Method and system for measuring height of moving goods based on hybrid solid-state radar

Abstract

The invention provides a method and system for measuring the height of a moving cargo based on a hybrid solid-state radar. The method comprises the following steps: converting a point cloud of a radar coordinate system into a point cloud in a world coordinate system; filtering ground point clouds and point clouds outside a scanning range by adopting a straight-through filtering algorithm; judging whether point clouds exist in the scanning range or not, if yes, eliminating equipment noise point clouds by adopting a statistical filtering algorithm, eliminating interference and diffraction effects by adopting a moving average filtering algorithm, extracting each frame of point clouds to obtain point cloud estimation height values, putting the point cloud estimation height values into a height estimation queue, and circulating in sequence until no point clouds exist in the scanning range; for all the estimated height values of the height estimation queue, estimating the maximum height value of the measured object by using the moving average filtering algorithm again; if the height is greater than or equal to the height early-warning value, alarming and reminding. During measurement, the highest moving speed of a measured object can reach about 10km / h, and the measurement error is about 1cm.

Description

Technical Field

[0001] The present application relates to the field of measuring the height of moving cargo, and in particular, to a method and system for measuring the height of moving cargo based on a hybrid solid-state radar. Background Art

[0002] During the transportation of goods or equipment inside industrial warehouses, height limit or height measurement is an important safety perception link, playing an important role in the safe stacking of goods and preventing collisions. Unlike the height limit on public roads, the height limit inside industrial warehouses requires more precision, and due to the irregularity of goods or transportation equipment, there will be slender items such as metal poles, columns, and brackets to be measured, which is more difficult. In addition, compared to outdoor scenes, the internal space of industrial warehouses is limited, and the installation of height measurement or height limit sensing equipment needs to be more flexible and convenient. Therefore, the height measurement of moving goods inside industrial warehouses currently faces many difficulties: high precision requirements, difficulty in measuring the height of items, and high installation flexibility requirements.

[0003] Currently, there are two mainstream technical solutions for industrial height measurement or height control: 1. Install a gantry in the driving path, with a ranging laser radar or 3D camera mounted on top. This scans for objects exceeding a height threshold and triggers an alarm. 2. Install a beaming light curtain in the driving path, blocking the light curtain to trigger an over-height alarm.

[0004] The above two solutions have the following defects and shortcomings:

[0005] Inconvenient installation: It takes up a certain amount of space in industrial warehouses and has the potential for collision during driving.

[0006] The supported travel speed is relatively low: When passing through the height-restricted measurement area, transport vehicles such as AGVs and forklifts need to slow down.

[0007] Insufficient accuracy: The measurement accuracy error is large, and the measurement error is far greater than 1cm.

[0008] High cost: The basic materials and personnel required for equipment installation and commissioning require a lot of time and effort, resulting in a high overall cost. Summary of the Invention

[0009] The purpose of this application is to provide a method and system for measuring the height of moving cargo based on a hybrid solid-state radar, which can support the measured object to move at a speed of up to about 10 km / h during measurement, and the measurement error is about 1 cm.

[0010] This application is implemented as follows:

[0011] In a first aspect, the present application provides a method for measuring the height of moving cargo based on a hybrid solid-state radar, comprising the following steps:

[0012] S1: Install the hybrid solid-state laser radar at the target location and adjust its scanning range to obtain the point cloud of the object entering the scanning range. The above point cloud is the point cloud of the radar coordinate system;

[0013] S2: Obtain the actual installation position of the hybrid solid-state lidar relative to the world coordinate system and convert the point cloud of the lidar coordinate system into the point cloud of the world coordinate system;

[0014] S3: Use the straight-through filtering algorithm to filter out the ground point cloud and the point cloud outside the scanning range;

[0015] S4: Determine whether there is a point cloud within the scanning range. If so, use a statistical filtering algorithm to eliminate device noise point clouds, use a sliding average filtering algorithm to eliminate interference and diffraction effects, extract each frame of point cloud to obtain the estimated point cloud height value and put it into the height estimation queue, then jump to step S1 and loop in sequence until there is no point cloud within the scanning range;

[0016] S5: For all estimated height values ​​in the height estimation queue, the sliding average filter algorithm is used again to estimate the maximum height value of the measured object;

[0017] S6: Output the maximum altitude value and compare it with the altitude warning value. If it is greater than or equal to the altitude warning value, an alarm will be issued.

[0018] Based on the first aspect, the steps of obtaining the actual installation position of the hybrid solid-state laser radar relative to the world coordinate system and converting the point cloud of the radar coordinate system into a point cloud in the world coordinate system specifically include:

[0019] Get the position of the hybrid solid-state lidar in the world coordinate system as the rotation quaternion q and the translation vector t:

[0020] q=q w +q x i+q y j+q z k (unit quaternion, ||q||=1);

[0021]

[0022] where q w is the real scalar part, which represents the cosine component of the rotation and is directly related to the rotation angle θ. q x ,q y ,q z is the imaginary vector part, which represents the sinusoidal component of the rotation and the direction of the rotation axis; Where (n x , n y , n z) is the unit rotation axis vector; i, j, k are imaginary units; t is a vector in three-dimensional space, indicating the direction from the origin to the point (t x , t y , t z ) directed line segment; t x , t y , t z are the components of vector t on the X-axis, Y-axis, and Z-axis; T is the transpose sign;

[0023] Convert quaternion q to rotation matrix

[0024]

[0025] The translation vector t directly constitutes the translation part, and the homogeneous transformation matrix is ​​obtained

[0026]

[0027] Among them, R is a matrix variable, ∈ is a relation symbol, is a set of real numbers, 3×3 is the matrix dimension;

[0028] Assume that the coordinates of the original point cloud in the radar local coordinate system are Its world coordinate system coordinate p world Calculated by the transformation matrix T:

[0029] p world =T·p local ;

[0030] Expands to:

[0031]

[0032] The complete formula chain is:

[0033]

[0034] Where R(q) is the rotation matrix corresponding to the quaternion q.

[0035] Based on the first aspect, the above-mentioned step of filtering out the ground point cloud and the point cloud outside the scanning range using the straight-through filtering algorithm specifically includes:

[0036] Get original point cloud coordinates (x, y, z): Get the original point cloud data. Each point cloud data contains three coordinate values: x, y, and z.

[0037] Determine whether the y-coordinate is within the scanning range: Assume that the y-axis starting point of the scanning range is ymin and the end point is ymax; determine whether the y-coordinate of the current point cloud is within this range, that is, whether it satisfies ymax>y>ymin; if not, discard the point cloud data and the process ends; if it is within the range, proceed to the next step;

[0038] Determine whether the z coordinate is within the scanning range: Assume that the z-axis starting point of the scanning range is zmin and the end point is zmax; determine whether the z coordinate of the current point cloud is within this range, that is, whether it satisfies zmax>z>zmin; if not, discard the point cloud data and the process ends; if within the range, it is considered to be a point within the reasonable scanning range and the point cloud data is retained;

[0039] Determine whether the z coordinate is greater than offset_z: Further check whether the z coordinate is greater than the preset offset_z; if not, discard the point cloud data and the process ends; if satisfied, retain the point cloud data and finally obtain the original point cloud set A.

[0040] Based on the first aspect, the above-mentioned steps of eliminating device noise point cloud using the statistical filtering algorithm specifically include:

[0041] Traverse each point P in the original point cloud set A;

[0042] Calculate the distance between point P and all other points in the original point cloud set A;

[0043] If the distance is less than 5cm, store the point in the neighborhood point cloud set B of P;

[0044] Determine whether all points in the original point cloud set A have been traversed;

[0045] If the traversal is not completed, continue to traverse the next point;

[0046] If the traversal is completed, determine whether the number of points in the neighborhood point set B is greater than 5;

[0047] If it is not greater than 5, clear the neighborhood point set B and delete point P in the original point cloud set A;

[0048] If it is greater than 5, keep the point and proceed to the next point;

[0049] Get the point cloud set A after filtering by the statistical filtering algorithm.

[0050] Based on the first aspect, the steps of eliminating interference and diffraction effects by using a sliding average filtering algorithm include:

[0051] Input single-frame point cloud data;

[0052] Sort by height value from large to small;

[0053] Extract the top 10 points by height and sort them from largest to smallest;

[0054] Initialize the valid point set A;

[0055] Start iteration from the second point;

[0056] Calculate the height difference △h between the current point and the previous point;

[0057] Determine whether △h is ≤3cm;

[0058] If the height difference △h is less than or equal to 3 cm, proceed to the next step; otherwise, skip the current point;

[0059] Add the current point to the valid set A;

[0060] Whether the first 10 points have been traversed;

[0061] If the traversal is not completed, return and continue to iterate the next point;

[0062] If the traversal is completed, execute the next step;

[0063] End the iteration;

[0064] Calculate the average height of points in the valid set A;

[0065] Output the current frame height estimate;

[0066] Store in the height estimation queue.

[0067] Based on the first aspect, the step of again using the sliding average filter algorithm to estimate the maximum height value of the measured object for all estimated height values ​​in the height estimation queue includes:

[0068] Sort the data in the height estimation queue by height value from large to small:

[0069] Extract the top 10 values ​​of height and sort them from largest to smallest;

[0070] Initialize the valid value set B;

[0071] Start iteration from the second point;

[0072] Calculate the height difference Δh between the current value and the previous value;

[0073] Determine whether Δh is less than or equal to 3cm;

[0074] If Δh is less than or equal to 3cm, proceed to the next step; otherwise, do nothing;

[0075] Add the current value to the valid value set B:

[0076] Determine whether the first 10 points have been traversed;

[0077] If the traversal is completed, the iteration ends;

[0078] Calculate the average height of points in the valid set B;

[0079] Output the final height estimate.

[0080] In a second aspect, the present application provides a system for measuring the height of moving cargo based on a hybrid solid-state radar, comprising:

[0081] Acquisition module: used to obtain the point cloud of the object under test that enters the scanning range. The above point cloud is the point cloud of the radar coordinate system;

[0082] Conversion module: used to obtain the actual installation position of the hybrid solid-state lidar relative to the world coordinate system and convert the point cloud of the radar coordinate system into the point cloud of the world coordinate system;

[0083] The first filtering module is used to filter out the ground point cloud and the point cloud outside the scanning range using a straight-through filtering algorithm;

[0084] The second filtering module is used to determine whether there is a point cloud within the scanning range. If so, a statistical filtering algorithm is used to eliminate device noise point clouds, and a sliding average filtering algorithm is used to eliminate interference and diffraction effects. The estimated height value of each point cloud frame is extracted and placed in the height estimation queue. The acquisition module is then executed and looped in sequence until there is no point cloud within the scanning range.

[0085] The third filtering module is used to estimate the maximum height value of the measured object again using the sliding average filtering algorithm for all estimated height values ​​in the height estimation queue;

[0086] Output warning module: used to output the maximum altitude value and compare it with the altitude warning value. If it is greater than or equal to the altitude warning value, an alarm will be issued.

[0087] In a third aspect, the present application provides an electronic device, comprising:

[0088] a memory for storing one or more programs;

[0089] processor;

[0090] When the one or more programs are executed by the processor, the method is implemented.

[0091] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program implements the above-mentioned method when executed by a processor.

[0092] Compared with the prior art, this application has at least the following advantages or beneficial effects:

[0093] The present invention provides a method and system for measuring the height of mobile cargo based on a hybrid solid-state radar. The method converts a point cloud in a radar coordinate system into a point cloud in a world coordinate system; uses a straight-through filtering algorithm to filter out ground point clouds and point clouds outside the scanning range; determines whether a point cloud exists within the radar's scanning range; and if so, uses a statistical filtering algorithm to eliminate device noise point clouds and a sliding average filtering algorithm to eliminate interference and diffraction effects. Each frame of the point cloud is extracted to obtain an estimated point cloud height value and placed in a height estimation queue. This process is repeated until no point cloud exists within the scanning range. For all estimated height values ​​in the height estimation queue, the sliding average filtering algorithm is used again to estimate the maximum height of the measured object; the maximum height value is output and compared with a height warning value; an alarm is triggered if the maximum height value is greater than or equal to the height warning value. This configuration makes installation more convenient, supports more types of warehouse mobile equipment passing through the measurement area, supports faster passage of measured objects through the measurement area, and supports slender objects such as metal poles, columns, and brackets. Compared to pure mechanical radars, hybrid solid-state radars have the advantage of more stable operation. In combination with the algorithm process of this patent, more accurate measurement is achieved, achieving overall precision and stability. The method is easy to maintain, takes up almost no space, and is less subject to environmental restrictions. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0095] Figure 1 A flowchart of a method for measuring the height of moving cargo based on a hybrid solid-state radar is provided for this application;

[0096] Figure 2 This is a schematic diagram of the installation position of a hybrid solid-state laser radar in a method for measuring the height of moving cargo based on a hybrid solid-state radar in this application;

[0097] Figure 3 A schematic diagram of using calibration software to convert a point cloud in a radar coordinate system into a point cloud in a world coordinate system in a method for measuring the height of moving cargo based on a hybrid solid-state radar in this application;

[0098] Figure 4 This is a flowchart of a method for measuring the height of moving cargo based on a hybrid solid-state radar, which uses a straight-through filtering algorithm to filter out ground point clouds and point clouds outside the scanning range;

[0099] Figure 5 This is a flowchart of a method for measuring the height of moving cargo based on a hybrid solid-state radar using a statistical filtering algorithm to eliminate equipment noise point clouds;

[0100] Figure 6 This is a flowchart of a method for measuring the height of moving cargo based on a hybrid solid-state radar using a sliding average filter algorithm to eliminate interference and diffraction effects;

[0101] Figure 7 This is a flow chart of a method for measuring the height of moving cargo based on a hybrid solid-state radar, which again uses a sliding average filter algorithm to estimate the maximum height value of the measured object;

[0102] Figure 8 This is a schematic structural diagram of a system for measuring the height of moving cargo based on a hybrid solid-state radar in this application;

[0103] Figure 9 This is a structural diagram of an electronic device in this application.

[0104] icon:

[0105] 1. Hybrid solid-state radar; 2. Terminal device; 3. Measured object; 4. Acquisition module; 5. First filtering module; 6. Second filtering module; 7. Third filtering module; 8. Output warning module; 9. Processor; 10. Memory; 11. Communication interface. DETAILED DESCRIPTION

[0106] To make the objectives, technical solutions, and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Generally, the components of the embodiments of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0107] The following describes some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features thereof may be combined with each other.

[0108] Example

[0109] The present invention provides a method and system for measuring the height of moving cargo based on a hybrid solid-state radar. The method and system can support the measured object moving at a speed of up to approximately 10 km / h, with a measurement error of approximately 1 cm.

[0110] Please refer to Figure 1 , a method for measuring the height of moving cargo based on a hybrid solid-state radar, comprising the following steps:

[0111] S1: Install the hybrid solid-state laser radar at the target location and adjust its scanning range to obtain the point cloud of the object entering the scanning range. The above point cloud is the point cloud of the radar coordinate system;

[0112] Specifically, this embodiment does not limit the model of the hybrid solid-state laser radar. The preferred model is Leishen Intelligent_CX1S3. This embodiment also does not limit the installation position of the hybrid solid-state laser radar. As a preferred option, please refer to Figure 2 , Figure 2 This diagram illustrates one possible installation location for a hybrid solid-state laser radar. The hybrid solid-state laser radar is installed 3-5 meters above the ground, with a downward inclination of approximately 45 degrees. This allows for a complete, 90-degree scan of the lower right corner, covering the object being measured. The solid-state laser radar is connected to a terminal device, which is used to run various algorithms, such as direct filtering, statistical filtering, and sliding average filtering. The coordinate system in the diagram represents the center point of the world coordinate system configured by the calibration software. After installation, the scanning range of the hybrid solid-state laser radar is debugged. Once the object being measured enters the scanning range, a point cloud of the object is acquired. This point cloud is in the radar coordinate system, and the data collected is relative to the radar coordinate system. Compared to the traditional method of installing a gantry on the driveway, with a ranging laser radar or 3D camera mounted on top to scan for objects exceeding a height threshold in the driveway, this invention does not require a gantry, making installation more convenient and enabling a wider range of warehouse mobile equipment to pass through the measurement area.

[0113] S2: Obtain the actual installation position of the hybrid solid-state lidar relative to the world coordinate system and convert the point cloud of the lidar coordinate system into the point cloud of the world coordinate system;

[0114] Specifically, as one of the methods of this embodiment, the step of converting the point cloud in the radar coordinate system into the point cloud in the world coordinate system specifically includes:

[0115] Get the position of the hybrid solid-state lidar in the world coordinate system as the rotation quaternion q and the translation vector t:

[0116] q=q w +q x i+q y j+q z k (unit quaternion, ||q||=1);

[0117]

[0118] where q w is the real scalar part, which represents the cosine component of the rotation and is directly related to the rotation angle θ. q x ,q y ,qz is the imaginary vector part, which represents the sinusoidal component of the rotation and the direction of the rotation axis; Where (n x , n y , n z ) is the unit rotation axis vector; i, j, k are imaginary units; t is a vector in three-dimensional space, indicating the direction from the origin to the point (t x , t y , t z ) directed line segment; t x , t y , t z are the components of vector t on the X-axis, Y-axis, and Z-axis; T is the transpose sign;

[0119] Convert quaternion q to rotation matrix

[0120]

[0121] The translation vector t directly constitutes the translation part, and the homogeneous transformation matrix is ​​obtained

[0122]

[0123] Among them, R is a matrix variable, ∈ is a relation symbol, is a set of real numbers, 3×3 is the matrix dimension;

[0124] Assume that the coordinates of the original point cloud in the radar local coordinate system are Its world coordinate system coordinate p world Calculated by the transformation matrix T:

[0125] p world =T·p local ;

[0126] Expands to:

[0127]

[0128] The complete formula chain is:

[0129]

[0130] Where R(q) is the rotation matrix corresponding to the quaternion q.

[0131] With this setup, the point cloud data collected by the lidar is initially located in a local coordinate system (radar coordinate system) with the radar itself as the origin. By obtaining the actual installation position of the radar in the world coordinate system (including three-dimensional coordinates and attitude angles), all point cloud data can be converted to the same fixed world coordinate system. This conversion eliminates the data deviation caused by differences in sensor position, allowing data collected at different times and by different sensors (such as cameras and millimeter-wave radars) to be aligned in a unified space. The calibration software achieves coordinate transformation by calculating the external parameter matrix of the radar relative to the world coordinate system.

[0132] This embodiment can also use calibration software to set the radar's position in the world coordinate system and adjust it visually in real time. The calibration software is a self-developed point cloud calibration software called ZLSOTACalibration. Its algorithm service uses grpc communication to transmit data and calibrate. The method of setting the radar's position in the world coordinate system and adjusting it visually in real time by using calibration software includes: Figure 3 TCP posture parameter settings in, Figure 3 A schematic diagram of using calibration software to convert the point cloud of the radar coordinate system into a point cloud in the world coordinate system. According to the actual installation position of the radar relative to the center of the world coordinate system, fill in the X, Y, Z and downward Euler angles here. Through this algorithm of the terminal device, the radar's measurement data is converted from the radar coordinate system to the world coordinate system. The actual installation of the hybrid solid-state laser radar does not require a precise 45-degree angle. If there is an installation deviation, it only needs to be corrected in the calibration software. This embodiment can also set the scanning range of the radar through the calibration software and adjust it visually in real time: limit the scanning range of the Y-axis and Z-axis of the radar in the world coordinate system, such as Figure 2 The YZ scanning range is shown on the left. This range can be freely configured within 5 meters on the Z axis and 5 meters on the Y axis. This embodiment also uses calibration software to set the ground filtering algorithm threshold and adjust it visually in real time. This configuration is due to the possibility that the site ground may be uneven, which may result in interfering point clouds. By setting the offset_z parameter in the software, uneven ground point clouds can be filtered out.

[0133] S3: Use the straight-through filtering algorithm to filter out the ground point cloud and the point cloud outside the scanning range;

[0134] For details, please refer to Figure 4 , Figure 4 The flowchart for using the straight-through filtering algorithm to filter out the ground point cloud and the point cloud outside the scanning range includes:

[0135] Get original point cloud coordinates (x, y, z): Get the original point cloud data. Each point cloud data contains three coordinate values: x, y, and z.

[0136] Determine whether the y-coordinate is within the scanning range: Assume that the y-axis starting point of the scanning range is ymin and the end point is ymax; determine whether the y-coordinate of the current point cloud is within this range, that is, whether it satisfies ymax>y>ymin; if not, discard the point cloud data and the process ends; if it is within the range, proceed to the next step;

[0137] Determine whether the z coordinate is within the scanning range: Assume that the z-axis starting point of the scanning range is zmin and the end point is zmax; determine whether the z coordinate of the current point cloud is within this range, that is, whether it satisfies zmax>z>zmin; if not, discard the point cloud data and the process ends; if within the range, it is considered to be a point within the reasonable scanning range and the point cloud data is retained;

[0138] Determine whether the z coordinate is greater than offset_z: Further check whether the z coordinate is greater than the preset offset_z; if not, discard the point cloud data and the process ends; if satisfied, retain the point cloud data and finally obtain the original point cloud set A.

[0139] Offset_z and zmin are functionally identical, both determining whether a point cloud is greater than this lower limit. However, their roles in actual products differ. zmin generally refers to the warehouse floor height and is used during calibration to filter the floor. Offset_z is a contingency parameter used in very special situations, such as when a radar laser beam is reflected by a relatively smooth glass or metal surface, causing mirror noise. This parameter is used to temporarily constrain the scanning range, filter out the noise, and quickly restore the system to normal operation. However, the presence of the offset_z parameter increases the minimum scanning height, making it impossible to properly scan some low objects. After relocating the radar or removing interfering objects, this value is reset to 0 to restore the height measurement range.

[0140] Through such settings, reasonable scanning points are screened out from the original point cloud data, and points outside the scanning range and the ground point cloud are filtered out.

[0141] S4: Determine whether there is a point cloud within the scanning range. If so, use a statistical filtering algorithm to eliminate device noise point clouds, use a sliding average filtering algorithm to eliminate interference and diffraction effects, extract each frame of point cloud to obtain the estimated point cloud height value and put it into the height estimation queue, then jump to step S1 and loop in sequence until there is no point cloud within the scanning range;

[0142] For details, please refer to Figure 5 , Figure 5This is a flow chart of using a statistical filtering algorithm to eliminate device noise point clouds. As one of the methods of this embodiment, the steps of using a statistical filtering algorithm to eliminate device noise point clouds specifically include:

[0143] Traverse each point P in the original point cloud set A;

[0144] Calculate the distance between point P and all other points in the original point cloud set A;

[0145] If the distance is less than 5cm, store the point in the neighborhood point cloud set B of P;

[0146] Determine whether all points in the original point cloud set A have been traversed;

[0147] If the traversal is not completed, continue to traverse the next point;

[0148] If the traversal is completed, determine whether the number of points in the neighborhood point set B is greater than 5;

[0149] If it is not greater than 5, clear the neighborhood point set B and delete point P in the original point cloud set A;

[0150] If it is greater than 5, keep the point and proceed to the next point;

[0151] Get the point cloud set A after filtering by the statistical filtering algorithm.

[0152] This setting can filter out noise points in the point cloud data. By traversing each point in the point cloud set, calculating its distance to other points, and judging whether to retain the point based on a set threshold (such as 5cm), the point cloud data is filtered. The threshold is set to a minimum number of points in the point cloud of 5 and a statistical spacing of 5cm. That is, when the number of nearby points within 5cm of a point is less than 5, this point and the points within 5cm of it are discarded.

[0153] For further information, please refer to Figure 6 , Figure 6 This is a flow chart of one method of eliminating interference and diffraction effects by using a sliding average filter algorithm. As one of the methods of this embodiment, the steps of eliminating interference and diffraction effects by using the sliding average filter algorithm include:

[0154] Input single-frame point cloud data;

[0155] Sort by height value from large to small;

[0156] Extract the top 10 points by height and sort them from largest to smallest;

[0157] Initialize the valid point set A;

[0158] Start iteration from the second point;

[0159] Calculate the height difference △h between the current point and the previous point;

[0160] Determine whether △h is ≤3cm;

[0161] If the height difference △h is less than or equal to 3 cm, proceed to the next step; otherwise, skip the current point;

[0162] Add the current point to the valid set A;

[0163] Whether the first 10 points have been traversed;

[0164] If the traversal is not completed, return and continue to iterate the next point;

[0165] If the traversal is completed, execute the next step;

[0166] End the iteration;

[0167] Calculate the average height of points in the valid set A;

[0168] Output the current frame height estimate;

[0169] Store in the height estimation queue.

[0170] With this setup, the height estimation value of a frame of image can be effectively extracted and calculated from the point cloud data.

[0171] S5: For all estimated height values ​​in the height estimation queue, the sliding average filter algorithm is used again to estimate the maximum height value of the measured object;

[0172] For details, please refer to Figure 7 , Figure 7 This is a flow chart of estimating the maximum height value of the measured object by using the sliding average filter algorithm again. As one embodiment, the steps of estimating the maximum height value of the measured object by using the sliding average filter algorithm again include:

[0173] Sort the data in the height estimation queue by height value from large to small:

[0174] Extract the top 10 values ​​of height and sort them from largest to smallest;

[0175] Initialize the valid value set B;

[0176] Start iteration from the second point;

[0177] Calculate the height difference Δh between the current value and the previous value;

[0178] Determine whether Δh is less than or equal to 3cm;

[0179] If Δh is less than or equal to 3cm, proceed to the next step; otherwise, do nothing;

[0180] Add the current value to the valid value set B:

[0181] Determine whether the first 10 points have been traversed;

[0182] If the traversal is completed, the iteration ends;

[0183] Calculate the average height of points in the valid set B;

[0184] Output the final height estimate.

[0185] With this setup, the first 10 height values ​​are extracted from the height estimation queue, and the changes in these values ​​are calculated. Points with changes within a certain range are selected, and finally the average of these valid points is calculated to obtain the final height estimate.

[0186] S6: Output the maximum altitude value and compare it with the altitude warning value. If it is greater than or equal to the altitude warning value, an alarm will be issued.

[0187] By setting the above steps, the present invention has the following advantages over the traditional solution:

[0188] 1. No gantry is required, which makes installation more convenient and can support more types of warehouse mobile equipment passing through the measurement area;

[0189] 2. Support faster passing through the measurement area;

[0190] 3. Supports slender objects to be tested such as metal rods, columns, brackets, etc.

[0191] 4. Compared with pure mechanical radar, hybrid solid-state radar has the advantage of more stable operation. At the same time, the patented algorithm process is integrated to achieve more accurate measurement and overall precision and stability.

[0192] 5. Easy to maintain, takes up almost no space, and is less restricted by the environment.

[0193] Please refer to Figure 8 , Figure 8 This is a schematic diagram of the structure of a system for measuring the height of moving cargo based on a hybrid solid-state radar according to the present invention. The system includes:

[0194] Acquisition module: used to obtain the point cloud of the object under test that enters the scanning range. The above point cloud is the point cloud of the radar coordinate system;

[0195] Conversion module: used to obtain the actual installation position of the hybrid solid-state lidar relative to the world coordinate system and convert the point cloud of the radar coordinate system into the point cloud of the world coordinate system;

[0196] The first filtering module is used to filter out the ground point cloud and the point cloud outside the scanning range using a straight-through filtering algorithm;

[0197] The second filtering module is used to determine whether there is a point cloud within the scanning range. If so, a statistical filtering algorithm is used to eliminate device noise point clouds, and a sliding average filtering algorithm is used to eliminate interference and diffraction effects. The estimated height value of each point cloud frame is extracted and placed in the height estimation queue. The acquisition module is then executed and looped in sequence until there is no point cloud within the scanning range.

[0198] The third filtering module is used to estimate the maximum height value of the measured object again using the sliding average filtering algorithm for all estimated height values ​​in the height estimation queue;

[0199] Output warning module: used to output the maximum altitude value and compare it with the altitude warning value. If it is greater than or equal to the altitude warning value, an alarm will be issued.

[0200] Specifically, for the specific implementation of the system for measuring the height of moving cargo based on a hybrid solid-state radar, please refer to the specific implementation of the method for measuring the height of moving cargo based on a hybrid solid-state radar, and no further details will be given here.

[0201] Please refer to Figure 9 , Figure 9 This is a schematic structural diagram of an electronic device according to the present invention, the electronic device comprising:

[0202] a memory for storing one or more programs;

[0203] processor;

[0204] When the one or more programs are executed by the processor, all or part of the method described above is implemented.

[0205] The present application also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements all or part of the above-mentioned method when executed by a processor.

[0206] It will be apparent to those skilled in the art that the present application is not limited to the details of the exemplary embodiments described above and that the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the present application is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A method for measuring the height of moving cargo based on hybrid solid-state radar, characterized in that: The following steps are involved: S1: Install the hybrid solid-state laser radar at the target location and adjust its scanning range to obtain the point cloud of the object entering the scanning range; S2: Obtain the actual installation position of the hybrid solid-state lidar relative to the world coordinate system and convert the point cloud of the lidar coordinate system into the point cloud of the world coordinate system; S3: Use the straight-through filtering algorithm to filter out the ground point cloud and the point cloud outside the scanning range; S4: Determine whether there is a point cloud within the scanning range. If so, use a statistical filtering algorithm to eliminate device noise point clouds, use a sliding average filtering algorithm to eliminate interference and diffraction effects, extract each frame of point cloud to obtain the estimated point cloud height value and put it into the height estimation queue, then jump to step S1 and loop in sequence until there is no point cloud within the scanning range; S5: For all estimated height values ​​in the height estimation queue, the sliding average filter algorithm is used again to estimate the maximum height value of the measured object; S6: Output the maximum altitude value and compare it with the altitude warning value. If it is greater than or equal to the altitude warning value, an alarm will be issued.

2. The method for measuring the height of moving cargo based on hybrid solid-state radar according to claim 1, characterized in that: The steps of obtaining the actual installation position of the hybrid solid-state laser radar relative to the world coordinate system and converting the point cloud of the radar coordinate system into the point cloud of the world coordinate system specifically include: Get the position of the hybrid solid-state lidar in the world coordinate system as the rotation quaternion q and the translation vector t: q=q w +q x i+q y j+q z k (unit quaternion, ||q||=1); where q ω is the real scalar part, which represents the cosine component of the rotation and is directly related to the rotation angle θ. q x ,q y ,q z is the imaginary vector part, which represents the sinusoidal component of the rotation and the direction of the rotation axis; Where (n x , n y , n z ) is the unit rotation axis vector; i, j, k are imaginary units; t is a vector in three-dimensional space, indicating the direction from the origin to the point (t x , t y , t z ) directed line segment; t x , t y , t z are the components of vector t on the X-axis, Y-axis, and Z-axis; T is the transpose sign; Convert quaternion q to rotation matrix The translation vector t directly constitutes the translation part, and the homogeneous transformation matrix is ​​obtained Among them, R is a matrix variable, ∈ is a relation symbol, is a set of real numbers, 3×3 is the matrix dimension; Assume that the coordinates of the original point cloud in the radar local coordinate system are Its world coordinate system coordinate p world Calculated by the transformation matrix T: p world =T·p local ; Expands to: The complete formula chain is: Where R(q) is the rotation matrix corresponding to the quaternion q.

3. The method for measuring the height of moving cargo based on hybrid solid-state radar according to claim 2, characterized in that: The step of using the straight-through filtering algorithm to filter out the ground point cloud and the point cloud outside the scanning range specifically includes: Get original point cloud coordinates (x, y, z): Get the original point cloud data. Each point cloud data contains three coordinate values: x, y, and z. Determine whether the y-coordinate is within the scanning range: Assume that the y-axis starting point of the scanning range is ymin and the end point is ymax; determine whether the y-coordinate of the current point cloud is within this range, that is, whether it satisfies ymax>y>ymin; if not, discard the point cloud data and the process ends; if it is within the range, proceed to the next step; Determine whether the z coordinate is within the scanning range: Assume that the z-axis starting point of the scanning range is zmin and the end point is zmax; determine whether the z coordinate of the current point cloud is within this range, that is, whether it satisfies zmax>z>zmin; if not, discard the point cloud data and the process ends; if within the range, it is considered to be a point within the reasonable scanning range and the point cloud data is retained; Determine whether the z coordinate is greater than offset_z: Further check whether the z coordinate is greater than the preset offset_z; if not, discard the point cloud data and the process ends; if satisfied, retain the point cloud data and finally obtain the original point cloud set A.

4. The method for measuring the height of moving cargo based on hybrid solid-state radar according to claim 3, characterized in that: The step of eliminating the device noise point cloud using the statistical filtering algorithm specifically includes: Traverse each point P in the original point cloud set A; Calculate the distance between point P and all other points in the original point cloud set A; If the distance is less than 5cm, store the point in the neighborhood point cloud set B of P; Determine whether all points in the original point cloud set A have been traversed; If the traversal is not completed, continue to traverse the next point; If the traversal is completed, determine whether the number of points in the neighborhood point set B is greater than 5; If it is not greater than 5, clear the neighborhood point set B and delete point P in the original point cloud set A; If it is greater than 5, keep the point and proceed to the next point; Get the point cloud set A after filtering by the statistical filtering algorithm.

5. The method for measuring the height of moving cargo based on hybrid solid-state radar according to claim 1, characterized in that: The step of using a sliding average filtering algorithm to eliminate interference and diffraction effects includes: Input single-frame point cloud data; Sort by height value from large to small; Extract the top 10 points by height and sort them from largest to smallest; Initialize the valid point set A; Start iteration from the second point; Calculate the height difference △h between the current point and the previous point; Determine whether △h is ≤3cm; If the height difference △h is less than or equal to 3 cm, proceed to the next step; otherwise, skip the current point; Add the current point to the valid set A; Whether the first 10 points have been traversed; If the traversal is not completed, return and continue to iterate the next point; If the traversal is completed, execute the next step; End the iteration; Calculate the average height of points in the valid set A; Output the current frame height estimate; Store in the height estimation queue.

6. The method for measuring the height of moving cargo based on hybrid solid-state radar according to claim 5, characterized in that: The step of again using a sliding average filtering algorithm to estimate the maximum height value of the measured object for all estimated height values ​​in the height estimation queue includes: Sort the data in the height estimation queue by height value from large to small: Extract the top 10 values ​​of height and sort them from largest to smallest; Initialize the valid value set B; Start iteration from the second point; Calculate the height difference Δh between the current value and the previous value; Determine whether Δh is less than or equal to 3cm; If Δh is less than or equal to 3cm, proceed to the next step; otherwise, do nothing; Add the current value to the valid value set B: Determine whether the first 10 points have been traversed; If the traversal is completed, the iteration ends; Calculate the average height of points in the valid set B; Output the final height estimate.

7. A system for measuring the height of moving cargo based on a hybrid solid-state radar, characterized in that: include: Acquisition module: used to acquire the point cloud of the object under test that enters the scanning range, and the point cloud is the point cloud of the radar coordinate system; Conversion module: used to obtain the actual installation position of the hybrid solid-state lidar relative to the world coordinate system and convert the point cloud of the radar coordinate system into the point cloud of the world coordinate system; The first filtering module is used to filter out the ground point cloud and the point cloud outside the scanning range using a straight-through filtering algorithm; The second filtering module is used to determine whether there is a point cloud within the scanning range. If so, a statistical filtering algorithm is used to eliminate device noise point clouds, and a sliding average filtering algorithm is used to eliminate interference and diffraction effects. The estimated height value of each point cloud frame is extracted and placed in the height estimation queue. The acquisition module is then executed and looped in sequence until there is no point cloud within the scanning range. The third filtering module is used to estimate the maximum height value of the measured object again using the sliding average filtering algorithm for all estimated height values ​​in the height estimation queue; Output warning module: used to output the maximum altitude value and compare it with the altitude warning value. If it is greater than or equal to the altitude warning value, an alarm will be issued.

8. An electronic device, characterized in that: include: a memory for storing one or more programs; processor; When the one or more programs are executed by the processor, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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