Correction method, device and equipment for point cloud data of slope radar and storage medium

Abstract

The invention discloses a slope radar point cloud data correction method and device, equipment and a storage medium. The method comprises the following steps: acquiring multi-frame continuous point cloud data; the point cloud data comprises detection parameter information corresponding to a plurality of detection targets; based on the detection parameter information corresponding to each detection target in the point cloud data of the current frame, determining candidate detection targets from a plurality of detection targets included in the point cloud data of the current frame; correcting the azimuth angle parameter of the candidate detection target to obtain a calibrated azimuth angle parameter; determining a calibration motion state of the candidate detection target based on the pitch angle parameter, the calibration azimuth angle parameter, the point cloud speed parameter and the motion speed parameter; and correcting the detection parameter information of the candidate detection target based on the calibration motion state to obtain calibration detection parameter information. According to the method, the point cloud data is corrected, so that the influence of error identification data on a data processing process is reduced, and the accuracy of early warning information generated based on the point cloud data is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of slope imaging radar, and in particular to a correction method, device, equipment and storage medium for slope radar point cloud data. Background Art

[0002] Slope radar is a technology used for slope monitoring and target identification. It uses radar beams to scan the slope surface, and by receiving and analyzing the returned radar signals, it can detect and identify target objects on the slope. During slope monitoring, when the radar signal encounters an obstacle, multiple reflections may occur, forming multiple signal paths, or the target signal energy is weak, resulting in an error in the estimation of the location of a target, making it a false target, resulting in poor reliability and accuracy of the radar monitoring results.

[0003] Traditional methods use clustering methods to segment point cloud data during data processing, distinguish real targets from false targets, and filter out false targets; however, dynamic false targets in continuous multi-frame point cloud data cannot be accurately identified and filtered. Summary of the invention

[0004] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is how to improve the accuracy of filtering dynamic false targets in continuous multi-frame point cloud data.

[0005] In order to solve at least one of the above-mentioned technical problems, the present invention discloses a correction method, device, equipment and storage medium for slope radar point cloud data.

[0006] According to one aspect of the present disclosure, a method for correcting slope radar point cloud data is provided, comprising:

[0007] Acquire multiple frames of continuous point cloud data; the multiple frames of continuous point cloud data include the current frame point cloud data; each frame of point cloud data includes detection parameter information corresponding to multiple detection targets;

[0008] Based on the detection parameter information corresponding to each detection target in the current frame point cloud data, determine the candidate detection target from the multiple detection targets included in the current frame point cloud data; the detection parameter information includes an azimuth angle parameter, a pitch angle parameter and a point cloud speed parameter;

[0009] Correcting the azimuth angle parameters corresponding to the candidate detection target to obtain calibrated azimuth angle parameters;

[0010] Determining the calibration motion state of the candidate detection target based on the pitch angle parameter, the calibration azimuth angle parameter, the point cloud speed parameter and the motion speed parameter; the motion speed parameter is determined based on the motion speed of the radar body when the current frame point cloud data is acquired;

[0011] Based on the calibration motion state, the detection parameter information corresponding to the candidate detection target is corrected to obtain calibration detection parameter information.

[0012] In some possible embodiments, determining a candidate detection target from a plurality of detection targets included in the current frame point cloud data based on the detection parameter information corresponding to each detection target in the current frame point cloud data includes:

[0013] Based on a preset acquisition distance, a preset target acquisition quantity and the distance parameter information, determining a first detection target and a second detection target from a plurality of detection targets included in the current frame point cloud data;

[0014] Determining a quantitative relationship between the number of the first detection targets, the number of the second detection targets, and a preset target number threshold;

[0015] If the number of the first detection targets is greater than the preset target number threshold, and / or the number of the second detection targets is greater than the preset target number threshold, the candidate detection targets are determined based on the motion state information, the distance parameter information, the azimuth angle parameter and the pitch angle parameter.

[0016] In some possible embodiments, the correcting the azimuth angle parameter corresponding to the candidate detection target to obtain the calibrated azimuth angle parameter includes:

[0017] Obtain array length data and radar wavelength data;

[0018] Calculating a first phase value based on the array length data, the radar wavelength data, and the azimuth angle parameter;

[0019] Performing phase unwrapping processing on the first phase value to obtain a second phase value;

[0020] The calibration azimuth angle parameter is determined based on the array length data, the radar wavelength data, and the second phase value.

[0021] In some possible embodiments, determining the calibration motion state of the candidate detection target based on the pitch angle parameter, the calibration azimuth angle parameter, the point cloud velocity parameter, and the motion velocity parameter includes:

[0022] Calculating a relative velocity component based on the pitch angle parameter, the calibration azimuth angle parameter, and the motion velocity parameter;

[0023] Determining the quantitative relationship between the relative speed component, the point cloud speed absolute value and a preset speed threshold; the point cloud speed absolute value is determined based on the point cloud speed parameter in the detection parameter information;

[0024] If the difference between the relative velocity component and the absolute value of the point cloud velocity is greater than the preset velocity threshold, determining that the calibration motion state of the candidate detection target is dynamic;

[0025] If the difference between the relative velocity component and the absolute value of the point cloud velocity is less than the preset velocity threshold, it is determined that the calibration motion state of the candidate detection target is static.

[0026] In some possible embodiments, the step of correcting the detection parameter information corresponding to the candidate detection target based on the calibration motion state to obtain the calibration detection parameter information includes:

[0027] Determining whether the calibration motion state is consistent with the motion state information;

[0028] If the two are inconsistent, the azimuth angle parameter and the motion state information in the detection parameter information are replaced with the calibration azimuth angle parameter and the calibration motion state to obtain the calibration detection parameter information;

[0029] If the two are consistent, it is determined that the detection parameter information is calibration detection parameter information.

[0030] In some possible embodiments, after the detection parameter information corresponding to the candidate detection target is corrected based on the calibration motion state to obtain the calibration detection parameter information, the method further includes:

[0031] The current frame point cloud data is updated based on the calibration detection parameter information to obtain calibration point cloud data;

[0032] The warning information is updated based on the calibration point cloud data to obtain target warning information.

[0033] In some possible embodiments, the method further includes:

[0034] From the multiple frames of continuous point cloud data, determine the next frame of point cloud data of the current frame of point cloud data as the current frame of point cloud data.

[0035] According to a second aspect of the present disclosure, a point cloud data correction device is provided, the device comprising:

[0036] A data acquisition module is used to acquire multiple frames of continuous point cloud data; the multiple frames of continuous point cloud data include the current frame point cloud data; each frame of point cloud data includes detection parameter information corresponding to multiple detection targets;

[0037] A target determination module, configured to determine a candidate detection target from a plurality of detection targets included in the current frame point cloud data based on detection parameter information corresponding to each detection target in the current frame point cloud data; the detection parameter information includes an azimuth angle parameter and a pitch angle parameter;

[0038] An angle correction module, used to correct the azimuth angle parameters corresponding to the candidate detection target to obtain calibration azimuth angle parameters;

[0039] a state determination module, configured to determine the calibration motion state of the candidate detection target based on the pitch angle parameter, the calibration azimuth angle parameter, the point cloud velocity parameter, and a motion speed parameter; the motion speed parameter is determined based on the motion speed of the radar body when the current frame point cloud data is acquired;

[0040] The information correction module is used to correct the detection parameter information corresponding to the candidate detection target based on the calibration motion state to obtain the calibration detection parameter information.

[0041] According to a third aspect of the present disclosure, an electronic device is provided, comprising a processor and a memory, wherein the memory stores at least one instruction and at least one program, and the at least one instruction and the at least one program are loaded and executed by the processor to implement the method for correcting slope radar point cloud data as described above.

[0042] According to a fourth aspect of the present disclosure, a computer storage medium is provided, in which at least one instruction and at least one program are stored, and the at least one instruction and the at least one program are loaded and executed by a processor to implement the method for correcting slope radar point cloud data as described above.

[0043] The implementation of the present invention has the following beneficial effects:

[0044] In the present invention, multiple frames of continuous point cloud data are obtained, wherein the point cloud data includes detection parameter information corresponding to multiple detection targets, and candidate detection parameter information is determined according to the detection parameter information corresponding to each detection target, and then the detection target corresponding to the candidate detection parameter information is determined as the candidate detection target, which can improve the accuracy and reliability of the detection target determination; the azimuth angle parameter of each candidate detection target is angle corrected to obtain the calibration azimuth angle parameter, and then the calibration azimuth angle parameter, pitch angle parameter and motion speed parameter are used to determine the calibration motion state of the candidate detection target, and the detection parameter information is corrected based on the calibration motion state, which can make the detection parameters contained in the candidate detection target meet the characteristics of the real detection target, thereby effectively reducing the impact of erroneous point cloud data on the data processing results; in addition, for each frame of point cloud data in the multiple frames of continuous point cloud data, the correction method of the slope radar point cloud data is used to calibrate, which can eliminate the errors in the multiple frames of continuous point cloud data and improve the accuracy of identifying the motion state of the detection target. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 A schematic diagram of a flow chart corresponding to a method for correcting slope radar point cloud data provided by an embodiment of the present invention;

[0047] Figure 2 A schematic diagram of a corresponding process for determining a candidate detection target provided by an embodiment of the present invention;

[0048] Figure 3 A schematic diagram of a process flow corresponding to angle correction provided by an embodiment of the present invention;

[0049] Figure 4 A schematic diagram of a corresponding flow chart for determining a motion state provided in an embodiment of the present invention;

[0050] Figure 5 A schematic diagram of the structure of a point cloud data correction device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0053] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0054] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0055] The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set consisting of A, B, and C.

[0056] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.

[0057] Figure 1The flowchart corresponding to the slope radar point cloud data correction method provided by the embodiment of the present invention is shown; the slope radar point cloud data correction method provided by the embodiment of the present invention can be applied to any application scenario that requires the use of slope radar for monitoring, and the execution subject can be any processor or mobile terminal that can implement the slope radar point cloud data correction method; the surrounding environment is detected based on the millimeter wave radar to generate point cloud data, and the point cloud data may include detection parameter information corresponding to the real detection target and detection parameter information corresponding to the false detection target. The surrounding environment is detected by the millimeter wave radar to obtain point cloud data, and the false detection target is determined according to the point cloud data, and the detection parameter information corresponding to the false detection target is corrected. Please refer to Figure 1 , a correction method for slope radar point cloud data, comprising:

[0058] Step S101: Acquire multiple frames of continuous point cloud data; the multiple frames of continuous point cloud data include current frame point cloud data; each frame of point cloud data includes detection parameter information corresponding to multiple detection targets;

[0059] In a feasible embodiment, the point cloud data may be detection data corresponding to objects in the environment generated by detecting the environment during radar monitoring, such as data generated by a slope monitoring millimeter wave radar system when detecting objects included in the surrounding environment; the point cloud data includes detection parameter information corresponding to multiple detection targets, wherein the multiple detection targets may include both real detection targets and false detection targets, and the detection parameter information may include at least azimuth angle parameters, pitch angle parameters, point cloud speed parameters and distance parameter information corresponding to each detection target; the current frame of point cloud data may be any frame of point cloud data in multiple frames of continuous point cloud data.

[0060] Specifically, since detection parameter information corresponding to dynamic false detection targets may be continuously acquired during the point cloud data acquisition process, multiple frames of continuous point cloud data are acquired to eliminate false detection targets in the continuous multiple frames of point cloud data.

[0061] Step S102: determining a candidate detection target from a plurality of detection targets included in the current frame point cloud data based on detection parameter information corresponding to each detection target in the current frame point cloud data; the detection parameter information includes an azimuth angle parameter, a pitch angle parameter, and a point cloud velocity parameter;

[0062] In a feasible embodiment, each detected target corresponds to detection parameter information, which may include distance parameter information, speed parameter information and angle parameter information, wherein the distance parameter information includes the distance of the detected target in the three-dimensional direction relative to the radial direction of the radar, the angle parameter information may include pitch angle information and azimuth angle information, and the speed parameter information may include the radial speed of each detected target relative to the millimeter wave radar; the detection parameter information may also include motion state information, wherein the motion state information may be reflected by a motion state flag, that is, the motion state flag is used to record the motion state of the detected target, and the motion state includes static and dynamic; accordingly, the motion state flag may use 0 to represent static and 1 to represent dynamic, or 1 to represent static and 0 to represent dynamic, and there is no specific limitation on the expression form of the motion state flag.

[0063] The candidate detection targets may include real detection targets or false detection targets. Based on the detection parameter information corresponding to each detection target, a detection target that meets the conditions can be determined from multiple detection targets to become a candidate detection target. Figure 2 , Figure 2 A schematic diagram of a corresponding process for determining a candidate detection target provided by an embodiment of the present invention; Figure 2 As shown, the method for determining the candidate detection target may include:

[0064] Step S201: determining a first detection target and a second detection target from a plurality of detection targets included in the current frame point cloud data based on a preset acquisition distance, a preset target acquisition quantity and the distance parameter information;

[0065] In a feasible embodiment, the preset acquisition distance and the preset target acquisition number can be determined based on actual feedback data collected during multiple tests, and different preset acquisition distances and preset target acquisition numbers can be set for different scenarios and different needs.

[0066] Specifically, the detection parameter information corresponding to the multiple detection targets included in the current frame point cloud data is traversed, and according to the distance parameter information and the motion state information in the detection parameter information, the number of static detection targets within the horizontal distance x and the vertical distance y on the left and right sides of the radar body are counted respectively. The left side count is num_left and corresponds to the first detection target, and the right side count is num_right and corresponds to the second detection target. The counting is stopped when the number of the first detection targets is greater than the preset target acquisition number num_max, or when the number of the second detection targets is greater than the preset target acquisition number.

[0067] Step S202: determining whether the number of the first detection targets is greater than a preset target number threshold;

[0068] If the number of the first detected targets is greater than the preset target number threshold, step S204 is executed; if the number of the first detected targets is less than the preset target number threshold, step S203 is executed;

[0069] Step S203: determining whether the number of the second detection targets is greater than the preset target number threshold;

[0070] If the number of the second detected targets is greater than the preset target number threshold, step S204 is executed; if the number of the second detected targets is less than the preset target number threshold, the execution is terminated;

[0071] Specifically, when the number of the first detection targets and the number of the second detection targets are both less than the preset target number threshold, it means that there are fewer detection targets in the radar's monitoring environment at this time, and it is not easy to form dynamic false detection targets. Therefore, the current frame point cloud data does not need to be corrected and the execution is terminated.

[0072] Furthermore, if the current frame point cloud data is not the last frame point cloud data in multiple frames of continuous point cloud data, the next frame point cloud data adjacent to the current frame point cloud data is determined as the new current frame point cloud data based on the time sequence of point cloud data acquisition, and the slope radar point cloud data correction method is executed on the new current frame point cloud data.

[0073] Step S204: Determine the candidate detection target based on the motion state information, the distance parameter information, the azimuth angle parameter and the pitch angle parameter.

[0074] Specifically, the preset target quantity threshold can be determined based on actual feedback data collected during multiple tests. For example, different preset target quantity thresholds can be set for different scenarios and different needs. The quantitative relationship between the number of first detection targets, the number of second detection targets, and the preset target quantity threshold is determined respectively; if the number of first detection targets is greater than the preset target quantity threshold, and / or the number of second detection targets is greater than the preset target quantity threshold, it indicates that there may be detection parameter information corresponding to dynamic false detection targets in the current frame point cloud data, and the current frame point cloud data needs to be corrected, so step S204 needs to be executed to determine the candidate detection targets.

[0075] In a feasible embodiment, step S204 may include:

[0076] Traversing the detection parameter information corresponding to the multiple detection targets included in the current frame point cloud data, and determining a dynamic detection target from the multiple detection targets according to the motion state information included in the detection parameter information; the dynamic detection target is a detection target whose motion state information is dynamic;

[0077] Determine a candidate detection target from multiple dynamic detection targets based on detection parameter information corresponding to the dynamic detection target, a preset three-dimensional distance range, and a preset angle range;

[0078] Specifically, the preset angle range includes an azimuth angle range and a pitch angle range; the preset three-dimensional distance range includes a distance range in a three-dimensional direction; the preset angle range and the preset three-dimensional distance range can be determined based on actual feedback data collected during multiple tests, for example, different preset angle ranges and preset three-dimensional distance ranges can be set for different scenarios and different needs. The distance parameter information, azimuth angle parameters, and pitch angle parameters corresponding to the dynamic detection target are traversed to determine the candidate detection target; wherein the candidate detection target is a dynamic detection target whose distance parameter information is within the preset three-dimensional distance range, and whose azimuth angle parameters and pitch angle parameters are both within the preset angle range.

[0079] In an embodiment of the present invention, candidate detection targets are screened out from multiple detection targets in combination with the detection parameter information corresponding to each detection target, and possible false detection targets can be screened out, and then the false detection targets can be corrected, which can improve the reliability and accuracy of determining false detection targets.

[0080] Step S103: correcting the azimuth angle parameters corresponding to the candidate detection target to obtain calibration azimuth angle parameters;

[0081] In a feasible embodiment, the main reason for the occurrence of dynamic false detection targets may be that the azimuth angle parameter is measured incorrectly, resulting in azimuth angle winding; therefore, the azimuth angle parameter needs to be corrected to obtain a calibrated azimuth angle parameter.

[0082] For details on the correction of the azimuth angle parameters, please refer to Figure 3 , Figure 3 A schematic diagram of a process flow corresponding to the angle correction provided in an embodiment of the present invention; Figure 3 As shown, step S103 may include:

[0083] Step S301: Acquire array length data and radar wavelength data;

[0084] Specifically, to calculate the first phase value, it is necessary to use array length data and radar wavelength data; wherein the array length data is the array azimuth baseline length corresponding to the millimeter wave radar, and the radar wavelength data is the wavelength of the radar electromagnetic wave; the array length data and the radar wavelength data are the array length data and the radar wavelength data corresponding to the acquisition of the current frame point cloud data.

[0085] Step S302: Calculating a first phase value based on the array length data, the radar wavelength data and the azimuth angle parameter;

[0086] In a feasible embodiment, the phase value corresponding to the azimuth angle parameter is a first phase value, and the first phase value is solved using array length data, radar wavelength data, and the azimuth angle parameter. The calculation formula of the first phase value is as follows:

[0087]

[0088] Among them, φ1 is the first phase value, θ1 is the azimuth angle parameter, λ is the wavelength of the radar electromagnetic wave, and dx is the array azimuth baseline length.

[0089] Step S303: performing phase unwrapping processing on the first phase value to obtain a second phase value;

[0090] In a feasible embodiment, after obtaining the first phase value φ1, it is necessary to perform phase unwrapping processing on it to obtain the second phase value; the phase unwrapping processing is phase value unwrapping, also known as phase unwrapping, and the calculation formula of the second phase value is as follows:

[0091]

[0092] Wherein, φ1 is the first phase value, φ2 is the second phase value, and j is an imaginary number.

[0093] Step S304: Determine the calibration azimuth angle parameter based on the array length data, the radar wavelength data and the second phase value.

[0094] In a feasible embodiment, the angle value is solved using the second phase value, the array length data, and the radar wavelength data. The obtained angle value is the calibration azimuth angle parameter. The calculation formula of the calibration azimuth angle parameter is:

[0095]

[0096] Among them, θ2 is the calibration azimuth angle parameter, φ2 is the second phase value, λ is the wavelength of the radar electromagnetic wave, and dx is the array azimuth baseline length.

[0097] Step S104: determining the calibration motion state of the candidate detection target based on the pitch angle parameter, the calibration azimuth angle parameter, the point cloud speed parameter and the motion speed parameter; the motion speed parameter is determined based on the motion speed of the radar body when the current frame point cloud data is acquired;

[0098] In a feasible embodiment, after obtaining the calibration azimuth parameter, the pitch angle parameter, the calibration azimuth angle parameter, the point cloud velocity parameter and the motion velocity parameter are used to re-determine the motion state of the candidate detection target, and the re-determined motion state is used as the calibration motion state of the candidate detection target. The motion velocity parameter is the motion velocity of the radar body when the current frame point cloud data is acquired.

[0099] Specifically, for the determination of the calibration motion state, please refer to Figure 4 , Figure 4 FIG. 2 is a schematic diagram showing a corresponding flow chart of determining a motion state according to an embodiment of the present invention; Figure 4 As shown, step S104 may include:

[0100] Step S401: Calculating a relative velocity component based on the pitch angle parameter, the calibration azimuth angle parameter and the motion velocity parameter;

[0101] In a feasible embodiment, the motion state of the detection target can be determined based on the relative velocity component of the radar body velocity in the radial direction of the candidate detection target relative to the millimeter-wave radar, the point cloud velocity of the candidate detection target relative to the radar body, and a preset velocity threshold. Therefore, it is necessary to first calculate the relative velocity component, and the calculation of the relative velocity component requires the use of pitch angle parameters, azimuth angle parameters, and motion speed parameters; since the calibration motion state is to be determined, the azimuth angle parameters are replaced with calibration azimuth angle parameters; the specific calculation formula of the relative velocity component is as follows:

[0102]

[0103] Among them, V r is the relative velocity component, v1 is the motion speed parameter, is the pitch angle parameter, and θ2 is the calibration azimuth angle parameter.

[0104] Step S402: determining whether the difference between the relative velocity component and the absolute value of the point cloud velocity is greater than a preset velocity threshold;

[0105] In a feasible embodiment, the point cloud speed is the aforementioned point cloud speed parameter, and the point cloud speed direction of the static detection target is determined based on the movement direction of the radar body; the point cloud speed parameter v of the radar detection target is opposite to the movement direction of the radar body, and the movement speed parameter v1 is a positive value, and the point cloud speed parameter v of the static detection target is a negative value.

[0106] After determining the relative velocity component, determine the quantitative relationship between the relative velocity component, the absolute value of the point cloud velocity, and the preset velocity threshold; calculate the difference between the relative velocity component and the absolute value of the point cloud velocity, and compare the difference with the preset velocity threshold v thrIf the difference is less than the preset speed threshold, the calibration motion state of the candidate detection target is determined to be static; otherwise, if the difference is greater than the preset speed threshold, the calibration motion state of the candidate detection target is determined to be dynamic.

[0107] If the difference between the relative speed component and the absolute value of the point cloud speed is greater than the preset speed threshold, step S403 is executed; if the difference between the relative speed component and the absolute value of the point cloud speed is less than the preset speed threshold, step S404 is executed;

[0108] Step S403: Determine that the calibration motion state of the candidate detection target is dynamic;

[0109] Step S404: Determine whether the calibration motion state of the candidate detection target is static.

[0110] In the embodiment of the present invention, it can be understood that steps S401-S404 calibrate the motion state of the candidate detection target using the calibration azimuth angle parameters, which can improve the accuracy of determining the motion state of the candidate detection target, thereby making the detection parameter information more reliable.

[0111] Step S105: Based on the calibration motion state, the detection parameter information corresponding to the candidate detection target is corrected to obtain calibration detection parameter information.

[0112] Determine whether the calibration motion state is consistent with the motion state information; if the two are inconsistent, replace the azimuth angle parameter and motion state information in the detection parameter information with the calibration azimuth angle parameter and the calibration motion state to obtain the calibration detection parameter information;

[0113] In a feasible embodiment, the motion state information and the calibrated motion state of the same candidate detection target are compared. If the motion state information of the candidate detection target is inconsistent with the calibrated motion state, that is, the motion state information is dynamic and the calibrated motion state is static, it means that the candidate detection target may be a dynamic false detection target; the azimuth angle parameters in the detection parameter information of the candidate detection target are replaced with the calibrated azimuth angle parameters, and the motion state information is replaced with the calibrated motion state to obtain the calibrated detection parameter information.

[0114] If the two are consistent, it is determined that the detection parameter information is calibration detection parameter information.

[0115] In a feasible embodiment, the motion state information and the calibration motion state of the same candidate detection target are compared. If the motion state information of the candidate detection target is consistent with the calibration motion state information, it means that the candidate detection target is a dynamic and real detection target. There is no need to update the detection parameter information of the candidate detection target, and its detection parameter information is retained. That is, for the candidate detection target whose motion state has not changed before and after calibration, it is determined that the detection parameter information is consistent with the calibration detection parameter information.

[0116] According to the above formula, the azimuth angle parameter corresponding to the candidate detection target affects the determination of the motion state of the candidate detection target. If the angle estimation is wrong, it will lead to errors in dynamic and static judgments and generate dynamic false detection targets. If there are multiple consecutive frames of dynamic false detection targets, it is difficult to filter them in data processing, which will lead to the formation of false tracking targets based on false detection targets. Therefore, by correcting the azimuth angle parameters, calibrating the motion state, and updating the detection parameter information, it is possible to filter out false detection targets among multiple detection targets, thereby improving the accuracy and reliability of detection target distinction and tracking target generation.

[0117] In an embodiment of the present invention, multiple frames of continuous point cloud data are obtained, wherein the point cloud data includes detection parameter information corresponding to multiple detection targets, and candidate detection parameter information is determined based on the detection parameter information corresponding to each detection target, and the detection target corresponding to the candidate detection parameter information is then determined as the candidate detection target, which can improve the accuracy and reliability of detection target determination; the azimuth angle parameters of each candidate detection target are angle corrected to obtain calibrated azimuth angle parameters, and then the calibrated azimuth angle parameters, pitch angle parameters and motion speed parameters are used to determine the calibrated motion state of the candidate detection target, and the detection parameter information is corrected based on the calibrated motion state, so that the detection parameters contained in the candidate detection target can meet the characteristics of the real detection target, thereby effectively reducing the impact of erroneous point cloud data on data processing results; in addition, each frame of point cloud data in the multiple frames of continuous point cloud data is calibrated using the correction method for slope radar point cloud data, which can eliminate errors in the multiple frames of continuous point cloud data.

[0118] In another specific embodiment, after executing steps S101 to S105, the slope radar point cloud data correction method further includes:

[0119] The current frame point cloud data is updated based on the calibration detection parameter information to obtain calibration point cloud data;

[0120] In a feasible embodiment, in step S105, the azimuth angle parameters and motion state information in the detection parameter information are updated; the detection parameter information also includes distance parameter information, so the distance parameter information also needs to be updated, and the calibration point cloud data is the point cloud data after the detection parameter information is updated.

[0121] Specifically, since the distance parameter information includes distance parameters in three-dimensional directions, that is, it includes three distance parameters, which can be horizontal distance, longitudinal distance and height distance; therefore, it is necessary to use the radial distance parameter, calibration azimuth angle parameter and pitch angle parameter of the candidate detection target relative to the millimeter wave radar in three-dimensional directions to update the distance parameter information. The specific update formula is:

[0122]

[0123] Where θ2 is the calibration azimuth angle parameter, is the pitch angle parameter; x, y, z are the updated distance parameter information, where x is the lateral distance, y is the longitudinal distance, and z is the height distance; ρ is the radial distance parameter of the candidate detection target relative to the millimeter wave radar.

[0124] It should be noted that the detection parameter information corresponding to the candidate detection target whose motion state information and the calibration motion state information are consistent, that is, the detection parameter information of the candidate detection target being a dynamic real detection target, does not need to be updated, and the original acquired detection parameter information can be directly output.

[0125] The warning information is updated based on the calibration point cloud data to obtain target warning information.

[0126] In a feasible embodiment, after obtaining the calibration point cloud data, the calibration point cloud data is output, and the calibration point cloud data and corresponding target warning information are used to update the warning information.

[0127] From the multiple frames of continuous point cloud data, determine the next frame of point cloud data of the current frame of point cloud data as the current frame of point cloud data.

[0128] In a feasible embodiment, for multiple frames of continuous point cloud data, any frame of point cloud data is determined as the current frame of point cloud data from the multiple frames of continuous point cloud data, and generally the first frame of point cloud data in the multiple frames of continuous point cloud data determined based on the acquisition time sequence is selected as the current point cloud data; based on the acquisition time sequence, the next frame of point cloud data of the current frame of point cloud data is determined as the current frame of point cloud data, that is, after performing point cloud data correction on the current frame of point cloud data, the current frame of point cloud data is updated, and the point cloud data correction as described above is continued to be performed on the updated current frame of point cloud data until the current frame of point cloud data is the last frame of point cloud data in the multiple frames of continuous point cloud data.

[0129] In an embodiment of the present invention, by cyclically updating the current frame point cloud data until the current frame point cloud data is the last frame point cloud data, different dynamic false detection targets appearing in each frame point cloud data in multiple frames of continuous point cloud data can be eliminated, and the same dynamic false detection target appearing continuously in each frame in multiple frames of continuous point cloud data can be eliminated, thereby avoiding determining false tracking targets based on erroneous false detection targets, and further avoiding generating erroneous warning information based on false tracking targets; correcting the dynamic false point cloud targets can effectively reduce the impact of false moving targets on radar functions and improve the accuracy of radar monitoring results.

[0130] The embodiment of the present invention also provides a point cloud data correction device, such as Figure 5 As shown, the device comprises:

[0131] The data acquisition module 510 is used to acquire multiple frames of continuous point cloud data; the multiple frames of continuous point cloud data include the current frame of point cloud data; each frame of point cloud data includes detection parameter information corresponding to multiple detection targets;

[0132] A target determination module 520 is used to determine a candidate detection target from multiple detection targets included in the current frame point cloud data based on the detection parameter information corresponding to each detection target in the current frame point cloud data; the detection parameter information includes an azimuth angle parameter, a pitch angle parameter, and a point cloud velocity parameter;

[0133] An angle correction module 530 is used to correct the azimuth angle parameters corresponding to the candidate detection target to obtain calibration azimuth angle parameters;

[0134] A state determination module 540 is used to determine the calibration motion state of the candidate detection target based on the pitch angle parameter, the calibration azimuth angle parameter, the point cloud speed parameter and the motion speed parameter; the motion speed parameter is determined based on the motion speed of the radar body when the current frame point cloud data is acquired;

[0135] The information correction module 550 is used to correct the detection parameter information corresponding to the candidate detection target based on the calibration motion state to obtain calibration detection parameter information.

[0136] In some other embodiments, the target determination module 520 further includes:

[0137] A first determination module, configured to determine a first detection target and a second detection target from a plurality of detection targets included in the current frame point cloud data based on a preset acquisition distance, a preset target acquisition quantity and the distance parameter information;

[0138] A first determination module, used to determine a quantitative relationship between the number of the first detection targets, the number of the second detection targets, and a preset target number threshold;

[0139] The second determination module is used to determine the candidate detection target based on the motion state information, the distance parameter information, the azimuth angle parameter and the pitch angle parameter if the number of the first detection targets is greater than the preset target number threshold, and / or the number of the second detection targets is greater than the preset target number threshold.

[0140] In some other embodiments, the angle correction module 530 includes:

[0141] Parameter acquisition module, used to obtain array length data and radar wavelength data;

[0142] A first phase calculation module, used for calculating a first phase value based on the array length data, the radar wavelength data and the azimuth angle parameter;

[0143] A second phase calculation module, configured to perform phase unwrapping processing on the first phase value to obtain a second phase value;

[0144] A parameter determination module is used to determine the calibration azimuth angle parameter based on the array length data, the radar wavelength data and the second phase value.

[0145] In some other embodiments, the state determination module 540 includes:

[0146] A velocity calculation module, configured to calculate a relative velocity component based on the pitch angle parameter, the calibration azimuth angle parameter, and the motion velocity parameter;

[0147] A second judgment module is used to judge the quantitative relationship between the relative speed component, the absolute value of the point cloud speed and a preset speed threshold; the absolute value of the point cloud speed is determined based on the point cloud speed parameter;

[0148] a first comparison module, configured to determine that the calibration motion state of the candidate detection target is dynamic if the difference between the relative velocity component and the absolute value of the point cloud velocity is greater than the preset velocity threshold;

[0149] The second comparison module is used to determine that the calibration motion state of the candidate detection target is static if the difference between the relative velocity component and the absolute value of the point cloud velocity is less than the preset velocity threshold.

[0150] In some other embodiments, the information correction module 550 includes:

[0151] A third judgment module is used to judge whether the calibration motion state is consistent with the motion state information;

[0152] A first data updating module, configured to replace the azimuth angle parameter and the motion state information in the detection parameter information with the calibration azimuth angle parameter and the calibration motion state if the two are inconsistent, so as to obtain the calibration detection parameter information;

[0153] The second data updating module is used to determine that the detection parameter information is calibration detection parameter information if the two are consistent.

[0154] In some other embodiments, the device further comprises:

[0155] A third data updating module, configured to update the current frame point cloud data based on the calibration detection parameter information to obtain calibration point cloud data;

[0156] The fourth data updating module is used to update the warning information based on the calibration point cloud data to obtain target warning information.

[0157] In some other embodiments, the device further comprises:

[0158] The fifth data updating module is used to determine, from the multiple frames of continuous point cloud data, the next frame of point cloud data of the current frame of point cloud data as the current frame of point cloud data.

[0159] The device and method embodiments in the device embodiment are based on the same inventive concept and are used to implement the above-mentioned slope radar point cloud data correction method.

[0160] An embodiment of the present invention also provides a device for correcting point cloud data, the device comprising: a processor and a memory, the memory storing at least one instruction, at least one program, a code set or an instruction set, the at least one instruction, the at least one program, the code set or the instruction set being loaded and executed by the processor to implement a method for correcting slope radar point cloud data as described in any one of the method embodiments.

[0161] An embodiment of the present invention also provides a storage medium, which can be set in a server to store at least one instruction, at least one program, a code set or an instruction set for implementing the method for correcting slope radar point cloud data in the method embodiment. The at least one instruction, the at least one program, the code set or the instruction set are loaded and executed by the processor to implement the method for correcting slope radar point cloud data as described in any one of the method embodiments.

[0162] Optionally, in an embodiment of the present invention, the above-mentioned storage medium may be located in at least one of a plurality of network servers in a computer network. Optionally, in an embodiment of the present invention, the above-mentioned storage medium may include, but is not limited to, various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0163] It can be seen from the embodiments provided by the present invention that, in the present invention, multiple frames of continuous point cloud data are obtained, wherein the point cloud data includes detection parameter information corresponding to multiple detection targets, and candidate detection parameter information is determined according to the detection parameter information corresponding to each detection target, and then the detection target corresponding to the candidate detection parameter information is determined as the candidate detection target, which can improve the accuracy and reliability of the detection target determination; the azimuth angle parameters of each candidate detection target are angle corrected to obtain the calibrated azimuth angle parameters, and then the calibrated azimuth angle parameters, pitch angle parameters and motion speed parameters are used to determine the calibrated motion state of the candidate detection target, and the detection parameter information is corrected based on the calibrated motion state, so that the detection parameters contained in the candidate detection target can meet the characteristics of the real detection target, thereby effectively reducing the impact of erroneous point cloud data on the data processing results and improving the accuracy of the radar monitoring results.

[0164] It should be noted that various embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The choice of terms used in this article is intended to best explain the principles of the embodiments, practical applications, or technical improvements in the market, or to enable other ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for correcting slope radar point cloud data, characterized in that: The method comprises: Acquire multiple frames of continuous point cloud data; the multiple frames of continuous point cloud data include the current frame point cloud data; each frame of point cloud data includes detection parameter information corresponding to multiple detection targets; Based on the detection parameter information corresponding to each detection target in the current frame point cloud data, determine the candidate detection target from the multiple detection targets included in the current frame point cloud data; the detection parameter information includes an azimuth angle parameter, a pitch angle parameter and a point cloud speed parameter; Correcting the azimuth angle parameters corresponding to the candidate detection target to obtain calibrated azimuth angle parameters; Determining the calibration motion state of the candidate detection target based on the pitch angle parameter, the calibration azimuth angle parameter, the point cloud speed parameter and the motion speed parameter; the motion speed parameter is determined based on the motion speed of the radar body when the current frame point cloud data is acquired; Based on the calibration motion state, the detection parameter information corresponding to the candidate detection target is corrected to obtain calibration detection parameter information.

2. The method for correcting slope radar point cloud data according to claim 1, characterized in that: The detection parameter information also includes motion state information and distance parameter information; The determining of a candidate detection target from a plurality of detection targets included in the current frame point cloud data based on the detection parameter information corresponding to each detection target in the current frame point cloud data comprises: Based on a preset acquisition distance, a preset target acquisition quantity and the distance parameter information, determining a first detection target and a second detection target from a plurality of detection targets included in the current frame point cloud data; Determining a quantitative relationship between the number of the first detection targets, the number of the second detection targets, and a preset target number threshold; If the number of the first detection targets is greater than the preset target number threshold, and / or the number of the second detection targets is greater than the preset target number threshold, the candidate detection targets are determined based on the motion state information, the distance parameter information, the azimuth angle parameter and the pitch angle parameter.

3. The method for correcting slope radar point cloud data according to claim 1, characterized in that: The correcting the azimuth angle parameters corresponding to the candidate detection targets to obtain the calibrated azimuth angle parameters includes: Obtain array length data and radar wavelength data; Calculating a first phase value based on the array length data, the radar wavelength data, and the azimuth angle parameter; Performing phase unwrapping processing on the first phase value to obtain a second phase value; The calibration azimuth angle parameter is determined based on the array length data, the radar wavelength data, and the second phase value.

4. The method for correcting slope radar point cloud data according to claim 1, characterized in that: The determining the calibration motion state of the candidate detection target based on the pitch angle parameter, the calibration azimuth angle parameter, the point cloud velocity parameter, and the motion velocity parameter includes: Calculating a relative velocity component based on the pitch angle parameter, the calibration azimuth angle parameter, and the motion velocity parameter; Determining the quantitative relationship between the relative speed component, the point cloud speed absolute value and a preset speed threshold; the point cloud speed absolute value is determined based on the point cloud speed parameter; If the difference between the relative velocity component and the absolute value of the point cloud velocity is greater than the preset velocity threshold, determining that the calibration motion state of the candidate detection target is dynamic; If the difference between the relative velocity component and the absolute value of the point cloud velocity is less than the preset velocity threshold, it is determined that the calibration motion state of the candidate detection target is static.

5. The method for correcting slope radar point cloud data according to claim 4, characterized in that: The detection parameter information includes motion state information; The step of correcting the detection parameter information corresponding to the candidate detection target based on the calibration motion state to obtain the calibration detection parameter information includes: Determining whether the calibration motion state is consistent with the motion state information; If the two are inconsistent, the azimuth angle parameter and the motion state information in the detection parameter information are replaced with the calibration azimuth angle parameter and the calibration motion state to obtain the calibration detection parameter information; If the two are consistent, it is determined that the detection parameter information is calibration detection parameter information.

6. The method for correcting slope radar point cloud data according to claim 5, characterized in that: After the detection parameter information corresponding to the candidate detection target is corrected based on the calibration motion state to obtain the calibration detection parameter information, the method further includes: The current frame point cloud data is updated based on the calibration detection parameter information to obtain calibration point cloud data; The warning information is updated based on the calibration point cloud data to obtain target warning information.

7. The method for correcting slope radar point cloud data according to claim 1, characterized in that: The method further comprises: From the multiple frames of continuous point cloud data, determine the next frame of point cloud data of the current frame of point cloud data as the current frame of point cloud data.

8. A correction device for slope radar point cloud data, characterized in that: The device comprises: A data acquisition module is used to acquire multiple frames of continuous point cloud data; the multiple frames of continuous point cloud data include the current frame point cloud data; each frame of point cloud data includes detection parameter information corresponding to multiple detection targets; A target determination module, configured to determine a candidate detection target from a plurality of detection targets included in the current frame point cloud data based on detection parameter information corresponding to each detection target in the current frame point cloud data; the detection parameter information includes an azimuth angle parameter, a pitch angle parameter, and a point cloud velocity parameter; An angle correction module, used to correct the azimuth angle parameters corresponding to the candidate detection target to obtain calibration azimuth angle parameters; a state determination module, configured to determine the calibration motion state of the candidate detection target based on the pitch angle parameter, the calibration azimuth angle parameter, the point cloud velocity parameter, and a motion speed parameter; the motion speed parameter is determined based on the motion speed of the radar body when the current frame point cloud data is acquired; The information correction module is used to correct the detection parameter information corresponding to the candidate detection target based on the calibration motion state to obtain the calibration detection parameter information.

9. An electronic device, comprising a processor and a memory, wherein the memory stores at least one instruction and at least one program, and the at least one instruction and the at least one program are loaded and executed by the processor to implement the slope radar point cloud data correction method as described in any one of claims 1 to 7.

10. A computer storage medium, wherein at least one instruction and at least one program are stored in the computer storage medium, wherein the at least one instruction and the at least one program are loaded and executed by a processor to implement the slope radar point cloud data correction method as described in any one of claims 1 to 7.

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