Sensor parameter online calibration method, device, equipment and storage medium

Abstract

This application discloses a method, apparatus, device, and storage medium for online calibration of sensor parameters. The technical solution provided by this application includes: constructing an optimization model based on sensor data and initial sensor parameters, solving the optimization model to obtain sensor optimization parameters, where the sensor optimization parameters are the optimal solution of the optimization model; determining whether the sensor optimization parameters meet preset convergence conditions; and, in response to a judgment result that the convergence conditions are met, setting the sensor optimization parameters as the initial sensor parameters and prior constraint values ​​for subsequent online calibration to perform subsequent online calibration. The above-mentioned technical means solve the problem of slow convergence speed during online calibration of sensor parameters in the prior art, thereby improving the efficiency of online calibration.

Description

Technical Field

[0001] The present application relates to the field of parameter calibration technology, and in particular to a sensor parameter online calibration method, device, equipment and storage medium. Background Art

[0002] When reconstructing a 3D map from sensor data collected by multiple sensors, the actual scale and pose of the sensor data are determined based on the intrinsic parameters of each sensor and the extrinsic parameters between them. Therefore, the extrinsic and intrinsic parameters of multiple sensors affect the accuracy of map reconstruction. Although multiple sensors are factory calibrated, factors such as calibration coverage, process control, and calibration modeling accuracy can still cause discrepancies between factory calibration results and actual observations.

[0003] Currently, online calibration of the intrinsic and extrinsic parameters of multiple sensors is used to address the discrepancy between factory calibration and real-world observations. The online calibration process involves constructing an online calibration model based on the physical processes of the sensor data collected by each sensor and the spatiotemporal relationships between them. This model is then incorporated into the residual model for state estimation to produce a calibration result that minimizes error and conforms to the sensor data. The inclusion of the online calibration model increases the dimension of the residual model's state vector, requiring multiple frames of data to produce a converged calibration result. This means that convergence of the calibration result takes a considerable amount of time. Summary of the Invention

[0004] The present application provides a sensor parameter online calibration method, apparatus, device and storage medium, which solves the problem of slow convergence speed when performing online calibration of the intrinsic and extrinsic parameters of multiple sensors in the prior art, and improves the online calibration efficiency.

[0005] In a first aspect, the present application provides a sensor parameter online calibration method, comprising:

[0006] Building an optimization model based on the sensor data and the initial parameters of the sensor, and solving the optimization model to obtain sensor optimization parameters, where the sensor optimization parameters are the optimal solution of the optimization model;

[0007] Determining whether the sensor optimization parameters meet preset convergence conditions;

[0008] In response to a judgment result that the convergence condition is satisfied, the sensor optimization parameters are set as initial sensor parameters and prior constraint values ​​for subsequent online calibration, so as to perform subsequent online calibration.

[0009] In a second aspect, the present application provides a sensor parameter online calibration device, comprising:

[0010] a first optimization module configured to construct an optimization model based on the sensor data and the initial sensor parameters, and solve the optimization model to obtain sensor optimization parameters, where the sensor optimization parameters are the optimal solution of the optimization model;

[0011] a first judgment module, configured to determine whether the sensor optimization parameters meet a preset convergence condition;

[0012] The first setting module is configured to set the sensor optimization parameters as the sensor initial parameters and prior constraints of the corresponding optimization model during subsequent online calibration in response to the judgment result that the convergence condition is met, so as to perform online calibration of the parameters.

[0013] In a third aspect, the present application provides a sensor parameter online calibration device, comprising:

[0014] One or more processors; a memory storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the sensor parameter online calibration method as described in the first aspect.

[0015] In a fourth aspect, the present application provides a storage medium comprising computer-executable instructions, which, when executed by a computer processor, are used to perform the sensor parameter online calibration method as described in the first aspect.

[0016] This application constructs an optimization model based on sensor data and sensor parameters obtained from the last online calibration, and solves the optimization model to obtain optimized sensor parameters. By repeatedly constructing and solving the optimization model, the current sensor parameters of the sensor can be accurately determined, and accurate calibration of the sensor parameters can be achieved to ensure the accuracy of the three-dimensional map construction. After each optimized sensor parameter is obtained, it is determined whether the optimized sensor parameters have converged. When the sensor parameters converge, it can be determined that the sensor parameters are close to the true value. The convergence of the sensor parameters can be used as the judgment condition for the end of calibration to accurately obtain the current sensor parameters of the sensor, avoiding the need to correct and optimize the sensor parameters every time new sensor data is collected, resulting in the inability to quickly and stably export the three-dimensional map. The converged sensor parameters are used as the initial sensor parameters and prior constraint values ​​for the next online calibration of the sensor, so that the optimization model can be constructed based on the converged sensor parameters during the next online calibration, and the optimized sensor parameters can be determined within the limited range of the converged sensor parameters. Due to the existence of the prior constraints, the convergence of the sensor parameters will be accelerated, the sensor parameters of the current sensor can be quickly and accurately determined, and the efficiency of online calibration will be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a sensor parameter online calibration method provided by an embodiment of the present application;

[0018] Figure 2 This is a flowchart of determining whether sensor optimization parameters meet convergence conditions provided by an embodiment of the present application;

[0019] Figure 3 This is a flow chart of calculating the mean and variance of optimization parameters provided in an embodiment of the present application;

[0020] Figure 4 This is a flow chart for calculating the mean and variance of parameter variances provided in an embodiment of the present application;

[0021] Figure 5 This is a flow chart for performing subsequent online calibration of sensor parameters provided by an embodiment of the present application;

[0022] Figure 6 This is a flowchart of fast online calibration based on prior constraints provided in an embodiment of the present application;

[0023] Figure 7 This is a schematic structural diagram of a sensor parameter online calibration device provided in an embodiment of the present application;

[0024] Figure 8 This is a structural diagram of a sensor parameter online calibration device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solutions and advantages of the present application clearer, the specific embodiments of the present application are further described in detail below in conjunction with the accompanying drawings. It is understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. It should also be noted that, for ease of description, only parts related to the present application, not all of the contents, are shown in the accompanying drawings. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe each operation (or step) as a sequential process, many of the operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0026] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in an order other than that illustrated or described herein, and that the objects distinguished by "first," "second," and the like are generally of the same type, and do not limit the number of objects; for example, the first object can be one or more. In addition, the term "and / or" in the specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the objects connected are in an "or" relationship.

[0027] The sensor parameter online calibration method provided in this embodiment can be performed by an online sensor parameter calibration device. The online sensor parameter calibration device can be implemented via software and / or hardware. The online sensor parameter calibration device can be composed of two or more physical entities, or a single physical entity. For example, the online sensor parameter calibration device can be an intelligent device equipped with a sensor, such as an unmanned device, or a processor of the intelligent device. Unmanned devices refer to devices such as drones that can automatically execute tasks based on preset tasks.

[0028] The sensor parameter online calibration device is installed with at least one type of operating system. The sensor parameter online calibration device can install at least one application based on the operating system. The application can be an application that comes with the operating system or an application downloaded from a third-party device or server. In this embodiment, the sensor parameter online calibration device has at least an application that can execute the sensor parameter online calibration method. Therefore, the sensor parameter online calibration device can also be the application itself.

[0029] For ease of understanding, this embodiment is described by taking a drone as an example of the subject for executing the sensor parameter online calibration method.

[0030] In one embodiment, the unmanned device is equipped with multiple sensors, such as cameras, 3D lidars, and IMUs / GPS. The unmanned device collects corresponding sensor data through these sensors, such as image data from cameras, point cloud data from 3D lidars, and pose data from IMUs / GPS. Sensor parameters include intrinsic parameters of each sensor and extrinsic parameters between sensors. For example, intrinsic parameters of a camera include focal length and distortion parameters. Extrinsic parameters between sensors include pose conversion matrices and acquisition delays between sensors, such as the relative pose and acquisition time difference between a camera and lidar. The unmanned device can collect sensor data in real time from multiple sensors and, based on the multiple sensor data and sensor parameters, determine the actual scale and pose of each sensor data. Based on the actual scale and pose, it can reconstruct a three-dimensional map corresponding to the sensor data. Sensor parameters affect the accuracy of map reconstruction. During the reconstruction of the three-dimensional map, sensor parameters are calibrated online to accurately obtain the intrinsic parameters of each sensor and the extrinsic parameters between sensors. In this embodiment, traditional online calibration is to construct an online calibration model based on the physical process of collecting sensor data from each sensor and the spatiotemporal relationship between each sensor. The spatiotemporal relationship can be understood as the relative position and acquisition delay between each sensor, which is the external parameter in the initial sensor parameters. By incorporating the online calibration model into the construction of the residual model of state estimation, a calibration result with minimal error and consistent with the sensor data is calculated. The calibration result refers to the sensor parameters obtained after optimizing the initial sensor parameters based on the sensor data. Due to the addition of the online calibration model, the state vector dimension of the residual model is increased, so that the multi-sensor must collect sensor data with a frame number far greater than the state vector dimension of the residual model in order to calculate a converged calibration result. For example, if the state to be estimated is 10 poses, the residual model has a 60-dimensional state vector dimension. The state vector dimension of the intrinsic and extrinsic parameters in the online calibration model is 10. After adding the online calibration model, the state vector dimension of the residual model increases to 70. This means that the multi-sensor sensor must continuously collect far more than 70 frames of sensor data before the sensor parameters converge, that is, they approximate the true intrinsic and extrinsic parameters. Therefore, traditional online calibration methods require a long time to obtain accurate calibration results, resulting in slow map reconstruction and an inability to meet the requirements of real-time export of reconstruction results. When the calibration results of online calibration are added for real-time mapping, the 3D map cannot be quickly exported due to the slow convergence of the calibration results. In addition, the online calibration may undergo significant changes after each optimization, resulting in inconsistent 3D maps derived based on different calibration results at different times, layering of the 3D maps, and poor mapping results.

[0031] To solve the above problems, this embodiment provides a sensor parameter online calibration method to improve the online calibration efficiency.

[0032] Figure 1A flow chart of a sensor parameter online calibration method provided in an embodiment of the present application is given.

[0033] refer to Figure 1 , the sensor parameter online calibration method specifically includes:

[0034] S110 , constructing an optimization model based on the sensing data and the initial sensor parameters, and solving the optimization model to obtain the sensor optimization parameters, which are the optimal solution of the optimization model.

[0035] Sensor data refers to all frames of sensor data collected by the drone's multiple sensors during the current flight during this online calibration. Initial sensor parameters can be understood as the intrinsic parameters of each sensor and the extrinsic parameters between sensors. For example, if the sensor parameters have not been calibrated since factory calibration, the initial sensor parameters are the factory calibration parameters. If the sensor parameters have been calibrated during the current flight, other flights, or at another calibration location, the initial sensor parameters are the sensor parameters obtained from the previous calibration.

[0036] In one embodiment, before performing online calibration, the drone first determines whether prior constraint values ​​are stored locally. If so, the prior constraint values ​​are obtained and an optimization model is constructed based on the prior constraint values, initial sensor parameters, and sensor data. If no prior constraint values ​​are stored locally, an optimization model is constructed based on the initial sensor parameters and sensor data. The optimization model can be used to optimize the initial sensor parameters based on the sensor data and output optimized sensor parameters.

[0037] In this embodiment, the optimization model constructed based on the initial sensor parameters and sensor data can be a residual model after adding an online calibration model, that is, the residual model used to solve the intrinsic and extrinsic parameters in traditional online calibration. For example, the residual model is constructed based on all the frame sensor data collected by the current multi-sensor flight, the internal parameters of each sensor and the external parameters between each sensor, as well as other state parameters to be estimated, such as the parameters of each sensor. It should be noted that the residual model can be constructed based only on the sensor parameters to be calibrated. For example, if only the intrinsic parameters of the camera and the external parameters of the camera and IMU are to be calibrated, the corresponding residual model can be constructed based on the image frames collected by the camera and the pose data collected by the IMU, as well as the current internal and external parameters of the camera. After solving the residual model, the optimized camera internal and external parameters can be obtained. This embodiment can flexibly construct the residual model according to the actual calibration requirements. For some sensors whose calibration parameters are not easy to change, online calibration can be omitted to reduce the dimension of the residual model and improve the online calibration speed.

[0038] In one embodiment, the optimization model is solved using a recursive least squares method to obtain an optimal solution to the optimization model, and the optimal solution of the optimization model is used as the sensor optimization parameters after this online calibration. The sensor optimization parameters can be understood as the internal and external parameters obtained by optimizing the internal parameters of each sensor and the external parameters between each sensor through online calibration. Because the optimization model is constructed based on all currently observed frames of sensor data, the optimal solution of the optimization model will satisfy the physical conditions of all frames of sensor data. Therefore, the sensor optimization parameters will be closer to the true value of the sensor parameters than the corresponding initial sensor parameters. In other words, the sensor optimization parameters can be regarded as the internal and external parameters of the current sensor.

[0039] S120: Determine whether the sensor optimization parameters meet a preset convergence condition.

[0040] The convergence condition can be considered as the condition satisfied when the sensor parameters are in a converged state. When the sensor parameters are in a converged state, the sensor parameters obtained by optimizing the sensor parameters fluctuate stably within a small range. This fluctuation does not affect the sensor parameter accuracy and map construction accuracy. The sensor parameters in the converged state can be approximated to the sensor's true intrinsic and extrinsic parameters. For example, when the sensor optimization parameters meet the convergence condition, the sensor optimization parameters can be determined to be the sensor's true intrinsic and extrinsic parameters, and online calibration is then terminated. A 3D map is then constructed based on the sensor optimization parameters. This avoids the need to calibrate and optimize the sensor parameters each time new sensor data is collected, which can result in the inability to quickly and stably derive the 3D map. Conversely, when the sensor optimization parameters do not meet the convergence condition, it can be determined that there is a significant error between the sensor optimization parameters and the sensor's true intrinsic and extrinsic parameters. This error can affect the sensor parameter accuracy and map construction accuracy. The sensor optimization parameters can then be further adjusted through the next online calibration until the sensor optimization parameters meet the convergence condition.

[0041] In one embodiment, the difference between the optimized sensor parameters and the corresponding initial sensor parameters is calculated, and the difference is compared with a preset difference threshold. If the difference is less than the preset difference threshold, the sensor optimization parameters are determined to have met the convergence condition. The preset difference threshold can be understood as the maximum fluctuation value of the sensor parameters when they are in a converged state. That is, when the sensor parameters are optimized based on newly added sensor data while in a converged state, the sensor parameters vary within the maximum fluctuation value, and this variation does not affect the sensor parameter accuracy and map construction accuracy. Therefore, when the difference between the optimized sensor parameters and the corresponding initial sensor parameters is less than the maximum fluctuation value, the sensor optimization parameters have reached a converged state.

[0042] In another embodiment, whether the sensor optimization parameters meet convergence conditions is determined based on historical optimization parameters and their variance, as well as sensor optimization parameters and their variance. The sensor parameter variance is obtained when solving the corresponding optimization model. The sensor parameter variance refers to the variance between the optimal solution of the optimization model and the solutions to the various equations in the optimization model, and can be calculated when solving the optimization model. The sensor parameter variance can indicate the estimation accuracy of the corresponding sensor optimization parameters. A smaller sensor parameter variance indicates that the current sensor optimization parameters satisfy more equations in the residual model, resulting in higher estimation accuracy. The historical optimization parameters refer to the sensor optimization parameters obtained during online calibration within a fixed time window prior to the current online calibration in the current flight. The historical parameter variance refers to the sensor parameter variance obtained during online calibration within a fixed time window prior to the current online calibration in the current flight. This embodiment utilizes statistical properties to calculate the numerical fluctuations of the sensor optimization parameters and the numerical fluctuations of the sensor parameter variance obtained during online calibration over a period of time to determine whether the sensor optimization parameters obtained during the current online calibration meet convergence conditions.

[0043] In this embodiment, Figure 2 This is a flow chart of determining that the sensor optimization parameters meet the convergence conditions provided by the embodiment of the present application. Figure 2 As shown, the step of determining that the sensor optimization parameters meet the convergence condition specifically includes S1201-S1203:

[0044] S1201. Calculate the mean and variance of the corresponding optimization parameters based on the historical optimization parameters and the sensor optimization parameters.

[0045] Figure 3 This is a flow chart of calculating the mean and variance of the optimization parameters provided in the embodiment of the present application. Figure 3 As shown, the step of calculating the mean and variance of the optimization parameters specifically includes S12011-S12012:

[0046] S12011. Storing the sensor optimization parameters in a preset historical parameter queue. If the length of the historical parameter queue exceeds the preset length, deleting the optimization parameters first stored in the historical parameter queue.

[0047] The historical parameter queue is used to store sensor optimization parameters. The preset length of the historical parameter queue is equal to the number of online calibrations that can be performed within a fixed time window plus one. The historical parameter queue can store sensor optimization parameters for the current online calibration and those performed online within the previous fixed time window. For example, assuming the preset length of the historical parameter queue is 10, when the historical parameter queue already stores 10 optimization parameters, the sensor optimization parameters obtained from the current online calibration are added, which exceeds the length of the historical parameter queue. The optimization parameters stored first in the historical parameter queue can be deleted from the queue, and then the sensor optimization parameters obtained from the current online calibration can be stored in the historical parameter queue. In this embodiment, since the queue follows the first-in-first-out principle, the optimization parameters stored first in the historical parameter queue are located at the head of the queue. The data at the head of the historical parameter queue can be deleted, and all data in the historical parameter queue can be moved to the head. The sensor optimization parameters obtained from the current online calibration are then stored at the tail of the queue.

[0048] S12012. Calculate the mean and variance of each optimization parameter stored in the historical parameter queue.

[0049] The mean of the optimized parameters is the mean of the optimized parameters of the sensor in this online calibration and the optimized parameters in the corresponding fixed time window. The variance of the optimized parameters refers to the variance of the optimized parameters of the sensor in this online calibration and the optimized parameters in the corresponding fixed time window. For example, assuming that the optimized parameters in the historical parameter queue are x1, x2, ..., x n , then the mean of the optimized parameters is The variance of the optimized parameter is n is the length of the historical parameter queue.

[0050] S1202: Calculate the mean and variance of the corresponding parameter variance based on the historical parameter variance and the sensor parameter variance.

[0051] Figure 4 This is a flow chart of calculating the mean and variance of parameter variances provided in the embodiment of the present application. Figure 4 As shown, the step of calculating the mean and variance of the parameter variance specifically includes S12021-S12022:

[0052] S12021. Storing the sensor parameter variance in a preset historical variance queue. If the length of the historical variance queue exceeds the preset length, deleting the parameter variance stored first in the historical variance queue.

[0053] The historical variance queue is used to store sensor parameter variances. The preset length of the historical variance queue is equal to the number of online calibrations that can be performed within a fixed time window plus one. The historical parameter queue stores the sensor parameter variances for the current online calibration and those performed within the previous fixed time window. The storage rules for the historical variance queue are similar to those for the historical parameter queue. If the historical variance queue already stores parameter variances for the preset length, adding the sensor parameter variances obtained during the current online calibration will exceed the preset length of the historical variance queue. The oldest parameter variance stored in the historical parameter queue can be deleted from the queue before the sensor parameter variances obtained during the current online calibration are stored in the historical variance queue.

[0054] S12022. Calculate the mean and variance of each parameter variance based on the parameter variance stored in the historical variance queue.

[0055] The mean of the parameter variance is the mean of the sensor parameter variances in this online calibration and the corresponding fixed time window. The variance of the parameter variance refers to the variance of the sensor parameter variance in this online calibration and the parameter variance in the corresponding fixed time window. For example, assuming that the parameter variances in the historical variance queue are y1, y2, ..., y m , then the mean of the optimized parameters is The variance of the optimized parameter is m is the length of the historical variance queue, m=n.

[0056] S1203: When the variance of the parameter variance is less than the first preset threshold, the mean of the parameter variance is less than the second preset threshold, the variance of the optimized parameter is less than the third preset threshold, and the mean of the optimized parameter satisfies the preset range, it is determined that the sensor optimization parameter meets the convergence condition.

[0057] Among them, the first preset threshold can be understood as the maximum fluctuation value of the estimated accuracy of the sensor optimization parameters calibrated online in the corresponding time period when the sensor optimization parameters of this online calibration reach the convergence state. The online calibration in the corresponding time period includes this online calibration and the online calibration in the fixed time window before it. The variance of the parameter variance can characterize the fluctuation of the estimated accuracy of the sensor optimization parameters corresponding to multiple online calibrations, and the corresponding multiple online calibrations include this online calibration and the online calibration in the fixed time window before it. Therefore, when the variance of the parameter variance is less than the first preset threshold, it can be determined that the fluctuation of the estimated accuracy of the sensor optimization parameters calibrated online in the corresponding time period meets the fluctuation requirement of the estimated accuracy in the convergence state, that is, the fluctuation of the estimated accuracy in the corresponding time period is relatively stable. In this embodiment, the first preset threshold can be set to the noise value in the convergence state.

[0058] Among them, the second preset threshold can be understood as the minimum average value of the estimated accuracy of the sensor optimization parameters calibrated online in the corresponding time period when the sensor optimization parameters of this online calibration reach the convergence state. Since the mean of the parameter variance can represent the mean value of the estimated accuracy of the sensor optimization parameters corresponding to multiple online calibrations, the larger the mean value of the parameter variance, the lower the accuracy of the estimated mean value. Therefore, when the mean value of the parameter variance is less than the second preset threshold, that is, the mean value of the estimated accuracy is greater than the minimum average value of the estimated accuracy in the convergence state, it can be determined that the mean value of the estimated accuracy of the sensor optimization parameters calibrated online in the corresponding time period meets the mean value requirement of the estimated accuracy in the convergence state, that is, the estimated accuracy in the corresponding time period is large. In this embodiment, the second preset threshold can be set according to empirical data.

[0059] The third preset threshold can be understood as the maximum fluctuation value of the sensor optimization parameters during the online calibration period when the sensor optimization parameters of the current online calibration reach a convergence state. Since the variance of the optimization parameters can represent the fluctuation of the sensor optimization parameters obtained from multiple online calibrations, when the variance of the parameter variance is less than the third preset threshold, it can be determined that the fluctuation of the sensor optimization parameters during the online calibration period meets the fluctuation requirements for the sensor optimization parameters in the convergence state, that is, the fluctuation of the sensor optimization parameters during the corresponding period is relatively stable. In this embodiment, the third preset threshold can be set based on empirical data.

[0060] The preset range can be understood as the average value range formed by the maximum and minimum average values ​​of the sensor optimization parameters within a corresponding time period in the convergence state. When the average value of the optimization parameters is less than the maximum average value and greater than the minimum average value, it can be determined that the average value of the optimization parameters meets the average value requirement for the sensor optimization parameters in the convergence state. In this embodiment, the preset range can be set based on empirical data.

[0061] Exemplarily, when the parameter thresholds or ranges under the various convergence states mentioned above are all met, it can be determined that the sensor optimization parameters and estimation accuracy of this online calibration are within the effective range of convergence and the fluctuations of the optimization parameters and the estimation accuracy are sufficiently stable. At this time, it can be determined that the sensor optimization parameters meet the convergence conditions.

[0062] It should be noted that compared with the embodiment of determining whether the sensor parameters are in a convergence state by presetting the difference threshold, this embodiment focuses on a longer time window to determine more stable sensor parameters, so as to obtain sensor parameters that are closer to the true values ​​and improve calibration accuracy.

[0063] S130 . In response to a judgment result that the convergence condition is satisfied, setting the sensor optimization parameters as the sensor initial parameters and prior constraint values ​​for subsequent online calibration to perform the subsequent online calibration.

[0064] For example, when the sensor optimization parameters of the current online calibration meet the convergence conditions, the sensor optimization parameters can be set as the prior constraint values ​​and sensor initial parameters of the residual model for the next online calibration. Since the sensor optimization parameters that meet the convergence conditions can be approximated to the true values ​​of the sensor parameters, setting the sensor optimization parameters as the prior constraint values ​​of the residual model for the next online calibration is equivalent to providing a true value constraint, which can provide a more accurate range of variation for the residual model solution process and is equivalent to reducing the state vector dimension of the residual model. For example, in a residual model with 70 state vector dimensions, there are sensor parameters with a state vector dimension of 10. When the true value constraints of the sensor parameters are added to the residual model, it is equivalent to knowing the 10 state vector dimensions in the residual model. The remaining 60 state parameters can then be solved. At this time, the state vector dimension of the residual model is reduced, which reduces the solution time and effectively improves the speed of each online calibration. Furthermore, as the dimensionality of the residual model's state vector increases, more sensor data is required to ensure convergence of the sensor parameters. If the sensor data collected within the effective time is insufficient to ensure convergence of the sensor parameters, the sensor data collection period must be extended to collect new sensor data until the sensor parameters converge. Therefore, when the dimensionality of the residual model's state vector decreases, the required sensor data also decreases. This allows the sensor data collected within the effective time to ensure convergence of the sensor parameters, eliminating the need to extend the sensor data collection period and improving the overall convergence speed of the sensor parameters.

[0065] In one embodiment, when sensor optimization parameters do not meet convergence conditions, indicating that there is still an error between the optimized sensor parameters and the true values, the current sensor parameters can be further optimized through subsequent online calibration. When sensor optimization parameters do not meet convergence conditions, the corresponding subsequent online calibration can be understood as the next online calibration performed by the UAV during the current flight. Figure 5 This is a flow chart of the subsequent online calibration of sensor parameters provided by the embodiment of the present application. Figure 5 As shown, the subsequent online calibration steps for the sensor optimization parameters specifically include S140-S150:

[0066] S140 : In response to a judgment result that the convergence condition is not satisfied, comparing the sensor optimization parameter with a preset range.

[0067] Generally, due to the constraints of newly acquired sensor data, sensor optimization parameters are closer to the true values ​​than the corresponding initial sensor parameters. In this case, a corresponding residual model can be constructed based on these sensor optimization parameters and the newly acquired sensor data during the next online calibration. This residual model is then solved to obtain sensor optimization parameters that are closer to the true values ​​until the sensor optimization parameters meet the convergence criteria. However, there are special cases where errors in the newly acquired sensor data cause the sensor optimization parameters to diverge. In other words, the sensor optimization parameters are further away from the true values ​​than the corresponding initial sensor parameters. In such special cases, using these sensor optimization parameters will cause the optimization direction to deviate, severely reducing the convergence speed of the online calibration. Based on this, during the next online calibration, the sensor optimization parameters are compared with the valid range. If the sensor optimization parameters are outside the valid range, the optimization direction of the sensor optimization parameters can be determined to be correct. If the sensor optimization parameters are within the valid range, the optimization direction of the sensor optimization parameters can be determined to be deviated.

[0068] S150 : Setting initial sensor parameters for subsequent online calibration based on a comparison result between the optimized sensor parameters and the preset range, so as to perform subsequent online calibration.

[0069] In this embodiment, in response to a comparison result indicating that the sensor optimization parameters are outside a preset range, the initial sensor parameters are set as the initial sensor parameters for subsequent online calibration, thereby continuing to optimize the sensor initial parameters from the previous online calibration. This prevents deviation from the optimization direction and improves online calibration efficiency. Conversely, in response to a comparison result indicating that the sensor optimization parameters are within a preset range, the sensor optimization parameters are set as the initial sensor parameters for subsequent online calibration, and optimization of the sensor optimization parameters continues in the correct optimization direction until the sensor optimization parameters meet convergence conditions.

[0070] In one embodiment, when the sensor parameters meet the convergence conditions, the corresponding subsequent online calibration can be the next online calibration performed by the drone in the current sortie, or the online calibration performed by the drone in the next sortie. In this embodiment, before the drone performs the first online calibration in the next sortie, it is determined that the prior constraint value retained from the previous sortie is stored locally, and then a fast online calibration is performed based on the prior constraint value. For example, Figure 6 This is a flowchart of fast online calibration based on prior constraint values ​​provided by the embodiment of the present application. Figure 6 As shown, the steps of performing fast online calibration based on the prior constraint value specifically include S160-S190:

[0071] S160: Construct a corresponding optimization model based on the sensor data, the initial sensor parameters, and the prior constraint values, and solve the optimization model to obtain the sensor optimization parameters.

[0072] In this embodiment, a corresponding optimization model is constructed based on the sensor data, the initial sensor parameters, and the prior constraint values, which includes a residual model constructed based on the sensor data and the initial sensor parameters, and a constraint equation generated based on the prior constraint values. For example, a residual model HX=0 is constructed based on the sensor data and the initial sensor parameters, where H is the state matrix of the residual model and X is the state parameter to be solved, which includes the sensor parameter x. A corresponding constraint equation is generated based on the prior constraint value, for example, the prior constraint value is P, and the constraint equation is x=P. The residual model and the constraint equation are combined to obtain the optimization model, and the expression of the optimization model is as follows:

[0073]

[0074] The optimization model is solved to obtain the corresponding sensor optimization parameters. Since the equation x = P is added to the residual model, the sensor parameters calculated by the residual model vary within the limited range of P. This allows the optimal solution of the residual model to be quickly calculated, improving the solution efficiency.

[0075] S170: Determine whether the sensor optimization parameters meet a preset convergence condition.

[0076] Exemplarily, step S170 may refer to step S120 .

[0077] S180 . In response to a judgment result that the convergence condition is not satisfied, setting the priori constraint value and the sensor optimization parameter as the priori constraint value and the sensor initial parameter for subsequent online calibration, respectively, to perform subsequent online calibration.

[0078] For example, the a priori constraint values ​​constrain the sensor parameters calculated from the residual model to a limited range within the a priori constraint values, accelerating convergence of the sensor parameters. Therefore, the a priori constraint values ​​used in subsequent online calibrations are identical to those used in this online calibration. Furthermore, the presence of the a priori constraint prevents significant changes in sensor parameters before and after optimization, ensuring consistency of the 3D map and preventing layering.

[0079] S190 . In response to a judgment result that the convergence condition is satisfied, setting the sensor optimization parameters as the sensor initial parameters and prior constraint values ​​for subsequent online calibration to perform the subsequent online calibration.

[0080] For example, step S190 may correspond to step S130. The converged sensor optimization parameters may be used as a priori constraint values ​​for the next online calibration, so that the subsequent online calibration can be quickly performed based on the a priori constraint values.

[0081] In one embodiment, after the sensor optimization parameters meet convergence conditions, they are sent to a server, which then determines the initial sensor parameters for sensors that have not undergone factory calibration based on the received sensor optimization parameters. The server can be a device with high computing power, such as a cloud server. This embodiment allows the cloud server to integrate the sensor optimization parameters obtained through online calibration of multiple drones, providing common sensor parameters for multiple sensors configured on other drones that have not undergone factory calibration. This addresses the issue of online calibration accuracy being affected by the lack of factory-calibrated sensor parameters.

[0082] In summary, the sensor parameter online calibration method provided by the embodiment of the present application constructs an optimization model through sensor data and sensor parameters obtained from the last online calibration, and solves the optimization model to obtain optimized sensor parameters. By repeatedly constructing and solving the optimization model, the current sensor parameters of the sensor can be accurately determined, thereby achieving accurate calibration of the sensor parameters and ensuring the accuracy of three-dimensional map construction. After each optimized sensor parameter is obtained, it is determined whether the optimized sensor parameters have converged. When the sensor parameters converge, it can be determined that the sensor parameters are approximate to the true values. The convergence of the sensor parameters can be used as the judgment condition for the end of calibration to accurately obtain the current sensor parameters of the sensor, avoiding the need to calibrate and optimize the sensor parameters every time new sensor data is collected, which results in the inability to quickly and stably export the three-dimensional map. The converged sensor parameters are used as the initial sensor parameters and prior constraint values ​​for the next online calibration of the sensor, so that the optimization model can be constructed based on the converged sensor parameters during the next online calibration, and the optimized sensor parameters can be determined within a limited range of the converged sensor parameters. Due to the existence of prior constraints, the convergence of sensor parameters will be accelerated, the sensor parameters of the current sensor will be determined quickly and accurately, and the efficiency of online calibration will be improved, which in turn can improve the efficiency of map reconstruction and realize real-time reconstruction of three-dimensional maps.

[0083] Based on the above embodiments, Figure 7 This is a schematic diagram of the structure of a sensor parameter online calibration device provided in an embodiment of the present application. Figure 7 The sensor parameter online calibration device provided in this embodiment specifically includes: a first optimization module 21, a first judgment module 22 and a first setting module 23.

[0084] The first optimization module is configured to construct an optimization model based on the sensor data and the initial sensor parameters, and solve the optimization model to obtain the sensor optimization parameters, which are the optimal solutions of the optimization model.

[0085] A first judgment module is configured to determine whether the sensor optimization parameters meet a preset convergence condition;

[0086] The first setting module is configured to set the sensor optimization parameters as the sensor initial parameters and prior constraint values ​​for subsequent online calibration in response to the judgment result that the convergence condition is met, so as to perform the subsequent online calibration.

[0087] Based on the above embodiment, the sensor parameter online calibration device also includes: a comparison module, which is configured to compare the sensor optimization parameters with a preset range in response to a judgment result that the convergence condition is not met; a second setting module, which sets the initial parameters of the sensor for subsequent online calibration based on the comparison result of the sensor optimization parameters with the preset range, so as to perform subsequent online calibration.

[0088] Based on the above embodiment, the second setting module includes: a first setting unit, configured to set the sensor initial parameters as the sensor initial parameters for subsequent online calibration in response to a comparison result that the sensor optimization parameters exceed a preset range; a second setting unit, configured to set the sensor optimization parameters as the sensor initial parameters for subsequent online calibration in response to a comparison result that the sensor optimization parameters are within a preset range.

[0089] On the basis of the above embodiment, the sensor parameter online calibration device also includes: a second optimization module, configured to construct a corresponding optimization model based on the sensor data, the sensor initial parameters and the prior constraint values, and solve the optimization model to obtain the sensor optimization parameters; a second judgment module, configured to determine whether the sensor optimization parameters meet the preset convergence conditions; a third setting module, configured to set the prior constraint values ​​and the sensor optimization parameters as the prior constraint values ​​and the sensor initial parameters of the subsequent online calibration respectively in response to the judgment result that the convergence conditions are not met, so as to perform the subsequent online calibration; a fourth setting module, configured to set the sensor optimization parameters as the sensor initial parameters and the prior constraint values ​​of the subsequent online calibration in response to the judgment result that the convergence conditions are met, so as to perform the subsequent online calibration.

[0090] Based on the above embodiment, the second optimization module includes: a residual model construction unit, configured to construct a residual model based on sensor data and sensor initial parameters; a constraint equation generation unit, configured to generate corresponding constraint equations according to prior constraint values; and an optimization model generation unit, configured to combine the residual model and the constraint equation group to obtain an optimization model.

[0091] On the basis of the above embodiment, the first judgment module or the second judgment module includes: a judgment submodule, which is configured to determine whether the sensor optimization parameters meet the convergence conditions based on the historical optimization parameters and the historical parameter variance, as well as the sensor optimization parameters and the sensor parameter variance, which is obtained when the sensor parameter variance is solved corresponding to the optimization model.

[0092] Based on the above embodiment, the judgment submodule includes: a first calculation unit, configured to calculate the mean and variance of the corresponding optimization parameters based on the historical optimization parameters and the sensor optimization parameters; a second calculation unit, configured to calculate the mean and variance of the corresponding parameter variances based on the historical parameter variances and the sensor parameter variances; and a judgment unit, configured to determine that the sensor optimization parameters meet the convergence conditions when the variance of the parameter variances is less than a first preset threshold, the mean of the parameter variances is less than a second preset threshold, the variance of the optimization parameters is less than a third preset threshold, and the mean of the optimization parameters meets a preset range.

[0093] Based on the above embodiment, the first calculation unit includes: a first storage subunit, configured to store the sensor optimization parameters in a preset historical parameter queue, and if the length of the historical parameter queue exceeds the preset length, the optimization parameters first stored in the historical parameter queue are deleted; the first calculation subunit, configured to calculate the mean and variance of the optimization parameters based on each optimization parameter stored in the historical parameter queue.

[0094] Based on the above embodiment, the second calculation unit includes: a second storage subunit, configured to store the sensor parameter variance in a preset historical variance queue, and if the length of the historical variance queue exceeds the preset length, the parameter variance stored first in the historical variance queue is deleted; the second calculation subunit is configured to calculate the mean and variance of the parameter variance based on each parameter variance stored in the historical variance queue.

[0095] Based on the above embodiment, the sensor parameter online calibration device also includes: a parameter upload module, which is configured to send the sensor optimization parameters to the server, so that the server can determine the sensor initial parameters of the non-factory calibrated sensor based on multiple received sensor optimization parameters.

[0096] As described above, the sensor parameter online calibration device provided in the embodiment of the present application constructs an optimization model based on sensor data and sensor parameters obtained from the previous online calibration, and solves the optimization model to obtain optimized sensor parameters. By repeatedly constructing and solving the optimization model, the current sensor parameters of the sensor can be accurately determined, thereby achieving accurate calibration of the sensor parameters and ensuring the accuracy of three-dimensional map construction. After each optimized sensor parameter is obtained, it is determined whether the optimized sensor parameters have converged. When the sensor parameters converge, it can be determined that the sensor parameters are approximately close to the true values. The convergence of the sensor parameters can be used as the judgment condition for the end of calibration to accurately obtain the current sensor parameters of the sensor, avoiding the need to calibrate and optimize the sensor parameters every time new sensor data is collected, which results in the inability to quickly and stably export the three-dimensional map. The converged sensor parameters are used as the initial sensor parameters and prior constraint values ​​for the next online calibration of the sensor, so that the optimization model can be constructed based on the converged sensor parameters during the next online calibration, and the optimized sensor parameters can be determined within a limited range of the converged sensor parameters. Due to the existence of prior constraints, the convergence of sensor parameters will be accelerated, the sensor parameters of the current sensor will be determined quickly and accurately, and the efficiency of online calibration will be improved, which in turn can improve the efficiency of map reconstruction and realize real-time reconstruction of three-dimensional maps.

[0097] The sensor parameter online calibration device provided in the embodiment of the present application can be used to execute the sensor parameter online calibration method provided in the above embodiment, and has corresponding functions and beneficial effects.

[0098] Figure 8 This is a schematic diagram of the structure of a sensor parameter online calibration device provided in an embodiment of the present application, with reference to Figure 8 The sensor parameter online calibration device includes: a processor 31, a memory 32, a communication device 33, an input device 34, and an output device 35. The number of processors 31 in the sensor parameter online calibration device can be one or more, and the number of memories 32 in the sensor parameter online calibration device can be one or more. The processor 31, memory 32, communication device 33, input device 34, and output device 35 of the sensor parameter online calibration device can be connected via a bus or other means.

[0099] The memory 32 is a computer-readable storage medium that can be used to store software programs, computer executable programs, and modules, such as the program instructions / modules corresponding to the sensor parameter online calibration method of any embodiment of the present application (for example, the first optimization module 21, the first judgment module 22, and the first setting module 23 in the sensor parameter online calibration device). The memory 32 may mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the device, etc. In addition, the memory 32 may include a high-speed random access memory and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory may further include a memory remotely located relative to the processor, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0100] The communication device 33 is used for data transmission.

[0101] The processor 31 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 32, that is, realizes the above-mentioned sensor parameter online calibration method.

[0102] The input device 34 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the device. The output device 35 may include a display device such as a display screen.

[0103] The sensor parameter online calibration device provided above can be used to execute the sensor parameter online calibration method provided in the above embodiment, and has corresponding functions and beneficial effects.

[0104] An embodiment of the present application also provides a storage medium containing computer-executable instructions, which, when executed by a computer processor, are used to execute a sensor parameter online calibration method. The sensor parameter online calibration method includes: constructing an optimization model based on sensor data and sensor initial parameters, solving the optimization model to obtain sensor optimization parameters, and the sensor optimization parameters are the optimal solution of the optimization model; determining whether the sensor optimization parameters meet preset convergence conditions; and in response to a judgment result that the convergence conditions are met, setting the sensor optimization parameters as the sensor initial parameters and prior constraint values ​​for subsequent online calibration to perform subsequent online calibration.

[0105] Storage medium - any of various types of memory devices or storage devices. The term "storage medium" is intended to include: installation media, such as CD-ROMs, floppy disks, or tape drives; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media (such as hard disks or optical storage); registers or other similar types of memory elements, etc. Storage media may also include other types of memory or combinations thereof. In addition, the storage medium may be located in the first computer system in which the program is executed, or it may be located in a different second computer system that is connected to the first computer system via a network (such as the Internet). The second computer system can provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media residing in different locations (e.g., in different computer systems connected via a network). The storage medium may store program instructions (e.g., embodied as a computer program) that can be executed by one or more processors.

[0106] Of course, the storage medium containing computer-executable instructions provided in an embodiment of the present application is not limited to the above-mentioned online calibration method for sensor parameters, and can also execute related operations in the online calibration method for sensor parameters provided in any embodiment of the present application.

[0107] The sensor parameter online calibration device, storage medium and sensor parameter online calibration equipment provided in the above embodiments can execute the sensor parameter online calibration method provided in any embodiment of the present application. For technical details not described in detail in the above embodiments, please refer to the sensor parameter online calibration method provided in any embodiment of the present application.

[0108] The above are only preferred embodiments of the present application and the technical principles employed. The present application is not limited to the specific embodiments described herein, and any obvious changes, readjustments, and substitutions that are apparent to those skilled in the art will not depart from the scope of protection of the present application. Therefore, although the present application has been described in detail through the above embodiments, the present application is not limited to the above embodiments and may include many other equivalent embodiments without departing from the scope of the present application. The scope of the present application is determined by the scope of the claims.

Claims

1. A sensor parameter online calibration method, characterized in that: include: Building an optimization model based on the sensor data and the initial parameters of the sensor, and solving the optimization model to obtain sensor optimization parameters, where the sensor optimization parameters are the optimal solution of the optimization model; Determining whether the sensor optimization parameters meet preset convergence conditions; The method includes: determining the fluctuation of the optimization parameters based on the sensor optimization parameters and the historical optimization parameters, determining the fluctuation of the parameter variance based on the sensor parameter variance and the historical parameter variance, and determining whether the sensor optimization parameters meet the preset convergence conditions based on the fluctuation of the optimization parameters and the fluctuation of the parameter variance; wherein the sensor parameter variance is the variance between the optimal solution of the optimization model and the solution of each equation in the optimization model, the historical optimization parameters are the sensor optimization parameters obtained by online calibration within a fixed time window before the current online calibration in the current flight, and the historical parameter variance is the sensor parameter variance corresponding to the historical optimization parameters; In response to a judgment result that the convergence condition is satisfied, the sensor optimization parameters are set as initial sensor parameters and prior constraint values ​​for subsequent online calibration, so as to perform subsequent online calibration.

2. The sensor parameter online calibration method according to claim 1, characterized in that: The method further comprises: In response to a determination that the convergence condition is not satisfied, comparing the sensor optimization parameter with a preset range; According to the comparison result of the sensor optimization parameter and the preset range, the initial parameters of the sensor for subsequent online calibration are set to perform the subsequent online calibration.

3. The sensor parameter online calibration method according to claim 2, characterized in that: The step of setting initial sensor parameters for subsequent online calibration based on a comparison result between the optimized sensor parameters and a preset range includes: In response to a comparison result that the optimized parameter of the sensor exceeds a preset range, setting the initial parameter of the sensor as the initial parameter of the sensor for subsequent online calibration; In response to a comparison result that the sensor optimization parameter is within a preset range, the sensor optimization parameter is set as an initial parameter of the sensor for subsequent online calibration.

4. The sensor parameter online calibration method according to claim 1, characterized in that: The method further comprises: Building a corresponding optimization model based on the sensor data, the sensor initial parameters and the prior constraint values, and solving the optimization model to obtain sensor optimization parameters; Determining whether the sensor optimization parameters meet preset convergence conditions; In response to a judgment result that the convergence condition is not satisfied, setting the priori constraint value and the sensor optimization parameter as the priori constraint value and the sensor initial parameter for subsequent online calibration, respectively, to perform subsequent online calibration; In response to a judgment result that the convergence condition is satisfied, the sensor optimization parameters are set as initial sensor parameters and prior constraint values ​​for subsequent online calibration, so as to perform subsequent online calibration.

5. The sensor parameter online calibration method according to claim 4, characterized in that: The constructing of a corresponding optimization model based on the sensor data, sensor initial parameters and prior constraint values ​​includes: constructing a residual model based on the sensing data and the initial parameters of the sensor; generating a corresponding constraint equation according to the prior constraint value; The residual model and the constraint equation are combined to obtain the optimization model.

6. The sensor parameter online calibration method according to claim 1, characterized in that: The determining of the optimization parameter fluctuation according to the sensor optimization parameter and the historical optimization parameter, determining the parameter variance fluctuation according to the sensor parameter variance and the historical parameter variance, and determining whether the sensor optimization parameter meets the preset convergence condition according to the optimization parameter fluctuation and the parameter variance fluctuation include: Calculating the mean and variance of the corresponding optimization parameters based on the historical optimization parameters and the sensor optimization parameters; According to the historical parameter variance and sensor parameter variance, the mean and variance of the corresponding parameter variance are calculated; When the variance of the parameter variance is less than a first preset threshold, the mean of the parameter variance is less than a second preset threshold, the variance of the optimization parameter is less than a third preset threshold, and the mean of the optimization parameter satisfies a preset range, it is determined that the sensor optimization parameter satisfies the convergence condition.

7. The sensor parameter online calibration method according to claim 6, characterized in that: Calculating the mean and variance of the corresponding optimization parameters based on the historical optimization parameters and the sensor optimization parameters includes: Storing the sensor optimization parameters in a preset historical parameter queue, and if the length of the historical parameter queue exceeds a preset length, deleting the optimization parameters stored first in the historical parameter queue; According to each optimization parameter stored in the historical parameter queue, the mean and variance of the optimization parameter are calculated.

8. The sensor parameter online calibration method according to claim 6, characterized in that: The step of calculating the mean and variance of the corresponding parameter variances based on the historical parameter variances and the sensor parameter variances includes: Storing the sensor parameter variance in a preset historical variance queue, and if the length of the historical variance queue exceeds a preset length, deleting the parameter variance stored first in the historical variance queue; According to each parameter variance stored in the historical variance queue, the mean and variance of the parameter variance are calculated.

9. The sensor parameter online calibration method according to any one of claims 1 to 5, characterized in that: After the response to the judgment result that the convergence condition is met, the method further includes: The sensor optimization parameters are sent to a server, so that the server determines the initial sensor parameters of the non-factory calibrated sensor according to a plurality of received sensor optimization parameters.

10. A sensor parameter online calibration device, characterized in that: include: a first optimization module configured to construct an optimization model based on the sensor data and the initial sensor parameters, and solve the optimization model to obtain sensor optimization parameters, where the sensor optimization parameters are the optimal solution of the optimization model; A first judgment module is configured to determine whether the sensor optimization parameters meet preset convergence conditions; wherein the first judgment module is specifically configured to determine the optimization parameter fluctuation according to the sensor optimization parameters and historical optimization parameters, determine the parameter variance fluctuation according to the sensor parameter variance and the historical parameter variance, and determine whether the sensor optimization parameters meet the preset convergence conditions according to the optimization parameter fluctuation and the parameter variance fluctuation; wherein the sensor parameter variance is the variance between the optimal solution of the optimization model and the solution of each equation in the optimization model, the historical optimization parameters are the sensor optimization parameters obtained by online calibration in a fixed time window before the current online calibration in the current flight, and the historical parameter variance is the sensor parameter variance corresponding to the historical optimization parameters; The first setting module is configured to set the sensor optimization parameters as sensor initial parameters and prior constraint values ​​for subsequent online calibration in response to a judgment result that the convergence condition is met, so as to perform subsequent online calibration.

11. A sensor parameter online calibration device, characterized in that: include: one or more processors; A memory stores one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the sensor parameter online calibration method according to any one of claims 1 to 9.

12. A storage medium containing computer-executable instructions, characterized in that: When executed by a computer processor, the computer executable instructions are used to perform the sensor parameter online calibration method according to any one of claims 1 to 9.

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