CN115752540A - Automatic calibration method and device suitable for unmanned container transport vehicle - Google Patents

Abstract

The application provides an automatic calibration method, an automatic calibration device, electronic equipment and a computer-readable storage medium, which are suitable for an unmanned container transport vehicle, wherein the method comprises the following steps: acquiring positioning information of a first sensor and a second sensor; calculating the installation relation between the first sensor and the second sensor through the speed difference and/or the position difference between the positioning information of the first sensor and the second sensor; and determining the installation relation smaller than the error threshold value as the calibration parameters of the first sensor and the second sensor.

Description

Automatic calibration method and device suitable for unmanned container transport vehicle

Technical Field

The present invention relates to the field of sensor calibration technologies, and in particular, to a method and an apparatus for automatic calibration of an unmanned container transportation vehicle, an electronic device, and a computer-readable storage medium.

Background

The vehicle automatic driving technology obtains the information of the vehicle and the information of the surrounding driving environment through a sensing system, and analyzes, calculates and processes the information to realize the automatic driving of the vehicle. Therefore, in the automatic driving technology, whether the parameters of each sensor arranged on the vehicle are accurate or not is of great importance for the automatic driving technology. Generally, before a vehicle leaves a factory, parameters of each sensor of the vehicle are calibrated offline, multiple links such as manual measurement, offline processing and parameter review are needed when the parameters are determined, operation is complex, and tire pressure and the like of the vehicle are changed along with time after the vehicle leaves the factory, so that actual parameters of the vehicle sensor, particularly external parameters, are further caused to deviate, and the parameters of the vehicle sensor need to be calibrated according to actual change conditions of the vehicle.

However, in the prior art, the parameters of the vehicle sensors are generally calibrated by using an off-line calibration method. However, the off-line calibration process is complicated, and the off-line time of the vehicle is increased; in addition, the offline calibration also has the condition of external parameter calibration limitation, such as yaw (course) angle of the installation error of IMU (Inertial Measurement Unit) can only be calibrated, and pitch (pitch) angle cannot be calibrated; meanwhile, the problem of large calibration error exists in offline calibration, for example, when the IMU installation error is calibrated offline, the offline calibration precision can only reach 0.3deg, and the calibration error of some parameters is larger.

Therefore, how to automatically calibrate the parameters of the vehicle sensor is a technical problem which needs to be solved in the field.

Disclosure of Invention

The application provides an automatic calibration method suitable for an unmanned container transport vehicle, which is characterized by comprising the following steps:

acquiring positioning information of a first sensor and a second sensor;

calculating the installation relation between the first sensor and the second sensor through the speed difference and/or the position difference between the positioning information of the first sensor and the second sensor;

and determining the installation relation smaller than the error threshold value as the calibration parameters of the first sensor and the second sensor.

Optionally, the first sensor, or the second sensor, includes:

one or a combination of an inertial measurement unit, a satellite receiver and a wheel speed meter.

Optionally, the installation relationship includes:

one or a combination of lever arms, installation error angles, and indexing coefficients.

Optionally, the method further includes:

determining that the vehicle is in a driving state and has a turning engine;

and if the ratio of the parking time length to the total calibration time length of the vehicle in the calibration process is greater than a first proportional threshold, determining that the fault occurs in the calibration process.

Optionally, the method further includes:

determining the accuracy of the positioning information acquired by the sensor;

and if the proportion of the quantity of the positioning information with the accuracy not meeting the calibration requirement in the positioning information acquired by the sensor in the calibration process is greater than a second proportion threshold, determining that the calibration process fails.

Optionally, the calculating an installation relationship between the first sensor and the second sensor further includes:

and if the time length of the installation relation is calculated to be larger than a time length threshold value, determining that the calibration process fails.

The application provides an automatic calibration device suitable for unmanned container haulage vehicle, its characterized in that, the device includes:

the information acquisition module is used for acquiring positioning information of the first sensor and the second sensor;

the calculation module is used for calculating the installation relation between the first sensor and the second sensor through the speed difference and/or the position difference between the positioning information of the first sensor and the positioning information of the second sensor;

and the checking module is used for determining the installation relation smaller than the error threshold as the calibration parameters of the first sensor and the second sensor.

Optionally, the first sensor, or the second sensor, includes:

one or a combination of an inertial measurement unit, a satellite receiver and a wheel speed meter.

The present application further provides an electronic device, including:

a processor;

a memory for storing processor-executable instructions;

wherein the processor implements the steps of the above method by executing the executable instructions.

The present application also provides a computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of the above-described method.

Through the embodiment, the calibration parameters can be determined by calculating the installation relation among the sensors, so that the automatic calibration of the sensors is realized, the complex manual operation in the offline calibration process is overcome, and the calibration efficiency of the sensors is improved.

Drawings

FIG. 1 is a flow chart illustrating an exemplary embodiment of an automatic calibration method for an unmanned container transport vehicle;

FIG. 2 is a block diagram of an automatic calibration arrangement suitable for use with an unmanned container transport vehicle in accordance with an exemplary embodiment;

fig. 3 is a hardware configuration diagram of an electronic device in which an automatic calibration apparatus for an unmanned container transportation vehicle according to an exemplary embodiment is provided.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

It should be noted that: in other embodiments, the steps of the corresponding methods are not necessarily performed in the order shown and described herein. In some other embodiments, the methods may include more or fewer steps than those described herein. Moreover, a single step described in this specification may be broken down into multiple steps for description in other embodiments; multiple steps described in this specification may be combined into a single step in other embodiments.

In order to make those skilled in the art better understand the technical solutions in the embodiments of the present disclosure, the following briefly describes the related art for automatic calibration of vehicle sensors related to the embodiments of the present disclosure.

GNSS: the GNSS is a Satellite-level radio Navigation System using an artificial Satellite as a Navigation station, and provides all-weather and high-precision position, speed and time information for various Global military and civil carriers, namely, a space-based positioning, navigation and time service System.

An IMU: an Inertial Measurement Unit (IMU) and an onboard positioning system may measure the position and state of the vehicle according to data output by the IMU and in combination with data of other sensors, so as to obtain state quantities of the vehicle, such as speed, position, attitude, acceleration, angular velocity, and the like. The IMU is characterized in that the frequency of the acquired data is more than 200Hz, and the IMU is more sensitive to the change of speed and posture. However, if no external auxiliary information is corrected for a long time, the speed and posture obtained according to the data acquired by the IMU tend to diverge rapidly.

And (3) WSS: wheel Speed Sensors (WSSs), which are used to detect the rotational Speed of the wheels of a vehicle, provide valuable Wheel Speed information for devices, apparatuses or systems, such as an electronic stability program (esp), an anti-lock braking system (abs), a control system of an automatic transmission, a power assisting system, etc., which are configured on the vehicle, and are important to ensure and enhance the operation control, safe driving, etc. of the vehicle.

GPS: the Global Positioning System (GPS) is the most widely used satellite navigation Positioning System, and has the advantages of high Positioning and speed measurement accuracy, good long-term working stability, convenient use, and low cost.

Pose: and the transformation matrix corresponding to the relative relation between the pose of the vehicle and the global coordinate system is used for describing the position and the orientation of the vehicle.

EKF: extended Kalman Filter (EKF), which performs first-order linear truncation on the Taylor expansion of a nonlinear function, ignores other high-order terms, thereby converting the nonlinear problem into linearity, and applying Kalman linear filtering algorithm to a nonlinear system. In this way, the non-linearity problem is solved. Although the EKF has been accepted by academia and is widely used in a nonlinear state estimation system, the method also has two disadvantages, one is that when the EKF violates the local linear assumption, and when a high order term ignored in the Taylor expansion brings a large error, the EKF algorithm may make the filtering diverge; in addition, because the EKF needs to use Jacobian (Jacobian) matrix in the linearization process, the complicated calculation process thereof causes the method to be relatively difficult to realize. Therefore, when the 3 assumptions of linear system, white Gaussian noise, and obeying Gaussian (Gaussian) distribution to all random variables are satisfied, the EKF is a suboptimal filter under the minimum variance criterion, and the performance depends on local nonlinearity.

Application scenario overview

The unmanned container transport vehicle comprises an unmanned card collection, an intelligent mobile transport flat car and the like, and the vehicles are provided with sensors such as an Inertial Measurement Unit (IMU), a satellite receiver (GNSS), a wheel speed meter (WSS) and the like. The mounting relationship (e.g., lever arm, angle, etc.) between the sensors varies from vehicle to vehicle. When the sensor calibration is not carried out or the sensor calibration is inaccurate, the residual error of the installation relation is brought into navigation calculation, and the positioning precision is directly influenced. Therefore, before the vehicle is put into high-precision automatic driving operation, the fine calibration of the sensor must be completed. During the trial run of the prototype, the following protocol was used: firstly, under the parking condition, a lever arm between sensors is manually measured; then, under the driving condition, the installation angle between the sensor and the vehicle is fitted in an 'online data acquisition + offline' mode; and finally, after the calibration parameters are obtained, the vehicle needs to stop to a reference point, and the accuracy of the calibration result is verified.

With the continuous batch production of various unmanned container transport vehicles, the defects of the scheme are gradually shown: firstly, if the sensors to be calibrated are arranged in different cabins (the sensors cannot be reached by eyes), a measurer can only add the lengths in the cabins and the thicknesses of steel plates or glass one by one, and the measurement randomness is high and the repeatability is poor; secondly, WSS resolution of part of vehicle types is insufficient, wheel speed meter dead zones exist, offline fitting accuracy is low, and universality is to be improved; thirdly, after the calibration is completed, the vehicle needs to be manually driven to stop at a specific point position, the mounting relation needs to be measured again through a geometric relation reinspection lever arm if the vehicle does not pass the reinspection, the calibration efficiency is low, and the requirement of mass production is difficult to meet.

Inventive concept

In view of this, the present specification aims to provide a technical solution for determining automatic calibration parameters of a vehicle sensor by reconstructing a calibration model by integrating factors such as sensor and vehicle characteristics, usage environment, and the like.

The core concept of the specification is as follows:

an automatic calibration model for the vehicle sensors is established, in particular, the installation relation between the sensors is calculated through the speed difference and/or the position difference between the sensors, and the installation relation which passes the inspection is determined as a calibration parameter, wherein the installation relation meets the error threshold value.

The present application is described below with reference to specific embodiments and specific application scenarios.

Referring to fig. 1, fig. 1 is a flow chart illustrating an exemplary embodiment of an automatic calibration method for an unmanned container transport vehicle, the method performing the steps of:

step 102: positioning information of the first sensor and the second sensor is acquired.

Step 104: and calculating the installation relation between the first sensor and the second sensor through the speed difference and/or the position difference between the positioning information of the first sensor and the second sensor.

Step 106: and determining the installation relation smaller than the error threshold value as the calibration parameters of the first sensor and the second sensor.

The unmanned container transport vehicle can comprise an unmanned card collection, an intelligent mobile transport flat car and the like, and the vehicles are provided with sensors such as an Inertial Measurement Unit (IMU), a satellite receiver (GNSS), a wheel speed meter (WSS) and the like. The mounting relationship (e.g., lever arm, angle, etc.) between the sensors varies from vehicle to vehicle. When the sensor calibration is not carried out or the sensor calibration is inaccurate, the residual error of the installation relation is brought into navigation calculation, and the positioning precision is directly influenced. Therefore, before the vehicle is put into high-precision automatic driving operation, the refined calibration of the sensor needs to be completed.

In the solution proposed in this specification, positioning information of a first sensor and a second sensor may be obtained, an installation relationship between the first sensor and the second sensor may be calculated by a speed difference and/or a position difference between the positioning information of the first sensor and the positioning information of the second sensor, and the installation relationship smaller than an error threshold may be determined as a calibration parameter of the first sensor and the second sensor.

In one embodiment shown, the first sensor may include one or a combination of an inertial measurement unit, a satellite receiver, and a wheel speed meter.

For example, taking the first sensor as an inertial measurement unit IMU and the second sensor as a satellite receiver GNSS as an example, the inertial measurement unit IMU is a device for measuring the three-axis attitude angle (or angular velocity) and acceleration of an object. Generally, an IMU may include three single-axis accelerometers and three single-axis gyroscopes, where the accelerometers detect acceleration signals of an object in three independent axes of a carrier coordinate system, and the gyroscopes detect angular velocity signals of the carrier relative to a navigation coordinate system, measure angular velocity and acceleration of the object in a three-dimensional space, and solve the attitude of the object as positioning information; the satellite receiver can receive positioning information sent by a satellite, and because the two sensors are installed at different positions, the positioning information acquired by the sensors comes from different positions on the vehicle, and a measurement equation can be constructed through the speed difference and/or the position difference in the two positioning information, so that the installation relation between the two positioning information can be calculated, such as an x-arm between the inertial measurement unit and the satellite receiver, and a z-arm between the inertial measurement unit and the satellite receiver.

For example, taking the first sensor as an inertial measurement unit IMU and the second sensor as a wheel speed meter WSS as an example, the IMU may calculate the pose of the vehicle as positioning information, and the wheel speed meter may fit the current positioning information of the vehicle by observing the rotation speed of the wheels. Because the two sensors are installed at different positions, the positioning information acquired by the sensors comes from different positions on the vehicle, and a measurement equation can be constructed through the speed difference and/or the position difference in the two positioning information, and the installation relation between the two positioning information can be calculated, such as an x-rod arm between the inertial measurement unit and the wheel speed meter, a z-rod arm between the inertial measurement unit and the wheel speed meter, course installation errors between the inertial measurement unit and the wheel speed meter, pitching installation errors, scale factors of the wheel speed meter and the like. According to different vehicle types, parameters and wheel speed models of the vehicle are adjusted from aspects such as WSS output characteristics and sensor installation relations, and vehicle adaptability is improved.

Meanwhile, the scheme provided by the specification can also finish automatic calibration of more than two sensors.

For example, the installation relationship between the satellite receiver and the inertial measurement unit may be calibrated, then the positioning information observed by the wheel speed meter is acquired, the installation relationship between the wheel speed meter and the inertial measurement unit is calculated, whether the installation relationship is smaller than an error threshold is verified, and the installation relationship smaller than the error threshold is determined as the calibration parameter.

In the calibration process, the calibration accuracy is the most important performance index, and the navigation positioning accuracy can be directly influenced, so that the calibration accuracy also needs to be improved.

In one embodiment shown, in order to improve the effectiveness of the source information and thus improve the calibration accuracy, the initial conditions used by the source, such as the working status word, the working quality word, etc., may be set according to the communication protocol of the location source and the velocity source. In addition, according to motion constraints such as speed and position jump variables, observation field points are removed; distortion observations can also be rejected in the EKF process using the kafang test.

In one embodiment shown, it may be determined that the vehicle is in a driving state and has a turning engine; and if the ratio of the parking time length of the vehicle in the calibration process to the total calibration time length is greater than a first proportional threshold, determining that the calibration process has a fault.

Vehicle parking or travel may be determined based on vehicle dynamics, integrating the IMU, satellite speed and position outputs. The lever arm and the installation error angle cannot be observed in the parking process, so that the vehicle is required to have turning maneuver in the calibration process, and meanwhile, if the parking duration is too large in the calibration process, effective data are reduced, and the calibration precision is reduced; therefore, a fault word mark which is convenient for a worker to find out a fault subsequently can be added to the calibration process with the parking time length being larger than the parking time length, and the failure of the calibration process can be marked.

In one illustrated embodiment, the calibration accuracy of the sensors on the unmanned card may be verified online.

For example, taking the example that the installation relationship between the satellite receiver and the inertial measurement unit is calibrated, then the positioning information observed by the wheel speed meter is obtained, the installation relationship between the wheel speed meter and the inertial measurement unit is calculated, whether the installation relationship is smaller than an error threshold is verified, and the installation relationship smaller than the error threshold is determined as a calibration parameter, the inventor verifies the calibration accuracy of the sensor on the unmanned truck for 6 times, and the verification result is as follows:

from the above table, it can be seen that: the repeatability of the x-direction lever arm calibration is better than 1cm (1 sigma); the repeatability of the calibration of the z-direction lever arm is better than 0.8cm (1 sigma); the repeatability of course installation error is better than 0.022 degrees (1 sigma); the repeatability of the pitch installation error is better than 0.012 degrees (1 sigma); the wheel speed scale factor repeatability is better than 130ppm (1 sigma).

In one embodiment shown, the accuracy of the positioning information acquired by the sensor may be determined; and if the proportion of the quantity of the positioning information with the accuracy not meeting the calibration requirement in the positioning information acquired by the sensor in the calibration process is greater than a second proportion threshold, determining that the calibration process fails.

For example, if the ratio of the positioning information that does not meet the calibration requirement in the positioning information received by the satellite receiver is greater than the second ratio threshold, a fault word mark that facilitates the subsequent fault finding of a worker may be added to the calibration process, and the failure of the calibration process may be marked.

In one embodiment shown, if the time length for calculating the installation relationship is greater than the time length threshold, it is determined that the calibration process is failed. The requirements on the sensor output during calibration are more strict, and the measurement value of the isolation distortion needs to be maximized. If the temperature of the sensor exceeds the limit, the angular speed and the acceleration output are abnormal, and the like, even if the calibration process is continuously executed, the fault of the calibration process is also judged, so that the reliability of the sensor is evaluated.

For example, a calculation duration threshold for each installation relationship may be set according to the convergence rate of the EKF covariance: and finishing each installation relation within 5-10 min, otherwise, judging that the calibration process has a fault.

Referring to fig. 2, fig. 2 shows an exemplary embodiment of an automatic calibration device for an unmanned container transport vehicle, wherein the device comprises:

an information obtaining module 210, configured to obtain positioning information of the first sensor and the second sensor;

a calculating module 220, configured to calculate an installation relationship between the first sensor and the second sensor according to a speed difference and/or a position difference between the positioning information of the first sensor and the second sensor;

a checking module 230, configured to determine the installation relationship smaller than the error threshold as the calibration parameter of the first sensor and the second sensor.

Optionally, the first sensor, or the second sensor, includes:

one or a combination of an inertial measurement unit, a satellite receiver, and a wheel speed meter.

Optionally, the installation relationship includes:

one or a combination of lever arms, installation error angles, and indexing coefficients.

Optionally, the apparatus further comprises:

determining that the vehicle is in a driving state and has a turning engine;

and if the ratio of the parking time length of the vehicle in the calibration process to the total calibration time length is greater than a first proportional threshold, determining that the calibration process has a fault.

Optionally, the apparatus further comprises:

determining the accuracy of the positioning information acquired by the sensor;

and if the proportion of the quantity of the positioning information of which the accuracy does not meet the calibration requirement in the positioning information acquired by the sensor in the calibration process is greater than a second proportion threshold, determining that the calibration process has a fault.

Optionally, the calculating the installation relationship between the first sensor and the second sensor further includes:

and if the time length of the installation relation is calculated to be larger than a time length threshold value, determining that the calibration process fails.

The calibration model is reconstructed by integrating factors such as sensor and vehicle characteristics, use environment and the like, and calibration parameters such as GNSS and IMU lever arms, IMU and WSS course and pitching installation errors, and WSS scale coefficients are determined. In the whole calibration process, the vehicle only needs to be in a running state and can be turned flexibly, the tedious and split links such as manual measurement, offline processing, parameter review and the like are overcome, and the vehicle is integrated into an automatic calibration module. In addition, after the installation relation is verified to be qualified, the installation relation is determined as a calibration parameter and is solidified to the system, and the reliability is greatly improved. For the failed calibration process, the fault reason can be quickly positioned through the fault word, and the maintenance and the upgrade are convenient.

Referring to fig. 3, fig. 3 is a hardware structure diagram of an electronic device in which an automatic calibration device for an unmanned container transportation vehicle is located according to an exemplary embodiment. At the hardware level, the device includes a processor 302, an internal bus 304, a network interface 306, a memory 308, and a non-volatile memory 310, although it may include hardware required for other services. One or more embodiments of the present description may be implemented in software, such as by processor 302 reading a corresponding computer program from non-volatile storage 310 into memory 308 and then executing. Of course, besides software implementation, the one or more embodiments in this specification do not exclude other implementations, such as logic devices or combinations of software and hardware, and so on, that is, the execution subject of the following processing flow is not limited to each logic unit, and may also be hardware or logic devices.

For the device embodiment, since it basically corresponds to the method embodiment, reference may be made to the partial description of the method embodiment for relevant points. The above-described embodiments of the apparatus are only illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution in the present specification. One of ordinary skill in the art can understand and implement it without inventive effort.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. A typical implementation device is a computer, which may be in the form of a personal computer, laptop, cellular telephone, camera phone, smart phone, personal digital assistant, media player, navigation device, email messaging device, game console, tablet computer, wearable device, or a combination of any of these devices.

In a typical configuration, a computer includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory. The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transmission medium, that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or apparatus comprising the element.

The foregoing description of specific embodiments has been presented for purposes of illustration and description. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

The terminology used in the description of the one or more embodiments is for the purpose of describing the particular embodiments only and is not intended to be limiting of the description of the one or more embodiments. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

It should be understood that although the terms first, second, third, etc. may be used in one or more embodiments of the present description to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of one or more embodiments herein. The word "if," as used herein, may be interpreted as "at \8230; \8230when" or "when 8230; \823030when" or "in response to a determination," depending on the context.

The above description is intended only to be exemplary of the one or more embodiments of the present disclosure, and should not be taken as limiting the one or more embodiments of the present disclosure, as any modifications, equivalents, improvements, etc. that come within the spirit and scope of the one or more embodiments of the present disclosure are intended to be included within the scope of the one or more embodiments of the present disclosure.

Claims (10)

1. An automatic calibration method for an unmanned container transport vehicle, the method comprising:

acquiring positioning information of a first sensor and a second sensor;

calculating the installation relation between the first sensor and the second sensor through the speed difference and/or the position difference between the positioning information of the first sensor and the second sensor;

and determining the installation relation smaller than the error threshold value as the calibration parameters of the first sensor and the second sensor.

2. The method of claim 1, wherein the first sensor, or second sensor, comprises:

one or a combination of an inertial measurement unit, a satellite receiver, and a wheel speed meter.

3. The method of claim 1, wherein the installation relationship comprises:

one or a combination of a lever arm, a mounting error angle and a scale factor.

4. The method of claim 1, further comprising:

determining that the vehicle is in a driving state and has a turning engine;

and if the ratio of the parking time length to the total calibration time length of the vehicle in the calibration process is greater than a first proportional threshold, determining that the fault occurs in the calibration process.

5. The method of claim 1, further comprising:

determining accuracy of positioning information acquired by the sensor;

and if the proportion of the quantity of the positioning information with the accuracy not meeting the calibration requirement in the positioning information acquired by the sensor in the calibration process is greater than a second proportion threshold, determining that the calibration process fails.

6. The method of claim 1, wherein the calculating the mounting relationship between the first sensor and the second sensor further comprises:

and if the time length for calculating the installation relation is greater than the time length threshold value, determining that the calibration process fails.

7. An automatic calibration device suitable for use with an unmanned container transport vehicle, the device comprising:

the information acquisition module is used for acquiring positioning information of the first sensor and the second sensor;

the calculation module is used for calculating the installation relation between the first sensor and the second sensor through the speed difference and/or the position difference between the positioning information of the first sensor and the positioning information of the second sensor;

and the checking module is used for determining the installation relation smaller than the error threshold value as the calibration parameters of the first sensor and the second sensor.

8. The apparatus of claim 7, wherein the first sensor, or second sensor, comprises:

one or a combination of an inertial measurement unit, a satellite receiver and a wheel speed meter.

9. A computer readable storage medium having stored thereon computer instructions which, when executed by a processor, carry out the steps of the method according to any one of claims 1 to 6.

10. An electronic device, comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor implements the steps of the method of any one of claims 1-6 by executing the executable instructions.

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