CN114167393A - Position calibration method and device for traffic radar, storage medium and electronic equipment - Google Patents
Position calibration method and device for traffic radar, storage medium and electronic equipment Download PDFInfo
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Abstract
The embodiment of the disclosure relates to a position calibration method and device of a traffic radar, a storage medium and electronic equipment, and relates to the technical field of point cloud data processing, wherein the method comprises the following steps: acquiring first current point cloud data of a first target object detected by a traffic radar in a radar coordinate system; the first target object is a corner reflector rotating from an axis, and the first current point cloud data is data generated by the corner reflector rotating from the axis in the rotating process; preprocessing the first current point cloud data to obtain first target point cloud data; performing coordinate conversion on the first target point cloud data to obtain a first coordinate position of the first target object in a global coordinate system; and calibrating the current position of the traffic radar according to the first coordinate position to obtain the target position of the traffic radar. The embodiment of the disclosure improves the accuracy of the target position of the traffic radar.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of point cloud data processing, in particular to a traffic radar position calibration method, a traffic radar position calibration device, a computer readable storage medium and electronic equipment.
Background
The intelligent intersection plays an important role in intelligent traffic, and can sense the environment by utilizing multiple sensors such as a camera, a laser radar and the like, so that the aim of accurate positioning is fulfilled. Meanwhile, in sensor fusion, calibration of the radar is a very important step, and the process directly influences the accuracy of a sensing result.
In the existing calibration method of the traffic radar, the offset between the installation position of a camera and the installation position of the radar in the X direction and the Y direction is mainly measured, and then the calibration is carried out through the measured offset.
However, the offset calculated by the method is rough, and high-precision calibration cannot be realized, so that the accuracy of a calibration result is low.
Therefore, it is necessary to provide a new position calibration method that can be adapted to the traffic radar with high accuracy.
It is to be noted that the information invented in the background section above is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
An object of the present disclosure is to provide a method for calibrating a position of a traffic radar, a device for calibrating a position of a traffic radar, a computer-readable storage medium, and an electronic device, thereby overcoming, at least to some extent, the problem of low accuracy of a calibration result due to limitations and drawbacks of the related art.
According to one aspect of the disclosure, a method for calibrating a position of a traffic radar is provided, which includes:
acquiring first current point cloud data of a first target object detected by a traffic radar in a radar coordinate system; the first target object is a corner reflector rotating from an axis, and the first current point cloud data is data generated by the corner reflector rotating from the axis in the rotating process;
preprocessing the first current point cloud data to obtain first target point cloud data;
performing coordinate conversion on the first target point cloud data to obtain a first coordinate position of the first target object in a global coordinate system;
and calibrating the current position of the traffic radar according to the first coordinate position to obtain the target position of the traffic radar.
In an exemplary embodiment of the present disclosure, coordinate transforming the first target point cloud data to obtain a first coordinate position of the first target object in a global coordinate system includes:
and performing coordinate conversion on the first target point cloud data through a preset rotation matrix and a preset translation matrix to obtain a first coordinate position of the first target object in a global coordinate system.
In an exemplary embodiment of the present disclosure, coordinate conversion is performed on the first target point cloud data through a preset rotation matrix and a preset translation matrix, so as to obtain a first coordinate position of the first target object in a global coordinate system, including:
wherein the content of the first and second substances,
is a first coordinate position of the first target object in a global coordinate system,
the first target point cloud data is stored in a memory,
is a preset rotation matrix and has:
t is a preset translation matrix and has:
in an exemplary embodiment of the present disclosure, preprocessing the first current point cloud data to obtain first target point cloud data includes:
and denoising the first current point cloud data, and clustering the denoised first current point cloud data to obtain the first target point cloud data.
In an exemplary embodiment of the present disclosure, denoising the first current point cloud data, and clustering the denoised first current point cloud data to obtain the first target point cloud data includes:
acquiring a radar scattering cross section value of a first current coordinate point included in the first current point cloud data, and sequencing the first current coordinate point according to the radar scattering cross section value;
extracting a first current coordinate point with a radar scattering cross section value larger than a first preset threshold value from the sequencing result as a target coordinate point, and clustering the target coordinate point to obtain a clustering result;
determining a central coordinate point included in the clustering result, and determining a target area with a preset size from the clustering result according to the central coordinate point;
and obtaining the first target point cloud data according to the target coordinate points included in the target area.
In an exemplary embodiment of the present disclosure, the method for calibrating a position of a traffic radar further includes:
acquiring a distance difference value between the traffic radar and the first target object, atmospheric transmission loss when the traffic radar reaches the first target object, and target return power received by the traffic radar;
and calculating the radar scattering cross section value of the first current coordinate point according to the distance difference value, the atmospheric transmission loss and the target echo power.
In an exemplary embodiment of the present disclosure, the method for calibrating a position of a traffic radar further includes:
acquiring a signal to be processed of a second target object detected by the traffic radar after the position adjustment; wherein the second target object is in a moving state;
calculating a target distance between the traffic radar after the position adjustment and a second target object according to the signal to be processed and the target position;
determining the position of the object to be detected according to the target position and the target distance
According to an aspect of the present disclosure, there is provided a position calibration apparatus for a traffic radar, including:
the system comprises a point cloud data acquisition module, a radar coordinate system acquisition module and a data processing module, wherein the point cloud data acquisition module is used for acquiring first current point cloud data of a first target object detected by a traffic radar in the radar coordinate system; the first target object is a corner reflector rotating from an axis, and the first current point cloud data is data generated by the corner reflector rotating from the axis in the rotating process;
the point cloud data preprocessing module is used for preprocessing the first current point cloud data to obtain first target point cloud data;
the coordinate conversion module is used for carrying out coordinate conversion on the first target point cloud data to obtain a first coordinate position of the first target object in a global coordinate system;
and the position calibration module is used for calibrating the current position of the traffic radar according to the first coordinate position to obtain the target position of the traffic radar.
According to an aspect of the present disclosure, there is provided a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the method for location calibration of a traffic radar according to any one of the above.
According to an aspect of the present disclosure, there is provided an electronic device including:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to execute any one of the above described position calibration methods for traffic radar via execution of the executable instructions.
On one hand, according to the position calibration method for the traffic radar, first current point cloud data of a first target object detected by the traffic radar in a radar coordinate system can be obtained, and the first current point cloud data is preprocessed to obtain first target point cloud data; then, coordinate conversion is carried out on the first target point cloud data to obtain a first coordinate position of a first target object in a global coordinate system, and finally the position of the traffic radar is calibrated according to the first coordinate position, so that the calibration of the position of the traffic radar through the high-precision point cloud data of the dynamic self-axis rotating corner reflector is realized, the calibration of the traffic radar can be realized without mapping radar points to an image obtained by a camera, and the problem that the dynamic traffic radar cannot be calibrated in the prior art is solved; on the other hand, the calibration of the position of the traffic radar is realized through the high-precision point cloud data of the corner reflector rotating from the axis, so that the problem that the precision of a calibration result is low because the high-precision calibration cannot be realized in the prior art is solved, and the precision of the calibration result is improved; on the other hand, the first current point cloud data is data generated by the self-axis rotating corner reflector in the rotating process, namely, the dynamic target can be detected based on the traffic radar, so that the problem that the traditional radar can only detect a static target but cannot detect the dynamic target is solved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
Fig. 1 schematically illustrates a flow chart of a method for location calibration of a traffic radar according to an example embodiment of the present disclosure.
FIG. 2 schematically illustrates a block diagram of a position calibration system for traffic radar, according to an example embodiment of the present disclosure.
Fig. 3 schematically illustrates a structural example diagram of a corner reflector rotated from an axis according to an example embodiment of the present disclosure.
Fig. 4 schematically shows a flowchart of a method for denoising the first current point cloud data and clustering the denoised first current point cloud data to obtain the first target point cloud data according to an exemplary embodiment of the present disclosure.
FIG. 5 schematically illustrates a flow chart of another method for position calibration of a traffic radar, according to an example embodiment of the present disclosure.
Fig. 6 schematically illustrates an example diagram of a preset traffic scene according to an example embodiment of the present disclosure.
Fig. 7 schematically illustrates an example diagram of first current point cloud data at a rotation angle of 0 degrees according to an example embodiment of the present disclosure.
Fig. 8 schematically illustrates an example diagram of first current point cloud data at a rotation angle of ± 15 degrees, according to an example embodiment of the present disclosure.
Fig. 9 schematically illustrates an example diagram of first current point cloud data at a rotation angle of ± 30 degrees according to an example embodiment of the present disclosure.
Fig. 10 schematically illustrates an example diagram of first current point cloud data at a rotation angle of ± 40 degrees according to an example embodiment of the present disclosure.
Fig. 11 schematically illustrates a block diagram of a position calibration apparatus for traffic radar according to an example embodiment of the present disclosure.
Fig. 12 schematically illustrates an electronic device for implementing the above-described traffic radar position calibration method according to an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
The current intelligent intersection mainly makes statistics of some traffic flows, and the calibration process is very rough. The main implementation mode comprises the following steps: firstly, measuring the offset between the installation position of a camera and the installation position of a radar in the X direction and the Y direction; secondly, calibrating through the offset between the camera and the radar, thereby achieving the purpose of ensuring that each target detected by the radar is in the corresponding area of the automobile in the image so as to monitor the traffic flow. However, this method is a very rough one-to-one detection method with very low accuracy.
Further, in solutions where robots and autonomous vehicles calibrate the radar and camera, a static detector board with angular reversals is typically used to match the radar coordinates to the camera coordinates. However, this method is only suitable for radar targeting static objects and is not suitable for traffic radar.
Meanwhile, the above scheme has the following defects: on one hand, the existing radar calibration technology can only calibrate one radar at a time, and when a plurality of radars need to be calibrated, the efficiency is low; however, in transportation and other related scenarios, there may be many scenarios in which the radar needs to be calibrated at the same time; for example, 4 radars are fully covered in each direction of the intersection, and the scheme is suitable for a scene needing to calibrate a plurality of radars at the same time; on the other hand, the current traffic radar calibration technology is very rough, and only supports use scenes with very low precision requirements such as traffic counting, vehicle tracking and the like; therefore, the method cannot be applied to vehicle cooperative infrastructure system scenes and other scenes with high requirements on precision; on the other hand, the current radar calibration technology has many limitations, which are only effective when the radar outputs a static object as a target; however, for a traffic radar scene, it needs to support a moving object as a target, so the current radar calibration technology is not suitable for the scene; moreover, the current calibration is to map radar points into images obtained by the camera, so strict synchronization between the camera and the radar is required, which poses a serious challenge to the hardware and software design of the sensor.
Based on this, in the example embodiments provided by the present disclosure, first, a method for calibrating a position of a traffic radar is provided, where the method may be performed in a terminal device, a server cluster or a cloud server, etc.; of course, those skilled in the art may also operate the method of the present disclosure on other platforms as needed, which is not particularly limited in the exemplary embodiment. Referring to fig. 1, the method for calibrating the position of the traffic radar may include the following steps:
s110, acquiring first current point cloud data of a first target object detected by a traffic radar in a radar coordinate system; the first target object is a corner reflector rotating from an axis, and the first current point cloud data is data generated by the corner reflector rotating from the axis in the rotating process;
step S120, preprocessing the first current point cloud data to obtain first target point cloud data;
s130, performing coordinate conversion on the first target point cloud data to obtain a first coordinate position of the first target object in a global coordinate system;
and S140, calibrating the current position of the traffic radar according to the first coordinate position to obtain the target position of the traffic radar.
In the method for calibrating the position of the traffic radar, on one hand, first current point cloud data of a first target object detected by the traffic radar in a radar coordinate system can be obtained, and the first current point cloud data is preprocessed to obtain first target point cloud data; then, coordinate conversion is carried out on the first target point cloud data to obtain a first coordinate position of a first target object in a global coordinate system, and finally the position of the traffic radar is calibrated according to the first coordinate position, so that the calibration of the position of the traffic radar through the high-precision point cloud data of the dynamic self-axis rotating corner reflector is realized, the calibration of the traffic radar can be realized without mapping radar points to an image obtained by a camera, and the problem that the dynamic traffic radar cannot be calibrated in the prior art is solved; on the other hand, the calibration of the position of the traffic radar is realized through the high-precision point cloud data of the corner reflector rotating from the axis, so that the problem that the precision of a calibration result is low because the high-precision calibration cannot be realized in the prior art is solved, and the precision of the calibration result is improved; on the other hand, the first current point cloud data is data generated by the self-axis rotating corner reflector in the rotating process, namely, the dynamic target can be detected based on the traffic radar, so that the problem that the traditional radar can only detect a static target but cannot detect the dynamic target is solved.
Hereinafter, the position calibration method of the traffic radar according to the exemplary embodiment of the present disclosure will be explained and explained in detail with reference to the drawings.
First, an application scenario of the exemplary embodiment of the present disclosure is explained and explained. Specifically, in an application scenario of intelligent transportation, environmental awareness is crucial to improving traffic safety and providing high-quality services; meanwhile, sensor fusion can provide accurate, real-time and reliable environmental perception; in a specific application process, various sensors such as a laser radar, a millimeter wave radar and a camera are usually installed at a crossroad to carry out all-area coverage; for better multi-sensor fusion, different sensors must be calibrated; therefore, the disclosed exemplary embodiment provides an efficient and high-precision method for calibrating the positions of traffic radars, all the traffic radars can be calibrated at one time, and the calibration precision can reach 20 cm.
Next, the objects of the exemplary embodiments of the present disclosure are explained and explained. Specifically, the embodiment of the disclosure discloses a high-precision and high-efficiency calibration method for traffic radar in intelligent traffic, which calibrates the radar by adopting a differential global positioning system and directly maps the radar coordinate to a GPS coordinate, so that a plurality of traffic radars can be calibrated at one time; meanwhile, an angle reflector which can be detected by a traffic radar and can rotate around the axis is built, coordinate points in the point cloud data can be clustered to one point according to the point cloud data of a plurality of different angles of the angle reflector rotating around the axis and radar scattering cross-section values of the coordinate points included in the point cloud data, then a Global Positioning System (GPS) coordinate of the position where the angle reflector rotating around the axis is located is generated by using a differential global positioning system, and the coordinate position detected by the radar is matched with the GPS coordinate, so that the position of the traffic radar is calibrated; meanwhile, a plurality of corner reflectors rotating from the shaft are placed at different positions, so that a plurality of traffic radar coordinates can be calibrated to global GPS coordinates, and the calibration of the positions of a plurality of traffic radars is realized at the same time.
Further, the position calibration system of the traffic radar of the exemplary embodiment of the present disclosure is explained and illustrated.
Specifically, referring to fig. 2, the position calibration system 200 for traffic radar may include a traffic radar 210, a corner reflector 220 rotating from an axis, and a server 230. The traffic radar is connected with the server through a network, and can be used for collecting first current point cloud data of a plurality of different angles generated in the process of rotating the corner reflector rotating from the axis, wherein the traffic radar can be a millimeter wave radar, and other radars can be configured according to actual needs, which is not limited in this example; meanwhile, the server may be used to implement the method for calibrating the position of the traffic radar described in the exemplary embodiment of the present disclosure.
Further, as shown in fig. 3, the corner reflector rotated from the axis may include a spin axis 301 disposed at a left position or a right position or a middle position of the corner reflector, and a corner reflector 302; in a specific application process, the spin axis rotates in a clockwise rotation (or anticlockwise rotation) mode, so that the corner reflector is controlled to rotate along with the spin axis, meanwhile, in the rotation process of the corner reflector, a plurality of different rotation angles can be generated, and then the traffic radar can collect the generated position information of each different rotation angle, and further obtain first current point cloud data.
Hereinafter, the steps included in the position calibration method for the traffic radar shown in fig. 1 will be explained and explained in detail with reference to fig. 2 and 3.
In step S110, first current point cloud data of a first target object detected by a traffic radar in a radar coordinate system is acquired; the first target object is a corner reflector rotating from an axis, and the first current point cloud data is data generated by the corner reflector rotating from the axis in a rotating process.
Specifically, first current point cloud data acquired by the traffic radar in the process of controlling the spin axis to automatically rotate the corner reflector may be acquired. In the process of rotating the corner reflector rotating from the shaft, the rotating speed may be 1000r/min, and of course, the corresponding rotating speed may be selected according to actual needs, which is not particularly limited in this example; furthermore, the traffic radar may be a millimeter wave radar of 24Ghz to 77Ghz, or a millimeter wave radar of other frequencies may be selected according to actual needs, which is not particularly limited in this example; it should be added that, in the exemplary embodiment of the present disclosure, it is specially limited that the first current point cloud data is data generated by a corner reflector rotating from an axis in a rotating process, and it is to indicate that the traffic radar described in the exemplary embodiment of the present disclosure can detect a position of a dynamic target, and further can solve a problem that a conventional radar can only perform static target detection but cannot perform dynamic target detection.
In step S120, the first current point cloud data is preprocessed to obtain first target point cloud data.
Specifically, the first target point cloud data may be obtained by performing noise reduction on the first current point cloud data and performing clustering on the first current point cloud data subjected to noise reduction. Referring to fig. 4, performing noise reduction on the first current point cloud data, and performing clustering on the first current point cloud data subjected to noise reduction to obtain the first target point cloud data may include the following steps:
step S410, obtaining radar scattering cross section values of a first current coordinate point included in the first current point cloud data, and sequencing the first current coordinate point according to the radar scattering cross section values;
step S420, extracting a first current coordinate point with a radar scattering cross section value larger than a first preset threshold value from the sequencing result as a target coordinate point, and clustering the target coordinate point to obtain a clustering result;
step S430, determining a center coordinate point included in the clustering result, and determining a target area with a preset size from the clustering result according to the center coordinate point;
step S440, obtaining the first target point cloud data according to the target coordinate points included in the target area.
Hereinafter, steps S410 to S440 will be explained and explained.
First, a method of calculating a radar cross-section value of the first current coordinate point is explained and explained. Specifically, the radar cross section value of the first current coordinate point may be calculated as follows: firstly, acquiring a distance difference value between the traffic radar and the first target object, atmospheric transmission loss when the traffic radar reaches the first target object, and target return power received by the traffic radar; and secondly, calculating the radar scattering cross section value of the first current coordinate point according to the distance difference value, the atmospheric transmission loss and the target echo power. That is, a specific calculation method of the radar scattering cross-section value of the first current coordinate point may be as shown in the following formula (1):
wherein, PrThe unit of target echo power received by the traffic radar is W; ptThe unit is the transmitting power of the traffic radar, and is W; gtGain of a transmitting antenna of the traffic radar in dB in a target direction of the traffic radar and the first target object；GrThe gain of a receiving antenna of the traffic radar in the target direction of the traffic radar and the first target object is dB; λ is the radar wavelength of the traffic radar in m; sigma is a radar scattering cross section value of the first current coordinate point; l istThe transmission feeder line branch loss between the traffic radar and the first target object is expressed in dB; rtThe distance difference value of a transmitting antenna of the traffic radar to a first target object is m; rrThe distance from a receiving antenna of the traffic radar to the first target object is m; l ismtAtmospheric transmission loss between a transmitting antenna of the traffic radar and a first target object is in dB; l ismrThe atmospheric transmission loss between a receiving antenna of the traffic radar and the first target object is dB; l isrThe loss of a receiving feeder line branch circuit between the traffic radar and the first target object is dB; l ispThe unit is the polarization loss of the traffic radar in dB.
Secondly, after the radar scattering cross section value of the first current coordinate point is obtained, the first current coordinate point can be sequenced according to the radar scattering cross section value; for example, the first current coordinate point may be sorted in a big-to-small manner; secondly, extracting a first current coordinate point with a radar scattering cross section value larger than a first preset threshold value from the sorting result as a target coordinate point; the first preset threshold may be 5000, and of course, other first preset thresholds, such as 5500 or 6000, etc., may also be determined according to actual needs, which is not limited in this example; then clustering the target coordinate points to obtain a clustering result; further, determining a central coordinate point included in the clustering result, and determining a target area with a preset size from the clustering result according to the central coordinate point, for example, the size of the target area may be 20cm by 20 cm; and finally, obtaining first target point cloud data according to the target coordinate points included in the target area. By the method, the coordinate error of the traffic radar can be limited to 20cm by 20cm, and the accuracy of the position of the traffic radar can be further improved.
It should be added that, in the process of clustering target coordinate points, all the target coordinate points may be clustered into K categories by using a K-means algorithm, and then the categories with too few target center points (for example, less than 6) are removed, so as to obtain the clustering result; of course, other clustering methods may be adopted, and this example is not particularly limited thereto.
It should be further added that the accuracy of the clustering result can be further improved by limiting the number of target center points included in the clustering result, so as to achieve the purpose of improving the accuracy of the position calibration result of the traffic radar.
In step S130, coordinate conversion is performed on the first target point cloud data to obtain a first coordinate position of the first target object in the global coordinate system.
Specifically, coordinate conversion can be performed on the first target point cloud data through a preset rotation matrix and a preset translation matrix, so as to obtain a first coordinate position of the first target object in a global coordinate system. The first target point cloud data is subjected to coordinate conversion through a preset rotation matrix and a preset translation matrix to obtain a first coordinate position of the first target object in a global coordinate system, which can be specifically shown in the following formula (2):
wherein the content of the first and second substances,
is a first coordinate position of the first target object in a global coordinate system,
the first target point cloud data is stored in a memory,
is a preset rotation matrix and has:
t is a preset translation matrix and has:
it should be further added here that the preset rotation matrix and the preset translation matrix may be calculated as follows: first, a historical coordinate point in a radar coordinate system and a standard coordinate point corresponding to the historical coordinate point in a differential global positioning system, which are obtained through measurement, may be obtained, and then a preset rotation matrix and a preset translation matrix may be calculated according to the historical coordinate point and the standard coordinate point.
In step S140, calibrating the current position of the traffic radar according to the first coordinate position to obtain a target position of the traffic radar.
Specifically, after the first coordinate position is obtained, the current position of the traffic radar can be calibrated based on the first coordinate position to obtain the target position of the traffic radar, and then the object to be detected can be positioned according to the distance between the calibrated traffic radar and the object to be detected, so that the accuracy of the positioning result of the object to be detected is improved. The positioning of the object to be detected can be realized by the following method: firstly, acquiring a signal to be processed of a second target object detected by a traffic radar after position adjustment; wherein the second target object is in a moving state; secondly, calculating a target distance between the traffic radar after the position adjustment and a second target object according to the signal to be processed and the target position; and finally, determining the position of the object to be detected according to the target position and the target distance, thereby achieving the purpose of accurately positioning the position of the object to be detected. Of course, the traffic radar after calibration may also be used to perform traffic monitoring, traffic prediction, and the like, which is not particularly limited in this example.
Hereinafter, the method for calibrating the position of the traffic radar according to the exemplary embodiment of the present disclosure is further explained and explained with reference to fig. 5. Specifically, referring to fig. 5, the method for calibrating the position of the traffic radar may include the following steps:
step S501, placing a corner reflector rotating from an axis into a preset traffic scene; the preset traffic scene is shown in fig. 6, and in the preset traffic scene, a plurality of corner reflectors 220 rotating from an axis and a plurality of traffic radars 210 may be included;
step S502, collecting position information of a corner reflector rotating from an axis through a traffic radar, and generating first current point cloud data;
step S503, noise reduction and clustering processing are carried out on the first current point cloud data to obtain first target point cloud data;
step S504, coordinate conversion is carried out on the first target point cloud data to obtain a first coordinate position;
step S505, judging whether the number of coordinate points included in the first target point cloud data is greater than a preset threshold value; if yes, jumping to step S506, otherwise, jumping to step S501;
and S506, calibrating the position of the traffic radar through the first coordinate position.
Further, in order to verify the correctness and the validity of the position calibration method for the traffic radar provided by the exemplary embodiment of the present disclosure, a specific experimental result is given below.
Specifically, an angle reflection with the edge length of 13cm is designed, the radar scattering cross section under a 24Ghz radar can reach 2mm, and the radar scattering cross section under a 77Ghz radar can reach 10 mm. Mounting a corner reflector rotating from an axis on a rotating platform and controlling it to rotate at a speed of 1000 revolutions per minute; meanwhile, the corner reflector rotating from the axis is placed at a distance of about 20 meters from the traffic radar, the corner reflector is irradiated by using a 24GHz radar, and then position information of a plurality of different angles is collected, and then the position information collected in each frame is counted, so that the first current point cloud data can be obtained. The different angles may include 0 degree, ± 15 degrees, ± 30 degrees and ± 40 degrees, and the radar scattering cross-section values of the first current coordinate point included in the first current point cloud data and the first current point cloud data obtained at the different angles may specifically refer to fig. 7, 8, 9 and 10. It should be added here that, in the practical application process, other angles may be selected according to the practical needs, and this example is not particularly limited to this.
Further, based on the results shown in fig. 7, 8, 9, and 10 described above, it can be seen that the first current point cloud data has different radar cross-section values due to the rotation of the corner reflector rotated from the axis and is dispersed over a large area, and the position of the high radar cross-section is more accurate without noise. In order to be more accurate, a point with a radar scattering cross section larger than 5000 is selected for clustering, then the obtained center position is used as a matching point (a first coordinate position) of a differential global positioning system, and the position of the traffic radar is calibrated based on the matching point; meanwhile, based on the results shown in fig. 7, 8, 9 and 10, it can be seen that the positional angle deviation of the traffic radar after calibration is within 0.5 degrees and the accuracy is within 20cm, thereby achieving the purpose of high-accuracy calibration.
The embodiment of the disclosure also provides a position calibration device of the traffic radar. Referring to fig. 11, the position calibration apparatus for traffic radar may include a point cloud data acquisition module 1110, a point cloud data preprocessing module 1120, a coordinate conversion module 1130, and a position calibration module 1140. Wherein:
a point cloud data obtaining module 1110, configured to obtain first current point cloud data of a first target object detected by a traffic radar in a radar coordinate system; the first target object is a corner reflector rotating from an axis, and the first current point cloud data is data generated by the corner reflector rotating from the axis in the rotating process;
a point cloud data preprocessing module 1120, configured to preprocess the first current point cloud data to obtain first target point cloud data;
a coordinate conversion module 1130, configured to perform coordinate conversion on the first target point cloud data to obtain a first coordinate position of the first target object in a global coordinate system;
the position calibration module 1140 may be configured to calibrate the current position of the traffic radar according to the first coordinate position, so as to obtain a target position of the traffic radar.
In the position calibration device for the traffic radar, on one hand, first current point cloud data of a first target object detected by the traffic radar in a radar coordinate system can be obtained, and the first current point cloud data is preprocessed to obtain first target point cloud data; then, coordinate conversion is carried out on the first target point cloud data to obtain a first coordinate position of a first target object in a global coordinate system, and finally the position of the traffic radar is calibrated according to the first coordinate position, so that the calibration of the position of the traffic radar through the high-precision point cloud data of the dynamic self-axis rotating corner reflector is realized, the calibration of the traffic radar can be realized without mapping radar points to an image obtained by a camera, and the problem that the dynamic traffic radar cannot be calibrated in the prior art is solved; on the other hand, the calibration of the position of the traffic radar is realized through the high-precision point cloud data of the corner reflector rotating from the axis, so that the problem that the precision of a calibration result is low because the high-precision calibration cannot be realized in the prior art is solved, and the precision of the calibration result is improved; on the other hand, the first current point cloud data is data generated by the self-axis rotating corner reflector in the rotating process, namely, the dynamic target can be detected based on the traffic radar, so that the problem that the traditional radar can only detect a static target but cannot detect the dynamic target is solved.
In an exemplary embodiment of the present disclosure, coordinate transforming the first target point cloud data to obtain a first coordinate position of the first target object in a global coordinate system includes:
and performing coordinate conversion on the first target point cloud data through a preset rotation matrix and a preset translation matrix to obtain a first coordinate position of the first target object in a global coordinate system.
In an exemplary embodiment of the present disclosure, coordinate conversion is performed on the first target point cloud data through a preset rotation matrix and a preset translation matrix, so as to obtain a first coordinate position of the first target object in a global coordinate system, including:
wherein the content of the first and second substances,
is a first coordinate position of the first target object in a global coordinate system,
the first target point cloud data is stored in a memory,
is a preset rotation matrix and has:
t is a preset translation matrix and has:
in an exemplary embodiment of the present disclosure, preprocessing the first current point cloud data to obtain first target point cloud data includes:
and denoising the first current point cloud data, and clustering the denoised first current point cloud data to obtain the first target point cloud data.
In an exemplary embodiment of the present disclosure, denoising the first current point cloud data, and clustering the denoised first current point cloud data to obtain the first target point cloud data includes:
acquiring a radar scattering cross section value of a first current coordinate point included in the first current point cloud data, and sequencing the first current coordinate point according to the radar scattering cross section value;
extracting a first current coordinate point with a radar scattering cross section value larger than a first preset threshold value from the sequencing result as a target coordinate point, and clustering the target coordinate point to obtain a clustering result;
determining a central coordinate point included in the clustering result, and determining a target area with a preset size from the clustering result according to the central coordinate point;
and obtaining the first target point cloud data according to the target coordinate points included in the target area.
In an exemplary embodiment of the present disclosure, the position calibration apparatus for a traffic radar may further include:
a distance difference value obtaining module, configured to obtain a distance difference value between the traffic radar and the first target object, an atmospheric transmission loss between the traffic radar and the first target object, and a target return power received by the traffic radar;
and the radar scattering cross section value calculating module can be used for calculating the radar scattering cross section value of the first current coordinate point according to the distance difference value, the atmospheric transmission loss and the target return power.
In an exemplary embodiment of the present disclosure, the position calibration apparatus for a traffic radar may further include:
the to-be-processed signal acquisition module can be used for acquiring to-be-processed signals of a second target object detected by the traffic radar after position adjustment; wherein the second target object is in a moving state;
the target distance calculation module can be used for calculating the target distance between the traffic radar after the position adjustment and a second target object according to the signal to be processed and the target position;
and the position determining module of the object to be detected can be used for determining the position of the object to be detected according to the target position and the target distance.
The specific details of each module in the above traffic radar position calibration apparatus have been described in detail in the corresponding traffic radar position calibration method, and therefore are not described herein again.
It should be noted that although in the above detailed description several modules or units of the device for action execution are mentioned, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Moreover, although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
As will be appreciated by one skilled in the art, aspects of the present disclosure may be embodied as a system, method or program product. Accordingly, various aspects of the present disclosure may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 1200 according to this embodiment of the disclosure is described below with reference to fig. 12. The electronic device 1200 shown in fig. 12 is only an example and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 12, the electronic device 1200 is embodied in the form of a general purpose computing device. The components of the electronic device 1200 may include, but are not limited to: the at least one processing unit 1210, the at least one memory unit 1220, a bus 1230 connecting various system components (including the memory unit 1220 and the processing unit 1210), and a display unit 1240.
Wherein the storage unit stores program code that is executable by the processing unit 1210 to cause the processing unit 1210 to perform steps according to various exemplary embodiments of the present disclosure described in the above section "exemplary methods" of this specification. For example, the processing unit 1210 may perform step S110 as shown in fig. 1: acquiring first current point cloud data of a first target object detected by a traffic radar in a radar coordinate system; the first target object is a corner reflector rotating from an axis, and the first current point cloud data is data generated by the corner reflector rotating from the axis in the rotating process; step S120: preprocessing the first current point cloud data to obtain first target point cloud data; step S130: performing coordinate conversion on the first target point cloud data to obtain a first coordinate position of the first target object in a global coordinate system; step S140: and calibrating the current position of the traffic radar according to the first coordinate position to obtain the target position of the traffic radar.
The storage unit 1220 may include a readable medium in the form of a volatile memory unit, such as a random access memory unit (RAM)12201 and/or a cache memory unit 12202, and may further include a read only memory unit (ROM) 12203.
The electronic device 1200 may also communicate with one or more external devices 1300 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 1200, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 1200 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 1250. Also, the electronic device 1200 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 1260. As shown, the network adapter 1260 communicates with the other modules of the electronic device 1200 via the bus 1230. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 1200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, various aspects of the disclosure may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the disclosure described in the "exemplary methods" section above of this specification, when the program product is run on the terminal device.
According to the program product for implementing the above method of the embodiments of the present disclosure, it may employ a portable compact disc read only memory (CD-ROM) and include program codes, and may be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
Furthermore, the above-described figures are merely schematic illustrations of processes included in methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. A position calibration method of a traffic radar is characterized by comprising the following steps:
acquiring first current point cloud data of a first target object detected by a traffic radar in a radar coordinate system; the first target object is a corner reflector rotating from an axis, and the first current point cloud data is data generated by the corner reflector rotating from the axis in the rotating process;
preprocessing the first current point cloud data to obtain first target point cloud data;
performing coordinate conversion on the first target point cloud data to obtain a first coordinate position of the first target object in a global coordinate system;
and calibrating the current position of the traffic radar according to the first coordinate position to obtain the target position of the traffic radar.
2. The method for calibrating the position of the traffic radar according to claim 1, wherein performing coordinate transformation on the first target point cloud data to obtain a first coordinate position of the first target object in a global coordinate system comprises:
and performing coordinate conversion on the first target point cloud data through a preset rotation matrix and a preset translation matrix to obtain a first coordinate position of the first target object in a global coordinate system.
3. The method for calibrating the position of the traffic radar according to claim 2, wherein the step of performing coordinate transformation on the first target point cloud data through a preset rotation matrix and a preset translation matrix to obtain a first coordinate position of the first target object in a global coordinate system comprises:
wherein the content of the first and second substances,
is a first coordinate position of the first target object in a global coordinate system,
the first target point cloud data is stored in a memory,
is a preset rotation matrix and has:
t is a preset translation matrix and has:
4. the method for calibrating the position of the traffic radar according to claim 1, wherein the step of preprocessing the first current point cloud data to obtain first target point cloud data comprises:
and denoising the first current point cloud data, and clustering the denoised first current point cloud data to obtain the first target point cloud data.
5. The method for calibrating the position of the traffic radar according to claim 4, wherein the step of denoising the first current point cloud data and clustering the denoised first current point cloud data to obtain the first target point cloud data comprises:
acquiring a radar scattering cross section value of a first current coordinate point included in the first current point cloud data, and sequencing the first current coordinate point according to the radar scattering cross section value;
extracting a first current coordinate point with a radar scattering cross section value larger than a first preset threshold value from the sequencing result as a target coordinate point, and clustering the target coordinate point to obtain a clustering result;
determining a central coordinate point included in the clustering result, and determining a target area with a preset size from the clustering result according to the central coordinate point;
and obtaining the first target point cloud data according to the target coordinate points included in the target area.
6. The method of claim 5, further comprising:
acquiring a distance difference value between the traffic radar and the first target object, atmospheric transmission loss when the traffic radar reaches the first target object, and target return power received by the traffic radar;
and calculating the radar scattering cross section value of the first current coordinate point according to the distance difference value, the atmospheric transmission loss and the target echo power.
7. The method for calibrating the position of the traffic radar according to claim 1, further comprising:
acquiring a signal to be processed of a second target object detected by the traffic radar after the position adjustment; wherein the second target object is in a moving state;
calculating a target distance between the traffic radar after the position adjustment and a second target object according to the signal to be processed and the target position;
and determining the position of the object to be detected according to the target position and the target distance.
8. A position calibration device of a traffic radar is characterized by comprising:
the system comprises a point cloud data acquisition module, a radar coordinate system acquisition module and a data processing module, wherein the point cloud data acquisition module is used for acquiring first current point cloud data of a first target object detected by a traffic radar in the radar coordinate system; the first target object is a corner reflector rotating from an axis, and the first current point cloud data is data generated by the corner reflector rotating from the axis in the rotating process;
the point cloud data preprocessing module is used for preprocessing the first current point cloud data to obtain first target point cloud data;
the coordinate conversion module is used for carrying out coordinate conversion on the first target point cloud data to obtain a first coordinate position of the first target object in a global coordinate system;
and the position calibration module is used for calibrating the current position of the traffic radar according to the first coordinate position to obtain the target position of the traffic radar.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method for position calibration of a traffic radar according to any one of claims 1 to 7.
10. An electronic device, comprising:
a processor; and
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the method of location calibration of a traffic radar of any of claims 1-7 via execution of the executable instructions.
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