Sensor extrinsic calibration method, device, equipment and autonomous vehicle

Abstract

The present disclosure provides a sensor extrinsic parameter calibration method, device, equipment and automatic driving vehicle, relates to the technical field of artificial intelligence, and specifically relates to the technical field of automatic driving, navigation positioning and the like. The sensor extrinsic parameter calibration method comprises: acquiring basic extrinsic parameters between an IMU and a wheel speed meter of an automatic driving vehicle; acquiring measurement data generated by the automatic driving vehicle under a preset driving condition; the preset driving condition comprises a preset site, and the preset site is a site with a lateral slope; acquiring attitude information based on the basic extrinsic parameters and the measurement data, wherein the attitude information comprises an absolute roll angle; determining a relative roll angle between the IMU and the wheel speed meter based on the absolute roll angle and the lateral slope of the preset site; and taking the basic extrinsic parameters and the relative roll angle as target extrinsic parameters between the IMU and the wheel speed meter. The present disclosure can improve the comprehensiveness of sensor extrinsic parameter calibration.

Description

Technical Field

[0001] This disclosure relates to the field of artificial intelligence technology, specifically to the fields of autonomous driving, navigation and positioning, and in particular to a sensor extrinsic parameter calibration method, apparatus, device and autonomous vehicle. Background Technology

[0002] The navigation and positioning system of autonomous vehicles needs to perform navigation and positioning autonomously to provide position and attitude information to downstream modules such as perception, planning, and control. To improve accuracy and robustness, navigation and positioning systems are typically based on multi-sensor fusion technology.

[0003] To fuse measurement data from different sensors, it is necessary to obtain the relative positions and attitude relationships between the sensors; these relationships are called extrinsic parameters. Extrinsic parameters between different sensors are typically obtained through offline calibration, and the accuracy of the extrinsic parameter calibration will affect the accuracy of the final navigation and positioning. Summary of the Invention

[0004] This disclosure provides a sensor extrinsic parameter calibration method, apparatus, device, and autonomous vehicle.

[0005] According to one aspect of this disclosure, a sensor extrinsic parameter calibration method is provided, comprising: acquiring basic extrinsic parameters between an IMU and a wheel speed meter of an autonomous vehicle; acquiring measurement data generated by the autonomous vehicle under preset driving conditions; the preset driving conditions including a preset site, the preset site being a site with a lateral slope; acquiring attitude information based on the basic extrinsic parameters and the measurement data, the attitude information including an absolute roll angle; determining a relative roll angle between the IMU and the wheel speed meter based on the absolute roll angle and the lateral slope of the preset site; and using the basic extrinsic parameters and the relative roll angle as target extrinsic parameters between the IMU and the wheel speed meter.

[0006] According to another aspect of this disclosure, a sensor extrinsic parameter calibration device is provided, comprising: a first acquisition module for acquiring basic extrinsic parameters between an IMU and a wheel speed meter of an autonomous vehicle; a second acquisition module for acquiring measurement data generated by the autonomous vehicle under preset driving conditions; the preset driving conditions including a preset site, the preset site being a site with a lateral slope; a third acquisition module for acquiring attitude information based on the basic extrinsic parameters and the measurement data, the attitude information including an absolute roll angle; a fourth acquisition module for determining a relative roll angle between the IMU and the wheel speed meter based on the absolute roll angle and the lateral slope of the preset site; and a determination module for using the basic extrinsic parameters and the relative roll angle as target extrinsic parameters between the IMU and the wheel speed meter.

[0007] According to another aspect of this disclosure, an autonomous vehicle is provided, comprising: an IMU and a wheel speed meter, wherein extrinsic parameters between the IMU and the wheel speed meter are calibrated using the method described in any of the preceding aspects.

[0008] According to another aspect of this disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to said at least one processor; wherein the memory stores instructions executable by said at least one processor, said instructions being executed by said at least one processor to enable said at least one processor to perform the method as described in any of the foregoing aspects.

[0009] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method according to any of the preceding aspects.

[0010] According to another aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method according to any of the preceding aspects.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0012] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0013] Figure 1 This is a schematic diagram based on the first embodiment of the present disclosure;

[0014] Figure 2 This is a schematic diagram of a sensor extrinsic parameter calibration scenario provided according to an embodiment of this disclosure;

[0015] Figure 3 This is a schematic diagram of a preset site provided according to an embodiment of the present disclosure;

[0016] Figure 4 This is a schematic diagram according to the second embodiment of the present disclosure;

[0017] Figure 5 This is a schematic diagram of the driving trajectory of an autonomous vehicle when solving basic extrinsic parameters according to an embodiment of this disclosure;

[0018] Figure 6 This is a schematic diagram illustrating the process of an autonomous vehicle driving on a lateral slope according to an embodiment of this disclosure;

[0019] Figure 7 This is a schematic diagram according to the third embodiment of the present disclosure;

[0020] Figure 8 This is a schematic diagram according to the fourth embodiment of the present disclosure;

[0021] Figure 9 This is a schematic diagram of an electronic device used to implement the sensor extrinsic parameter calibration method of the embodiments of this disclosure. Detailed Implementation

[0022] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0023] Inertial Measurement Units (IMUs) are crucial sensors for autonomous vehicles. They can measure the acceleration and angular velocity of autonomous vehicles at high frequencies, unaffected by external factors. By integrating the IMU's measurement data, the vehicle's position, velocity, and attitude can be obtained. However, due to the presence of measurement noise, long-term integration can lead to accumulated errors.

[0024] Wheel speed sensors are also a type of sensor in autonomous vehicles. They measure wheel rotation speed (wheel speed) using the Hall effect, thus providing wheel speed data for multi-sensor fusion. Wheel diameter affects the wheel speed measurement results; a scaling factor is typically used to correct the nominal wheel diameter to obtain the actual wheel diameter.

[0025] In related technologies, the calibration of extrinsic parameters between an IMU and a wheel speedometer typically involves six degrees of freedom. In a physical system, a degree of freedom describes the number of independent ways in which the system's state can be changed. Generally, each parameter that can be changed independently without affecting other parts of the system can be considered a degree of freedom. For an IMU and wheel speedometer, the six degrees of freedom include the following parameters: three relative positions, two relative rotation angles (yaw and pitch), and one wheel speedometer scale factor. Roll calibration is not considered.

[0026] Therefore, the calibration parameters of the relevant technologies are not comprehensive enough.

[0027] To improve the comprehensiveness of external parameter calibration, the present disclosure provides the following embodiments.

[0028] Figure 1 Based on a schematic diagram of the first embodiment of this disclosure, this embodiment provides a sensor extrinsic parameter calibration method, the method comprising:

[0029] 101. Obtain basic external parameters between the IMU and wheel speedometer of the autonomous vehicle.

[0030] 102. Obtain measurement data generated by the autonomous vehicle under preset driving conditions; the preset driving conditions include a preset site, which is a site with a lateral slope.

[0031] 103. Obtain attitude information based on the basic extrinsic parameters and the measurement data, wherein the attitude information includes: absolute roll angle.

[0032] 104. Based on the absolute roll angle and the lateral slope of the preset field, determine the relative roll angle between the IMU and the wheel speed gauge.

[0033] 105. The basic extrinsic parameters and the relative roll angle are used as the target extrinsic parameters between the IMU and the wheel speed gauge.

[0034] In this embodiment, the relative roll angle between the IMU and the wheel speed meter is determined and used as one of the target extrinsic parameters. Since the basic extrinsic parameters are usually 6 degrees of freedom, while the target extrinsic parameters in this embodiment include the basic extrinsic parameters and the relative roll angle, the extrinsic parameter calibration of 7 degrees of freedom can be achieved, thereby improving the comprehensiveness of the sensor extrinsic parameter calibration.

[0035] The basic extrinsic parameters between the IMU and the wheel speed meter include parameters with six degrees of freedom: three relative positions, two relative rotation angles, and one wheel speed meter scale factor.

[0036] As can be seen from the subsequent solution process for the basic extrinsic parameters, the relative roll (relative roll angle) is ignored in this process, that is, the relative roll is not observable. In order to ensure the comprehensiveness of the extrinsic parameter calibration between the IMU and the wheel speed meter, this embodiment also solves for the relative roll.

[0037] To solve for relative roll, after installing an IMU and wheel speedometers and determining the basic extrinsic parameters between the IMU and wheel speedometers, the autonomous vehicle can be driven under preset driving conditions. These preset driving conditions are designed to ensure that the autonomous vehicle maintains a stable driving posture during driving. The stable driving posture mainly ensures that the roll angle between the IMU and the horizontal plane (which can be called the absolute roll angle) remains unchanged, thereby ensuring that the absolute roll angle is solvable.

[0038] To achieve the aforementioned stable driving posture, the autonomous vehicle can drive on a pre-set site, which is a site with a lateral slope.

[0039] Autonomous vehicles generate measurement data during driving under preset driving conditions. This measurement data is collected by the sensors (including IMU and wheel speedometers) of the autonomous vehicle. Based on this measurement data and the already determined basic extrinsic parameters, navigation solutions are performed to obtain the attitude information of the autonomous vehicle. The attitude information includes the absolute roll angle, which is the roll angle between the IMU and the horizontal plane. Specifically, it is the angle between the IMU's y-axis (the positive direction axis pointing towards the front of the vehicle) and the horizontal plane.

[0040] The lateral slope of a site is the angle between the lateral road surface and the horizontal plane.

[0041] Then, the difference between the absolute roll angle and the lateral slope can be used as the relative roll angle between the IMU and the wheel speed gauge.

[0042] After obtaining the basic extrinsic parameters and the relative roll angle, the six basic extrinsic parameters and the relative roll angle can be combined to form the seven-degree-of-freedom target extrinsic parameters between the IMU and the wheel speed gauge.

[0043] To better understand the embodiments of this disclosure, the application scenarios of these embodiments are described. These embodiments can be applied to autonomous driving scenarios.

[0044] Figure 2 This is a schematic diagram illustrating the application scenarios used to implement the embodiments of this disclosure.

[0045] like Figure 2 As shown, the autonomous vehicle is equipped with an IMU 201 and a wheel speedometer 202. There is a relative relationship between the two, including relative position (such as relative translation) and relative rotation angle. In order to ensure correct navigation and positioning, it is necessary to calibrate the external parameters between the IMU and the wheel speedometer. The external parameters include the aforementioned relative translation and relative rotation angle, as well as the wheel speedometer scale factor.

[0046] The wheel speed meter scaling factor can be represented by s. The actual speed of the vehicle and the wheel speed measured by the wheel speed meter have the following relationship:

[0047]

[0048] Among them, v v It is the vehicle's actual speed. s is the wheel speed measured by the wheel speed gauge, and s is the wheel speed gauge scale factor.

[0049] Assume the IMU coordinate system is called the b system and the wheel speedometer coordinate system is called the v system. Both follow the orientation of the vehicle body at the right front and top, but the origins of the coordinate systems are different. The origin of the b system is located at the IMU centroid, and the origin of the v system is located at the rear wheel axle center. There is a certain installation error angle between the two, which can be described by a relative rotation matrix.

[0050] like Figure 2 As shown, the three coordinate axes of the IMU coordinate system (b system) are represented by x... b y b z b The origin is represented by O. b Indicated; the three coordinate axes of the wheel speed gauge coordinate system (v-frame) are represented by x, x, and x respectively. v y v z v The origin is represented by O. v express.

[0051] The coordinate transformation relationship from the v system to the b system can be expressed as:

[0052]

[0053] Where, p v This represents the coordinates of a point p in the v frame; p b This represents the coordinates of point p in frame b; The rotation matrix from the v-frame to the b-frame can be called the relative rotation matrix; The coordinates of the origin of the v system in the b system can be called the relative translation matrix.

[0054] Rotation matrix The rotation can be determined based on the rotation angles and rotation order corresponding to the three coordinate axes. In the field of autonomous driving, the rotation order is usually zxy. Based on this rotation order, the rotation matrix can be obtained. The specific expression is:

[0055]

[0056]

[0057]

[0058]

[0059]

[0060] Where ψ, θ, and γ are the relative yaw, pitch, and roll angles between the v and b frames, respectively. The relative yaw angle ψ is the z-angle of the b frame along the v frame. v The angle of rotation of the axis, the relative pitch angle θ is the x-axis of the b-frame along the v-frame. v The angle of rotation of the shaft, the relative roll angle γ is the y-axis of the b-system along the v-system. v The angle of rotation of the axis.

[0061] Relative translation matrix It can be represented as:

[0062] These are the x, y, and z components of the coordinates of the origin in the v system in the b system, representing the relative translations in the three directions.

[0063] The basic extrinsic parameters include three relative positions, two relative rotation angles, and a wheel speedometer scale factor. The three relative positions refer to the relative translation amounts in the three directions mentioned above, i.e. The two relative rotation angles include the relative heading angle and the relative pitch angle, i.e., ψ and θ; and a wheel speed meter scale factor, i.e., s mentioned above.

[0064] The basic extrinsic parameters can be obtained by minimizing the cost function, which can be constructed from the residual terms, which are based on the extrinsic parameter variables. See subsequent examples for details.

[0065] The basic extrinsic parameters mentioned above are for 6 degrees of freedom and lack a calibration process for the relative roll angle. However, in certain scenarios, relative roll angle is also required to obtain accurate state information (position, speed, and attitude) of autonomous vehicles. Specific scenarios include special turning scenarios, such as right turns which are usually left-high and right-low, but there are also right-high and left-low right turn scenarios. In these cases, the absolute roll of the vehicle chassis (wheel speed sensor) is required. This absolute roll can be calculated based on the relative roll between the IMU and the wheel speed sensor. Therefore, it is necessary to calibrate the relative roll angle between the IMU and the wheel speed sensor.

[0066] In the usual calibration method for the extrinsic parameters between the IMU and the wheel speed gauge, the relative roll is not observable. For details, please refer to the calibration process for the 6 degrees of freedom later. In this process, the relative roll will be hidden and cannot be solved.

[0067] To obtain relative roll, this embodiment can be based on preset driving conditions. The preset driving conditions are those conditions that allow the vehicle to maintain a stable driving posture.

[0068] Preset driving conditions include: preset location, such as Figure 3 As shown, the preset site 301 is a site with a lateral slope.

[0069] During the autonomous vehicle's operation in the aforementioned preset area, measurement data collected by the vehicle's sensors can be obtained. Based on this measurement data, the vehicle's attitude information can be obtained, including absolute roll. Absolute roll refers to the IMU's roll relative to the horizontal plane.

[0070] The aforementioned measurement data can specifically include various measurement data collected by multiple sensors. Navigation solutions are performed based on the measurement data and basic extrinsic parameters, such as Kalman filtering or nonlinear optimization techniques, to obtain state information. The state information includes velocity information, position information, and attitude information, among which the attitude information includes absolute roll.

[0071] After obtaining the absolute roll, the relative roll can be calculated based on the absolute roll and the lateral slope, and used as the parameter for the 7th degree of freedom.

[0072] The formula for calculating relative roll can be:

[0073] γ=β-α

[0074] Where γ is relative roll; β is absolute roll; and α is lateral slope, which is the angle of the slope relative to the horizontal plane.

[0075] Then, the parameters of the relative roll and the six degrees of freedom in the basic extrinsic parameters can be used as the target extrinsic parameters, that is, the final calibrated target extrinsic parameters include parameters of seven degrees of freedom.

[0076] In conjunction with the above application scenarios, this disclosure also provides a method for calibrating sensor extrinsic parameters.

[0077] Figure 4 Based on a schematic diagram of the second embodiment of this disclosure, this embodiment provides a sensor extrinsic parameter calibration method, the method comprising:

[0078] 401. Obtain the basic external parameters between the IMU and wheel speedometer of the autonomous vehicle.

[0079] Specifically, basic data generated by the autonomous vehicle under basic driving conditions can be obtained; residual terms can be constructed based on the basic data and extrinsic variables; a cost function can be constructed based on the residual terms; and the cost function can be minimized to obtain the basic extrinsic parameters.

[0080] In this embodiment, a cost function is constructed based on the residual term. By minimizing the cost function, the basic extrinsic parameters can be solved simply and efficiently.

[0081] Basic data refers to measurement data generated under basic driving conditions.

[0082] Basic driving conditions may include: the autonomous vehicle is traveling on a level (or near-level) surface, following a non-linear trajectory. Traveling along a non-linear trajectory ensures that the relative heading and pitch angles are considerable.

[0083] like Figure 5As shown, the non-linear trajectory can be a loop (or a semi-loop) 501, or it can also be a figure-eight 502.

[0084] For example, sensors can be installed on autonomous vehicles, including IMUs and wheel speedometers, and may also include other sensors such as Global Navigation Satellite System (GNSS). The vehicle equipped with these sensors travels in a figure-eight pattern several times on a roughly level, open, ordinary road surface, collecting measurement data from the IMU, wheel speedometer, and GNSS as basic data.

[0085] In this embodiment, based on non-linear trajectory driving, it can be ensured that the relative heading angle and relative pitch angle are considerable, thereby accurately solving the basic extrinsic parameters.

[0086] After acquiring the basic data, navigation solutions can be performed based on this data to obtain the state information of the autonomous vehicle, and residual terms can be constructed based on the state information. The navigation solution process can be implemented using relevant techniques, such as Kalman filtering or nonlinear optimization.

[0087] The expression for the residual term is:

[0088]

[0089] Where r is the residual term;

[0090] It is the rotation matrix from the b-frame (IMU coordinate system) to the v-frame (wheel speedometer coordinate system), and is related to the above. The relationship is: The superscript T indicates the transpose operation;

[0091] v b It is the speed of the autonomous vehicle in the b-frame obtained after performing navigation solutions on the measurement data of the IMU and the measurement data of the GSNN;

[0092] It is the angular velocity of the autonomous vehicle in the b-frame, measured by the IMU;

[0093] It is the relative translation matrix from the v system to the b system; for details, please refer to the aforementioned related descriptions.

[0094] {v} [2] It takes the second dimension of vector v;

[0095] s is the wheel speed gauge scale factor;

[0096] It is the wheel speed measured by the wheel speed gauge.

[0097] It should be noted that the quantities corresponding to the basic extrinsic parameters in the residual term are unknown and can be called extrinsic variables. For example, in the residual term mentioned above... and These are unknown. By subsequently solving the cost function, the specific values ​​of these basic extrinsic parameters can be obtained, which can then be used as the basic extrinsic parameters for calibration.

[0098] During the autonomous vehicle's operation, a residual term as described above can be constructed at each data collection point. The cost function can be constructed from the residual terms at multiple points along the entire trajectory as follows:

[0099]

[0100] in, This is the cost function, and the meaning of its independent variable can be found in the aforementioned related content;

[0101] k is the time index, N is the number of times, and represents the residual term at time k.

[0102] After obtaining the cost function described above, we can solve for the six degrees of freedom corresponding to the cost function, namely ψ, θ, by minimizing the cost function. These 6 parameters are 's'.

[0103] Based on the cost function described above, we know that the independent variables of this cost function include 6 degrees of freedom, but do not include the relative roll angle γ. This is because when constructing the residual term, the parameters of the second dimension are taken, and this dimension's parameters do not include γ, meaning the relative roll angle is not observable.

[0104] In related technologies, although there are some external parameter calibration schemes between IMU and wheel speed gauge, they do not realize that the relative roll angle is not observable. They use the same method to solve for other angles (ψ, θ). However, due to the inobservability of the relative roll angle, the relative roll angle cannot be solved using other angle solution methods.

[0105] To solve for the relative roll angle, this embodiment also includes:

[0106] 402. Obtain the relative roll angle between the IMU and wheel speedometer of the autonomous vehicle.

[0107] Specifically, this may include: acquiring measurement data generated by the autonomous vehicle under preset driving conditions; the preset driving conditions include a preset site, which is a site with a lateral slope; acquiring attitude information based on the basic extrinsic parameters and the measurement data, the attitude information including: absolute roll angle; and determining the relative roll angle between the IMU and the wheel speed gauge based on the absolute roll angle and the lateral slope of the preset site.

[0108] The stage of solving the basic extrinsic parameters can be called the first stage, and the stage of solving the relative roll can be called the second stage.

[0109] In the first stage, taking figure-eight driving as an example, during the process of the vehicle driving around the figure-eight, there is a difference in the absolute roll of the IMU relative to the horizontal plane when turning and when going straight, resulting in inaccurate solution. In order to ensure the accuracy of absolute roll, the vehicle needs to maintain a stable driving state, that is, to ensure that the above-mentioned absolute roll remains basically unchanged during the driving process.

[0110] Therefore, the second phase requires autonomous vehicles to operate in pre-designated areas, such as... Figure 3 As shown, the preset site is a road surface with a lateral slope.

[0111] The aforementioned road surface with lateral slope can, to a certain extent, ensure the stability of autonomous vehicles.

[0112] To further ensure the smoothness of vehicle travel, the preset site can be a circular or semi-circular site; and / or, the autonomous vehicle travels along the center line of the road surface of the preset site.

[0113] like Figure 6 As shown, an autonomous vehicle can travel from point A to point B along centerline 601 on a semi-circular lateral slope. During this process, measurement data collected by sensors can be acquired. Navigation calculations based on this measurement data can obtain the autonomous vehicle's attitude information, including absolute roll, which is the IMU's roll relative to the horizontal plane.

[0114] In this embodiment, based on the aforementioned preset driving conditions, the smooth driving of the autonomous vehicle can be guaranteed, so as to accurately obtain the absolute roll, and thus accurately obtain the relative roll of the IMU relative to the wheel speed meter.

[0115] After obtaining the absolute roll, the relative roll can be calculated based on the absolute roll and the lateral slope, and used as the parameter for the 7th degree of freedom.

[0116] The formula for calculating relative roll can be:

[0117] γ=β-α

[0118] Where γ is relative roll; β is absolute roll; and α is lateral slope, which is the angle of the slope relative to the horizontal plane.

[0119] In this embodiment, the difference between the absolute roll and the lateral slope is used as the relative roll, which can easily and accurately obtain the relative roll.

[0120] 403. The basic extrinsic parameters and the relative roll angle are used as the target extrinsic parameters between the IMU and the wheel speed gauge.

[0121] Among them, the parameters of the relative roll and the six degrees of freedom in the basic extrinsic parameters are used as the target extrinsic parameters, that is, the final calibrated target extrinsic parameters include parameters of seven degrees of freedom.

[0122] In certain scenarios, downstream modules (such as the Planning and Control (PNC) module) need to know the chassis roll angle component (i.e., the roll of the wheel speed sensor coordinate system relative to the road surface). This roll can be calculated from the IMU roll relative to the road surface (which can be obtained through the navigation solution process) and the relative roll between the IMU and the wheel speed sensor. In this way, the PNC can perform more accurate planning and control of highly maneuverable vehicles based on the roll of the wheel speed sensor coordinate system relative to the road surface. Conventional calibration methods lack calibration of the IMU roll angle component relative to the wheel speed sensor, resulting in the inability to obtain accurate chassis attitude. This embodiment uses a two-stage calibration method to calibrate seven degrees of freedom parameters, including the roll angle component, improving the comprehensiveness of extrinsic parameter calibration. This allows the chassis roll to be determined based on the relative roll, providing accurate data to downstream modules and facilitating their planning and execution.

[0123] Figure 7 The schematic diagram is based on the third embodiment of the present disclosure. This embodiment provides a sensor extrinsic parameter calibration device. The device 700 includes: a first acquisition module 701, a second acquisition module 702, a third acquisition module 703, a fourth acquisition module 704, and a determination module 705.

[0124] The first acquisition module 701 is used to acquire basic extrinsic parameters between the IMU and the wheel speed meter of the autonomous vehicle; the second acquisition module 702 is used to acquire measurement data generated by the autonomous vehicle under preset driving conditions; the preset driving conditions include a preset site, which is a site with a lateral slope; the third acquisition module 703 is used to acquire attitude information based on the basic extrinsic parameters and the measurement data, the attitude information including: absolute roll angle; the fourth acquisition module 704 is used to determine the relative roll angle between the IMU and the wheel speed meter based on the absolute roll angle and the lateral slope of the preset site; the determination module 705 is used to use the basic extrinsic parameters and the relative roll angle as target extrinsic parameters between the IMU and the wheel speed meter.

[0125] In this embodiment, the relative roll angle between the IMU and the wheel speed meter is determined and used as one of the target extrinsic parameters. Since the basic extrinsic parameters are usually 6 degrees of freedom, while the target extrinsic parameters in this embodiment include the basic extrinsic parameters and the relative roll angle, the extrinsic parameter calibration of 7 degrees of freedom can be achieved, thereby improving the comprehensiveness of the sensor extrinsic parameter calibration.

[0126] In some embodiments, the preset driving conditions further include:

[0127] The preset site is a circular or semi-circular site; and / or,

[0128] The autonomous vehicle travels along the center line of the road surface of the preset site.

[0129] In this embodiment, based on the aforementioned preset driving conditions, the smooth driving of the autonomous vehicle can be guaranteed, so as to accurately obtain the absolute roll, and thus accurately obtain the relative roll of the IMU relative to the wheel speed meter.

[0130] In some embodiments, the fourth acquisition module 704 is further configured to:

[0131] The difference between the absolute roll angle and the lateral slope is taken as the relative roll angle.

[0132] In this embodiment, the difference between the absolute roll and the lateral slope is used as the relative roll, which can easily and accurately obtain the relative roll.

[0133] In some embodiments, the first acquisition module 701 is further configured to:

[0134] Acquire basic data generated by the autonomous vehicle under basic driving conditions;

[0135] The residual term is constructed based on the basic data and the extrinsic variables;

[0136] Construct a cost function based on the residual term;

[0137] The cost function is minimized to obtain the basic extrinsic parameters.

[0138] In this embodiment, a cost function is constructed based on the residual term. By minimizing the cost function, the basic extrinsic parameters can be solved simply and efficiently.

[0139] In some embodiments, the first acquisition module 701 is further configured to:

[0140] Acquire basic data generated by the autonomous vehicle as it travels along a non-linear trajectory on a horizontal road surface.

[0141] In this embodiment, based on non-linear trajectory driving, it can be ensured that the relative heading angle and relative pitch angle are considerable, thereby accurately solving the basic extrinsic parameters.

[0142] Figure 8 This is a schematic diagram based on the fourth embodiment of the present disclosure. This embodiment provides an autonomous driving vehicle 800, which includes an IMU 801 and a wheel speed meter 802. There is an installation error between the two, namely, there is a relative translation and a relative rotation angle. In addition, the wheel speed meter has a scale factor. The extrinsic parameters between the IMU 801 and the wheel speed meter 802 are calibrated using the method described in any of the above embodiments.

[0143] It is understood that the same or similar content in different embodiments of this disclosure can be referred to each other.

[0144] It is understood that the terms "first" and "second" in the embodiments of this disclosure are only used for distinction and do not indicate the degree of importance or the order of events.

[0145] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0146] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0147] Figure 9 A schematic block diagram of an example electronic device 900 that can be used to implement embodiments of the present disclosure is shown. Electronic device 900 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 900 may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0148] like Figure 9As shown, the electronic device 900 includes a computing unit 901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 902 or a computer program loaded from a storage unit 909 into a random access memory (RAM) 903. The RAM 903 may also store various programs and data required for the operation of the electronic device 900. The computing unit 901, ROM 902, and RAM 903 are interconnected via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0149] Multiple components in electronic device 900 are connected to I / O interface 905, including: input unit 906, such as keyboard, mouse, etc.; output unit 907, such as various types of displays, speakers, etc.; storage unit 908, such as disk, optical disk, etc.; and communication unit 909, such as network card, modem, wireless transceiver, etc. Communication unit 909 allows electronic device 900 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0150] The computing unit 901 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 901 performs the various methods and processes described above, such as sensor extrinsic calibration methods. For example, in some embodiments, the sensor extrinsic calibration method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 908. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 900 via ROM 902 and / or communication unit 909. When the computer program is loaded into RAM 903 and executed by the computing unit 901, one or more steps of the sensor extrinsic calibration method described above can be performed. Alternatively, in other embodiments, the computing unit 901 can be configured to perform the sensor extrinsic calibration method by any other suitable means (e.g., by means of firmware).

[0151] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0152] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to the processor or controller of a general-purpose computer, special-purpose computer, or other programmable load balancing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0153] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0154] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0155] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0156] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service ecosystem, addressing the shortcomings of traditional physical hosts and VPS (Virtual Private Server, or simply "VPS") services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0157] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0158] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for calibrating sensor extrinsic parameters, comprising: Obtaining the basic extrinsic parameters between the inertial measurement unit (IMU) and wheel speedometer of an autonomous vehicle includes: acquiring basic data generated by the autonomous vehicle under basic driving conditions; constructing residual terms based on the basic data and extrinsic parameter variables; constructing a cost function based on the residual terms; and minimizing the cost function to obtain the basic extrinsic parameters. Acquire measurement data generated by the autonomous vehicle under preset driving conditions; the preset driving conditions include a preset site, which is a site with a lateral slope, and the lateral slope is the angle between the lateral road surface of the preset site and the horizontal plane. Attitude information is obtained based on the basic extrinsic parameters and the measurement data. The attitude information includes: absolute roll angle, which is the roll angle between the IMU and the horizontal plane. Based on the absolute roll angle and the lateral slope of the preset field, the relative roll angle between the IMU and the wheel speed gauge is determined; The basic extrinsic parameters and the relative roll angle are used as the target extrinsic parameters between the IMU and the wheel speed gauge. The target extrinsic parameters are a seven-degree-of-freedom target extrinsic parameter consisting of a basic extrinsic parameter with six degrees of freedom and a relative roll angle.

2. The method according to claim 1, wherein, The preset driving conditions also include: The preset site is a circular or semi-circular site; and / or, The autonomous vehicle travels along the center line of the road surface of the preset site.

3. The method according to claim 1, wherein, Determining the relative roll angle between the IMU and the wheel speedometer based on the absolute roll angle and the lateral slope of the preset field includes: The difference between the absolute roll angle and the lateral slope is taken as the relative roll angle.

4. The method according to claim 1, wherein, The acquisition of basic data generated by the autonomous vehicle under basic driving conditions includes: Acquire basic data generated by the autonomous vehicle as it travels along a non-linear trajectory on a horizontal road surface.

5. A sensor extrinsic parameter calibration device, comprising: The first acquisition module is used to acquire basic extrinsic parameters between the IMU and wheel speed meter of the autonomous vehicle, including: acquiring basic data generated by the autonomous vehicle under basic driving conditions; constructing residual terms based on the basic data and extrinsic parameter variables; constructing a cost function based on the residual terms; and minimizing the cost function to obtain the basic extrinsic parameters. The second acquisition module is used to acquire measurement data generated by the autonomous vehicle under preset driving conditions; the preset driving conditions include a preset site, which is a site with a lateral slope, and the lateral slope is the angle between the lateral road surface of the preset site and the horizontal plane. The third acquisition module is used to acquire attitude information based on the basic extrinsic parameters and the measurement data. The attitude information includes: absolute roll angle, which is the roll angle between the IMU and the horizontal plane. The fourth acquisition module is used to determine the relative roll angle between the IMU and the wheel speed gauge based on the absolute roll angle and the lateral slope of the preset field. The determination module is used to take the basic extrinsic parameters and the relative roll angle as the target extrinsic parameters between the IMU and the wheel speed gauge. The target extrinsic parameters are the target extrinsic parameters of 7 degrees of freedom, which consist of 6 basic extrinsic parameters and one relative roll angle.

6. The apparatus according to claim 5, wherein, The preset driving conditions also include: The preset site is a circular or semi-circular site; and / or, The autonomous vehicle travels along the center line of the road surface of the preset site.

7. The apparatus according to claim 5, wherein, The fourth acquisition module is further used for: The difference between the absolute roll angle and the lateral slope is taken as the relative roll angle.

8. The apparatus according to claim 5, wherein, The first acquisition module is further used for: Acquire basic data generated by the autonomous vehicle as it travels along a non-linear trajectory on a horizontal road surface.

9. An autonomous vehicle, comprising: The IMU and the wheel speed meter, wherein the external parameters between the IMU and the wheel speed meter are calibrated using the method described in any one of claims 1-4.

10. An electronic device, comprising: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.

11. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-4.

12. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1-4.

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