed to determine the external parameters of the sensor to be calibrated with the vehicle coordinate system as the reference, that is, the rotation and translation parameters.

[0043] In the sensor calibration method provided by the embodiments of the present disclosure, by using the inertial measurement unit and the motion speed corresponding to the reference pose of the vehicle coordinate system to perform trajectory deduction in three-dimensional space, odometer integration in three-dimensional space is realized, reducing the influence of the direction error corresponding to the motion speed on the calibration result, improving the calibration accuracy, and at the same time realizing the calibration of height; at the same time, this calibration method does not require a calibration board for assistance nor a high-precision three-dimensional map of the calibration site to be established in advance, realizing high convenience.

[0044] The following provides an exemplary description of the implementation steps for performing trajectory deduction on the motion speed corresponding to the combined inertial measurement unit and the reference pose, and determining the pose of the odometer corresponding to the vehicle coordinate system.

[0045] In some embodiments, based on Figure 1 , in combination with Figure 2 , "determining the pose of the odometer corresponding to the vehicle coordinate system" in S120 may specifically include:

[0046] S121. Use interpolation to align the timestamps of the reference pose, the motion speed, and the pose of the inertial measurement unit.

[0047] Among them, to align the timestamps of the reference pose, the motion speed, and the pose of the inertial measurement unit, the specific method may be: fix the timestamp of the motion speed, perform interpolation on the timestamp of the pose of the inertial measurement unit, and perform interpolation on the timestamp of the reference pose to fill in the data, so as to improve the data processing accuracy in subsequent steps.

[0048] In other embodiments, the timestamp alignment of the pose of the sensor to be calibrated is also synchronously performed, which is not limited herein.

[0049] It should be noted that "interpolation" in this article mainly includes two parts. One part is the spherical interpolation of the rotation quaternion, and the other part is the linear interpolation of the translation vector, so as to achieve the external parameter calibration of rotation and translation.

[0050] S122. Based on the reference pose and the pose of the inertial measurement unit after timestamp alignment, determine the rotation external parameter of the inertial measurement unit relative to the vehicle coordinate system.

[0051] Among them, the rotation external parameter includes the rotation matrix of the pitch angle and the roll angle of the pose of the inertial measurement unit relative to the reference pose, and the default yaw angle is 0 degrees. In this step, the reference pose transformation of the inertial measurement unit relative to the vehicle coordinate system is performed to obtain the rotation external parameter of the inertial measurement unit relative to the vehicle coordinate system.

[0052] In some embodiments, the first formula is used to determine the rotation external parameter. Among them, the first formula may be:

[0053]

[0054] Among them, represents the rotation quaternion from the i-th frame to the i + 1-th frame of the vehicle coordinate system, represents the rotation quaternion from the i-th frame to the i + 1-th frame of the inertial measurement unit, q yx1 represents the rotation external parameter of the inertial measurement unit relative to the vehicle coordinate system; L and R respectively represent the left multiplication symbol and the right multiplication symbol of the rotation quaternion.

[0055] Thus, based on the first formula, a pose transformation is performed to obtain the external rotation parameter q of the inertial measurement unit relative to the vehicle coordinate system yx1 .

[0056] In some embodiments, the pose of the inertial measurement unit can be initialized by gravity. The vehicle coordinate system can perform planar trajectory deduction using the front wheel speed and steering angle through a bicycle model, and record the rotation pose of the vehicle at each timestamp. Further, by performing rotational calibration on the rotation pose of the vehicle and the integrated rotation pose of the inertial measurement unit, the external rotation parameter between the inertial measurement unit and the vehicle coordinate system is obtained, which can be specifically determined by the first formula.

[0057] It should be noted that in this embodiment, it is default that the Y-axis direction of the inertial measurement unit is the vehicle forward direction, that is, the Y-axis direction of the vehicle coordinate system is the forward direction; both the inertial measurement unit and the vehicle coordinate system are right-handed systems, that is, Cartesian coordinate systems. And among them, the X-axis and Z-axis of the inertial measurement unit may not be parallel to the X-axis and Z-axis of the vehicle coordinate system. Here, calibration is performed using the pitch angle and roll angle, and can be calculated by the first formula exemplarily.

[0058] It can be understood that in this embodiment, the spatial transformation relationship between different sensors (including the vehicle coordinate system, the inertial measurement unit, and the sensor to be calibrated) is fixed and unchanged. Therefore, on the basis of the above timestamp alignment, the data for the entire time period is used for calibration once, that is, the external parameters are calibrated jointly; at the algorithm level, the first formula can be solved using data at multiple different times in the entire time period, that is, an overdetermined equation is solved; the least squares method is used to solve the solution of the overdetermined equation to obtain the rotation external parameter.

[0059] S123. Determine the angular velocity integral rotation increment of the odometer corresponding to the vehicle coordinate system based on the rotation external parameter and the rotation increment between two adjacent frames of the inertial measurement unit.

[0060] Among them, the rotation external parameter determined in the previous step and the rotation increment between two adjacent frames determined based on the pose of the inertial measurement unit are used to determine the angular velocity integral rotation increment of the odometer corresponding to the vehicle coordinate system, which is convenient for subsequent trajectory deduction in combination with this angular velocity integral rotation increment.

[0061] In some embodiments, the second formula is used to determine the angular velocity integral rotation increment. Among them, the second formula can be:

[0062]

[0063] Among them, R(α) represents the angular velocity integral rotation increment represents the rotation external parameter of the inertial measurement unit relative to the vehicle coordinate system represents the rotation increment of the (t + 1)-th frame of the inertial measurement unit relative to the t-th frame.

[0064] In this way, by combining the rotational increments of two adjacent frames of the inertial measurement unit and the external rotational parameters determined in the above steps, the angular velocity integrated rotational increment is obtained, providing data on the angular velocity integrated rotational increment related to the inertial measurement unit for the trajectory deduction in the subsequent steps.

[0065] S124. Perform a closed-form three-dimensional space integration in the group space based on the angular velocity integrated rotational increment and the reference pose to determine the pose of the odometer corresponding to the vehicle coordinate system.

[0066] Among them, coupling the angular velocity integrated rotational increment related to the inertial measurement unit into the odometer integration realizes the three-dimensional space trajectory deduction. Thus, when the motion speed is obtained based on the data collected by the wheel speedometer, the influence of the wheel speedometer direction error on the calibration result is reduced, which is beneficial to improving the accuracy of the external parameter calibration; at the same time, using the closed-form three-dimensional space integration in the group space improves the integration accuracy, which is also beneficial to improving the calibration accuracy.

[0067] In some embodiments, the third formula is used for the closed-form integration in the group space; the third formula is:

[0068]

[0069] where, R t represents the rotational attitude of the vehicle coordinate system at the t-th frame, corresponding to three angular quantities, P t represents the position of the vehicle coordinate system at the t-th frame, corresponding to three translational quantities; R(α) represents the angular velocity integrated rotational increment, A(α) represents the left multiplication BCH approximate Jacobian of the Lie algebra, α represents the axis angle of the rotational increment, u represents the position increment obtained by integrating the motion speed; and

[0070]

[0071] where, a represents the rotation axis, v b represents the motion speed.

[0072] Specifically, there are mainly three ways of wheel speedometer trajectory deduction, namely: Euler integration, second-order Runge-Kutta integration, and closed-form integration in the group space. The integration schematic diagrams are as Figure 3 shown.

[0073] Figure 3 In, 001 represents the integration principle of Euler integration, 002 represents the integration principle of second-order Runge-Kutta integration, and 003 represents the integration principle of closed-form integration in the group space. Among them, q k and q k+1Represent two poses before and after integration. The dotted line marked by △s abstracts the curved track of the vehicle's movement, and the solid line indicates the degree of coincidence between the integrated track and it; the central angle of the sector corresponding to △θ corresponds to the vehicle's curved driving; the intersection of the two straight dotted lines corresponds to the virtual center of motion.

[0074] Among them, Euler integration is relatively simple, and the error mainly comes from the assumption of constant angle during the translational integration process. This is not affected during straight-line motion, and the error is also small when the integration time is very small. In the second-order Runge-Kutta integration process, it is considered that the angle is the average value before and after the motion, and the error is a little smaller than that of Euler integration. Closed-loop integration in the group space, that is, integrating the wheel speedometer in the Lie group space, has almost no error.

[0075] Exemplarily, the closed-loop integration formula in the group space is as shown in the third formula above, that is:

[0076]

[0077] And among them:

[0078]

[0079] In this embodiment, if a single vehicle model is adopted, the motion speed can be expressed as:

[0080] v b =[0,v f cos(δ),0] T

[0081] It is the velocity vector in the vehicle coordinate system. During calibration, it is considered that the forward direction of the vehicle is the Y-axis direction, and u is the position increment integrated from the motion speed.

[0082] Among the above three integration methods, the closed-loop integration in the group space has the highest accuracy. Therefore, in this embodiment, the closed-loop integration in the group space is adopted for the integration of the wheel speedometer, and combined with the observation of the inertial measurement unit, the integration of the wheel speedometer is extended to three-dimensional space for closed-loop integration in the group space.

[0083] Specifically, the data related to the inertial measurement unit is incorporated into the integration, that is, the angular velocity integration rotation increment uses the angular velocity integration rotation increment determined based on the rotation increment of two adjacent frames of the inertial measurement unit to obtain the angular velocity integration rotation increment of the wheel speedometer.

[0084] In the embodiment of the present disclosure, through the second formula and the third formula, the inertial measurement unit and the wheel speedometer can be coupled for three-dimensional space integration, so as to obtain the integration result of six degrees of freedom (6-Dof), in which the plane assumption is avoided, and the pose of the odometer corresponding to the vehicle coordinate system in three-dimensional space is obtained.

[0085] In the embodiments of the present disclosure, the assumption of planar vehicle motion is abandoned, and instead, the pose of the inertial measurement unit is introduced. Combining with the wheel speedometer, the trajectory deduction in three-dimensional space is realized, and the vehicle trajectory (i.e., the pose of the odometer corresponding to the vehicle coordinate system) is obtained. Each point on the trajectory represents a three-dimensional pose of the vehicle coordinate system, so that the extrinsic calibration of the vehicle-mounted sensor based on hand-eye calibration can be realized in three-dimensional space.

[0086] In some embodiments, when the sensor to be calibrated is an image sensor (i.e., a camera), obtaining the pose of the camera and representing it in a six-degree-of-freedom manner may include: The Visual Odometry (VO) part uses the open-source ORB-SLAM algorithm to calculate the pose of the camera.

[0087] Among them, eliminating the error caused by scale drift by unifying the scale may include: The motion of the vehicle is set to a closed-loop motion, enabling the VO part to perform loop detection and optimize in the similarity transformation group (i.e., Sim(3)) to reduce scale drift. Thus, all trajectories are unified to the same scale S. In this embodiment, loop detection is used to eliminate scale drift and unify the scale S, so that the global scale of the VO part is consistent, providing a better visual prior information for the subsequent calibration process, that is, the six-degree-of-freedom visual pose of the camera.

[0088] Among them, the closed-loop motion can be a motion in the shape of an "8", a "0", a double-track "s", or other closed curve trajectories, as long as it satisfies the closed trajectory motion with curvature. Loop detection, that is, closed-loop detection, can judge whether it has returned to the starting point based on the real-time detected data, that is, judge whether the closed loop is completed.

[0089] In the embodiments of the present disclosure, complementary trajectory deduction between the vehicle coordinate system and the inertial measurement unit is realized, that is, odometer integration, to obtain an integration result with six degrees of freedom, that is, to obtain the pose of the odometer corresponding to the vehicle coordinate system.

[0090] In the embodiments of the present disclosure, considering the scale drift problem of the visual odometer of the monocular camera, the optimization in the similarity transformation group after the monocular loop of ORB-SLAM is fully utilized to ensure the consistency of the global scale. Furthermore, in the subsequent calibration process, the scale of the visual pose can be used as a fixed variable for optimization iteration, reducing the calibration difficulty and improving the accuracy.

[0091] The following is an exemplary description of the implementation steps for determining the conversion relationship between the sensor to be calibrated and the vehicle coordinate system based on the pose of the odometer corresponding to the vehicle coordinate system.

[0092] In some embodiments, on the basis of Figure 1 combining with Figure 4, "Determining the rotation and translation parameters of the sensor to be calibrated relative to the vehicle coordinate system" in S130 may specifically include:

[0093] S131. Using interpolation to align the timestamps of the pose of the sensor to be calibrated and the reference pose of the vehicle coordinate system.

[0094] Among them, the specific method of aligning the timestamps of the pose of the sensor to be calibrated and the reference pose may be: fixing the timestamp of the sensor to be calibrated and interpolating the timestamp of the reference pose to fill in the data, so as to improve the accuracy of data processing in subsequent steps.

[0095] In other embodiments, all data to be processed may also be synchronized with timestamp alignment, which is not limited herein.

[0096] The subsequent calibration is performed in three steps. First, by rotating the sensor to be calibrated and the pose of the odometer corresponding to the vehicle coordinate system, the pitch angle and roll angle of the external parameters of the pose from the sensor to be calibrated to the odometer corresponding to the vehicle coordinate system are calibrated, as in S132 below; then, the rough calibration of the external parameters is mainly to obtain the scale information of the pose of the sensor to be calibrated, and the rotation yaw angle of the external parameters, the x and y of the translation external parameters, and the detection scale S are calibrated within the plane range, as shown in S133 below; finally, using the detection scale S as prior information, the 6-degree-of-freedom external parameter calibration is performed in three-dimensional space, as shown in S134 below. Thus, the external parameter calibration from the sensor to be calibrated to the vehicle coordinate system can be effectively automated; the external parameter data can be provided for the autonomous driving vehicle, providing the initial value of the external parameters for the multi-sensor fusion of the vehicle and the automatic calibration of the external parameters during the driving application process.

[0097] S132. Based on the pose of the sensor to be calibrated and the pose of the odometer corresponding to the vehicle coordinate system after timestamp alignment, perform rotation angle calibration to determine the rotation quaternion composed of the pitch angle and roll angle of the external rotation of the sensor to be calibrated.

[0098] The rotation angle calibration is performed in this step.

[0099] In some embodiments, the fourth formula is used for rotation angle calibration; the fourth formula is:

[0100]

[0101] Among them, represents the rotation quaternion from the i-th frame to the i + 1-th frame of the odometer corresponding to the vehicle coordinate system, represents the rotation quaternion from the i-th frame to the i + 1-th frame of the sensor to be calibrated, q yx2represents the rotation quaternion synthesized by the pitch angle and roll angle of the rotation extrinsic parameter of the sensor to be calibrated relative to the odometer corresponding to the vehicle coordinate system; L and R respectively represent the left multiplication symbol and right multiplication symbol of the rotation quaternion.

[0102] The formula principle in this embodiment is the same as that of the first formula in the previous text, and the only difference lies in the distinction of the rotation quaternion. Similarly, the SVD least squares solution can be used to obtain the rotation quaternion synthesized by the pitch angle and roll angle of the rotation extrinsic parameter of the sensor to be calibrated.

[0103] S133. Based on the poses of the sensor to be calibrated and the odometer corresponding to the vehicle coordinate system after timestamp alignment, perform two-dimensional calibration to determine the detection scale.

[0104] In this step, the detection scale is unified, and the rotation yaw angle of the rotation extrinsic parameter and the x and y of the translation extrinsic parameter are solved.

[0105] In some embodiments, the fifth formula is used for two-dimensional calibration. Among them, the fifth formula can be:

[0106]

[0107] Among them, represents the rotation matrix of the odometer corresponding to the vehicle coordinate system, represents the x and y of the extrinsic translation vector from the sensor to be calibrated to the vehicle coordinate system; α represents the yaw angle of the rotation extrinsic parameter from the sensor to be calibrated to the vehicle coordinate system, S represents the detection scale, represents the position change amount of the sensor to be calibrated in x and y, represents the translation change amount of the odometer corresponding to the vehicle coordinate system; ( ) row：1，2；col：1，2 means taking the data of the first row, second row, first column, and second column of the matrix.

[0108] In the embodiments of the present disclosure, pose transformation is solved by using the sensor to be calibrated and the odometer corresponding to the vehicle coordinate system, and the least squares solution is constructed by using the above fifth formula. First, the least squares initial value is solved by the SVD method, and then the least squares solution is optimized by the iterative optimization method. In this step, the same detection scale S in the previous text is solved and used in the sixth formula in the following text to achieve three-dimensional full-parameter calibration.

[0109] S134. Based on the detection scale, the rotation quaternion synthesized by the pitch angle and roll angle, and the poses of the sensor to be calibrated and the odometer corresponding to the vehicle coordinate system after timestamp alignment, perform three-dimensional full-parameter calibration to determine the yaw angle of the rotation extrinsic parameter of the sensor to be calibrated and the extrinsic translation vector.

[0110] In this step, in the three-dimensional space, the extrinsic parameter calibration with 6 degrees of freedom is performed.

[0111] In some embodiments, the sixth formula is used for three-dimensional full-parameter calibration; the sixth formula is as follows:

[0112]

[0113] Wherein, represents the rotation matrix of the odometer corresponding to the vehicle coordinate system, represents the external parameter translation vector from the sensor to be calibrated to the vehicle coordinate system, including three translation amounts of x, y, and z, and is more accurate than the x and y of the external parameter translation vector obtained by solving based on the fifth formula; α represents the yaw angle of the external parameter rotation from the sensor to be calibrated to the vehicle coordinate system; S represents the detection scale, and the detection scale S obtained by solving based on the fifth formula is substituted; represents the position change amounts of the sensor to be calibrated in the x and y directions, represents the translation change amount of the odometer corresponding to the vehicle coordinate system.

[0114] Exemplarily, the sixth formula can be converted into the seventh formula through matrix transformation, as follows:

[0115]

[0116] Wherein:

[0117]

[0118] Similarly, the least-squares initial value is solved by SVD first, and then the optimal solution of the external parameters is calculated through iterative optimization. At the same time, since the detection scale S in the sixth formula is a known fixed value, it avoids the contradictory phenomenon that occurs when -Scosα, -Ssinα, and S are solved as independent variables during the solution process, reduces the solution difficulty, and improves the accuracy.

[0119] In some embodiments, the moving object includes a vehicle; the vehicle coordinate system takes the center of the rear axle of the vehicle as the coordinate origin, and the sensor to be calibrated includes at least one of an image sensor, a lidar, and a Real Time Kinematic (RTK) sensor.

[0120] In some embodiments, the moving object includes a vehicle; the vehicle coordinate system coincides with the lidar coordinate system, and the sensor to be calibrated includes at least one of an image sensor and a real-time dynamic positioning sensor.

[0121] In other embodiments, the moving object can also be other moving objects such as unmanned aerial vehicles that can achieve autonomous driving or assisted driving, and the sensor to be calibrated can also be other types of sensors loaded on the moving object, which is not limited herein.

[0122] In some embodiments, Figure 5This is a technical roadmap of a calibration method for a sensor provided by an embodiment of the present disclosure, showing the calibration method when the sensor to be calibrated is a camera. Refer to Figure 5 , this method may include:

[0123] Determination of the pose of the odometer corresponding to the vehicle coordinate system, specifically including: calibrating the rotation between the IMU and the vehicle coordinate system based on the IMU and wheel speed, and coupling the IMU with the wheel speed to obtain the pose of the odometer corresponding to the vehicle coordinate system, which can be understood with reference to S120 above;

[0124] Determination of the monocular pose (i.e., the pose of the monocular camera), specifically including: performing monocular VO based on the data collected by the camera to determine the camera pose;

[0125] Calibration, specifically including: rotation calibration, two-dimensional calibration, determination of the detection scale S, and three-dimensional hand-eye calibration, so as to achieve three-dimensional full-parameter calibration, which can be understood with reference to S130 above.

[0126] The calibration method for the sensor provided by the embodiment of the present disclosure has at least the following beneficial effects:

[0127] First, this calibration method does not require the assistance of a calibration board, nor does it require the prior establishment of a high-precision three-dimensional map of the calibration site, reducing the dependence on the environment and manual labor for calibration, enabling the moving object to achieve automatic external parameter calibration of the sensor to be calibrated in the driving environment without manual intervention, and improving the calibration convenience.

[0128] Second, in this calibration method, during the track deduction process of coupling the inertial measurement unit to the three-dimensional space, it no longer depends on the assumption of vehicle planar motion, reducing the influence of the direction error of the motion speed on the calibration result; moreover, the closed-form integration in the Lie group space improves the accuracy of odometer integration, which is conducive to improving the calibration accuracy. In addition, it can achieve height calibration of the sensor to be calibrated, corresponding to the external parameter z in the sixth equation.

[0129] Third, the VO part is optimized in the similarity transformation group through loop detection, ensuring the consistency of the global scale, reducing the influence of the detection scale error on the calibration result, and improving the calibration accuracy.

[0130] Fourth, in this calibration method, the calibration process of the pose of the sensor to be calibrated relative to the odometer corresponding to the vehicle coordinate system is divided into three steps. The rotation is split and calibrated, so that the solution no longer depends on the excitation of the Z axis to adapt to the situation where the excitation of the Z axis is relatively low during vehicle driving; and first calibrating in the two-dimensional space and then in the three-dimensional space, combined with using the unified detection scale S of loop detection, can greatly reduce the influence of the detection scale error on the calibration result and can adapt to the situation where the VO scale is uncertain.

[0131] Fifth, taking the sensor to be calibrated as a camera as an example, the external parameter calibration of all three-dimensional parameters from the camera to the vehicle coordinate system based on the hand-eye calibration algorithm is realized. Moreover, this method does not require the prior establishment of a high-precision three-dimensional map, nor does it require the assistance of a calibration board; since there are no special requirements for the calibration site, it can also be widely used for the external parameter automatic calibration of various sensors, such as lidar, real-time kinematic positioning sensors, etc.

[0132] Based on the above embodiments, the embodiments of the present disclosure further provide a calibration device for a sensor loaded on a moving object, and this device can execute the steps of any of the methods provided in the above embodiments to achieve the corresponding beneficial effects.

[0133] Exemplarily, Figure 6 is a schematic structural diagram of a calibration device for a sensor provided by an embodiment of the present disclosure. Refer to Figure 6 , the device 30 may include: an acquisition module 310, configured to acquire the reference pose of the vehicle coordinate system, the motion speed of the vehicle coordinate system, the pose of the sensor to be calibrated, and the pose of the inertial measurement unit; the vehicle coordinate system, the sensor to be calibrated, and the inertial measurement unit all move synchronously with the moving object; a first determination module 320, configured to perform coupled integration based on the reference pose, the motion speed, and the pose of the inertial measurement unit to determine the pose of the odometer corresponding to the vehicle coordinate system; a second determination module 330, configured to determine the rotation and translation parameters of the sensor to be calibrated relative to the vehicle coordinate system based on the pose of the odometer corresponding to the vehicle coordinate system and the pose of the sensor to be calibrated.

[0134] In the calibration device for a sensor provided by the embodiments of the present disclosure, through the cooperation among the above functional modules, it is possible to perform track deduction in three-dimensional space by using the motion speed corresponding to the reference pose of the inertial measurement unit and the vehicle coordinate system, realize odometer integration in three-dimensional space, reduce the influence of the direction error corresponding to the motion speed on the calibration result, improve the calibration accuracy, and at the same time realize altitude calibration; at the same time, this calibration method does not require the assistance of a calibration board nor the prior establishment of a high-precision three-dimensional map of the calibration site, achieving relatively high convenience.

[0135] In some embodiments, the first determination module 320 is configured to determine the pose of the odometer corresponding to the vehicle coordinate system, and specifically may include: using interpolation to align the timestamps of the reference pose, the motion speed, and the pose of the inertial measurement unit; based on the reference pose and the pose of the inertial measurement unit after timestamp alignment, determining the external rotation parameter of the inertial measurement unit relative to the vehicle coordinate system; based on the external rotation parameter and the rotation increment between two adjacent frames of the inertial measurement unit, determining the angular velocity integration rotation increment of the odometer corresponding to the vehicle coordinate system; and performing closed-form three-dimensional space integration in the group space based on the angular velocity integration rotation increment and the reference pose to determine the pose of the odometer corresponding to the vehicle coordinate system.

[0136] In some embodiments, the external rotation parameters are determined using the first formula; the first formula is:

[0137]

[0138] Wherein, represents the rotation quaternion from the i-th frame to the (i + 1)-th frame of the vehicle coordinate system, represents the rotation quaternion from the i-th frame to the (i + 1)-th frame of the inertial measurement unit, q yx1 represents the external rotation parameters of the inertial measurement unit relative to the vehicle coordinate system; L and R respectively represent the left multiplication symbol and the right multiplication symbol of the rotation quaternion.

[0139] In some embodiments, the angular velocity integrated rotation increment is determined using the second formula; the second formula is:

[0140]

[0141] Wherein, R(α) represents the angular velocity integrated rotation increment, represents the external rotation parameters of the inertial measurement unit relative to the vehicle coordinate system, represents the rotation increment of the (t + 1)-th frame of the inertial measurement unit relative to the t-th frame.

[0142] In some embodiments, the group space closed-form integration is performed using the third formula; the third formula is:

[0143]

[0144] Wherein, R t represents the rotation attitude of the vehicle coordinate system at the t-th frame, P t represents the position of the vehicle coordinate system at the t-th frame, R(α) represents the angular velocity integrated rotation increment, A(α) represents the left multiplication BCH approximate Jacobian of the Lie algebra, α represents the axis angle of the rotation increment, u represents the position increment obtained by integrating the motion velocity; and

[0145]

[0146] Wherein, a represents the rotation axis, v b represents the motion velocity.

[0147] In some embodiments, the second determination module 330 is configured to determine the rotation and translation parameters of the sensor to be calibrated relative to the vehicle coordinate system, which may specifically include: using interpolation to align the timestamps of the pose of the sensor to be calibrated and the reference pose of the vehicle coordinate system; based on the pose of the sensor to be calibrated after timestamp alignment and the pose of the odometer corresponding to the vehicle coordinate system, performing rotation angle calibration to determine the rotation quaternion synthesized by the pitch angle and roll angle of the external parameters rotation of the sensor to be calibrated; based on the pose of the sensor to be calibrated after timestamp alignment and the pose of the odometer corresponding to the vehicle coordinate system, performing two-dimensional calibration to determine the detection scale; based on the detection scale, the rotation quaternion synthesized by the pitch angle and roll angle, and the pose of the sensor to be calibrated after timestamp alignment and the pose of the odometer corresponding to the vehicle coordinate system, performing three-dimensional full-parameter calibration to determine the yaw angle of the external parameters rotation of the sensor to be calibrated and the external parameter translation vector.

[0148] In some embodiments, the fourth formula is used for rotation angle calibration; the fourth formula is:

[0149]

[0150] Wherein, represents the rotation quaternion from the i-th frame to the (i + 1)-th frame of the odometer corresponding to the vehicle coordinate system, represents the rotation quaternion from the i-th frame to the (i + 1)-th frame of the sensor to be calibrated, q yx2 represents the rotation quaternion synthesized by the pitch angle and roll angle of the external rotation reference of the sensor to be calibrated relative to the odometer corresponding to the vehicle coordinate system; L and R respectively represent the left multiplication symbol and right multiplication symbol of the rotation quaternion.

[0151] In some embodiments, the fifth formula is used for two-dimensional calibration; the fifth formula is:

[0152]

[0153] Wherein, represents the rotation matrix of the odometer corresponding to the vehicle coordinate system, represents the x and y of the external parameter translation vector from the sensor to be calibrated to the vehicle coordinate system; α represents the yaw angle of the external rotation from the sensor to be calibrated to the vehicle coordinate system, S represents the detection scale, represents the position change amount of the sensor to be calibrated in x and y, represents the translation change amount of the odometer corresponding to the vehicle coordinate system; ( ) row：1，2；col：1，2 means taking the data of the first row, second row, first column, and second column of the matrix.

[0154] In some embodiments, the sixth formula is used for three-dimensional full-parameter calibration; the sixth formula is:

[0155]

[0156] Among them, represents the rotation matrix of the odometer corresponding to the vehicle coordinate system, represents the external parameter translation vector from the sensor to be calibrated to the vehicle coordinate system, α represents the yaw angle of the external parameter rotation from the sensor to be calibrated to the vehicle coordinate system, and S represents the detection scale. represents the position change of the sensor to be calibrated in the x and y directions, represents the translation change of the odometer corresponding to the vehicle coordinate system.

[0157] In some embodiments, the moving body includes a vehicle; the vehicle coordinate system has the center of the rear axle of the vehicle as the coordinate origin, and the sensor to be calibrated includes at least one of an image sensor, a lidar, and a real-time kinematic positioning sensor.

[0158] In some embodiments, the moving body includes a vehicle; the vehicle coordinate system coincides with the lidar coordinate system, and the sensor to be calibrated includes at least one of an image sensor and a real-time kinematic positioning sensor.

[0159] It can be understood that Figure 6 the shown device can implement any of the methods provided by the above embodiments, and has corresponding beneficial effects. For details, please refer to the above for understanding and will not be elaborated here.

[0160] Based on the above embodiments, as Figure 7 shown, it is a schematic structural diagram of a vehicle device provided by an embodiment of the present disclosure. Referring to Figure 7 , the vehicle device 40 includes: a processor 420; a memory 410 for storing executable instructions executable by the processor 420; the processor 420 is configured to read the executable instructions from the memory 410 and execute the executable instructions to implement the steps of any of the methods provided by the above embodiments, and has corresponding beneficial effects. To avoid repeated description, it will not be elaborated here.

[0161] Among them, the processor 420 may be a central processing unit (CPU) or other forms of processing units with data processing capabilities and / or instruction execution capabilities, and may control other components in the computer to perform desired functions.

[0162] The memory 410 may include one or more computer program products, and the computer program products 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, 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 media, and the processor 420 may run the program instructions to implement the method steps of the various embodiments of the present application described above and / or other desired functions.

[0163] In addition to the above methods and vehicle-mounted devices, embodiments of the present application may also be computer program products, which include computer program instructions that, when run by a processor, cause the processor to execute the method steps of the various embodiments of the present application.

[0164] The computer program products may be written in any combination of one or more programming languages for programming code to perform the operations of the embodiments of the present disclosure. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user device, executed as a stand-alone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0165] Furthermore, embodiments of the present disclosure may also be computer-readable storage media, on which computer program instructions are stored, and the computer program instructions, when run by the processor 420, cause the processor 420 to execute the method steps of the various embodiments of the present application.

[0166] The computer-readable storage media may adopt any combination of one or more readable media. The readable media may be a readable signal medium or a readable storage medium. The readable storage media may include, for example, but are not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above. More specific examples (non-exhaustive list) of the readable storage media include: electrical connections with one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0167] Based on the above embodiments, the embodiments of the present disclosure further provide a vehicle, including the above vehicle equipment, which has corresponding beneficial effects. To avoid repeated description, it will not be elaborated here.

[0168] In other embodiments, the vehicle may further include other structural components, which will not be elaborated or limited here.

[0169] It should be noted that in this text, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0170] The above are only specific embodiments of the present disclosure, enabling those skilled in the art to understand or implement the present disclosure. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments described herein, but rather will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A calibration method for a sensor mounted on a moving object, characterized in that, including: Obtaining a reference pose and a motion speed of a vehicle coordinate system, a pose of a sensor to be calibrated, and a pose of an inertial measurement unit; Performing coupled integration based on the reference pose, the motion speed, and the pose of the inertial measurement unit to determine a pose of an odometer corresponding to the vehicle coordinate system; Determining rotation and translation parameters of the sensor to be calibrated relative to the vehicle coordinate system based on the pose of the odometer corresponding to the vehicle coordinate system and the pose of the sensor to be calibrated; The determining the pose of the odometer corresponding to the vehicle coordinate system includes: Using interpolation to align time stamps of the reference pose, the motion speed, and the pose of the inertial measurement unit; Determining an external rotation parameter of the inertial measurement unit relative to the vehicle coordinate system based on the reference pose and the pose of the inertial measurement unit after time stamp alignment; Determining an angular velocity integrated rotation increment of the odometer corresponding to the vehicle coordinate system based on the external rotation parameter and a rotation increment between two adjacent frames of the inertial measurement unit; Performing a closed-form three-dimensional space integration in a group space based on the angular velocity integrated rotation increment and the reference pose to determine the pose of the odometer corresponding to the vehicle coordinate system.

2. The method according to claim 1, wherein The external rotation parameter is determined by using a first formula; the first formula is: Among them, represents the rotation quaternion from the i-th frame to the (i + 1)-th frame of the vehicle coordinate system, represents the rotation quaternion from the i-th frame to the (i + 1)-th frame of the inertial measurement unit, q yx1 represents the external rotation parameter of the inertial measurement unit relative to the vehicle coordinate system; L and R respectively represent the left multiplication symbol and the right multiplication symbol of the rotation quaternion.

3. The method according to claim 1, wherein The angular velocity integrated rotation increment is determined by using a second formula; the second formula is: where R(α) represents the angular velocity integrated rotation increment, represents the external rotation parameter of the inertial measurement unit relative to the vehicle coordinate system, represents the rotation increment of the (t + 1)-th frame of the inertial measurement unit relative to the t-th frame.

4. The method according to claim 1, wherein The closed-form integration in the group space is performed by using a third formula; the third formula is: where R t represents the rotational attitude of the vehicle coordinate system at the t-th frame, P t represents the position of the vehicle coordinate system at the t-th frame, R(α) represents the angular velocity integrated rotation increment, A(α) represents the left multiplication BCH approximation Jacobian of Lie algebra, α represents the axis angle of the rotation increment, and u represents the position increment obtained by integrating the motion speed; and Among them, a represents the rotation axis, and v b represents the moving speed.

5. The method according to claim 1, wherein Determining the rotation and translation parameters of the sensor to be calibrated relative to the vehicle coordinate system includes: Using interpolation to align the time stamp of the pose of the sensor to be calibrated with the time stamp of the reference pose of the vehicle coordinate system; Performing rotation angle calibration based on the pose of the sensor to be calibrated and the pose of the odometer corresponding to the vehicle coordinate system after time stamp alignment to determine a rotation quaternion synthesized by a pitch angle and a roll angle of an external parameter rotation of the sensor to be calibrated; Performing two-dimensional calibration based on the pose of the sensor to be calibrated and the pose of the odometer corresponding to the vehicle coordinate system after time stamp alignment to determine a detection scale; Performing three-dimensional full-parameter calibration based on the detection scale, the rotation quaternion synthesized by the pitch angle and the roll angle, and the pose of the sensor to be calibrated and the pose of the odometer corresponding to the vehicle coordinate system after time stamp alignment to determine a yaw angle of the external parameter rotation of the sensor to be calibrated and an external parameter translation vector.

6. The method according to claim 5, wherein The rotation angle calibration is performed by using a fourth formula; the fourth formula is: wherein, represents the rotation quaternion from the i-th frame to the (i + 1)-th frame of the odometer corresponding to the vehicle coordinate system, represents the rotation quaternion from the i-th frame to the (i + 1)-th frame of the sensor to be calibrated, q yx2 represents the rotation quaternion synthesized by the pitch angle and roll angle of the external rotation parameter of the sensor to be calibrated relative to the odometer corresponding to the vehicle coordinate system; L and R respectively represent the left multiplication symbol and right multiplication symbol of the rotation quaternion.

7. The method according to claim 5, wherein The two-dimensional calibration is performed by using a fifth formula; the fifth formula is: Among them, represents the rotation matrix of the odometer corresponding to the vehicle coordinate system, represents the x and y of the external parameter translation vector from the sensor to be calibrated to the vehicle coordinate system; α represents the yaw angle of the external parameter rotation from the sensor to be calibrated to the vehicle coordinate system, and S represents the detection scale, represents the position change amount of the sensor to be calibrated in the x and y directions, represents the translation change amount of the odometer corresponding to the vehicle coordinate system; ( ) row：1，2；col：1，2 means taking the data of the first row, second row, first column, and second column of the matrix.

8. The method according to claim 5, wherein The three-dimensional full-parameter calibration is performed by using a sixth formula; the sixth formula is: Among them, represents the rotation matrix of the odometer corresponding to the vehicle coordinate system, represents the external parameter translation vector from the sensor to be calibrated to the vehicle coordinate system, α represents the yaw angle of the external parameter rotation from the sensor to be calibrated to the vehicle coordinate system, and S represents the detection scale, represents the position change of the sensor to be calibrated in the x and y directions, represents the translation change of the odometer corresponding to the vehicle coordinate system.

9. The method according to any one of claims 1 to 8, characterized in that The moving object includes a vehicle; The vehicle coordinate system has the center of the rear axle of the vehicle as the origin of the coordinate system, and the sensor to be calibrated includes at least one of an image sensor, a lidar, and a real-time kinematic (RTK) sensor; or The vehicle coordinate system coincides with the lidar coordinate system, and the sensor to be calibrated includes at least one of an image sensor and a real-time kinematic (RTK) sensor.

10. A calibration device for a sensor mounted on a moving object, characterized in that, Comprising: An acquisition module, configured to acquire the reference pose and motion speed of the vehicle coordinate system, the pose of the sensor to be calibrated, and the pose of the inertial measurement unit; A first determination module, configured to perform coupled integration based on the reference pose, the motion speed, and the pose of the inertial measurement unit, and use interpolation to align the timestamps of the reference pose, the motion speed, and the pose of the inertial measurement unit; Based on the reference pose and the pose of the inertial measurement unit after timestamp alignment, determine the external rotation parameter of the inertial measurement unit relative to the vehicle coordinate system; Based on the external rotation parameter and the rotation increment between two adjacent frames of the inertial measurement unit, determine the angular velocity integrated rotation increment of the odometer corresponding to the vehicle coordinate system; Based on the angular velocity integrated rotation increment and the reference pose, perform a closed-form three-dimensional space integration in the Lie group space to determine the pose of the odometer corresponding to the vehicle coordinate system; A second determination module, configured to determine the rotation and translation parameters of the sensor to be calibrated relative to the vehicle coordinate system based on the pose of the odometer corresponding to the vehicle coordinate system and the pose of the sensor to be calibrated.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the computer program is used to execute the steps of the method according to any one of claims 1-9.

12. A vehicle-mounted device, characterized in that, Comprising: A processor; A memory for storing executable instructions executable by the processor; The processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the steps of the method according to any one of claims 1-9.

13. A vehicle, characterized in that, Including the vehicle device according to claim 12.

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