WO2022206519A1

WO2022206519A1 - External parameter calibration method and apparatus for vehicle-mounted radar

Info

Publication number: WO2022206519A1
Application number: PCT/CN2022/082560
Authority: WO
Inventors: 尹晓萌; 王建国; 陈默
Original assignee: 华为技术有限公司
Priority date: 2021-03-31
Filing date: 2022-03-23
Publication date: 2022-10-06
Also published as: CN115144825A

Abstract

An external parameter calibration method and apparatus for a vehicle-mounted radar, applied to autonomous driving or assisted driving. The method comprises: acquiring travel information of a vehicle, the travel information comprising at least one of traveling state information or travel road condition information, wherein the traveling state information comprises at least one of the speed, acceleration, yaw angle or yaw rate, and the travel road condition information is used for indicating at least one of a travel road of the vehicle or objects around the travel road; and performing external parameter calibration on the vehicle-mounted radar according to the travel information. In this way, the external parameter calibration for the vehicle-mounted radar can be completed during the traveling process of the vehicle, there is no need to return to the factory for calibration, and therefore, the efficiency is high. Furthermore, the method can be applied to the Internets of Vehicles, such as Vehicle to Everything (V2X), Long Term Evolution-Vehicle (LTE-V), and Vehicle to Vehicle (V2V).

Description

Method and device for external parameter calibration of vehicle-mounted radar

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the priority of the Chinese patent application filed on March 31, 2021 with the application number of 202110351002.5 and the application title of "A method and device for calibrating external parameters of a vehicle-mounted radar", the entire contents of which are incorporated by reference in this application.

technical field

The present application relates to the field of connected vehicles, and in particular, to a method and device for calibrating external parameters of a vehicle-mounted radar.

Background technique

Vehicle-mounted radar can realize functions such as obstacle measurement, collision prediction, adaptive cruise control, etc., which can effectively reduce the difficulty of driving, reduce the burden on drivers and reduce the incidence of accidents, so it has been widely used in the automotive field.

Referring to FIG. 1 , a vehicle-mounted radar (eg, the radar on vehicle A in FIG. 1 ) can perceive the coordinates of surrounding objects (eg, vehicle B on the road in FIG. 1 ) in the vehicle-mounted radar coordinate system. In order to restore the position of the object in the real environment, it is necessary to convert the coordinates of the object in the vehicle radar coordinate system to the vehicle coordinate system. Among them, the conversion from the vehicle radar coordinate system to the vehicle coordinate system requires the use of an important parameter, that is, the external parameters of the vehicle radar. Once the external parameters of the vehicle radar are inaccurate, it will affect the determination of the object's position in the real environment and cannot guarantee driving safety. The process of determining the external parameters of the vehicle radar is called the external parameter calibration process of the vehicle radar.

At present, the calibration methods of vehicle-mounted external parameters, such as return-to-factory calibration, are relatively cumbersome, and the vehicle-mounted radar cannot be used by users normally during the return-to-factory calibration, which affects the user experience.

SUMMARY OF THE INVENTION

The purpose of the present application is to provide a method and device for calibrating external parameters of a vehicle-mounted radar, which are used to realize the online calibration of the vehicle-mounted radar and improve the calibration efficiency.

In a first aspect, a method for calibrating external parameters of a vehicle-mounted radar is provided, which is applied to a vehicle, and the vehicle includes a radar. The method includes: acquiring driving information of the vehicle, where the driving information includes at least one of driving state information or driving road condition information; wherein the driving state information includes speed, acceleration, yaw angle, or yaw angle speed. The driving road condition information includes at least one of the driving road of the vehicle or the objects around the driving road; according to the driving information, the vehicle radar is calibrated with external parameters.

In this way, the vehicle can complete the external parameter calibration of the on-board radar during the driving process, without returning to the factory for calibration, the efficiency is high, the normal use of the on-board radar is not affected, and the user experience is high.

In the first case, the performing external parameter calibration on the vehicle radar according to the driving information includes: according to the driving state information, when it is determined that at least one of the following conditions is satisfied, performing external parameter calibration on the vehicle radar Calibration, wherein the conditions include: the vehicle is traveling at an average speed, the speed is less than a first threshold, the vehicle is traveling at an average speed, the acceleration is less than a second threshold, the yaw angle is within a preset range, Or the yaw angular velocity is less than the third threshold.

That is to say, when the vehicle is driving, it is determined that the current state is suitable for online calibration according to parameters such as speed, acceleration, yaw angle, and yaw angular velocity, and the external parameters of the vehicle radar are calibrated. For example, when the vehicle is driving at a constant speed, it is considered that the vehicle is relatively stable, and it can be calibrated online if it meets the calibration conditions.

In the second case, the performing external parameter calibration on the vehicle radar according to the driving information includes: determining, according to the driving road condition information, that the driving road is a straight road and/or that there are targets around the driving road When a reference object is used, external parameter calibration is performed on the vehicle-mounted radar.

That is to say, when the vehicle is driving on the straight road and/or there are target reference objects around the driving road, the external parameters of the vehicle-mounted radar are calibrated to realize the online calibration of the vehicle-mounted radar, and the efficiency is high. Moreover, the vehicle is calibrated using the target reference around the driving road, and the calibrated external parameters are more suitable for the real situation and have higher accuracy.

In a possible design, the external parameter includes at least one of a rotation angle or a translation distance of the vehicle-mounted radar; the method further includes: determining the vehicle-mounted radar according to a target reference around the driving road At least one of the rotation angle or the translation distance of the radar.

That is to say, the vehicle is calibrated using the target reference objects around the driving road during the driving process. In this way, not only online calibration can be achieved, but the calibrated external parameters are more suitable for the real situation and have higher accuracy.

For example, the determining the rotation angle of the vehicle-mounted radar according to the target reference around the driving road includes: determining the vehicle-mounted radar according to the ground of the driving road or a plane object parallel to the ground The pitch angle and roll angle of the vehicle are determined; the yaw angle of the vehicle-mounted radar is determined according to the objects arranged along the driving road.

That is to say, the pitch angle, roll angle and yaw angle can be calibrated using different target reference objects with high accuracy.

For example, the objects disposed along the travel road include: first type objects disposed along the travel road and parallel to the ground of the travel road, and/or along the travel road and the second type of objects arranged perpendicular to the ground of the driving road; the first type of objects includes: at least one of a road edge, a guardrail, a green belt or a lane line; the second type of objects includes: trees, At least one of a sign or street light.

In a possible design, determining the yaw angle of the vehicle-mounted radar according to the objects arranged along the driving road includes: determining the road direction of the driving road according to the objects arranged along the driving road , where the road direction satisfies y=C0+C1*x; where C1 is used to indicate the road direction; C0 is used to describe the distance between the object and the origin of the radar coordinate system, x and y are The coordinates of the object in the radar coordinate system; the yaw angle of the vehicle-mounted radar is determined according to the C1.

The yaw angle determined in this way is more accurate. Because a calibration scheme is to determine the rotation matrix R of the radar coordinate system relative to the vehicle coordinate system according to the deviation between the first normal vector of the point cloud data of the ground in the radar coordinate system and the standard normal vector of the ground, and then, according to The rotation matrix R can get the yaw angle. However, since the change of the yaw angle has no effect on the deviation between the first normal vector and the standard normal vector of the ground, in other words, the change of the yaw angle will not cause the change of the rotation matrix R. Therefore, pushing back the yaw angle by R is not accurate. However, in the embodiment of the present application, the yaw angle is determined by the objects arranged along the driving road, and the calibration accuracy of the yaw angle is improved.

In a possible design, the method further includes: acquiring external parameters of the vehicle-mounted radar; determining a target plane object around the driving road; using the external parameters to place the target plane object in the vehicle-mounted radar coordinate system Convert the corresponding first point cloud data in the inertial coordinate system to the second point cloud data in the inertial coordinate system; if the flatness of the second point cloud data does not meet the first condition, adjust the external parameter.

That is to say, in this embodiment of the present application, the vehicle can verify whether the external parameters of the vehicle-mounted radar are accurate. After the second point cloud data, the flatness of the second point cloud data does not meet the first condition, indicating that the external parameters are not accurate, then adjust the external parameters. This method can verify whether the external parameters are accurate and improve the accuracy of external parameter calibration.

In a possible design, the flatness of the second point cloud data does not satisfy the first condition, including: any two reflection points of different transmit beams in the second point cloud data on the target plane object The first vector between is not perpendicular to the normal vector of the point cloud data corresponding to any one or more transmit beams in the second point cloud data; or, any two different transmit beams in the second point cloud data are in the The included angle between the first vector between the reflection points on the target plane object and the normal vector of the point cloud data corresponding to any one or more transmit beams in the second point cloud data is not within a preset range.

It should be noted that, since the second point cloud data is the coordinates of the target plane object in the inertial coordinate system. If the flatness of the second point cloud data is good, it means that the second point cloud data obtained by using the current external parameters of the vehicle radar through a series of coordinate transformations is flat, which is more in line with the real situation, indicating that the current external parameters are relatively accurate of. If the flatness of the second point cloud data is poor, it means that the second point cloud data obtained through a series of coordinate transformations using the current external parameters is not flat and does not conform to the real situation (because the real situation is that the surface of the target plane object is flat ), indicating that the current external parameters are inaccurate, and the external parameters of the vehicle radar need to be calibrated. In this way, it can be accurately judged whether the current external parameter is accurate, and the accuracy is high.

In a possible design, the adjusting the external parameter includes: generating an objective function based on the external parameter, where the objective function is used to describe the angle between the first vector and the normal vector , or, the objective function is used to describe the projection distance of the normal vector on the first vector; within a preset extrinsic parameter adjustment range, search for an extrinsic parameter that makes the objective function reach a minimum value.

That is to say, in the embodiment of the present application, if it is determined that the external parameter is inaccurate, an objective function can be generated to find the optimal value of the external parameter to improve the accuracy of the external parameter.

In a second aspect, a method for calibrating external parameters of a vehicle-mounted radar is provided, which is applied to a vehicle, and the vehicle includes a radar. The method includes: acquiring external parameters of the vehicle-mounted radar, and using the external parameters to convert the first point cloud data corresponding to the target plane object around the vehicle's driving road in the vehicle-mounted radar coordinate system into the first point cloud data in the inertial coordinate system Two point cloud data; if the flatness of the second point cloud data does not meet the first condition, the external parameter is calibrated.

In some possible designs, the flatness of the second point cloud data does not satisfy the first condition, including: in the second point cloud data, between any two reflection points of different transmit beams on the target plane object The first vector of and the normal vector of the point cloud data corresponding to any one or more transmit beams in the second point cloud data are not perpendicular;

or,

The first vector between the reflection points of any two different transmit beams on the target plane object in the second point cloud data and the point cloud data corresponding to any one or more transmit beams in the second point cloud data The angle between the normal vectors of is not within the preset range.

One way of calibrating the external parameter is: the external parameter includes at least one of the rotation angle or the translation distance of the vehicle radar; the method further includes: according to the target reference around the driving road , and determine at least one of the rotation angle or the translation distance of the vehicle-mounted radar.

Wherein, the objects arranged along the driving road include: objects of the first type arranged along the driving road and parallel to the ground of the driving road, and/or, along the driving road and with The second type of objects vertically arranged on the ground of the driving road; the first type of objects includes: at least one of road edges, guardrails, green belts or lane lines; the second type of objects includes: trees, signs or at least one of street lights.

Another way of calibrating the external parameter is: based on the external parameter, generate an objective function, and the objective function is used to describe the angle between the first vector and the normal vector, or, The objective function is used to describe the projection distance of the normal vector on the first vector; within a preset extrinsic parameter adjustment range, search for an extrinsic parameter that makes the objective function reach a minimum value.

In a third aspect, a method for calibrating external parameters of a vehicle-mounted radar is also provided, which is applied to a vehicle, and the vehicle includes a radar. The method includes:

Determine the target reference around the road where the vehicle travels;

According to the target reference, the external parameters of the vehicle radar are calibrated.

In a possible design, the external parameter includes at least one of a rotation angle or a translation distance of the vehicle-mounted radar; according to the target reference, the external parameters of the vehicle-mounted radar are calibrated, including: according to the target reference The target reference around the driving road determines at least one of the rotation angle or the translation distance of the vehicle-mounted radar.

In a fourth aspect, a method for calibrating external parameters of a vehicle-mounted radar is also provided, which is applied to a vehicle, and the vehicle includes a radar. The method includes: acquiring external parameters of the vehicle-mounted radar; using the external parameters to convert the first point cloud data corresponding to the target plane object around the vehicle's driving road in the vehicle-mounted radar coordinate system into the first point cloud data in the inertial coordinate system Two point cloud data; generate an objective function, the objective function is used to describe the first vector and the second point between the reflection points of any two different emission beams on the target plane object in the second point cloud data the angle between the normal vectors of the point cloud data corresponding to any one or more transmit beams in the point cloud data, or the objective function is used to describe the projection distance of the normal vector on the first vector; Within the preset external parameter adjustment range, find the external parameter that makes the objective function reach the minimum value.

In a possible design, before searching for an external parameter that makes the objective function reach a minimum value, the method further includes: determining that the flatness of the second point cloud data does not satisfy the first condition.

In some possible designs, the flatness of the second point cloud data does not satisfy the first condition, including: in the second point cloud data, between any two reflection points of different transmit beams on the target plane object The first vector of the second point cloud data is not perpendicular to the normal vector of the point cloud data corresponding to any one or more transmit beams in the second point cloud data; or,

In a possible design, acquiring the external parameters of the vehicle-mounted radar includes: determining a target reference around the road where the vehicle travels; and calibrating the external parameters of the vehicle-mounted radar according to the target reference.

In a fifth aspect, an external parameter calibration device for a vehicle-mounted radar is provided. The device may be a vehicle or a device within a vehicle (such as a chip or a system-on-chip). The device includes: an acquisition unit for acquiring driving information of the vehicle, where the driving information includes at least one of driving state information or driving road condition information; wherein the driving state information includes speed, acceleration, yaw angle or At least one of yaw angular velocity; the driving road condition information includes at least one of the driving road of the vehicle or the objects around the driving road; the processing unit, according to the driving information, performs external processing on the vehicle radar; parameter calibration.

In a possible design, the processing unit is specifically configured to: according to the driving state information, when it is determined that at least one of the following conditions is satisfied, perform external parameter calibration on the vehicle radar, wherein the conditions include:

The speed is less than a first threshold, the vehicle is traveling at an average speed, the acceleration is less than a second threshold, the yaw angle is within a preset range, or the yaw angle speed is less than a third threshold.

In a possible design, the processing unit is specifically configured to: determine, according to the driving road condition information, that the driving road is a straight road and/or when there is a target reference object around the driving road, perform the detection on the vehicle radar Perform external parameter calibration.

In a possible design, the external parameter includes at least one of a rotation angle or a translation distance of the vehicle-mounted radar; the processing unit is further configured to: determine, according to the target reference around the driving road, the at least one of the rotation angle or the translation distance of the vehicle-mounted radar.

In a possible design, when the processing unit is used to determine the rotation angle of the vehicle radar according to the target reference objects around the driving road, it is specifically used for: according to the ground or For a plane object parallel to the ground, the pitch angle and the roll angle of the vehicle-mounted radar are determined; and the yaw angle of the vehicle-mounted radar is determined according to the objects arranged along the driving road.

In a possible design, the objects disposed along the travel road include: first type objects disposed along the travel road and parallel to the ground of the travel road, and/or along the travel road The second type of object on the driving road and perpendicular to the ground of the driving road; wherein, the first type of object includes: at least one of a road edge, a guardrail, a green belt or a lane line; the second type of object Type objects include: at least one of trees, signs, or street lights.

In a possible design, when the processing unit is used to determine the yaw angle of the vehicle-mounted radar according to the objects arranged along the driving road, it is specifically used for: according to the objects arranged along the driving road object, determine the road direction of the driving road, wherein the road direction satisfies y=C0+C1*x; wherein, C1 is used to indicate the road direction; C0 is used to describe the object and the radar coordinate system The distance between the origins, x and y are the coordinates of the object in the radar coordinate system; the yaw angle of the vehicle radar is determined according to the C1.

In a possible design, the acquisition unit is further configured to: acquire external parameters of the vehicle radar; the processing unit is further configured to: use the external parameters to record the target plane objects around the driving road on the vehicle The corresponding first point cloud data in the radar coordinate system is converted into the second point cloud data in the inertial coordinate system; if the flatness of the second point cloud data does not meet the first condition, the external parameter is adjusted.

In a possible design, when adjusting the external parameters, the processing unit is specifically configured to: generate an objective function based on the external parameters, where the objective function is used to describe the first vector and the method The included angle between the vectors, or the objective function is used to describe the projection distance of the normal vector on the first vector; within the preset external parameter adjustment range, find a value that makes the objective function reach the minimum value. External reference.

In a sixth aspect, an external parameter calibration device for a vehicle-mounted radar is provided. The device may be a vehicle, or a module (such as a chip or system-on-chip) in a vehicle that includes a radar. The device includes: an acquisition unit for acquiring external parameters of the vehicle-mounted radar; a processing unit for using the external parameters to obtain the first point corresponding to the target plane object around the vehicle's driving road in the vehicle-mounted radar coordinate system The cloud data is converted into second point cloud data in the inertial coordinate system; if the flatness of the second point cloud data does not meet the first condition, the external parameters are calibrated.

In some possible designs, the flatness of the second point cloud data does not satisfy the first condition, including: in the second point cloud data, between any two reflection points of different transmit beams on the target plane object The first vector of the second point cloud data is not perpendicular to the normal vector of the point cloud data corresponding to any one or more transmit beams in the second point cloud data; or, any two different transmit beams in the second point cloud data are at the target The included angle between the first vector between the reflection points on the plane object and the normal vector of the point cloud data corresponding to any one or more transmit beams in the second point cloud data is not within a preset range.

One way for the processing unit to calibrate the external parameter is: the external parameter includes at least one of the rotation angle or the translation distance of the vehicle-mounted radar; according to the target reference around the driving road, determine At least one of the rotation angle or the translation distance of the vehicle radar.

Another way for the processing unit to calibrate the external parameters is to generate an objective function based on the external parameters, where the objective function is used to describe the angle between the first vector and the normal vector , or, the objective function is used to describe the projection distance of the normal vector on the first vector; within a preset extrinsic parameter adjustment range, search for an extrinsic parameter that makes the objective function reach a minimum value.

In a seventh aspect, an external parameter calibration device for a vehicle-mounted radar is also provided. The device may be a vehicle, or a module (such as a chip or a system of chips) in a vehicle that includes a radar. The device includes: a determining unit for determining a target reference around the road where the vehicle travels; a processing unit for calibrating the external parameters of the vehicle-mounted radar according to the target reference.

In an eighth aspect, a device for calibrating external parameters of a vehicle-mounted radar is also provided. The device may be a vehicle, or a module (such as a chip or a system of chips) in a vehicle that includes a radar. The device includes: an acquisition unit for acquiring external parameters of the vehicle-mounted radar; a processing unit for using the external parameters to obtain the first point corresponding to the target plane object around the vehicle's driving road in the vehicle-mounted radar coordinate system The cloud data is converted into second point cloud data in the inertial coordinate system; the processing unit is further configured to generate an objective function, and the objective function is used to describe any two different emission beams in the second point cloud data on the target plane object The angle between the first vector between the reflection points on the second point cloud data and the normal vector of the point cloud data corresponding to any one or more transmit beams in the second point cloud data, or the objective function is used to describe the The projection distance of the normal vector on the first vector; the processing unit is further configured to search for the extrinsic parameter that makes the objective function reach the minimum value within the preset extrinsic parameter adjustment range.

In a ninth aspect, a device for calibrating external parameters of a vehicle-mounted radar is also provided, comprising a memory and one or more processors; wherein, the memory is used to store computer program codes, and the computer program codes include computer instructions; The computer instructions, when executed by the processor, cause the apparatus to perform the method of any one of the first to fourth aspects above.

According to a tenth aspect, a vehicle is also provided, which includes the above-mentioned device for calibrating external parameters of a vehicle-mounted radar according to any one of the fifth aspect to the tenth aspect. The vehicle-mounted radar external parameter calibration device may be, for example, a processing module in a vehicle, such as a vehicle-mounted processor or an electronic control unit (electronic control unit, ECU) or the like.

In an eleventh aspect, a computer-readable storage medium is also provided, comprising computer instructions, when the computer instructions are executed in the external parameter calibration device of the vehicle-mounted radar, the external parameter calibration device of the vehicle-mounted radar is made to perform the above-mentioned first. The method of any of the aspects to the fourth aspect.

A twelfth aspect further provides a computer program product that, when the computer program product runs on a processor, causes the processor to perform the method according to any one of the first to fourth aspects above.

For the beneficial effects of the second aspect to the twelfth aspect, please refer to the beneficial effects of the first aspect, and details will not be repeated.

Description of drawings

FIG. 1 is a schematic diagram of an application scenario provided by an embodiment of the present application;

2 is a schematic diagram of a radar coordinate system, a vehicle coordinate system, and an inertial coordinate system provided by an embodiment of the present application;

3 is a schematic diagram of a relative relationship between a radar coordinate system and a vehicle coordinate system provided by an embodiment of the present application;

FIG. 4 is an exemplary functional block diagram of a vehicle according to an embodiment of the present application;

FIG. 5 is an exemplary schematic flowchart of a method for calibrating a vehicle-mounted radar provided by an embodiment of the present application;

FIG. 6 is another exemplary schematic flowchart of a calibration method for a vehicle-mounted radar provided by an embodiment of the present application;

7 is a schematic diagram of judging the flatness of a point cloud according to an embodiment of the present application;

FIG. 8 is another exemplary schematic flowchart of a calibration method for a vehicle-mounted radar provided by an embodiment of the present application;

FIG. 9 is a schematic diagram of an image collected by a vehicle according to an embodiment of the present application;

FIG. 10 is a schematic structural diagram of an exemplary structure of a vehicle-mounted radar external parameter calibration device provided by an embodiment of the application;

FIG. 11 is a schematic structural diagram of another exemplary structure of a vehicle-mounted radar external parameter calibration device provided by an embodiment of the present application.

Detailed ways

In order to make the present application easier to understand, some basic concepts involved in the embodiments of the present application are first explained below. It should be noted that these explanations are for the purpose of making the embodiments of the present application easier to understand, and should not be regarded as limitations on the protection scope claimed by the present application.

(1) Vehicle radar

Vehicle radar refers to a radar set on a vehicle, which can be a vehicle carrier radar, such as a lidar, a microwave radar, or a millimeter-wave radar. Vehicle-mounted radar can realize functions such as obstacle measurement, collision prediction, adaptive cruise control, etc., which can effectively reduce the difficulty of driving, reduce the burden on drivers and reduce the incidence of accidents, so it has been widely used in the automotive field. For example, taking the vehicle radar as a vehicle carrier radar as an example, the vehicle carrier radar can detect the relative distance, relative speed and angle between the vehicle and the target (such as objects around the vehicle), and then track and identify the target according to the obtained information. After a reasonable decision, the driver will be informed or warned in various ways such as sound, light and touch, or the car will be actively intervened in time, so as to ensure the safety and comfort of the driving process and reduce the probability of accidents.

The following describes the principle of vehicle radar detection of targets (such as objects around the vehicle).

Vehicle radars may include transmitters and receivers. The transmitter is used to transmit the electromagnetic wave energy beam. The electromagnetic wave is transmitted to the antenna through the transceiver switch. The antenna then transmits the electromagnetic wave into the air along a certain direction and angle. If there is a target within a certain distance along the emission direction of the electromagnetic wave energy beam, then The electromagnetic wave energy beam is reflected by the target, and when the electromagnetic wave encounters the target object, a part of the energy will be reflected and received by the antenna of the vehicle carrier radar, and then transmitted to the receiver through the transceiver switch. The receiver is used to determine the information related to the target according to the received echo signal and the transmitted electromagnetic wave energy beam. For example, the distance to the target, the point cloud density of the target, etc. The radar sensor transmits an electromagnetic wave energy beam through the transmitter, and further processes the signal processor to obtain the relative distance, angle and relative speed of the target object.

(2) Vehicle LiDAR

Vehicle lidar is a type of vehicle radar. Vehicle lidars have transmitters and receivers. The transmitter emits a laser beam, and after the laser beam encounters a target (such as an object around the vehicle), it is reflected and returned to the receiver. Multiplying the interval between transmit and receive times by the speed of light and dividing by 2, the distance between the transmitter and the target can be calculated.

Vehicle lidar includes single-beam laser transmitter, four-line laser radar, sixteen-line laser radar, thirty-two-line laser radar and so on. Taking a single-beam laser transmitter as an example, the single-beam laser transmitter can rotate at a constant speed inside the lidar, and emits a laser every time it rotates a small angle. After a certain angle, a complete frame of data is generated. Therefore, the data of single-line lidar can be regarded as a row of lattices at the same height. Four-line LiDAR polls four laser transmitters. After one polling period, a frame of laser point cloud data is obtained. The laser point cloud data can be composed of planar information, and the height information of obstacles can be obtained. Therefore, the higher the number of laser emitters, the higher the efficiency and the richer the information obtained.

(3) Radar coordinate system, vehicle coordinate system, inertial coordinate system

Please refer to (a) in FIG. 2 , which is a schematic diagram of the vehicle coordinate system. The origin of the vehicle coordinate system can be at any position on the vehicle (such as the center of mass, the ground below the midpoint of the rear axle of the vehicle), the x-axis is forward along the front of the vehicle, and the z-axis is perpendicular to the chassis of the vehicle. When facing forward, the y-axis points to the left of the vehicle.

Please refer to (b) in Figure 2, which is a schematic diagram of the radar coordinate system. The origin of the radar coordinate system is at the location where the radar is installed on the vehicle. The x-axis, y-axis, and z-axis can be defined in various ways. For example, they are designed by the manufacturer of the vehicle-mounted radar, and the designs of different manufacturers are different. Generally speaking, there is a rotation and translation relationship between the radar coordinate system and the vehicle coordinate system.

Please refer to (c) in FIG. 2 , which is a schematic diagram of the inertial coordinate system. Inertial coordinate system, also known as public coordinate system, world coordinate system or global coordinate system, etc., its coordinate origin is a fixed point in space and is an absolute coordinate system. All objects in space can be based on inertial coordinate system. to determine the location of the object. Exemplarily, the common coordinate system may be a world coordinate system with east, north, and sky as the X-axis, Y-axis, and Z-axis.

Generally, there is a relationship of rotation and translation between the radar coordinate system and the vehicle coordinate system, and there is also a relationship of rotation and translation between the vehicle coordinate system and the inertial coordinate system. During the driving process of the vehicle, in order to ensure driving safety, the vehicle needs to know the location of surrounding objects (such as obstacles in front of the vehicle) in the real environment, that is, the position coordinates of the objects around the vehicle in the inertial coordinate system need to be obtained. One solution for this is that the on-board radar can sense the position of surrounding objects and represent that position in the radar coordinate system. Due to the rotation and translation relationship between the radar coordinate system, the vehicle coordinate system and the inertial coordinate system, the coordinates of the target in the radar coordinate system are different from those in the real world, so the target can be placed in the radar coordinate system. The coordinates are converted to the vehicle coordinate system, and then converted to the inertial coordinate system to determine the position of the target in the real environment. It should be understood that, in the process of converting one coordinate system to another coordinate system, the relative positional relationship (such as rotation and translation relationship) between the two coordinate systems needs to be used. Therefore, to realize the conversion from the radar coordinate system to the inertial coordinate system, it is necessary to use: 1. The relative positional relationship between the radar coordinate system and the vehicle coordinate system, which is used to convert the coordinates from the radar coordinate system to the vehicle coordinate system. 2. The relative positional relationship 2 between the vehicle coordinate system and the inertial coordinate system, the relative positional relationship 2 is used for coordinate transformation from the vehicle coordinate system to the world coordinate system. Among them, the parameters that need to be used when the radar coordinate system is converted to the vehicle coordinate system are called the external parameters of the vehicle radar.

(4) External parameters of vehicle radar (referred to as external parameters)

As mentioned above, the radar coordinate system and the vehicle coordinate system have a relative positional relationship, and the relative positional relationship includes a rotation and translation relationship. The relative position relationship is called the external parameters of the vehicle-mounted radar, that is to say, the external parameters of the vehicle-mounted radar include the rotation and translation relationship of the radar coordinate system of the vehicle-mounted radar with respect to the vehicle coordinate system.

Please refer to FIG. 3 , which is a schematic diagram of the relative positional relationship between the radar coordinate system and the vehicle coordinate system. As shown in Fig. 3, the origin of the radar coordinate system is defined as _OL , and its coordinate system is _OL -X _L Y _L Z _L. The origin of the vehicle coordinate system is defined as O _V , and its coordinate system is O _V -X _V Y _V Z _V . The external parameters of the vehicle-mounted radar include the rotation and translation relationship of the radar coordinate system relative to the vehicle coordinate system, wherein the rotation relationship is described by the rotation angle, and the translation relationship is described by the translation distance.

The rotation angle of the radar coordinate system relative to the vehicle coordinate system can be described by three attitude angles, namely the pitch angle β (pitch), the yaw angle γ (yaw), and the roll angle α (roll). Among them, the pitch angle β (pitch) refers to the counterclockwise rotation angle around the Y _L axis; the yaw angle γ (yaw) refers to the counterclockwise rotation angle around the Z _L axis; the roll angle α (roll) refers to the rotation angle around the _XL axis The angle by which the axis is rotated counterclockwise. In other words, after the radar coordinate system O _L -X _L Y _L Z _L is rotated around the Z _L axis by -γ, around the Y _L axis by -β, and then rotated around the X _L axis by -α, its coordinate axis is the same as the vehicle coordinate axis. The directions of the three axes in O _V -X _V Y _V Z _V are the same. Where -γ refers to the opposite direction to γ, in the same way, -β is opposite to the direction of β, and -α is opposite to the direction of α.

The translation distance of the radar coordinate system relative to the vehicle coordinate system can be described using three translation distances, ie, Δx, Δy, Δz. Among them, _Δx is the projection value on the x-axis direction of the distance from the origin of the coordinates of the radar coordinate system _OL to the origin of the coordinates of the vehicle coordinate system OV. _Δy is the projection value on the y-axis direction of the distance from the origin of the coordinates of the radar coordinate system _OL to the origin of the coordinates of the vehicle coordinate system OV. Δz is the projection value in the _z -axis direction of the distance from the origin of the coordinates of the radar coordinate system _OL to the origin of the coordinates of the vehicle coordinate system OV. That is to say, after the radar coordinate system coordinate origin _OL is translated on the x-axis by -Δx, on the y-axis by -Δy, and on the z-axis by -Δz, the radar coordinate system coordinate origin _OL is connected with the vehicle coordinate system. Among them, -Δx refers to the opposite direction to Δx, -Δy to the opposite direction to Δy, and -Δz to the opposite direction to Δz.

Therefore, after determining the rotation angle (including three attitude angles) and translation distance of the radar coordinate system relative to the vehicle coordinate system, the rotation angle and translation distance can be used to place a target (such as an object around the vehicle) in the radar coordinate system The coordinates are converted to the vehicle coordinate system. Since the rotation angle and translation distance are collectively referred to as the external parameters of the vehicle-mounted radar, the process of determining the rotation angle and translation distance of the radar coordinate system relative to the vehicle coordinate system can be called the calibration process of the external parameters of the vehicle-mounted radar. It can be understood as determining, obtaining, calculating, etc.

(5) External parameter calibration of vehicle radar

The external parameter calibration of the vehicle radar refers to the process of determining the external parameters of the vehicle radar. As mentioned above, the external parameters of the vehicle radar include the rotation angle and translation distance of the radar coordinate system relative to the vehicle coordinate system. Therefore, the external parameter calibration process of the vehicle radar can be understood as determining the rotation angle of the radar coordinate system relative to the vehicle coordinate system. (including three attitude angles) and the process of translation distance.

As mentioned earlier, the external parameters of the vehicle radar are the important parameters that need to be used in the process of converting from the coordinates in the radar coordinate system to the vehicle coordinate system. If the calibration of the external parameters of the vehicle radar is not accurate, the exact coordinates of the target in the vehicle coordinate system cannot be obtained, and the accurate position of the target in the inertial coordinate system cannot be obtained. That is, the vehicle cannot determine the real position of the surrounding objects, which will affect the safe driving of the vehicle. For example, due to the inaccurate external parameters of the on-board radar, the vehicle determines that the object in front is 2m away from the vehicle after a series of coordinate transformations, but in fact the object in front is only 1m away from the vehicle, which is prone to danger and cannot guarantee driving safety.

At present, there are calibration methods for vehicle-mounted external parameters, such as return-to-factory calibration. This method is cumbersome, and the vehicle-mounted radar cannot be used normally during the return-to-factory calibration, which affects the user experience.

The present application provides an external parameter calibration method for a vehicle-mounted radar. Specifically, the external parameters of the vehicle radar are calibrated according to the driving information of the vehicle. Wherein, the driving information of the vehicle includes at least one of driving road condition information or driving state information. The traveling state information includes at least one of speed, acceleration, yaw angle, or yaw angular velocity. The driving road condition information includes at least one of a driving road of the vehicle or objects around the driving road. That is to say, the external parameter calibration of the on-board radar can be completed while the vehicle is running, without the need to return to the factory for calibration, which is more convenient and has a better user experience. For example, assuming that the current external parameter of the on-board radar is external parameter 1, when the vehicle is driving, if the external parameters of the on-board radar are calibrated according to the driving information, after a period of driving, the external parameters of the on-board radar will be calibrated. Change, for example, from external parameter 1 to external parameter 2, then the vehicle uses external parameter 2 for calculation (such as determining the coordinates of the target in the inertial coordinate system). That is to say, the calibration of the external parameters of the vehicle radar is completed during the driving process of the vehicle.

Among them, the completion of the external parameter calibration of the vehicle radar during the driving process of the vehicle can be understood as online calibration, and the online calibration can be understood as the completion of the calibration during the operation of the vehicle radar or the operation of the vehicle system. In other words, during the external parameter calibration process The mid-vehicle radar is in an online state (or a working state or a running state). The calibration method that is different from online calibration is offline calibration, such as return-to-factory calibration. During this period, the on-board radar cannot be used normally, that is, it is in an offline state. Therefore, the external parameter calibration method of the vehicle radar provided in the embodiment of the present application is more practical, and can perform online calibration in real time, improve the accuracy of determining the position of a target (eg, objects around the vehicle), and ensure driving safety.

The external parameter calibration method of the vehicle-mounted radar provided in the embodiment of the present application can be applied to a vehicle. The vehicle includes an onboard radar.

As shown in FIG. 4 , a schematic diagram of a possible application scenario provided by the present application. In this scenario, in-vehicle radar (or radar sensor) is installed on vehicles (such as unmanned vehicles, smart vehicles, electric vehicles, digital vehicles, etc.). As shown in Figure 4, the on-board radar deployed on the vehicle can perceive the fan-shaped area shown by the solid line frame, and the fan-shaped area can be understood as the on-board radar sensing area. Signals, such as point cloud data, are transmitted to the processing module for further processing. After receiving the signal from the vehicle radar, the processing module outputs the measurement information of the target (for example, the relative distance, angle, and relative speed between the target and the vehicle). It should be noted that the processing module here can be either a hardware or software module independent of the vehicle-mounted radar, or a hardware or software module deployed in the vehicle-mounted radar, which is not limited here. It can be seen that by installing the on-board radar on the vehicle body, measurement information such as the position and relative distance of surrounding objects can be sensed in real time or periodically, and then assisted driving or unmanned driving of the vehicle can be realized according to these measurement information. For example, the distance of surrounding objects relative to the vehicle is used to determine the number, density, etc. of obstacles around the vehicle.

The vehicle-mounted radar in the present application may be a laser radar, a microwave radar, or a millimeter-wave radar, which is not limited in the embodiment of the present application.

It should be noted that this application does not limit the number of vehicle-mounted radars deployed on the vehicle in the scenario shown in FIG. 4 .

The following describes the external parameter calibration method for the vehicle radar provided by the embodiments of the present application with reference to the accompanying drawings.

Example 1

Please refer to FIG. 5 , which is a schematic flowchart of a method for calibrating external parameters of a vehicle-mounted radar according to an embodiment of the present application. This method can be applied to a vehicle as shown in FIG. 4 . As shown in Figure 5, the process includes:

S501 , acquiring driving information of the vehicle.

Wherein, the driving information of the vehicle includes at least one item of driving road condition information or driving state information. The driving status information and the driving road condition information are described below.

(1) The traveling state information includes at least one of speed, acceleration, yaw angle, or yaw angle velocity.

The speed and/or acceleration may be determined according to motion parameters collected by motion sensors in the vehicle. The motion sensor may be, for example, an accelerometer, a gyroscope, an inertial measurement unit (IMU), and the like. Taking the accelerometer as an example, the accelerometer can measure the linear acceleration of the three axes of the vehicle, which is used to calculate the driving speed of the vehicle. Alternatively, the velocity and/or acceleration can also be obtained through a satellite navigation and positioning system. Taking the IMU as an example, the IMU can output the speed, acceleration, etc. of the vehicle. The satellite navigation and positioning system includes, but is not limited to, global navigation satellite system (GNSS), global positioning system (global positioning system, GPS), Galileo system, etc. or enhanced systems of these systems. Taking GNSS as an example, GNSS is a radio navigation system with artificial satellites as "beacon", which provides all-weather, high-precision position, speed and time information for various carriers (such as vehicles) in the global land, sea, air and sky (such as vehicles). positioning navigation and timing, PNT). Thus, the driving speed and/or acceleration of the vehicle can be determined by means of GNSS. Alternatively, speed and/or acceleration can also be measured by a wheel velocity sensor (WSS). The WSS is a sensor for measuring the rotational speed of the wheels of the vehicle, and the traveling speed and/or acceleration of the vehicle can be determined by measuring the rotational speed of the wheels of the vehicle. The wheel speed sensor includes, but is not limited to, a magnetoelectric wheel speed sensor, a Hall-type wheel speed sensor, and the like.

Wherein, the yaw angle and/or the yaw angular velocity can be determined by on-board radar. For the introduction of the yaw angle, please refer to Figure 3. The yaw angle γ(yaw) refers to the rotation angle of the vehicle around the Z _L axis in the radar coordinate system O _L -X _L Y _L Z _L during the driving process, which can be simply understood as The angle of deviation from the route, for example, the vehicle turns left, right, U-turn, etc. The yaw angular velocity refers to the change of the yaw angle per unit time. Therefore, the yaw angular velocity can be determined by determining the yaw angle within a certain period of time. Taking the vehicle turning left as an example, the yaw angle can be understood as the left turn angle, and the yaw angle speed refers to the change of the yaw angle per unit time, so it can be understood as the change of the left turn angle, which reflects the left turn speed of the vehicle. Alternatively, the yaw angle and/or the yaw angular velocity can also be calculated by a steering wheel sensor (steering wheel sensor, SAS). For example, the SAS can determine the steering wheel rotation angle, rotation direction, and steering speed, etc., and the yaw angle can be estimated through these parameters. Heading angle and/or yaw angle. Alternatively, the yaw angle and/or the yaw rate can also be estimated by the IMU. For example, the IMU can detect angular velocity, angular acceleration, etc., and the yaw angle and/or yaw angular velocity can be calculated from the detected parameters.

(2) The driving road condition information is used to indicate at least one of the driving road of the vehicle or the object information around the driving road.

The use of the driving road condition information to indicate the driving road of the vehicle may be understood as being used to indicate the attribute of the driving road, such as a straight road, a right-turn road, a left-turn road, and the like.

For example, determine the driving road based on the map and the current location. Specifically, it is assumed that the current positioning of the vehicle (such as GPS positioning) is at position 1, and position 1 is on road 1 on the map, then road 1 is determined as the driving road of the vehicle, and then it can be determined according to the description information of the road 1 in the map Attributes of road 1 such as straight road, right turn road and so on.

Alternatively, the vehicle may collect images during the driving process, and determine the attributes of the driving road of the vehicle through image recognition, such as a straight road, a right-turn road, and the like.

Alternatively, it may also be a driver's manual input. For example, an input button may be provided on the vehicle, and the driver may input the attribute of the driving road through the input button.

Among them, the object information around the driving road of the vehicle can be determined in various ways. For example, the vehicle collects images during driving, and determines the objects around the road where the vehicle travels through image recognition. Alternatively, objects around the road where the vehicle travels can also be determined according to the point cloud data obtained by the vehicle-mounted radar. As mentioned above, the on-board radar can transmit electromagnetic waves that are emitted by surrounding objects and then collect the emitted electromagnetic waves. The reflected electromagnetic waves are point cloud data. According to the point cloud data, objects around the road where the vehicle is traveling can be identified. For example, objects around the driving road of the vehicle include trees, curbs, street lights, signs, and the like.

S502, according to the driving information of the vehicle, it is determined to calibrate the external parameters of the vehicle-mounted radar.

Since the driving information of the vehicle includes at least one item of driving road condition information or driving state information, S502 may include the following three situations.

In the first case, the driving information of the vehicle includes driving state information. Then, S502 can be refined as follows: according to the driving state information, it is determined to calibrate the external parameters of the vehicle radar. Specifically, it may include: according to the driving state information, when it is determined that at least one of the following conditions is satisfied, determining to calibrate the external parameters of the vehicle-mounted radar. Wherein, the conditions include:

(1) The vehicle travels at a constant speed. For example, whether the vehicle is traveling at a constant speed can be determined according to the speed, acceleration, etc. in the traveling state information.

(2) The traveling speed of the vehicle is less than the first threshold value or within the first range.

(3) The running acceleration of the vehicle is smaller than the second threshold value or within the second range. The first threshold, the first range, the second threshold, and the second range may be set by default (for example, set by default before the vehicle leaves the factory), or set by the user, which is not limited in this embodiment of the present application.

(4) The yaw angle is within the preset range. The preset range is, for example, -5 degrees to 5 degrees. That is, the vehicle is going straight or approximately straight.

(5) The yaw angular velocity is less than the third threshold or within the third range. The third threshold and the third range may be set by default (for example, set by default before the vehicle leaves the factory), or set by the user, which is not limited in the embodiment of the present application.

In the second case, the driving information of the vehicle includes driving road condition information. Then S502 can be refined as: according to the driving road condition information, it is determined to calibrate the external parameters of the vehicle radar. Specifically, it may include: according to the driving road condition information, when it is determined that at least one of the following conditions is satisfied, determining to calibrate the external parameters of the vehicle-mounted radar. Wherein, the conditions include:

(1) The driving road of the vehicle is a straight road.

As described above, the vehicle may determine the driving road condition information, and the driving road condition information is used to indicate the attributes of the driving road of the vehicle, such as a straight road, a right-turn road, and the like. Therefore, it can be determined whether the driving road is a straight road according to the driving road condition information.

(2) There are target reference objects around the driving road of the vehicle.

As described above, the vehicle may determine the driving road condition information, and the driving road condition information may be used to indicate objects around the driving road of the vehicle, such as trees, road edges, street lights, signs, and the like. Therefore, it can be determined whether there is a target reference around the vehicle driving road according to the vehicle driving road condition information.

In the embodiments of the present application, target references include the following two categories. The determining that a target reference exists around the vehicle driving road includes: determining that at least one of the two types of target references exists around the driving road. The two categories include:

The first type of target reference includes: at least one of the ground of the driving road or a plane object parallel to the ground.

The second type of target reference includes: objects set along the driving road. For example, at least one of a type A object along the driving road and parallel to the ground of the driving road, or a type B object along the driving road and perpendicular to the ground of the driving road. Wherein, the A-type object includes at least one of a road edge, a guardrail, a green belt or a lane line. Type B objects include: at least one of trees, signs, or street lights.

The third situation can be understood as the combination of the first situation and the second situation, that is, the driving information of the vehicle includes the driving road condition information and the driving state information. Then, S502 can be refined as follows: determine whether to calibrate the external parameters of the on-board radar according to the driving state information (see the first case for the realization principle); if so, continue to judge whether to calibrate the external parameters of the on-board radar according to the driving road condition information. Calibration (see the second case for the implementation principle), if not, it is determined not to calibrate the external parameters of the vehicle radar, and there is no need to continue to make judgments based on the driving road condition information. Alternatively, S502 can also be refined as follows: determine whether to calibrate the external parameters of the vehicle-mounted radar according to the driving road condition information; if so, continue to judge whether to calibrate the external parameters of the vehicle-mounted radar according to the driving state information; if not, determine not to calibrate the vehicle-mounted radar. It is not necessary to continue to make judgments based on the driving state information. These two methods can perform double judgment and improve the accuracy.

Embodiment 2

The first embodiment above is to determine whether to calibrate the external parameters of the vehicle radar according to the driving information of the vehicle. Different from the first embodiment, the second embodiment provides another way of determining whether to calibrate the external parameters of the vehicle-mounted radar.

Please refer to FIG. 6 , which is another schematic flowchart of a method for calibrating external parameters of a vehicle-mounted radar according to an embodiment of the present application. As shown in Figure 6, the process includes:

S601, obtain external parameters of the vehicle radar.

The acquired external parameters may be external parameters of the vehicle-mounted radar calibrated last time or initial external parameters (for example, external parameters set at the factory). The extrinsic parameter includes at least one of a rotation angle or a translation distance. The rotation angle includes three attitude angles, namely, the pitch angle β (pitch), the yaw angle γ (yaw), and the roll angle α (roll). The translation distance includes Δx, Δy, and Δz. For details about the rotation angle and the translation distance, please refer to the introduction in the previous terminology section, which will not be repeated here.

Assuming that the rotation angle is represented by R, then R can be expressed as:

Among them, r ₁₁ is the projection of the x-axis of the radar coordinate system on the x-axis of the vehicle coordinate system, r ₁₂ is the projection of the x-axis of the radar coordinate system on the y-axis of the vehicle coordinate system, and r ₁₃ is the x-axis of the radar coordinate system in the The projection of the z-axis of the vehicle coordinate system, r ₂₁ is the projection of the y-axis of the radar coordinate system on the x-axis of the vehicle coordinate system, r ₂₂ is the projection of the y-axis of the radar coordinate system on the y-axis of the vehicle coordinate system, and r ₂₃ is The projection of the y-axis of the radar coordinate system on the z-axis of the vehicle coordinate system, r ₃₁ is the projection of the z-axis of the radar coordinate system on the x-axis of the vehicle coordinate system, and r ₃₂ is the z-axis of the radar coordinate system on the y-axis of the vehicle coordinate system The projection of the axis, r ₃₃ is the projection of the z-axis of the radar coordinate system on the z-axis of the vehicle coordinate system.

Correspondingly, the three attitude angles satisfy:

yaw angle

Pitch angle pitc=arcsin(-r ₃₁ )

roll angle

Assuming that the translation distance is represented by T, then T is represented as:

For the meanings of Δx, Δy, and Δz, please refer to the previous section on terminology.

S602, using the external parameters to convert the first point cloud data of the target plane object in the radar coordinate system to the second point cloud data in the inertial coordinate system.

Optionally, before S602, the step may further include: determining a target plane object. The target plane object may be a sign, ground, billboard and other plane objects around the vehicle. For example, the vehicle can acquire images including surrounding objects, and determine the target planar object based on the images. Alternatively, point cloud data of surrounding objects may also be acquired, where the point cloud data corresponds to all surrounding objects, and the point cloud data is represented in a radar coordinate system. Then, the point cloud data of the target plane object is determined from the point cloud data. For example, it can be determined that points in the point cloud data that are on the same plane or approximately the same plane constitute the point cloud data of the target plane object.

Among them, S602 includes two processes. Process 1: Convert the first point cloud data of the target plane object in the radar coordinate system to the third point cloud data in the vehicle coordinate system. Process 2: Convert the third point cloud data to the second point cloud data in the inertial coordinate system.

Process 1: Convert the first point cloud data to the third point cloud data in the vehicle coordinate system according to the following formula (1).

Among them, (x ₀ , y ₀ , z ₀ ) is the coordinate of a point in the first point cloud data in the radar coordinate system, and (x ₁ , y ₁ , z ₁ ) is the coordinate of this point in the vehicle coordinate system. R and T are the external parameters of the vehicle radar, where R is the rotation angle and T is the translation distance. About R and T, please refer to the introduction of S601.

In process 2, the third point cloud data is converted to the second point cloud data in the inertial coordinate system according to the following formula (2).

Among them, (x ₁ , y ₁ , z ₁ ) are the coordinates of a point in the third point cloud data in the vehicle coordinate system, and (x ₂ , y ₂ , z ₂ ) are the coordinates of this point in the inertial coordinate system. R1 is the rotation angle of the vehicle coordinate system relative to the inertial coordinate system, and T1 is the translation distance of the vehicle coordinate system relative to the inertial coordinate system. Wherein, R1 and T1 are preset, or, R1 and T1 may also be calculated by sensing devices such as GNSS, IMU, WSS, and SAS, and the specific calculation process is not repeated in the embodiments of this application.

S603: Determine whether the flatness of the second point cloud data satisfies the first condition.

The flatness of the second point cloud data can be understood as the degree to which the second point cloud data are on the same plane. For example, if most or all of the point cloud data in the second point cloud data are on the same plane, the flatness of the second point cloud data is considered to be good.

The first way is to determine whether the flatness of the second point cloud data satisfies the first condition, which may include: when the first vector and the second vector in the second point cloud data are perpendicular or nearly perpendicular, determining the second point cloud data The flatness of satisfies the first condition, otherwise, it is determined that the flatness of the second point cloud data does not satisfy the first condition. Wherein, the first vector is the vector between the reflection points of any two different transmit beams on the target plane object in the second point cloud data, and the second vector is the corresponding one or more transmit beams in the second point cloud data The normal vector of the point cloud data. Optionally, "any two different emission beams" can be beams emitted by any two different transmitters on the vehicle radar, or any two different beams emitted by the same transmitter, or, two adjacent beams, or , are the two closest beams, etc., which are not limited in the embodiments of the present application.

The fact that the first vector and the second vector are nearly perpendicular can be understood as the angle between the first vector and the second vector being smaller than the preset angle or within the preset angle range. The value is not limited in this embodiment of the present application, for example, 85 degrees to 95 degrees.

Optionally, before judging whether the first vector and the second vector are perpendicular, the method may further include the step of: selecting part of the point cloud data from the second point cloud data, and determining the first vector based on the part of the point cloud data. As mentioned above, the vehicle-mounted radar can emit a beam, and the emitted beam is emitted on the target plane object to generate a reflection point, and the reflected beam is received by the vehicle-mounted radar. The vehicle-mounted radar calculates the reflection point according to the transmitted beam and the received reflected beam. The coordinate value in the system (ie the first point cloud data), and then the coordinate value in the radar coordinate system is converted to the coordinate value in the inertial coordinate system (ie the second point cloud data). Therefore, the points in the second point cloud data correspond to different reflection points, that is, correspond to different emission light beams. Therefore, the partial point cloud data may be point cloud data corresponding to a plurality of different emission beams in the second point cloud data, for example, point cloud data corresponding to emission beam 1 and emission beam 2, then the first vector can emit beams 1 The vector between the reflection point 1 on the target plane object and the reflection point 2 of the emission beam 2 on the target plane object, for example, see Figure 7, which is a schematic diagram of the second point cloud data, assuming points P and Point Q is a reflection point corresponding to two different transmit beams in the second point cloud data, so the vector between point P and point Q may be the first vector.

Optionally, before judging whether the first vector and the second vector are perpendicular, the step may further include: determining the second vector. The second vector may be the normal vector corresponding to all point cloud data in the second point cloud data, or the second vector may be part of the point cloud data in the second point cloud data (for example, part of the points selected when determining the first vector) The normal vector corresponding to the cloud data), or, the second vector may also be the normal vector of the point cloud data corresponding to any one or more transmit beams in the second point cloud data.

For example, referring to FIG. 7 , which is a schematic diagram of the second point cloud data, it is assumed that point P and point Q are points corresponding to two different transmit beams in the second point cloud data. If the first vector between point P and point Q is perpendicular or nearly perpendicular to the normal vector of the second point cloud data, the flatness of the second point cloud data is better, so the first vector and the normal vector are perpendicular or nearly perpendicular , it is determined that the flatness of the second point cloud data satisfies the first condition.

The second way is to determine whether the flatness of the second point cloud data satisfies the first condition, which may include: determining a plurality of sets of first vectors according to the second point cloud data, and there is a difference between each set of first vectors and the second vector. An included angle, if the average or weighted average of the multiple included angles is 90 degrees or close to 90 degrees, then it is determined that the flatness of the second point cloud data satisfies the first condition.

For example, as described above, when determining the first vector, the part of the point cloud data selected from the second point cloud data may be point cloud data corresponding to a plurality of different emitted light beams in the second point cloud data. Assuming that M (M is greater than 2) transmit beams are selected, there are corresponding M reflection points, and the M reflection points can generate multiple different first vectors. In this case, each first vector can be determined with the first vector The angle between the two vectors, get multiple angles, count the average or weighted average of the multiple angles, if the average or weighted average is 90 degrees or close to 90 degrees, then determine the second point cloud data. The flatness satisfies the first condition.

When the flatness of the second point cloud data does not satisfy the first condition, S604 may be performed. When the flatness of the second point cloud data satisfies the first condition, it is not necessary to calibrate the external parameters of the vehicle-mounted radar.

S604, if the flatness of the second point cloud data does not meet the first condition, determine to calibrate the external parameters of the vehicle-mounted radar.

It should be noted that, since the second point cloud data is the coordinates of the target plane object in the inertial coordinate system. If the flatness of the second point cloud data is good, it means that the second point cloud data obtained by using the current external parameters of the vehicle radar through a series of coordinate transformations is flat, which is more in line with the real situation, indicating that the current external parameters are relatively accurate of. If the flatness of the second point cloud data is poor, it means that the second point cloud data obtained through a series of coordinate transformations using the current external parameters is not flat and does not conform to the real situation (because the real situation is that the surface of the target plane object is flat ), indicating that the current external parameters are inaccurate, and the external parameters of the vehicle radar need to be calibrated. In this way, it can be accurately judged whether the current external parameters are accurate, that is, whether the external parameters of the vehicle-mounted radar need to be calibrated.

The above Embodiment 1 and Embodiment 2 provide two ways to determine whether to calibrate the external parameters of the vehicle-mounted radar. It is assumed that the method of the first embodiment is taken as the first method, and the method of the second embodiment is taken as the second method. In some embodiments, the vehicle may default to the first mode or default to the second mode. Alternatively, the user can specify to use the first method or the second method, for example, a switch button is set on the vehicle, and the switch between the first method and the second method is realized by the switch button. Alternatively, the vehicle can also determine whether to adopt the first method or the second method according to conditions (such as environmental conditions or speed conditions).

Embodiment 3

The third embodiment can be understood as a combination of the first embodiment and the second embodiment. Specifically, when it is determined that the external parameters of the vehicle-mounted radar are calibrated using the method of the first embodiment, the external parameters of the vehicle-mounted radar are calibrated. After the calibration is completed, the method of the second embodiment is used to determine whether the calibrated external parameters meet the conditions. If satisfied, adjust the calibrated external parameters.

Specifically, please refer to FIG. 8 , which is another schematic flowchart of a method for calibrating external parameters of a vehicle-mounted radar according to an embodiment of the present application. As shown in Figure 8, the process includes:

S801 , acquiring driving information of the vehicle. The driving information includes at least one item of driving state information or driving road condition information.

S802, according to the driving information, determine to calibrate the external parameters of the vehicle-mounted radar.

For the implementation principles of S801 to S802, please refer to the implementation principles of S501 and S502 in the embodiment shown in FIG.

S803, the external parameters of the vehicle radar are calibrated.

In the embodiment of the present application, S803 may be refined as: calibrating the external parameters of the vehicle-mounted radar according to the target reference. Because the external parameter includes at least one of a rotation angle or a translation distance of the radar coordinate system relative to the vehicle coordinate system. Therefore, S803 can be refined as: determining at least one of a rotation angle or a translation distance of the radar coordinate system relative to the vehicle coordinate system according to the target reference.

The process of determining the rotation angle and the translation distance is described below.

The rotation angle includes three attitude angles, namely pitch angle, roll angle and yaw angle. In this embodiment of the present application, different attitude angles may be determined according to different target reference objects. Specifically, the following two cases are included.

(1), according to the first type of target reference, determine the pitch angle and roll angle. Among them, the first type of target reference refers to the ground on the road of the vehicle and the plane objects parallel to the ground.

Specifically, determining the pitch angle and the roll angle according to the first type of target reference includes the following steps 1 to 4.

In step 1, point cloud data 1 is obtained, and point cloud data 1 is the point cloud data corresponding to the first type of target reference in the radar coordinate system.

Step 2: Determine the first normal vector of the point cloud data 1.

Step 3: Determine the rotation matrix of the radar coordinate system relative to the vehicle coordinate system according to the first normal vector and the standard normal vector.

Taking the first type of target reference as the ground as an example, the vehicle-mounted radar detects the point cloud data 1 corresponding to the ground. Since the vehicle-mounted radar is offset from the vehicle coordinate system (or inertial coordinate system), the detected point on the ground corresponds to Cloud data 1 and the real ground are not necessarily on the same plane. In other words, the normal vector of point cloud data 1 corresponding to the detected ground (ie the first normal vector) and the normal vector of the real ground (ie the standard normal vector) Not necessarily parallel, so the deviation between the first normal vector and the standard normal vector can reflect the rotation matrix of the radar coordinate system and the vehicle coordinate system. Therefore, the rotation matrix of the radar coordinate system and the vehicle coordinate system can be determined by the first normal vector and the standard normal vector.

Exemplarily, the rotation matrix satisfies the following formula:

where R is the rotation matrix. I is a unit diagonal matrix, which can be preset. [v] _× satisfies the following formula:

Among them, the calculation methods of v ₁ , v ₂ , and v ₃ are as follows:

in,

is the first vector,

is the nominal ground normal vector,

Therefore, the rotation matrix R of the radar coordinate system relative to the vehicle coordinate system can be obtained by the above formula.

Step 4, according to the rotation matrix, determine the pitch angle and the roll angle.

For example, the pitch and roll angles are determined according to the following formulas:

yaw angle

Pitch angle pitc=arcsin(-r ₃₁ )

roll angle

where R is the rotation matrix.

It can be seen that through the deviation between the first normal vector and the standard normal vector, the rotation matrix of the radar coordinate system and the vehicle coordinate system can be reversed, and then the pitch angle and roll angle can be obtained, with high accuracy.

It should be noted that the above step 4 can obtain not only the pitch angle and the roll angle, but also the yaw angle according to the rotation matrix R, but since the rotation matrix is based on the normal vector of the point cloud data 1 corresponding to the detected ground (that is, the first The deviation between the first normal vector and the normal vector of the real ground (ie the standard normal vector) is inferred, and the change of the yaw angle has no effect on the deviation between the first normal vector and the standard normal vector. The change of the sailing angle will not cause the first normal vector of the point cloud data 1 to change. Therefore, according to the deviation between the first normal vector of the point cloud data 1 corresponding to the detected ground and the standard normal vector of the real ground, it is inaccurate to obtain the yaw angle by reverse inference. Therefore, the following case (2) can be used to calibrate the yaw angle.

(2), according to the second type of target reference, determine the yaw angle. Wherein, the second type of target reference is an object set along the driving road. See above for an introduction to the second category of target references.

Specifically, according to the second type of target reference, determining the yaw angle includes the following steps 1 to 2.

Step 1: Determine the road direction of the driving road according to the objects set along the driving road, and the road direction satisfies: y=C0+C1*x; wherein, C1 is used to indicate the road direction; C0 is used to describe the The distance between the object and the origin of the radar coordinate system, x and y are the positions of the second type of target reference in the radar coordinate system.

Taking the second type of target reference as a street lamp as an example, please refer to FIG. 9 . An implementation method is, according to the street lamp, the process of determining the road direction of the driving road includes: acquiring point cloud data, and determining the street lamp 1 in the point cloud data. The feature point 1 on the street lamp 1, for example, the feature point 1 can be any point on the street lamp 1, and the feature point 2 on the street lamp 2 is determined, and the feature point 2 corresponds to the feature point 1. Therefore, based on feature point 1 and feature point 2, a linear relationship y=C0+C1*x can be determined.

Step 2: Determine the yaw angle of the vehicle-mounted radar according to C1.

For example, yaw angle=arctan(1/C1), or, yaw angle=90-arctan(C1).

It should be noted that, in the embodiment of the present application, the vehicle calibrates the external parameters (such as three attitude angles) of the vehicle-mounted radar according to the target reference objects around the driving road. In particular, calibrating the yaw angle based on street lights, signs, trees, etc. around the road helps to improve the accuracy of the calibrated yaw angle.

The above embodiment introduces the calibration process of the rotation angle, and the following describes the determination process of the translation distance.

In this embodiment of the present application, the translation distance may be determined according to the first type of target reference. Wherein, the first type of target reference includes at least one of the ground of the driving road or a plane object parallel to the ground. Among them, the translation distance includes Δ _z , Δ _x and Δ _y . Since _Δx and _Δy generally do not change, in the embodiment of the present application, _Δx =Δy ₌ 0. Therefore, the translation distance can be determined according to the first type of target reference and can be refined as follows: Δ _z can be determined according to the first type of target reference. Specifically, the following steps 1 to 2 are included.

In step 1, point cloud data 2 is determined, and point cloud data 2 corresponds to the first type of target reference in the radar coordinate system.

Step 2, according to the point cloud data 2, determine the ground fitting equation.

For example, using the ground segmentation algorithm, a ground fitting equation is obtained, and the ground fitting equation satisfies:

Ax+By-z+C=0

Step 3: Determine the translation distance according to the distance from the origin of the radar coordinate system to the ground fitting equation.

Among them, the distance from the origin of the radar coordinate system to the ground fitting equation can be determined by the principle of distance from point to plane, which is not repeated in this article. Assuming that the distance from the origin of the radar coordinate system to the ground fitting equation is D, then determine Δ _z according to D; for example, Δ _z =D.

In the above manner, the calibration of the external parameters (rotation angle and/or translation distance) of the vehicle-mounted radar is completed. After the calibration of the external parameters is completed, it can be further judged whether the calibrated external parameters are accurate, and if not, the calibrated external parameters can be adjusted, which specifically includes the following steps S804 to S806.

S804, using the calibrated external parameters to convert the first point cloud data of the target plane object in the radar coordinate system to the second point cloud data in the inertial coordinate system.

S805: Determine whether the flatness of the second point cloud data satisfies the first condition.

For the implementation principles of S804 to S805, please refer to the implementation principles of S602 and S603 in the embodiment shown in FIG.

S806, if the flatness of the second point cloud data does not meet the first condition, adjust the calibrated external parameters.

It should be noted that, if the flatness of the second point cloud data does not meet the first condition, it means that the accuracy of the calibrated external parameters is low. Therefore, the calibrated external parameters can be adjusted. The extrinsic parameter includes at least one of a rotation angle or a translation distance. Therefore, adjusting the external parameters can include adjusting the rotation angle, that is, the three attitude angles. The manner of adjusting the rotation angle may include at least one of the following manners A to C.

Method A, determine the objective function f, the objective function f is used to describe

and

the angle between. in,

is the vector between two points in the second point cloud data (the first vector between point P and point Q as shown in Figure 7);

is the normal vector of the second point cloud data (such as the second vector shown in Figure 7). Then, solve the optimal solution of this objective function.

Exemplary, the objective function

in,

and

The independent variables in include three attitude angles, so the independent variables of the above formula include three attitude angles, so the objective function f can be expressed as f(β, γ, α). Among them, the pitch angle is β (pitch), the yaw angle is γ (yaw), and the roll angle is α (roll).

After determining the objective function f, within the search range of the independent variable parameters (pitch angle, roll angle, yaw angle), find that the objective function f is within the threshold range (for example, [85 degrees, 95 degrees] interval range) The independent variables (that is, the three attitude angles). Among them, the search range of the independent variables (pitch angle, roll angle, yaw angle) may include: for example, the pitch angle is in the interval [0, M0], the roll angle is in the interval [0, M1], and the yaw angle is in the interval [0, M1]. [0, M2] interval. The specific values of M0, M1, and M2 are not limited in this embodiment of the present application.

That is to say, within the search range of the independent variable parameters, find the independent variable parameters that can make the objective function f be at [85 degrees, 95 degrees], and the independent variable parameters include the found accurate rotation angles (that is, the three attitude angles). ).

Method B, determine the objective function f, the objective function f is used to describe

and

vertical relationship between them. in,

Exemplary, the objective function

in,

and

The independent variables include three attitude angles, so f can be expressed as f(β, γ, α).

After determining the objective function f, within the search range of the independent variable parameters (pitch angle, roll angle, yaw angle), find the external parameter value that makes the objective function f reach the minimum value. Among them, the search range of the independent variables (pitch angle, roll angle, yaw angle) may include: for example, the pitch angle is in the interval [0, M0], the roll angle is in the interval [0, M1], and the yaw angle is in the interval [0, M1]. [0, M2] interval. The independent variable that makes the objective function f reach the minimum value is the exact rotation angle (ie, three attitude angles) found.

In mode C, the objective function f is determined, and the objective function f is used to describe the projection distance of the second vector on the first vector (for example, the projection distance of the second vector on the first vector in FIG. 7 ). The projection distance can be understood as the distance from the vertex of the second vector to the first vector. Alternatively, the objective function f may also describe the distance from a point to a plane, where the plane refers to a plane fitted according to the second point cloud data, and the point is any point in the second point cloud data.

Exemplarily, the objective function f satisfies the following formula:

In the above formula, p _k and m _k are two points in the second point cloud data. η _k is the normal vector of the fitted plane. If the flatness of the second point cloud data is good, the distance between each point in the second point cloud data and the fitted plane is small. Therefore, within the search range of the independent variable parameters (pitch angle, roll angle, yaw angle), find the extrinsic parameter value that makes the objective function f reach the minimum value. Among them, the search range of the independent variables (pitch angle, roll angle, yaw angle) may include: for example, the pitch angle is in the interval [0, M0], the roll angle is in the interval [0, M1], and the yaw angle is in the interval [0, M1]. [0, M2] interval. Wherein, the independent variable that makes the objective function f reach the minimum value is the exact rotation angle (ie, three attitude angles) found.

The above three methods use different objective functions to obtain the exact values of the three attitude angles. In practical applications, other objective functions can also be used to solve, which are not limited in the embodiments of the present application.

To sum up, in the third embodiment, not only can it be determined whether to calibrate the external parameters of the vehicle radar according to the driving information of the vehicle, but if it is determined to calibrate the external parameters of the vehicle radar, it can also be judged whether the calibrated external parameters meet the conditions. (ie S804 and S805), if not satisfied, adjust the calibrated external parameters to improve the accuracy of the external parameters.

The apparatus for implementing the above method in the embodiments of the present application will be described below with reference to the accompanying drawings. Therefore, the above content can be used in subsequent embodiments, and repeated content will not be repeated.

FIG. 10 is a structural block diagram of a device 1000 for calibrating external parameters of a vehicle-mounted radar according to an embodiment of the present application. The external parameter calibration device 1000 of the vehicle-mounted radar includes: an acquisition unit 1001 and a processing unit 1002 . Wherein, the obtaining unit 1001 is configured to obtain the driving information of the vehicle, where the driving information includes at least one of driving state information or driving road condition information; wherein the driving state information includes speed, acceleration, yaw angle or at least one of yaw angular velocity; the driving road condition information is used to indicate at least one of the driving road of the vehicle or the objects around the driving road; the processing unit 1002 is configured to determine, according to the driving information The vehicle-mounted radar is calibrated with external parameters.

Exemplarily, the acquiring unit 1001 and the processing unit 1002 may be processors, such as application processors or baseband processors, and the processors may include one or more central processing units (CPUs).

Exemplarily, the external parameter calibration device 1000 of the vehicle-mounted radar may be a vehicle or a vehicle-mounted device, or a processing module or a chip system in a vehicle or a vehicle-mounted device, such as a vehicle-mounted processor or an electronic control unit (ECU).

Exemplarily, the external parameter calibration apparatus 1000 of the vehicle-mounted radar may also be a processing module (such as a processor) in the vehicle-mounted radar.

Optionally, the external parameter calibration apparatus 1000 of the vehicle-mounted radar may further include a communication unit. The communication unit may include a receiving unit and a transmitting unit. The sending unit may be a functional module for performing a sending operation; the receiving unit may be a functional module for performing a receiving operation.

In a possible design, the processing unit 1002 is specifically configured to: determine to perform external parameter calibration on the vehicle radar when at least one of the following conditions is satisfied according to the driving state information, wherein the conditions include:

In a possible design, the processing unit 1002 is specifically configured to: according to the driving road condition information, determine that the driving road is a straight road and/or when there is a target reference object around the driving road, determine whether the driving road is a straight road. The vehicle radar performs external parameter calibration.

In a possible design, the external parameter includes at least one of a rotation angle or a translation distance of the vehicle-mounted radar; the processing unit 1002 is further configured to: determine, according to the target reference around the driving road, At least one of the rotation angle or the translation distance of the vehicle radar.

In a possible design, when the processing unit 1002 is used to determine the rotation angle of the vehicle radar according to the target reference around the driving road, it is specifically used for: according to the ground of the driving road Or a plane object parallel to the ground, determine the pitch angle and roll angle of the vehicle-mounted radar; determine the yaw angle of the vehicle-mounted radar according to the objects arranged along the driving road.

In a possible design, when the processing unit 1002 is used to determine the yaw angle of the vehicle-mounted radar according to the objects arranged along the driving road, the processing unit 1002 is specifically configured to: according to the setting along the driving road determine the road direction of the driving road, wherein the road direction satisfies y=C0+C1*x; wherein, C1 is used to indicate the road direction; C0 is used to describe the object and the radar coordinates The distance between the origins of the system, x and y are the coordinates of the object in the radar coordinate system; the yaw angle of the vehicle radar is determined according to the C1.

In a possible design, the acquisition unit 1001 is further configured to: acquire external parameters of the vehicle-mounted radar;

The processing unit 1002 is further configured to: use the external parameter to convert the first point cloud data corresponding to the target plane object around the driving road in the vehicle radar coordinate system into the second point cloud data in the inertial coordinate system; If the flatness of the second point cloud data does not meet the first condition, adjust the external parameter.

In a possible design, when adjusting the external parameters, the processing unit 1002 is specifically configured to: generate an objective function based on the external parameters, where the objective function is used to describe the first vector and the The angle between the normal vectors, or the objective function is used to describe the projection distance of the normal vector on the first vector; within the preset external parameter adjustment range, find the objective function to reach the minimum value of external parameters.

The division of units in the embodiments of the present application is schematic, and is only a logical function division. In actual implementation, there may be other division methods. In addition, the functional units in the various embodiments of the present application may be integrated into one processing unit. In the device, it can also exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.

Only one or more of the various elements in FIG. 10 may be implemented in software, hardware, firmware, or a combination thereof. The software or firmware includes, but is not limited to, computer program instructions or code, and can be executed by a hardware processor. The hardware includes, but is not limited to, various types of integrated circuits, such as central processing units (CPUs), digital signal processors (DSPs), field programmable gate arrays (FPGAs), or application specific integrated circuits (ASICs).

FIG. 11 is a structural block diagram of an external parameter calibration apparatus 1100 of a vehicle-mounted radar provided by an embodiment of the present application. The external parameter calibration apparatus 1100 of the vehicle-mounted radar shown in FIG. 11 includes at least one processor 1101 . The external parameter calibration device 1100 of the vehicle-mounted radar further includes at least one memory 1102 for storing program instructions and/or data. Memory 1102 and processor 1101 are coupled. The coupling in the embodiments of the present application is an indirect coupling or communication connection between devices, units or modules, which may be in electrical, mechanical or other forms, and is used for information exchange between devices, units or modules. The processor 1101 may cooperate with the memory 1102 , the processor 1101 may execute program instructions stored in the memory 1102 , and at least one of the at least one memory 1102 may be included in the processor 1101 . The external parameter calibration device 1100 of the vehicle radar may also include an interface circuit (not shown in the figure), the processor 1101 is coupled with the memory 1102 through the interface circuit, and the processor 1101 can execute the program code in the memory 1102 to achieve The external parameter calibration method of the vehicle-mounted radar provided by the embodiment of the present application.

Optionally, the external parameter calibration apparatus 1100 for vehicle radar may further include a communication interface 1103 for communicating with other devices through a transmission medium, so that the external parameter calibration apparatus 1100 for vehicle radar may communicate with other devices. In this embodiment of the present application, the communication interface may be a transceiver, a circuit, a bus, a module, or other types of communication interfaces. In the embodiment of the present application, when the communication interface is a transceiver, the transceiver may include an independent receiver and an independent transmitter; a transceiver with integrated transceiver functions, or an interface circuit, etc. may also be included.

It should be understood that a connection medium between the above-mentioned processor 1101 , the memory 1102 , and the communication interface 1103 is not limited in the embodiments of the present application. In this embodiment of the present application, the memory 1102, the processor 1101, and the communication interface 1103 are connected through a communication bus 1104 in FIG. 11. The bus is represented by a thick line in FIG. 11, and the connection between other components is only a schematic illustration. , not as a limitation. The bus may include an address bus, a data bus, a control bus, and the like. For convenience of presentation, only one thick line is used in FIG. 11 , but it does not mean that there is only one bus or one type of bus or the like.

For example, when the computer instructions in the memory 1102 are executed by the processor 1101, the device is caused to execute: acquiring the driving information of the vehicle, where the driving information includes at least one of driving state information or driving road condition information; wherein, The driving state information includes at least one of speed, acceleration, yaw angle or yaw angular velocity; the driving road condition information is used to indicate at least one of a driving road of the vehicle or objects around the driving road; According to the driving information, it is determined to perform external parameter calibration on the vehicle-mounted radar.

The present application provides a computer-readable storage medium, including computer instructions, when the computer instructions are executed by a processor, the external parameter calibration device of the vehicle-mounted radar can perform the external parameter calibration of the vehicle-mounted radar described in the embodiments of the present application. parameter calibration method.

The present application provides a computer program product, the computer program product includes a computer program, when the computer program product runs on a processor, the external parameter calibration device of the vehicle-mounted radar is made to execute the method described in the embodiments of the present application. External parameter calibration method of vehicle radar.

In this embodiment of the present application, the processor may be a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, which can implement or The methods, steps and logic block diagrams disclosed in the embodiments of this application are executed. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the methods disclosed in conjunction with the embodiments of the present application may be directly embodied as executed by a hardware processor, or executed by a combination of hardware and software modules in the processor.

In this embodiment of the present application, the memory may be a non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD), etc., or may also be a volatile memory (volatile memory), for example Random-access memory (RAM). Memory is any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto. The memory in this embodiment of the present application may also be a circuit or any other device capable of implementing a storage function, for storing program instructions and/or data.

In the embodiments of the present application, "at least one" refers to one or more, and "multiple" refers to two or more. "And/or", which describes the relationship of the associated objects, indicates that there can be three kinds of relationships, for example, A and/or B, it can indicate that A exists alone, A and B exist at the same time, and B exists alone, where A, B can be singular or plural. The character "/" generally indicates that the associated objects are an "or" relationship. "At least one item(s) below" or similar expressions thereof refer to any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (a) of a, b, or c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

The methods provided in the embodiments of the present application may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer may be a general purpose computer, a special purpose computer, a computer network, network equipment, user equipment, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be downloaded from a website site, computer, server or data center Transmission to another website site, computer, server or data center by means of wired (such as coaxial cable, optical fiber, digital subscriber line, DSL for short) or wireless (such as infrared, wireless, microwave, etc.) A computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The available media can be magnetic media (eg, floppy disks, hard disks, magnetic tape), optical media (eg, digital video disc (DVD) for short), or semiconductor media (eg, SSD), and the like.

Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

A method for calibrating external parameters of a vehicle-mounted radar, which is applied to a vehicle, wherein the vehicle includes a radar, and is characterized in that it includes:

Acquiring driving information of the vehicle, where the driving information includes at least one of driving status information or driving road condition information; wherein the driving status information includes at least one of speed, acceleration, yaw angle, or yaw angular velocity; The driving road condition information includes at least one of a driving road of the vehicle or objects around the driving road;

According to the driving information, external parameter calibration is performed on the vehicle-mounted radar.
The method according to claim 1, wherein the performing external parameter calibration on the vehicle-mounted radar according to the driving information comprises: determining, according to the driving state information, that when at least one of the following conditions is satisfied, Perform external parameter calibration on the vehicle radar, wherein the conditions include:

The speed is less than a first threshold, the vehicle is traveling at an average speed, the acceleration is less than a second threshold, the yaw angle is within a preset range, or the yaw angle speed is less than a third threshold.
The method according to claim 1, wherein the performing external parameter calibration on the vehicle radar according to the driving information comprises:

According to the driving road condition information, when it is determined that the driving road is a straight road and/or that there is a target reference object around the driving road, external parameter calibration is performed on the vehicle-mounted radar.
The method according to any one of claims 1-3, wherein the external parameter includes at least one of a rotation angle or a translation distance of the vehicle-mounted radar; the method further comprises:

At least one of a rotation angle or a translation distance of the vehicle-mounted radar is determined according to the target reference around the driving road.
The method according to claim 4, wherein the determining the rotation angle of the vehicle-mounted radar according to the target reference around the driving road comprises:

Determine the pitch angle and the roll angle of the vehicle-mounted radar according to the ground of the driving road or a plane object parallel to the ground;

The yaw angle of the vehicle-mounted radar is determined according to the objects arranged along the driving road.
The method according to claim 5, wherein the objects arranged along the driving road include: first type objects arranged along the driving road and parallel to the driving road, and/or , objects of the second type arranged along the travel road and perpendicular to the travel road;

Wherein, the first type of object includes at least one of a road edge, a guardrail, a green belt or a lane line; the second type of object includes at least one of a tree, a sign or a street lamp.
The method according to claim 5 or 6, wherein determining the yaw angle of the vehicle-mounted radar according to objects arranged along the driving road, comprising:

Determine the road direction of the driving road according to the objects arranged along the driving road, wherein the road direction satisfies y=C0+C1*x; wherein, C1 is used to indicate the road direction; C0 is used to describe the distance between the object and the origin of the radar coordinate system, where x and y are the coordinates of the object in the radar coordinate system;

The yaw angle of the vehicle radar is determined according to the C1.
The method according to any one of claims 1-7, wherein the method further comprises:

obtain the external parameters of the vehicle radar;

determining a target plane object around the driving road;

Using the external parameter to convert the first point cloud data corresponding to the target plane object in the vehicle radar coordinate system into the second point cloud data in the inertial coordinate system;

If the flatness of the second point cloud data does not meet the first condition, adjust the external parameter.
The method according to claim 8, wherein the flatness of the second point cloud data does not meet the first condition, comprising:

The first vector between the reflection points of any two different transmit beams on the target plane object in the second point cloud data and the point cloud data corresponding to any one or more transmit beams in the second point cloud data The normal vector of is not perpendicular;

or,

The first vector between the reflection points of any two different transmit beams on the target plane object in the second point cloud data and the point cloud data corresponding to any one or more transmit beams in the second point cloud data The angle between the normal vectors of is not within the preset range.
The method according to claim 9, wherein the adjusting the external parameter comprises:

Based on the external parameters, an objective function is generated, the objective function is used to describe the angle between the first vector and the normal vector, or the objective function is used to describe the normal vector in the first Projection distance on a vector;

Within the preset external parameter adjustment range, find the external parameter that makes the objective function reach the minimum value.
An external parameter calibration device for a vehicle-mounted radar, characterized in that it includes:

The acquiring unit acquires the driving information of the vehicle, where the driving information includes at least one of driving state information or driving road condition information; wherein the driving state information includes speed, acceleration, yaw angle or yaw angle speed. at least one; the driving road condition information includes at least one of the driving road of the vehicle or the objects around the driving road;

The processing unit performs external parameter calibration on the vehicle radar according to the driving information.
The apparatus according to claim 11, wherein the processing unit is specifically configured to:

According to the driving state information, when it is determined that at least one of the following conditions is satisfied, perform external parameter calibration on the vehicle-mounted radar, wherein the conditions include:

The speed is less than a first threshold, the vehicle is traveling at an average speed, the acceleration is less than a second threshold, the yaw angle is within a preset range, or the yaw angle speed is less than a third threshold.
The apparatus according to claim 11, wherein the processing unit is specifically configured to:

According to the driving road condition information, when it is determined that the driving road is a straight road and/or that there is a target reference object around the driving road, external parameter calibration is performed on the vehicle-mounted radar.
The device according to any one of claims 11-13, wherein the external parameter includes at least one of a rotation angle or a translation distance of the vehicle-mounted radar; the processing unit is further configured to:

At least one of a rotation angle or a translation distance of the vehicle-mounted radar is determined according to the target reference around the driving road.
The device according to claim 14, wherein when the processing unit is used to determine the rotation angle of the vehicle-mounted radar according to the target reference around the driving road, it is specifically used to:

Determine the pitch angle and the roll angle of the vehicle-mounted radar according to the ground of the driving road or a plane object parallel to the ground;

The yaw angle of the vehicle-mounted radar is determined according to the objects arranged along the driving road.
16. The device according to claim 15, wherein the objects disposed along the travel road comprise: first type objects disposed along the travel road and parallel to the travel road, and/or , objects of the second type arranged along the travel road and perpendicular to the travel road;

Wherein, the first type of object includes at least one of a road edge, a guardrail, a green belt or a lane line; the second type of object includes at least one of a tree, a sign or a street lamp.
The device according to claim 15 or 16, wherein when the processing unit is used to determine the yaw angle of the vehicle-mounted radar according to the objects arranged along the driving road, the processing unit is specifically used for:

Determine the road direction of the driving road according to the objects arranged along the driving road, wherein the road direction satisfies y=C0+C1*x; wherein, C1 is used to indicate the road direction; C0 is used to describe the distance between the object and the origin of the radar coordinate system, where x and y are the coordinates of the object in the radar coordinate system;

The yaw angle of the vehicle radar is determined according to the C1.
The device according to any one of claims 11-17, wherein the acquiring unit is further configured to:

obtain the external parameters of the vehicle radar;

determining a target plane object around the driving road;

The processing unit is further configured to: use the external parameters to convert the first point cloud data corresponding to the target plane object in the vehicle radar coordinate system into the second point cloud data in the inertial coordinate system; if the second point cloud data If the flatness of the point cloud data does not meet the first condition, adjust the external parameter.
The device according to claim 18, wherein the flatness of the second point cloud data does not meet the first condition, comprising:

The first vector between the reflection points of any two different transmit beams on the target plane object in the second point cloud data and the point cloud data corresponding to any one or more transmit beams in the second point cloud data The normal vector of is not perpendicular;

or,

The first vector between the reflection points of any two different transmit beams on the target plane object in the second point cloud data and the point cloud data corresponding to any one or more transmit beams in the second point cloud data The angle between the normal vectors of is not within the preset range.
The device according to claim 18 or 19, wherein when the processing unit adjusts the external parameter, it is specifically configured to:

Based on the external parameters, an objective function is generated, the objective function is used to describe the angle between the first vector and the normal vector, or the objective function is used to describe the normal vector in the first Projection distance on a vector;

Within the preset external parameter adjustment range, find the external parameter that makes the objective function reach the minimum value.
An external parameter calibration device for a vehicle-mounted radar, characterized in that it includes a memory and one or more processors; wherein, the memory is used to store computer program codes, and the computer program codes include computer instructions; when the computer instructions When executed by the processor, the apparatus is caused to perform the method of any of claims 1-10.
A computer-readable storage medium, characterized in that it includes computer instructions, when the computer instructions are executed in the external parameter calibration device of the vehicle-mounted radar, the external parameter calibration device of the vehicle-mounted radar is made to perform as in claims 1-10. The method of any one.
A vehicle, characterized in that, the vehicle comprises the external parameter calibration device of the vehicle-mounted radar according to any one of claims 11 to 21.