CN112034438A - Radar calibration method and device, electronic equipment and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses a radar calibration method, a radar calibration device, electronic equipment and a storage medium. The method comprises the following steps: acquiring initial point cloud data in a radar coordinate system and vehicle pose data in a global coordinate system; determining the pose data of the points in the initial point cloud data according to the point cloud time stamp of the points in the initial point cloud data and the pose time stamp of the vehicle pose data; and calibrating the conversion relation between the radar coordinate system and the global coordinate system according to the pose data of the point in the initial point cloud data and the initial point cloud data. By operating the technical scheme provided by the embodiment of the invention, the problems of complicated calibration operation, low calibration efficiency and precision and the like caused by manual calibration can be solved, and the effects of reducing the difficulty of the calibration operation and improving the calibration efficiency and precision are realized.
Description
Technical Field
The embodiments of the present invention relate to positioning technologies, and in particular, to a radar calibration method and apparatus, an electronic device, and a storage medium.
Background
With the development of science and technology, the automatic driving of automobiles becomes an important development direction of automobile industry in the future. Environmental awareness plays a role as a foundation in autonomous vehicle architectures. The automatic driving automobile avoids or even avoids the traffic problem caused by personal factors of the driver by means of technologies such as artificial intelligence, machine vision and the like. Automotive vehicles are involved in areas including: the comprehensive experiment carrier is a comprehensive experiment carrier for various leading-edge hot technologies at present. Therefore, the automatic driving technology in the automatic driving automobile has important scientific research value and wide application prospect.
For an automatic driving system, an environment perception technology is the basis of the whole system, calibration of a multi-sensor coordinate system conversion relation has direct influence on spatial information fusion acquired by a sensor, and the environment perception technology is the basis of researching a multi-source fusion navigation positioning algorithm.
At present, calibration is mainly carried out in a manual calibration mode, so that the problems of complex calibration operation, low calibration efficiency and precision and the like are caused.
Disclosure of Invention
The embodiment of the invention provides a radar calibration method, a radar calibration device, electronic equipment and a storage medium, and aims to achieve the effects of reducing the difficulty of calibration operation and improving the calibration efficiency and precision.
In a first aspect, an embodiment of the present invention provides a radar calibration method, where the method includes:
acquiring initial point cloud data in a radar coordinate system and vehicle pose data in a global coordinate system;
determining the pose data of the points in the initial point cloud data according to the point cloud time stamp of the points in the initial point cloud data and the pose time stamp of the vehicle pose data;
and calibrating the conversion relation between the radar coordinate system and the global coordinate system according to the pose data of the point in the initial point cloud data and the initial point cloud data.
In a second aspect, an embodiment of the present invention further provides a radar calibration apparatus, where the apparatus includes:
the data acquisition module is used for acquiring initial point cloud data in a radar coordinate system and vehicle pose data in a global coordinate system;
the pose data determining module is used for determining pose data of the points in the initial point cloud data according to the point cloud time stamp of the points in the initial point cloud data and the pose time stamp of the vehicle pose data;
and the conversion relation calibration module is used for calibrating the conversion relation between the radar coordinate system and the global coordinate system according to the pose data of the point in the initial point cloud data and the initial point cloud data.
In a third aspect, an embodiment of the present invention further provides an electronic device, where the electronic device includes:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the radar calibration method as described above.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the radar calibration method described above.
The method comprises the steps of acquiring initial point cloud data in a radar coordinate system and vehicle pose data in a global coordinate system; determining the pose data of the points in the initial point cloud data according to the point cloud time stamp of the points in the initial point cloud data and the pose time stamp of the vehicle pose data; and calibrating the conversion relation between the radar coordinate system and the global coordinate system according to the pose data of the point in the initial point cloud data and the initial point cloud data. The problem of through artifical demarcation mode mark, lead to marking operation complicacy, mark efficiency and precision lower scheduling problem is solved, realize reducing the effect of marking the operation degree of difficulty, improving mark efficiency and precision.
Drawings
Fig. 1 is a flowchart of a radar calibration method according to an embodiment of the present invention;
fig. 2 is a flowchart of a radar calibration method according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a radar calibration apparatus according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a radar calibration method according to an embodiment of the present invention, where the method is applicable to calibrating a transformation relationship between a radar coordinate system and a global coordinate system, and the method may be executed by a radar calibration apparatus provided in an embodiment of the present invention, and the apparatus may be implemented in a software and/or hardware manner. Referring to fig. 1, the radar calibration method provided in this embodiment includes:
and 110, acquiring initial point cloud data in a radar coordinate system and vehicle pose data in a global coordinate system.
Wherein the radar coordinate system may be a lidar coordinate system. The initial point cloud data can be acquired by a laser radar sensor, and the initial point cloud data is in a three-dimensional polar coordinate after being acquired by the laser radar sensor, so that the three-dimensional polar coordinate is required to be converted into a Cartesian coordinate system, and the conversion formula is as follows:
the conversion formula is as follows:
the radar three-dimensional polar coordinate of the point cloud data is (rho)i，θi，γi) The j point in each frame of point cloud data is converted from three-dimensional polar coordinates to coordinates of a Cartesian coordinate system as (x)j，yj，zj) Where ρ isiIndicating the Euclidean distance, θ, from the center of the sensor to the object to be sensediFor yaw launch angle, gammaiIs the pitch launch angle.
Vehicle pose data may be acquired by an Inertial Measurement Unit (IMU). The data measured by the IMU can be vehicle pose data under a WGS84 geographic coordinate system, and the data of the mth frame measured by the IMU can be recorded as [ b ]m lm hm ym rm pm]Respectively representing longitude, latitude, elevation, yaw angle, roll angle and pitch angle in the vehicle pose data. The global coordinate system may be constructed by using the IMU data of frame 1 as an origin, which is not limited by the embodiment. And converting the data of the mth frame into a global coordinate system, wherein the conversion formula is as follows:
tm＝utm(bm，lm，hm)-utm(bo，lo，ho)
wherein, tmFor the m frame data measured by IMU after conversion to global coordinate system, utm is standard Mercator projection transform, bo，lo，hoIs the longitude, latitude and elevation of the origin. Obtaining vehicle pose data T under global coordinate systemi，mThe formula of (1) is as follows:
wherein the content of the first and second substances,
and 120, determining the pose data of the points in the initial point cloud data according to the point cloud time stamp of the points in the initial point cloud data and the pose time stamp of the vehicle pose data.
The point cloud timestamp of the midpoint in the initial point cloud data is a timestamp corresponding to a single point in the initial point cloud data, and because motion distortion may exist in the radar in the rotation process and a deviation may exist between the pose corresponding to each point in each frame of point cloud data and the actual pose, the pose data of the midpoint in the initial point cloud data needs to be calibrated, that is, the actual pose data of the midpoint in the initial point cloud data is obtained.
The point cloud timestamp is obtained by the following formula:
tl，k，j＝tl，k+rj/2πf
wherein, tl，k，jPoint cloud time stamp, t, of the jth point of the kth frame of the initial point cloud datal，kFor the starting point cloud time stamp of the kth frame of the initial point cloud data obtained by measurement, the point cloud time stamp of each point in each frame of point cloud may be different due to motion distortion; f is the frequency of radar rotation, if the radar rotation is at a constant speed and the horizontal rotation angle is from 0 to 360 degrees, the horizontal rotation angle r of the jth pointjThe calculation formula of (a) is as follows:
rj＝atan2(yk，j，xk，j)
wherein, yk，jAs the ordinate, x, of the radar coordinate system of the jth point of the kth framek，jIs the radar coordinate system abscissa of the jth point of the kth frame. It should be noted that, since the processing manner of the point cloud in each frame is the same, the jth point in a certain frame of point cloud data is taken as an example for description in the following steps in this embodiment.
The position and attitude time stamp of the vehicle position and attitude data is the time stamp when the position and attitude data is acquired, each frame of data corresponds to one position and attitude time stamp, for example, the m-th frame of vehicle position and attitude data is Ti，mAnd the corresponding pose timestamp is ti，m。
And determining the pose data of the points in the initial point cloud data according to the point cloud time stamp of the points in the initial point cloud data and the pose time stamp of the vehicle pose data. The point cloud timestamp and the pose timestamp are matched, and pose data corresponding to the pose timestamp close to the point cloud timestamp time are used as pose data of the point in the initial point cloud data.
In this embodiment, optionally, determining the pose data of the midpoint in the initial point cloud data according to the point cloud timestamp of the midpoint in the initial point cloud data and the pose timestamp of the vehicle pose data includes:
selecting a first pose timestamp and a second pose timestamp from the pose timestamps of the vehicle pose data, wherein the first pose timestamp and the second pose timestamp are close to the point cloud timestamp of the midpoint of the initial point cloud data;
acquiring first vehicle pose data according to the first pose timestamp, and acquiring second vehicle pose data according to the second pose timestamp;
and determining the pose data of the point in the initial point cloud data according to the first vehicle pose data and the second vehicle pose data.
Selecting a first and second pose timestamp from the pose timestamps of the vehicle pose data that is close to the point cloud timestamp of the midpoint of the initial point cloud data, illustratively, tl，jPoint cloud time stamp of points in the initial point cloud data, ti，qIs the first attitude timestamp, ti，q+1As a second pose timestamp, a pose timestamp that is close to the point cloud timestamp of the point in the initial point cloud data may be tl，jAnd the pose timestamp with the minimum absolute value of the difference. The first position and posture timestamp is less than or equal to the point cloud timestamp and is closest to the point cloud timestamp, and the second position and posture timestamp is greater than or equal to the point cloud timestamp and is closest to the point cloud timestamp, namely t is satisfiedi，q≤tl，j≤ti，q+1。
And acquiring first vehicle pose data acquired at the corresponding time according to the first pose timestamp, and acquiring second vehicle pose data acquired at the corresponding time according to the second pose timestamp. Illustratively, according to ti，qObtaining first vehicle pose data Ti，qAccording to ti，q+1Obtaining second vehicle pose data Ti，q+1。
And determining the pose data of the point in the initial point cloud data through the first vehicle pose data and the second vehicle pose data. The accuracy of pose data calibration is improved, and therefore the accuracy of subsequent radar calibration is improved.
In this embodiment, optionally, determining the pose data of the midpoint in the initial point cloud data according to the first vehicle pose data and the second vehicle pose data includes:
determining pose data of the points in the initial point cloud data by the following formula:
ratio＝(tl，j-ti，q)/(ti，q+1-ti，j)；
wherein, Tl，jPoint cloud pose data T corresponding to the point j in the initial point cloud datai，qFor the first vehicle pose data, Ti，q+1Is the second vehicle pose data, i is the radar coordinate system, i is the global coordinate system, tl，jPoint cloud time stamp, t, for point j in the initial point cloud datai，qIs the first attitude timestamp, ti，q+1And the ratio is the ratio result of the difference between the point cloud time stamp and the first position posture time stamp and the difference between the second position posture time stamp and the point cloud time stamp.
An objective function can be constructed through the pose data of the point in the initial point cloud data and the initial point cloud data, the conversion relation between the radar coordinate system and the global coordinate system is used as a variable, the actual conversion relation between the radar coordinate system and the global coordinate system is obtained through solving the objective function, and therefore the conversion relation between the radar coordinate system and the global coordinate system is calibrated, and distortion generated by the laser radar in the motion process can be calibrated automatically.
The method comprises the steps of acquiring initial point cloud data in a radar coordinate system and vehicle pose data in a global coordinate system; determining the pose data of the points in the initial point cloud data according to the point cloud time stamp of the points in the initial point cloud data and the pose time stamp of the vehicle pose data; and calibrating the conversion relation between the radar coordinate system and the global coordinate system according to the pose data of the point in the initial point cloud data and the initial point cloud data. The problem of through artifical demarcation mode mark, lead to marking operation complicacy, mark efficiency and precision lower scheduling problem is solved, realize reducing the effect of marking the operation degree of difficulty, improving mark efficiency and precision.
Example two
Fig. 2 is a flowchart of a radar calibration method according to a second embodiment of the present invention, and the technical solution is supplementary explained for a process of calibrating a transformation relationship between the radar coordinate system and the global coordinate system according to the pose data of the midpoint in the initial point cloud data and the initial point cloud data. Compared with the scheme, the scheme is specifically optimized in that the conversion relation between the radar coordinate system and the global coordinate system is calibrated according to the pose data of the points in the initial point cloud data and the initial point cloud data, and comprises the following steps:
calibrating a conversion relation between the radar coordinate system and the global coordinate system by the following formula:
Pj(Tl，i)＝Tl，iTl，jQj；
specifically, a flow chart of the radar calibration method is shown in fig. 2:
and step 210, acquiring initial point cloud data in a radar coordinate system and vehicle pose data in a global coordinate system.
And step 220, determining the pose data of the points in the initial point cloud data according to the point cloud time stamp of the points in the initial point cloud data and the pose time stamp of the vehicle pose data.
Pj(Tl，i)＝Tl，iTl，jQj；
wherein, Pj(Tl，i) Is a point P obtained by converting the midpoint j of the initial point cloud data from a radar coordinate system to a global coordinate systemjCoordinate of (2), Tl，iIs the conversion relation between the radar coordinate system and the global coordinate system, i is the radar coordinate system, i is the global coordinate system, Tl，jCorresponding to the point j in the initial point cloud dataPoint cloud pose data of, QjThe coordinate of the point j in the initial point cloud data under the radar coordinate system is set; p'kAs a distance P from a target point in the coordinate conversion pointsjThe most recent point, J (T)l，i) To pass through point PjAnd point P'kConstructed about Tl，iThe objective function of (1).
By minimizing the objective function J (T)l，i) Can obtain
Optionally, the target equation is solved by using an optimization method GN _ DIRECT _ L in the nonlinear optimization library to obtain a conversion relation between the radar coordinate system and the global coordinate system, so that the calibration precision is improved.
In this embodiment, optionally, the method further includes: acquiring a point closest to a target point in the coordinate conversion points by:
constructing a K-D tree according to all the coordinate conversion points;
and acquiring a point which is closest to a target point in the coordinate conversion points by inquiring the K-D tree.
And constructing a K-D tree for all points obtained after the radar coordinate system is converted into the global coordinate system, wherein the K-D tree is a data structure for dividing a K-dimensional data space and can be used for searching key data of the multi-dimensional space. And acquiring a point closest to a specified target point in the coordinate conversion points by querying the K-D tree, wherein the target point is exemplarily an A point, and the query K-D tree acquires a B point closest to the A point. By constructing the K-D tree, the query speed of the data points is improved.
According to the embodiment of the invention, the conversion relation between the radar coordinate system and the global coordinate system is calibrated by solving the specific formula constructed by the position and posture data of the midpoint in the initial point cloud data and the initial point cloud data, so that the calibration difficulty is reduced, and the calibration efficiency and precision are improved.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a radar calibration apparatus according to a third embodiment of the present invention. The device can be realized in a hardware and/or software mode, can execute the radar calibration method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. As shown in fig. 3, the apparatus includes:
a data acquisition module 310, configured to acquire initial point cloud data in a radar coordinate system and vehicle pose data in a global coordinate system;
a pose data determination module 320, configured to determine pose data of the initial point cloud data according to the point cloud timestamp of the initial point cloud data and the pose timestamp of the vehicle pose data;
a conversion relation calibration module 330, configured to calibrate a conversion relation between the radar coordinate system and the global coordinate system according to the pose data of the point in the initial point cloud data and the initial point cloud data.
The method comprises the steps of acquiring initial point cloud data in a radar coordinate system and vehicle pose data in a global coordinate system; determining the pose data of the points in the initial point cloud data according to the point cloud time stamp of the points in the initial point cloud data and the pose time stamp of the vehicle pose data; and calibrating the conversion relation between the radar coordinate system and the global coordinate system according to the pose data of the point in the initial point cloud data and the initial point cloud data. The problem of through artifical demarcation mode mark, lead to marking operation complicacy, mark efficiency and precision lower scheduling problem is solved, realize reducing the effect of marking the operation degree of difficulty, improving mark efficiency and precision.
In this embodiment, optionally, the pose data determining module includes:
the timestamp selection unit is used for selecting a first position and posture timestamp and a second position and posture timestamp which are close to the point cloud timestamp of the midpoint of the initial point cloud data from the position and posture timestamps of the vehicle position and posture data;
the first position and posture data determining unit is used for acquiring first vehicle position and posture data according to the first position and posture timestamp and acquiring second vehicle position and posture data according to the second position and posture timestamp;
and the second position and posture data determining unit is used for determining the position and posture data of the point in the initial point cloud data according to the first vehicle position and posture data and the second vehicle position and posture data.
In this embodiment, optionally, the second posture data determining unit includes:
determining pose data of the points in the initial point cloud data by the following formula:
ratio＝(tl，j-ti，q)/(ti，q+1-ti，j)；
wherein, Tl，jPoint cloud pose data T corresponding to the point j in the initial point cloud datai，qFor the first vehicle pose data, Ti，q+1Is the second vehicle pose data, i is the radar coordinate system, i is the global coordinate system, tl，jPoint cloud time stamp, t, for point j in the initial point cloud datai，qIs the first attitude timestamp, ti，q+1And the ratio is the ratio result of the difference between the point cloud time stamp and the first position posture time stamp and the difference between the second position posture time stamp and the point cloud time stamp.
In this embodiment, optionally, the conversion relation calibration module includes:
calibrating a conversion relation between the radar coordinate system and the global coordinate system by the following formula:
Pj(Tl，i)＝Tl，iTl，jQj；
wherein, Pj(Tl，i) A coordinate conversion point P is obtained after the midpoint j of the initial point cloud data is converted from a radar coordinate system to a global coordinate systemjCoordinate of (2), Tl，iIs the conversion relation between the radar coordinate system and the global coordinate system, i is the radar coordinate system, i isGlobal coordinate system, Tl，jPoint cloud pose data Q corresponding to the point j in the initial point cloud datajThe coordinate of the point j in the initial point cloud data under the radar coordinate system is set; p'kAs a distance P from a target point in the coordinate conversion pointsjThe most recent point, J (T)l，i) To pass through point PjAnd point P'kConstructed about Tl，iThe objective function of (1).
In this embodiment, optionally, the point obtaining module includes:
the K-D tree construction unit is used for constructing a K-D tree according to all the coordinate conversion points;
and the point acquisition unit is used for acquiring a point which is closest to the target point in the coordinate conversion points by inquiring the K-D tree.
Example four
Fig. 4 is a schematic structural diagram of an electronic apparatus according to a fourth embodiment of the present invention, as shown in fig. 4, the electronic apparatus includes a processor 40, a memory 41, an input device 42, and an output device 43; the number of the processors 40 in the electronic device may be one or more, and one processor 40 is taken as an example in fig. 4; the processor 40, the memory 41, the input device 42 and the output device 43 in the electronic apparatus may be connected by a bus or other means, and the bus connection is exemplified in fig. 4.
The memory 41 is a computer-readable storage medium for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the radar calibration method in the embodiment of the present invention. The processor 40 executes various functional applications and data processing of the electronic device by executing software programs, instructions and modules stored in the memory 41, so as to implement the radar calibration method.
The memory 41 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 41 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, memory 41 may further include memory located remotely from processor 40, which may be connected to the electronic device through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
EXAMPLE five
An embodiment of the present invention further provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a radar calibration method, where the method includes:
acquiring initial point cloud data in a radar coordinate system and vehicle pose data in a global coordinate system;
determining the pose data of the points in the initial point cloud data according to the point cloud time stamp of the points in the initial point cloud data and the pose time stamp of the vehicle pose data;
and calibrating the conversion relation between the radar coordinate system and the global coordinate system according to the pose data of the point in the initial point cloud data and the initial point cloud data.
Of course, the storage medium provided by the embodiments of the present invention contains computer-executable instructions, and the computer-executable instructions are not limited to the operations of the method described above, and may also perform related operations in the radar calibration method provided by any embodiment of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods according to the embodiments of the present invention.
It should be noted that, in the embodiment of the radar calibration apparatus, each included unit and each included module are only divided according to functional logic, but are not limited to the above division, as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A radar calibration method is characterized by comprising the following steps:
acquiring initial point cloud data in a radar coordinate system and vehicle pose data in a global coordinate system;
determining the pose data of the points in the initial point cloud data according to the point cloud time stamp of the points in the initial point cloud data and the pose time stamp of the vehicle pose data;
and calibrating the conversion relation between the radar coordinate system and the global coordinate system according to the pose data of the point in the initial point cloud data and the initial point cloud data.
2. The method of claim 1, wherein determining pose data for the points in the initial point cloud data from the point cloud timestamps for the points in the initial point cloud data and the pose timestamps for the vehicle pose data comprises:
selecting a first pose timestamp and a second pose timestamp from the pose timestamps of the vehicle pose data, wherein the first pose timestamp and the second pose timestamp are close to the point cloud timestamp of the midpoint of the initial point cloud data;
acquiring first vehicle pose data according to the first pose timestamp, and acquiring second vehicle pose data according to the second pose timestamp;
and determining the pose data of the point in the initial point cloud data according to the first vehicle pose data and the second vehicle pose data.
3. The method of claim 2, wherein determining pose data for points in the initial point cloud data from the first vehicle pose data and the second vehicle pose data comprises:
determining pose data of the points in the initial point cloud data by the following formula:
ratio＝(tl，j-ti，q)/(ti，q+1-ti，j)；
wherein, Tl，jPoint cloud pose data T corresponding to the point j in the initial point cloud datai，qFor the first vehicle pose data, Ti，q+1As second vehicle pose data, 1 is radar coordinate system, i is global coordinate system, tl，jPoint cloud time stamp, t, for point j in the initial point cloud datai，qIs the first attitude timestamp, ti，q+1And the ratio is the ratio result of the difference between the point cloud time stamp and the first position posture time stamp and the difference between the second position posture time stamp and the point cloud time stamp.
4. The method of claim 1, wherein calibrating the transformation relationship between the radar coordinate system and the global coordinate system according to the pose data of the points in the initial point cloud data and the initial point cloud data comprises:
calibrating a conversion relation between the radar coordinate system and the global coordinate system by the following formula:
Pj(Tl，i)＝Tl，iTl，jQj；
wherein, Pj(Tl，i) A coordinate conversion point P is obtained after the midpoint j of the initial point cloud data is converted from a radar coordinate system to a global coordinate systemjCoordinate of (2), Tl，iIs the conversion relation between the radar coordinate system and the global coordinate system, i is the radar coordinate system, i is the global coordinate system, Tl，jPoint cloud pose data Q corresponding to the point j in the initial point cloud datajThe coordinate of the point j in the initial point cloud data under the radar coordinate system is set; p'kAs a distance P from a target point in the coordinate conversion pointsjThe most recent point, J (T)l，i) To pass through point PjAnd point P'kConstructed about Tl，iThe objective function of (1).
5. The method of claim 4, further comprising: acquiring a point closest to a target point in the coordinate conversion points by:
constructing a K-D tree according to all the coordinate conversion points;
and acquiring a point which is closest to a target point in the coordinate conversion points by inquiring the K-D tree.
6. A radar calibration device, comprising:
the data acquisition module is used for acquiring initial point cloud data in a radar coordinate system and vehicle pose data in a global coordinate system;
the pose data determining module is used for determining pose data of the points in the initial point cloud data according to the point cloud time stamp of the points in the initial point cloud data and the pose time stamp of the vehicle pose data;
and the conversion relation calibration module is used for calibrating the conversion relation between the radar coordinate system and the global coordinate system according to the pose data of the point in the initial point cloud data and the initial point cloud data.
7. The apparatus of claim 6, wherein the pose data determination module comprises:
the timestamp selection unit is used for selecting a first position and posture timestamp and a second position and posture timestamp which are close to the point cloud timestamp of the midpoint of the initial point cloud data from the position and posture timestamps of the vehicle position and posture data;
the first position and posture data determining unit is used for acquiring first vehicle position and posture data according to the first position and posture timestamp and acquiring second vehicle position and posture data according to the second position and posture timestamp;
and the second position and posture data determining unit is used for determining the position and posture data of the point in the initial point cloud data according to the first vehicle position and posture data and the second vehicle position and posture data.
8. The apparatus according to claim 7, wherein the second posture data determining unit includes:
determining pose data of the points in the initial point cloud data by the following formula:
ratio＝(tl，j-ti，q)/(ti，q+1-ti，j)；
wherein, Tl，jPoint cloud pose data T corresponding to the point j in the initial point cloud datai，qFor the first vehicle pose data, Ti，q+1For second vehicle pose data, l radarCoordinate system, i is the global coordinate system, tl，jPoint cloud time stamp, t, for point j in the initial point cloud datai，qIs the first attitude timestamp, ti，q+1And the ratio is the ratio result of the difference between the point cloud time stamp and the first position posture time stamp and the difference between the second position posture time stamp and the point cloud time stamp.
9. An electronic device, characterized in that the electronic device comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the radar calibration method of any one of claims 1-5.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out a radar calibration method according to any one of claims 1 to 5.
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