Method, device and system for high-level fork truck mast calibration

Abstract

The application relates to a method, device and system for calibrating a high forklift mast, the method comprising: acquiring an initial position of a laser radar relative to a mast of the high forklift; projecting a laser line to a target feature by the laser radar to generate corresponding laser data; establishing a laser coordinate system with the laser radar as an origin; filtering and clustering the laser data when tines of the high forklift are respectively at a preset reference height and a target height, to obtain first and second center coordinates of the target feature in the laser coordinate system; and obtaining a calibration value of the mast of the high forklift according to the first and second center coordinates and the initial position of the laser radar relative to the mast of the high forklift. The application solves the problem of low mast-to-point accuracy of a forklift during automatic loading and unloading in the prior art, reduces system errors caused by hardware errors during mast lifting of the forklift, and improves the mast-to-point accuracy.

Description

Technical Field

[0001] This application relates to the field of logistics equipment technology, and in particular to a method, apparatus and system for calibrating a high-mounted forklift mast. Background Technology

[0002] With the rapid development of industrialization, smart logistics has become a hot research topic, and forklifts play a crucial role in it. Ensuring both forklift efficiency and precision within a given timeframe has become a primary criterion for evaluating the development level of smart logistics.

[0003] Currently, traditional smart forklifts use algorithms to control their movement, achieving efficient picking and unloading tasks, which to some extent frees up manpower and improves efficiency. However, the control methods described above do not calibrate the moving mast of the forklift, thus posing a risk of forklift picking and unloading tasks failing due to low mast accuracy.

[0004] There is currently no effective solution to the problem of low mast accuracy when forklifts are used for automated picking and unloading in the aforementioned technologies. Summary of the Invention

[0005] This embodiment provides a method, apparatus, and system for calibrating the mast of a high-mounted forklift to solve the problem of low mast accuracy when forklifts perform automated loading and unloading in related technologies.

[0006] Firstly, this embodiment provides a method for calibrating a high-mounted forklift mast, comprising:

[0007] The initial position of the lidar relative to the mast of the high-reach forklift is obtained; the lidar is mounted on the forks of the high-reach forklift; the lidar projects a laser line onto the target feature to generate corresponding laser data; the target feature is positioned at the target location.

[0008] Establish a laser coordinate system with the aforementioned lidar as the origin;

[0009] When the forks of the high-mounted forklift are at a preset reference height, the laser data is filtered and clustered to obtain first target laser data; based on the center coordinate algorithm, the first target laser data is processed to obtain the first center coordinates of the target feature in the laser coordinate system; when the forks of the high-mounted forklift are at the target height, the laser data is filtered and clustered to obtain second target laser data; based on the center coordinate algorithm, the second target laser data is processed to obtain the second center coordinates of the target feature in the laser coordinate system.

[0010] The calibration value of the mast of the high-reach forklift is obtained based on the first center coordinates, the second center coordinates, and the initial position of the lidar relative to the mast of the high-reach forklift.

[0011] In some embodiments, when the forks of the high-mounted forklift are at a preset reference height, the laser data is filtered and clustered to obtain first target laser data; based on a center coordinate algorithm, the first target laser data is processed to obtain the first center coordinates of the target feature in the laser coordinate system, including:

[0012] When the forks of the high-mounted forklift are at a preset reference height, the laser data is filtered and clustered to obtain a first target point cloud group.

[0013] Based on the first target point cloud group, the first center coordinates of the target feature in the laser coordinate system are obtained.

[0014] In some embodiments, filtering and clustering the laser data to obtain a first target point cloud group includes:

[0015] The laser data is processed to obtain first target data; a first reflectivity threshold is calculated based on the first target data; a first target laser reflection point is obtained based on the first target data and the first reflectivity threshold; and the coordinates of the first target laser reflection point in the laser coordinate system are acquired.

[0016] Cluster the first target laser reflection points to obtain a first point cloud group; based on the target location, obtain the first point cloud group where the target feature is located, and record the first point cloud group where the target feature is located as the first target point cloud group.

[0017] In some embodiments, when the forks of the high-mounted forklift are at the target height, the laser data is filtered and clustered to obtain second target laser data; based on the center coordinate algorithm, the second target laser data is processed to obtain the second center coordinates of the target feature in the laser coordinate system, including:

[0018] When the forks of the high-mounted forklift are at the target height, the laser data is filtered and clustered to obtain a second target point cloud group.

[0019] Based on the second target point cloud group, the second center coordinates of the target feature in the laser coordinate system are obtained.

[0020] In some embodiments, filtering and clustering the laser data to obtain a second target point cloud group includes:

[0021] The laser data is processed to obtain second target data; a second reflectivity threshold is calculated based on the second target data; a second target laser reflection point is obtained based on the second target data and the second reflectivity threshold; and the coordinates of the second target laser reflection point in the laser coordinate system are acquired.

[0022] Cluster the second target laser points to obtain a second point cloud group; based on the target location, obtain the second point cloud group where the target feature is located, and record the second point cloud group where the target feature is located as the second target point cloud group.

[0023] In some embodiments, obtaining the first point cloud group where the target feature is located based on the target location, and denoting the first point cloud group where the target feature is located as the first target point cloud group, includes:

[0024] When the number of points in the first point cloud group is less than the number of target features, the target features and the target location are checked; the first point cloud group is reacquired; the first point cloud group where the target features are located is reacquired based on the target location; the first point cloud group where the target features are located is recorded as the first target point cloud group.

[0025] When the number of points in the first point cloud group is equal to the number of target features, the first point cloud group is the first target point cloud group; obtain the first target point cloud group;

[0026] When the number of the first point cloud clusters is greater than the number of the target features, the first target point cloud clusters are obtained based on the target location.

[0027] In some embodiments, obtaining the second point cloud group where the target feature is located based on the target location, and denoting the second point cloud group where the target feature is located as the second target point cloud group, includes:

[0028] When the number of points in the second cloud cluster is less than the number of target features, the target features and the target location are checked; the second cloud cluster is reacquired; the second cloud cluster where the target feature is located is reacquired based on the target location; the second cloud cluster where the target feature is located is recorded as the second target cloud cluster.

[0029] When the number of points in the second point cloud group is equal to the number of target features, the second point cloud group is the second target point cloud group; acquire the second target point cloud group;

[0030] When the number of the second point cloud clusters is greater than the number of the target features, the second target point cloud clusters are obtained based on the target location.

[0031] In some embodiments, the lidar is mounted on the forks of the high-reach forklift, including:

[0032] The lidar is located at the exact center of the fork tooth;

[0033] The LiDAR scanner is positioned downwards and perpendicular to the ground.

[0034] Secondly, this embodiment provides a high-mounted forklift mast calibration device, comprising:

[0035] A radar setting module is used to obtain the initial position of the lidar relative to the mast of the high-reach forklift; the lidar is set on the fork teeth of the high-reach forklift; the lidar projects a laser line onto the target feature to generate corresponding laser data; the target feature is set at the target location;

[0036] The coordinate acquisition module is used to establish a laser coordinate system with the lidar as the origin; when the forks of the high-mounted forklift are at a preset reference height, the laser data is filtered and clustered to obtain first target laser data; based on the center coordinate algorithm, the first target laser data is processed to obtain the first center coordinates of the target feature in the laser coordinate system; when the forks of the high-mounted forklift are at the target height, the laser data is filtered and clustered to obtain second target laser data; based on the center coordinate algorithm, the second target laser data is processed to obtain the second center coordinates of the target feature in the laser coordinate system.

[0037] The calibration module is used to obtain the calibration value of the mast of the high-reach forklift based on the first center coordinates, the second center coordinates, and the initial position of the lidar relative to the mast of the high-reach forklift.

[0038] Thirdly, this embodiment provides a high-lift forklift mast calibration system, including a high-lift forklift, a memory, and a processor. The memory and the processor are located on the high-lift forklift. The memory stores a computer program, and when the processor executes the computer program, it implements the high-lift forklift mast calibration method described in the first aspect above.

[0039] Compared with related technologies, the method, apparatus, and system for calibrating the mast of a high-lift forklift provided in this embodiment solves the problem of low mast accuracy during automated forklift loading and unloading in related technologies. This is achieved by obtaining the initial position of a laser radar relative to the mast of the high-lift forklift; the laser radar is mounted on the forks of the high-lift forklift; the laser radar projects laser lines onto a target feature to generate corresponding laser data; the target feature is positioned at the target location; a laser coordinate system is established with the laser radar as the origin; when the forks of the high-lift forklift are at a preset reference height, the laser data is processed using a center coordinate algorithm to obtain the first center coordinate of the target feature in the laser coordinate system; when the forks of the high-lift forklift are at the target height, the laser data is processed again using the center coordinate algorithm to obtain the second center coordinate of the target feature in the laser coordinate system; and based on the first center coordinate, the second center coordinate, and the initial position of the laser radar relative to the mast of the high-lift forklift, the calibration value of the mast of the high-lift forklift is obtained.

[0040] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0041] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0042] Figure 1 This is a hardware structure block diagram of the high-mounted forklift mast calibration method in this embodiment;

[0043] Figure 2 This is a flowchart of the high-mounted forklift mast calibration method in this embodiment;

[0044] Figure 3 This is a flowchart of the high-mounted forklift mast calibration method according to a preferred embodiment;

[0045] Figure 4 This is a structural block diagram of the high-mounted forklift mast calibration device in this embodiment.

[0046] Reference numerals: 102, processor; 104, memory; 106, transmission device; 108, input / output device; 10, radar setting module; 20, coordinate acquisition module; 30, calibration module. Detailed Implementation

[0047] To better understand the purpose, technical solution, and advantages of this application, the application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0048] Unless otherwise defined, the technical or scientific terms used in this application shall have the general meaning as understood by one of ordinary skill in the art to which this application pertains. Words such as “a,” “an,” “an,” “the,” “the,” and “these,” used in this application, do not indicate quantitative limitation and may be singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or modules (units) is not limited to the listed steps or modules (units) but may include steps or modules (units) not listed, or may include other steps or modules (units) inherent to such processes, methods, products, or devices. The terms “connected,” “linked,” and “coupled,” used in this application, are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. The term “multiple” used in this application refers to two or more. The "and / or" operator describes the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: A alone, A and B simultaneously, and B alone. Typically, the character " / " indicates that the objects before and after it are in an "or" relationship. The terms "first," "second," and "third," etc., used in this application are merely for distinguishing similar objects and do not represent a specific ordering of the objects.

[0049] The method embodiments provided in this example can be executed on a terminal, computer, or similar computing device. For example, it can run on a terminal. Figure 1 This is a hardware structure block diagram of the terminal of the high-mounted forklift mast calibration method in this embodiment. For example... Figure 1 As shown, a terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 and a memory 104 for storing data are also included. The processor 102 may be, but is not limited to, a microprocessor (MCU) or a programmable logic device (FPGA). The terminal may also include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that… Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the terminal described above. For example, the terminal may also include components that are larger than... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown are illustrated.

[0050] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the high-mounted forklift mast calibration method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0051] The transmission device 106 is used to receive or send data via a network. This network includes a wireless network provided by the terminal's communication provider. In one example, the transmission device 106 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 106 can be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0052] This embodiment provides a method for calibrating a high-mounted forklift mast. Figure 2 This is a flowchart of the high-mounted forklift mast calibration method in this embodiment, as follows: Figure 2 As shown, the process includes the following steps:

[0053] Step S201: Obtain the initial position of the lidar relative to the mast of the high-mounted forklift; the lidar is set on the fork of the high-mounted forklift; the lidar projects a laser line onto the target feature to generate corresponding laser data; the target feature is set at the target position.

[0054] Specifically, by recording the initial position of the lidar relative to the high-mounted forklift, the deviation of the forklift's fork height from the ground due to fork bending during vertical movement is calculated. The lidar is installed on the forklift's fork to generate and transmit laser data. A suitable target feature is selected so that the laser data received by the lidar and projected onto the reflection point of the target feature is different from the laser data received by the lidar and projected onto other objects. The target feature is set at the target location for subsequent determination of the position of the point cloud group and the center coordinates of the target feature in the laser coordinate system.

[0055] In this implementation, the target features used were two high-reflectivity stickers, each 4cm wide and 20cm long. Testing showed that these stickers, compared to other high-reflectivity objects, offered advantages such as ease of application and application, more stable point cloud ranging on the stickers, more accurate ranging results, and easier measurement comparison. Testing also indicated that a sticker width of 4cm was optimal. When the sticker width was less than 4cm, the number of points scanned by the laser at long distances was too small, leading to inaccurate ranging and introducing random ranging errors. Conversely, when the sticker width was greater than 4cm, the number of points scanned by the laser at close distances was too large, potentially generating noise and resulting in inaccurate center point extraction and lateral errors. Currently, there is no absolute requirement for the length of the high-reflectivity stickers. With a 20cm sticker length, and the fork tines positioned 5m above the ground, a 1.5-degree bend in the fork tines is permissible. Since the fork tines are generally rigid steel structures with minimal deformation, a 20cm sticker length generally meets the requirements of most forklifts. The above-mentioned technical means have improved the response speed and accuracy of acquiring laser data.

[0056] It should be noted that the ranging instrument used in this embodiment is a lidar. In other embodiments, ranging instruments such as infrared radar can also be used to generate and transmit distance-related data between the instrument and the target feature. In this embodiment, the target feature used is a high-reflectivity sticker. In other embodiments, other high-reflectivity materials can also be used as target features.

[0057] Step S202: Establish a laser coordinate system with the lidar as the origin.

[0058] Specifically, a laser coordinate system is established with the lidar as the origin, the x-axis perpendicular to the ground downwards, and the y-axis perpendicular to the x-axis and parallel to the gantry plane. This system is used to more conveniently describe the coordinate positions of the lidar, the reflection point of the lidar onto the target feature, and the center of the target feature on the coordinate system. It should be noted that this embodiment establishes the laser coordinate system with the lidar as the origin; in other embodiments, other reference points can also be used to establish a reasonable coordinate system.

[0059] Step S203: When the forks of the high-mounted forklift are at a preset reference height, the laser data is filtered and clustered to obtain the first target laser data; based on the center coordinate algorithm, the first target laser data is processed to obtain the first center coordinates of the target feature in the laser coordinate system; when the forks of the high-mounted forklift are at the target height, the laser data is filtered and clustered to obtain the second target laser data; based on the center coordinate algorithm, the second target laser data is processed to obtain the second center coordinates of the target feature in the laser coordinate system.

[0060] Specifically, based on the center coordinate algorithm, the laser data of the forks of the high-reach forklift at preset reference height and target height are processed to obtain the first center coordinate and the second center coordinate. The center coordinate algorithm is as follows: using the first and second target laser data, the average values ​​of the abscissa and ordinate of the laser reflection point on the target feature in the laser coordinate system are calculated, thereby obtaining the first and second center coordinates of the target feature in the laser coordinate system. This is used to calculate the downward bending height of the forks when the mast of the high-reach forklift moves vertically, improving the accuracy of the high-reach forklift mast calibration.

[0061] Step S204: Based on the first center coordinates, the second center coordinates, and the initial position of the lidar relative to the mast of the high-mounted forklift, obtain the calibration value of the mast of the high-mounted forklift.

[0062] Specifically, by using the first and second center coordinates, the height at which the fork teeth bend downwards when the mast of the high-lift forklift moves vertically is obtained. Then, based on the initial position of the lidar relative to the mast of the high-lift forklift, the calibration value of the high-lift forklift mast is obtained, which improves the accuracy of the calculated calibration value of the high-lift forklift mast.

[0063] Through the above-described high-mounted forklift mast calibration steps, the initial position of the lidar relative to the mast and the laser data generated by the lidar projecting laser lines onto the target feature are obtained. Then, a laser coordinate system is established with the lidar as the origin. Using the center coordinate algorithm, the laser data when the forks of the high-mounted forklift are at the preset reference height and target height are processed respectively, and the first center coordinate and the second center coordinate are obtained in sequence. Based on the first center coordinate, the second center coordinate, and the initial position of the lidar relative to the mast, the calibration value of the high-mounted forklift mast is finally obtained. This solves the problem of low mast accuracy when the forklift is automatically picking up and unloading goods in the prior art. The above calibration value is used to compensate for the system error introduced by hardware error during the lifting of the forklift mast, thereby improving the mast accuracy.

[0064] In some embodiments, when the forks of the high-mounted forklift are at a preset reference height, the laser data is filtered and clustered to obtain first target laser data; based on a center coordinate algorithm, the first target laser data is processed to obtain the first center coordinates of the target feature in the laser coordinate system, including the following steps:

[0065] Step S301: When the forks of the high-mounted forklift are at a preset reference height, the laser data is filtered and clustered to obtain the first target point cloud group.

[0066] Step S302: Based on the first target point cloud group, obtain the first center coordinates of the target feature in the laser coordinate system.

[0067] Specifically, when the forks of the high-mounted forklift are 50cm above the ground, the forks do not deform much. Therefore, in this embodiment, the reference height of the forks of the high-mounted forklift is set to 50cm. The laser data is filtered to obtain the filtered laser reflection points. The laser reflection points are clustered to obtain the first target point cloud group. The first target point cloud group is the first target laser data in this embodiment.

[0068] The following describes the specific steps for calculating the first center coordinates of the target feature in the laser coordinate system using the center coordinate algorithm and based on the first target point cloud group:

[0069] As explained above, the target features used in this embodiment are two high-reflectivity stickers, so the final number of first target point cloud groups is also 2. The mean of the abscissa of the laser reflection points in the laser coordinate system in the two first target point cloud groups is calculated and denoted as x. avg1 ,x avg2 Calculate the mean ordinate of the laser reflection points in the laser coordinate system for each of the two first target point cloud groups, denoted as y. avg1 ,y avg2 The first center coordinates of the two target features in the laser coordinate system are X1 = x avg1 +x avg2 / 2,Y2=y avg1 +y avg2 / 2.

[0070] By following the steps above, the first center coordinates of the target feature in the laser coordinate system can be accurately obtained, thereby improving the accuracy of subsequent calculations of the high-mounted forklift mast calibration values.

[0071] In some embodiments, the laser data is filtered and clustered to obtain a first target point cloud group, including the following steps:

[0072] Step S401: Process the laser data to obtain first target data; calculate the first reflectivity threshold based on the first target data; obtain the first target laser reflection point based on the first target data and the first reflectivity threshold; obtain the coordinate value of the first target laser reflection point in the laser coordinate system.

[0073] Step S402: Cluster the laser reflection points of the first target to obtain the first point cloud group; according to the target location, obtain the first point cloud group where the target feature is located, and record the first point cloud group where the target feature is located as the first target point cloud group.

[0074] Specifically, the laser data transmitted by the lidar is analyzed and processed to obtain the reflectivity of each laser reflection point, i.e., the first target data. Because the laser line projected by the lidar will reflect different light intensities when it hits different material surfaces due to differences in the material's smoothness, light transmittance, and other properties, different reflectivities will appear, which is used to determine the level of laser reflectivity. The reflectivity of the laser reflection points in the acquired first target data is sorted, and the highest reflectivity, `max_intensity`, is found. Then, the reflectivity of all other laser reflection points is summed, and `∑every_intensity` represents the sum of the reflectivity of all current points, where `point_size` represents the sum of the number of points. The average reflectivity of all laser reflection points is then calculated, yielding the average reflectivity `avg_intensity` = `∑every_intensity` / `point_size`. This average is then used to calculate a second average of the maximum reflectivity and the average reflectivity of all points, resulting in `avg_threshold` = `(max_intensity + avg_intensity) / 2`. This average is used as the first reflectivity threshold to distinguish high reflectivity laser reflection points in the first target data. Laser reflection points with reflectivity less than `avg_threshold` are filtered out, while those with reflectivity greater than `avg_threshold` are retained and designated as the first target laser reflection points. The coordinates of these first target laser reflection points in the laser coordinate system are then obtained. These first target laser reflection points are then clustered, and the coordinates of the n first target laser reflection points are defined as [x...]. n ,y n ], where n∈{1,2,...,∞}, x n y represents laser ranging. n This represents the deviation of the current point relative to the laser. For example, if the coordinates of two adjacent laser reflection points of the first target are [x0, y0] and [x1, y1], then the Euclidean distance between these two points is... Then, based on the laser reflection point of the first target and the lidar, the distance threshold T is set. threshold In this embodiment, the target feature used is a high-reflectivity sticker, with dimensions of 20cm in length and 4cm in width. Based on this parameter, and combined with the furthest distance from the laser radar to the first target laser reflection point on the high-reflectivity sticker, a distance threshold T is determined. When this furthest distance is within 3m, the distance threshold T... threshold It can be set to 2cm; the maximum distance mentioned above is greater than 3m, and the distance threshold T is... threshold It can be set to 4cm. If T≤T thresholdIf the two first target laser reflection points belong to the same first point cloud group, then they are considered to belong to the same first point cloud group; otherwise, they do not belong to the same first point cloud group. This process is repeated to cluster all points, resulting in m different first point cloud groups (m∈[1,2,...,∞)). Based on these first point cloud groups, the first target point cloud group where the target feature sits is obtained.

[0075] The first target point cloud group obtained in the above manner is used to calculate the first center coordinates of the target feature based on the coordinates of the first target laser reflection point in the first target point cloud group, thereby improving the accuracy of the obtained first center coordinates.

[0076] In some embodiments, when the forks of the high-mounted forklift are at the target height, the laser data is filtered and clustered to obtain second target laser data; based on the center coordinate algorithm, the second target laser data is processed to obtain the second center coordinates of the target feature in the laser coordinate system, including the following steps:

[0077] Step S501: When the forks of the high-mounted forklift are at the target height, the laser data is filtered and clustered to obtain the second target point cloud group.

[0078] Step S502: Based on the second target point cloud group, obtain the second center coordinates of the target feature in the laser coordinate system.

[0079] Specifically, similar to steps S301 and S302 above, when the forks of the high-mounted forklift are located at a height above the target on the ground, the laser data is filtered to obtain the filtered laser reflection points. The laser reflection points are then clustered to obtain a second target point cloud group. This second target point cloud group is the second target laser data in this embodiment.

[0080] The following describes the specific steps for calculating the second center coordinates of the target feature in the laser coordinate system using the center coordinate algorithm and based on the second target point cloud group:

[0081] As explained above, the target features used in this embodiment are two high-reflectivity stickers. Therefore, the final number of second target point cloud groups is also 2. Each second target point cloud group includes multiple laser reflection points. The mean of the abscissa of the laser reflection points in the laser coordinate system in the two second target point cloud groups is calculated and denoted as x. avg21 x avg22 Calculate the mean ordinate of the laser reflection points in the laser coordinate system for each of the two second target point cloud groups, denoted as y. avg21 y avg22 The second center coordinates of the two target features in the laser coordinate system are X. 21 =x avg21 +xavg22 / 2,Y 22 =y avg21 +y avg22 / 2.

[0082] By following the steps above, the second center coordinates of the target feature in the laser coordinate system can be accurately obtained, thereby improving the accuracy of subsequent calculations of the high-mounted forklift mast calibration values.

[0083] In some embodiments, the laser data is filtered and clustered to obtain a second target point cloud group, including the following steps:

[0084] Step S601: Process the laser data to obtain second target data; calculate the second reflectivity threshold based on the second target data; obtain the laser reflection point of the second target based on the second target data and the second reflectivity threshold; obtain the coordinate value of the laser reflection point of the second target in the laser coordinate system.

[0085] Step S602: Cluster the second target laser points to obtain a second point cloud group; based on the target location, obtain the second point cloud group where the target feature is located, and record the second point cloud group where the target feature is located as the second target point cloud group.

[0086] Specifically, similar to steps S401 and S402, the laser data transmitted by the lidar is analyzed and processed to obtain the reflectivity of each laser reflection point, i.e., the second target data. The reflectivity of the laser reflection points in the acquired second target data is sorted, and the highest reflectivity, `max_intensity`, is found. Then, the reflectivity of all other laser reflection points is summed, and `∑every_intensity` represents the sum of the reflectivity of all current points, where `point_size` represents the sum of the number of points. The average reflectivity of all laser reflection points is then calculated, yielding the average reflectivity `avg_intensity` = `∑every_intensity` / `point_size`. This average is then used to calculate a second average of the maximum reflectivity and the average reflectivity of all points, resulting in `avg_threshold` = `(max_intensity + avg_intensity) / 2`. This average is used as the second reflectivity threshold to distinguish high reflectivity laser reflection points in the second target data. Laser reflection points with reflectivity less than `avg_threshold` are filtered out, while those with reflectivity greater than `avg_threshold` are retained and designated as second target laser reflection points. The coordinates of these second target laser reflection points in the laser coordinate system are then obtained. These second target laser reflection points are then clustered, and the coordinates of the n second target laser points are defined as [x′...]. n ,y′ n], where n∈{1,2,...,∞}, x' n y' represents laser ranging. n This represents the offset of the current point relative to the laser. For example, if the coordinates of two adjacent second target laser points are [x'0, y'0] and [x'1, y'1], then the Euclidean distance between these two points is... Then, based on the laser reflection point of the second target and the lidar, the distance threshold T' is set. threshold If T'≤T' threshold If the two laser reflection points of the first target are considered to belong to the same second point cloud group, then the two laser reflection points of the second target are not considered to belong to the same second point cloud group. This process is repeated to cluster all points, resulting in m different second point cloud groups m∈[1,2,...,∞]. Based on the above second point cloud groups, the second target point cloud group where the target feature sits is obtained.

[0087] The second target point cloud group obtained in the above manner is used to calculate the second center coordinates of the target feature based on the coordinates of the second target laser reflection point in the second target point cloud group, thereby improving the accuracy of the obtained second center coordinates.

[0088] In some embodiments, based on the target location, a first point cloud group containing the target feature is obtained, and this first point cloud group is denoted as the first target point cloud group. This includes the following steps:

[0089] Step S701: When the number of first point cloud groups is less than the number of target features, check the target features and target locations; reacquire the first point cloud groups; reacquire the first point cloud groups where the target features are located based on the target locations; and record the first point cloud groups where the target features are located as the first target point cloud groups.

[0090] Step S702: When the number of points in the first point cloud group is equal to the number of target features, the first point cloud group is the first target point cloud group; obtain the first target point cloud group.

[0091] Step S703: When the number of the first point cloud group is greater than the number of target features, obtain the first target point cloud group according to the target location.

[0092] Specifically, first determine the specific value of the first cloud cluster m. Since the number of target features used in this embodiment is 2, when m < 2, it is considered that the desired target feature has not been obtained. At this time, it is necessary to check whether the pasted target feature is a high reflectivity material, whether it is obviously different from the surrounding ground, whether the laser is facing the feature, and whether the laser can scan the feature. After checking, repeat the above steps.

[0093] When m = 2, it indicates that the first point cloud group obtained is the point cloud group containing the two target features, i.e., the first target point cloud group. When m ≥ 2, it indicates that there is noise affecting the currently extracted target features, resulting in more extracted features than the actual number of target features. At this point, further searching for point cloud groups that meet the requirements is necessary. Calculate the length of each first point cloud group and the distance of each laser reflection point in the laser coordinate system, using the coordinates [x, y] of each point obtained above. Since the laser's fixed position is perpendicular to the ground, the length of the point cloud group can be directly calculated using the y-value of the laser reflection point coordinates. Sort each point in the point cloud group along the y-direction and find the maximum y-value. max and minimum y min According to the above maximum value y max and minimum value y min To calculate the length length = |y max -y min Since the laser reflection points fluctuate, if the absolute value of the calculated length minus the target feature width of 4cm is less than 1cm, it means that the first cloud group found currently meets the requirement |length-0.04|<0.01. Then, the x-values ​​of the laser point coordinates are sorted in a similar way, and the maximum x-value is selected. max and minimum x min Then subtract x from x. err =|x max -x min | Determine the fluctuation of the point cloud before and after x err A value less than 0.01 is considered acceptable. This method is used to filter all first point cloud groups, retaining those that meet the requirements. If fewer than 2 point cloud groups are retained, no target feature is found; otherwise, the mean x of the coordinates of all laser points in each first point cloud group is calculated. avg = (x1+x2+...+x) n ) / n, y avg = (y1+y2+...+y n ) / n, and then take the average of the coordinates of all laser points in each first point cloud group [x avg ,y avg Calculate the difference between the two first-point cloud clusters, then subtract the actual measured distance to satisfy err_y=||y avg1 -y avg2 |-width|<0.01, err_x=|x avg1 -x avg2 |<0.01 where err_y represents the difference between the distance between the two first-point cloud clusters and the actual distance to the target feature, y avg1 ,y avg2The x and y coordinates of the two point cloud means are respectively, and the width represents the actual distance between the two target features. avg1 ,x avg2 Let err_y and err_x represent the x-coordinates of the two point cloud means, respectively. If both err_y and err_x are less than 1 cm, then the two first point cloud groups found are considered to correspond to the actual feature location. These two first point cloud groups are the first target point cloud groups. The first target point cloud group containing the target feature is obtained in this way. This is used to verify whether the first point cloud group contains a first target point cloud group that overlaps with the target location of the target feature, thus ensuring the accuracy of the found first target point cloud group.

[0094] In some embodiments, based on the target location, a second point cloud group containing the target feature is obtained, and this second point cloud group is denoted as the second target point cloud group. This includes the following steps:

[0095] Step S801: When the number of second point cloud groups is less than the number of target features, check the target features and target locations; reacquire the second point cloud groups; reacquire the second point cloud groups where the target features are located based on the target locations; and record the second point cloud groups where the target features are located as the second target point cloud groups.

[0096] Step S802: When the number of points in the second point cloud group is equal to the number of target features, the second point cloud group is the second target point cloud group; obtain the second target point cloud group.

[0097] Step S803: When the number of second point cloud groups is greater than the number of target features, obtain the second target point cloud group based on the target location.

[0098] Specifically, similar to steps S701, S702, and S703 above, the specific value of the obtained first point cloud group m is first determined. Since the number of target features used in this embodiment is 2, when m < 2, it is considered that no target feature has been obtained. At this time, it is necessary to check whether the pasted target feature is a high reflectivity material, whether it is clearly distinguishable from the surrounding ground, whether the laser is directly facing the feature, and whether the laser can scan the feature. After checking, the above steps are repeated. When m = 2, it means that the obtained second point group is a point cloud group where two target features are located, that is, the second target point cloud group. When m ≥ 2, it means that there is noise in the currently extracted high reflectivity feature, resulting in more than the actual number of target features extracted. At this time, a point cloud group that meets the requirements is further searched. The length of each second point cloud group and the distance of each laser reflection point in the laser coordinate system are calculated, and the coordinates [x', y'] of each point obtained above are used. Since the laser's fixed position is perpendicular to the ground, the length of the point cloud can be directly calculated using the y-value of the laser reflection point. By sorting each point in the point cloud along the y-direction, the maximum y' value can be found. max and minimum y' min The length is calculated based on the maximum and minimum values: length = |y' max -y' min Since the laser reflection point fluctuates, if the absolute value of the calculated length minus the target feature width of 4cm is less than 1cm, it means that the currently found second point cloud group meets the requirement |length-0.04|<0.01. Then, the x-values ​​of the laser point coordinates are sorted in a similar way, and the maximum x' is selected. max and minimum x' min Then subtract x' from x. err =|x' max -x' min | Determine the fluctuation of the point cloud before and after x' err A value less than 0.01 is considered acceptable. This method is used to filter all second point cloud groups, retaining those that meet the requirements. If the number of retained second point cloud groups is less than 2, it is considered that no target feature has been found; otherwise, the mean x' of the coordinates of all laser points in each second point cloud group is calculated. avg = (x'1+x'2+...+x') n ) / n,y' avg =(y'1+y'2+...+y' n ) / n, and then take the mean [x'] of the coordinates of all laser points in each second point cloud group. avg ,y' avg Calculate the difference between the two second-point cloud clusters, then subtract the actual measured distance to satisfy err_y'=||y' avg1 -y'avg2 |-width|<0.01, err_x'=|x' avg1 -x' avg2 |<0.01 where err_y' represents the difference between the distance between the two second-point cloud clusters and the actual distance to the target feature, y' avg1 ,y' avg2 Let x' and y' represent the y-coordinates of the two point cloud means, respectively, and width represent the actual distance between the two target features. avg1 ,x' avg2 Let err_y' and err_x' represent the x-coordinates of the two point cloud means, respectively. If both err_y' and err_x' are less than 1 cm, then the two second point cloud groups found are considered to correspond to the actual feature location. The two second point cloud groups found above are the second target point cloud groups. The second target point cloud group containing the target feature is obtained in the above way and used to verify whether the second point cloud group contains a second target point cloud group that coincides with the target location of the target feature, thereby ensuring the accuracy of the found second target point cloud group.

[0099] In some embodiments, the lidar is mounted on the forks of the overhead forklift, including the following steps:

[0100] Step S901: The lidar is positioned at the exact center of the fork.

[0101] In step S902, the scanning direction of the lidar is downward and perpendicular to the ground.

[0102] Specifically, the lidar is fixed at the center of the fork teeth of the high-mounted forklift, with the scanning direction facing downwards and perpendicular to the ground. This installation allows the lidar to scan target features on the ground more effectively and accurately.

[0103] The present embodiment will now be described and illustrated through preferred embodiments.

[0104] Figure 3 This is a flowchart of the high-mounted forklift mast calibration method according to a preferred embodiment, such as... Figure 3 As shown, the calibration method for the high-mounted forklift mast includes the following steps:

[0105] Step S1: Fix the laser radar on the fork teeth. The laser radar is fixed in the center of the fork teeth and the scanning direction is downward and perpendicular to the ground. This installation can make the laser scan the object to be inspected on the ground better and more accurately.

[0106] Step S2: Measure the initial position of the lidar relative to the gantry. After the lidar is fixed, measure the coordinates of the lidar relative to the gantry. The direction of the fork is x. Since the lidar is fixed at the center of the fork, the lateral y is 0. Record the measured coordinates for later use.

[0107] Step S3: Fix the target feature. First, manually cut out two high-reflectivity stickers with a width of 4cm and a length of 20cm. Then, symmetrically paste the two stickers with respect to the center of the fork teeth, ensuring that the high-reflectivity stickers are aligned and symmetrical with the fork teeth and located directly below the fork teeth. Then, manually control the fork teeth to raise them to a height of 50cm above the ground, because the fork teeth have almost no deformation at around 50cm. Use this height as the standard to start the measurement.

[0108] Step S4: Extract target features. First, connect the lidar, retrieve and analyze the data transmitted by the lidar, and calculate the reflectivity corresponding to each laser point from the analyzed data. Because the intensity of light reflected by a laser hitting different material surfaces varies due to differences in the material's smoothness, light transmittance, etc., different reflectivities will appear, thus determining the level of laser reflectivity. The reflectivity of each laser point is sorted, and the highest reflectivity, `max_intensity`, is found. Then, the reflectivity of all other laser points is summed, and the sum of the reflectivity of all points is represented by `∑every_intensity`, where `point_size` represents the total number of points. The average reflectivity, `avg_intensity`, is then calculated as `∑every_intensity` / `point_size`. This average is then used to calculate a second average of the maximum reflectivity and the average reflectivity of all points. The final result is `avg_threshold` = `(max_intensity + avg_intensity) / 2`. This average is used as the threshold for distinguishing high reflectivity; points with reflectivity greater than `avg_threshold` are marked as high reflectivity points, while points with reflectivity less than `avg_threshold` are discarded. This process yields the high reflectivity target features.

[0109] Step S5: Calculate the center coordinates of the two high-reflectivity target features. Based on the high-reflectivity features extracted above, cluster them. Assume the coordinates of each high-reflectivity laser point are [x...]. n ,y n Let x represent the laser range and y represent the offset of the current point relative to the laser. Here, n∈{1,2,...,∞} represents the number of pairs of [x,y] points equal to the number of highly reflective points. If the coordinates of two adjacent highly reflective laser beams are [x0,y0] and [x1,y1], then the Euclidean distance between these two points is... If T≤T thresholdIf two points belong to the same class, they are considered to belong to another class. This process is repeated to cluster all points, resulting in m different point cloud groups, m∈[1,2,...,∞]. The specific value of the obtained point cloud group m is then determined. If m<2, it is considered that the desired high reflectivity feature has not been obtained. In this case, it is necessary to check whether the pasted feature is a high reflectivity material, whether it is clearly distinguishable from the surrounding ground, whether the laser is directly facing the feature, and whether the laser can scan the feature. After checking, the above steps are repeated. If m≥2, it indicates that there is noise affecting the extracted high reflectivity feature, resulting in more extracted features than the actual number of features. At this point, further searching for point cloud groups that meet the requirements is needed. The length of each point cloud group and the distance of each point in the laser coordinate system are calculated, using the coordinates [x,y] of each point obtained above. Since the laser's fixed position is perpendicular to the ground, the length of the point cloud group can be directly calculated using the y-value of the laser point coordinates. Each point in the point cloud group is sorted in the y-direction, and the maximum y-value is found. max and minimum y min The length is calculated based on the maximum and minimum values: length = |y max -y min Since the laser points fluctuate, if the absolute value of the calculated length minus the actual feature width of 4cm is less than 1cm, it means that the current point cloud group meets the requirement |length-0.04|<0.01. Then, the x-values ​​of the laser point coordinates are sorted in a similar way, and the maximum x-value is selected. max and minimum x min Then subtract x from x. err =|x max -x min | Determine the fluctuation of the point cloud before and after x err A value less than 0.01 is considered acceptable. This method is used to filter all point cloud groups, retaining those that meet the requirements. If fewer than 2 point cloud groups are retained, no feature objects are considered found; otherwise, the mean x of the coordinates of all laser points in each point cloud group is calculated. avg = (x1+x2+...+x) n ) / n, y avg = (y1+y2+...+y n Then, take the average of the coordinates of all laser points in each point cloud group. Calculate the distance between the two point cloud groups by taking the difference between them, then subtract the actual measured distance to satisfy err_y=||y avg1 -y avg2 |-width|<0.01, err_x=|x avg1 -x avg2 |<0.01 where err_y represents the difference between the distance between two point cloud groups and the actual distance to the target feature, y avg1,y avg2 The x and y coordinates of the two point cloud means are respectively, and the width represents the actual distance between the two target features. avg1 ,x avg2 Let err_y and err_x represent the x-coordinates of the two point cloud means, respectively. If both err_y and err_x are less than 1 cm, then the two point cloud groups found are considered to correspond to the actual target feature. Then, calculate the center coordinates X = x between the two features. avg1 +x avg2 / 2,Y=y avg1 +y avg2 / 2.

[0110] Step S6: Calculate the coordinates of the gantry relative to the ground and the gantry calibration value. Raise the fork to 50cm above the ground, as the fork has virtually no deformation at around 50cm; use this height as the standard. Calculate the coordinates of the feature center in the laser coordinate system when the fork height is 50cm as [x...]. ori y ori The initial measured height is recorded as height. ori First, calculate the standard height error h. err =|height ori -x ori |, Standard angular error deg err =acos(height) ori / x ori Then, the fork teeth are raised to different heights for gantry calibration. Assuming the height of the fork teeth is h1 at this moment, and the coordinates of the two feature centers detected by the lidar are [x1, y1], the current offset angle deg of the fork teeth is calculated based on the current coordinates and height. cur =acos(h1 / x1), calculate the downward bending height h of the fork based on the angle = tan(deg) cur -deg err *fork length y err =y1-y ori Where h represents the height to which the fork bends downwards, fork length The x-value and y-value represent the x-coordinate of the lidar relative to the gantry in step S2. err This indicates lateral deviation. We obtain (h, y) err That is, to calculate the calibration values ​​corresponding to different heights.

[0111] The high-mounted forklift mast calibration method described in the above preferred embodiment solves the problem of low mast accuracy during automated forklift loading and unloading in the prior art by fixing a lidar on the fork teeth; measuring the initial position of the lidar relative to the mast; fixing target features; extracting target features; calculating the center coordinates of two high-reflectivity target features; and calculating the mast coordinates and mast calibration value relative to the ground. It also reduces the system error introduced by hardware errors during the forklift mast lifting process and improves the mast accuracy.

[0112] This embodiment also provides a high-mounted forklift mast calibration device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. The terms "module," "unit," "subunit," etc., used below refer to combinations of software and / or hardware that perform a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0113] Figure 4 This is a structural block diagram of the high-mounted forklift mast calibration device in this embodiment, as shown below. Figure 4 As shown, the device includes: a radar setting module 10, a coordinate acquisition module 20, and a calibration module 30.

[0114] Specifically, the radar setting module 10 is used to acquire the initial position of the lidar relative to the mast of the high-reach forklift; the lidar is set on the forks of the high-reach forklift; the lidar projects a laser line onto the target feature to generate corresponding laser data; the target feature is set at the target position; the coordinate acquisition module 20 is used to establish a laser coordinate system with the lidar as the origin; when the forks of the high-reach forklift are at a preset reference height, the laser data is filtered and clustered to obtain the first target laser data; based on the center coordinate algorithm, the first target laser data is processed to obtain the first center coordinate of the target feature in the laser coordinate system; when the forks of the high-reach forklift are at the target height, the laser data is filtered and clustered to obtain the second target laser data; based on the center coordinate algorithm, the second target laser data is processed to obtain the second center coordinate of the target feature in the laser coordinate system; the calibration module 30 is used to obtain the calibration value of the mast of the high-reach forklift based on the first center coordinate, the second center coordinate, and the initial position of the lidar relative to the mast of the high-reach forklift.

[0115] The above-mentioned high-mounted forklift mast calibration device solves the problem of low mast accuracy when forklifts are automatically picking up and unloading goods in the existing technology. It reduces the system error introduced by hardware error during the lifting of the forklift mast and improves the mast accuracy.

[0116] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0117] In some embodiments, the coordinate acquisition module 20 is also used to filter and cluster the laser data to obtain a first target point cloud group when the fork of the high-mounted forklift is at a preset reference height.

[0118] Based on the first target point cloud group, the first center coordinates of the target feature in the laser coordinate system are obtained.

[0119] In some embodiments, the coordinate acquisition module 20 is further configured to process the laser data to obtain first target data; calculate a first reflectivity threshold based on the first target data; obtain a first target laser reflection point based on the first target data and the first reflectivity threshold; and obtain the coordinate values ​​of the first target laser reflection point in the laser coordinate system.

[0120] Cluster the laser reflection points of the first target to obtain the first point cloud group; based on the target location, obtain the first point cloud group where the target feature is located, and record the first point cloud group where the target feature is located as the first target point cloud group.

[0121] In some embodiments, the coordinate acquisition module 20 is also used to filter and cluster the laser data when the forks of the high-mounted forklift are at the target height to obtain a second target point cloud group.

[0122] Based on the second target point cloud group, the second center coordinates of the target feature in the laser coordinate system are obtained.

[0123] In some embodiments, the coordinate acquisition module 20 is further configured to process the laser data to obtain second target data; calculate a second reflectivity threshold based on the second target data; obtain the second target laser reflection point based on the second target data and the second reflectivity threshold; and acquire the coordinate values ​​of the second target laser reflection point in the laser coordinate system.

[0124] Cluster the second target laser points to obtain a second point cloud group; based on the target location, obtain the second point cloud group where the target features are located, and record the second point cloud group where the target features are located as the second target point cloud group.

[0125] In some embodiments, the coordinate acquisition module 20 is further configured to check the target features and target positions when the number of first point cloud groups is less than the number of target features; reacquire the first point cloud groups; reacquire the first point cloud groups where the target features are located based on the target positions; and record the first point cloud groups where the target features are located as the first target point cloud groups.

[0126] When the number of points in the first point cloud group is equal to the number of target features, the first point cloud group is the first target point cloud group; obtain the first target point cloud group;

[0127] When the number of points in the first cloud cluster is greater than the number of target features, the first target point cloud cluster is obtained based on the target location.

[0128] In some embodiments, the coordinate acquisition module 20 is further configured to check the target features and target positions when the number of second point cloud groups is less than the number of target features; reacquire the second point cloud groups; reacquire the second point cloud groups where the target features are located based on the target positions; and record the second point cloud groups where the target features are located as the second target point cloud groups.

[0129] When the number of points in the second point cloud group is equal to the number of target features, the second point cloud group is the second target point cloud group; obtain the second target point cloud group.

[0130] When the number of second point cloud clusters is greater than the number of target features, the second target point cloud cluster is obtained based on the target location.

[0131] In some embodiments, the radar setting module 10 further includes: a lidar at the exact center of the fork; the lidar's scanning direction is downward and perpendicular to the ground.

[0132] This embodiment also provides a high-lift forklift mast calibration system, including a high-lift forklift, a lidar, a target feature, a memory, and a processor. The memory and processor are located on the high-lift forklift, the lidar is mounted on the forks of the high-lift forklift, and the target feature is located directly below the forks of the high-lift forklift. The memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0133] Optionally, the system may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0134] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0135] S11, Obtain the initial position of the lidar relative to the mast of the high-reach forklift; The lidar is set on the fork of the high-reach forklift; The lidar projects a laser line onto the target feature to generate corresponding laser data; The target feature is set at the target position.

[0136] S12, establish a laser coordinate system with the lidar as the origin.

[0137] S13, when the forks of the high-mounted forklift are at a preset reference height, the laser data is filtered and clustered to obtain the first target laser data; based on the center coordinate algorithm, the first target laser data is processed to obtain the first center coordinate of the target feature in the laser coordinate system; when the forks of the high-mounted forklift are at the target height, the laser data is filtered and clustered to obtain the second target laser data; based on the center coordinate algorithm, the second target laser data is processed to obtain the second center coordinate of the target feature in the laser coordinate system.

[0138] S14. Based on the first center coordinates, the second center coordinates, and the initial position of the lidar relative to the mast of the high-mounted forklift, obtain the calibration value of the mast of the high-mounted forklift.

[0139] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated in this embodiment.

[0140] It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. All other embodiments derived by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0141] Obviously, the accompanying drawings are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar situations based on these drawings without any creative effort. Furthermore, it is understood that although the work done in this development process may be complex and lengthy, for those skilled in the art, certain design, manufacturing, or production modifications made based on the technical content disclosed in this application are merely conventional technical means and should not be considered as insufficient disclosure of this application.

[0142] The term "embodiment" in this application refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily imply the same embodiment, nor does it imply that it is mutually exclusive with or independent of other embodiments. It will be clearly or implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0143] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of patent protection. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the appended claims.

Claims

1. A method for calibrating a high-mounted forklift mast, characterized in that, include: Obtain the initial position of the lidar relative to the mast of the high-mounted forklift; The lidar is mounted on the fork teeth of the high-mounted forklift; The lidar projects a laser line onto the target feature to generate corresponding laser data; the target feature is positioned at the target location. Establish a laser coordinate system with the aforementioned lidar as the origin; When the forks of the high-mounted forklift are at a preset reference height, the laser data is filtered and clustered to obtain first target laser data; based on the center coordinate algorithm, the first target laser data is processed to obtain the first center coordinates of the target feature in the laser coordinate system; when the forks of the high-mounted forklift are at the target height, the laser data is filtered and clustered to obtain second target laser data; based on the center coordinate algorithm, the second target laser data is processed to obtain the second center coordinates of the target feature in the laser coordinate system. The calibration value of the mast of the high-reach forklift is obtained based on the first center coordinates, the second center coordinates, and the initial position of the lidar relative to the mast of the high-reach forklift.

2. The high-mounted forklift mast calibration method according to claim 1, characterized in that, When the forks of the high-mounted forklift are at a preset reference height, the laser data is filtered and clustered to obtain the first target laser data; Based on the center coordinate algorithm, the laser data of the first target is processed to obtain the first center coordinates of the target feature in the laser coordinate system, including: When the forks of the high-mounted forklift are at a preset reference height, the laser data is filtered and clustered to obtain a first target point cloud group. Based on the first target point cloud group, the first center coordinates of the target feature in the laser coordinate system are obtained. 。 3. The high-mounted forklift mast calibration method according to claim 2, characterized in that, The process of filtering and clustering the laser data to obtain a first target point cloud group includes: The laser data is processed to obtain first target data; a first reflectivity threshold is calculated based on the first target data; a first target laser reflection point is obtained based on the first target data and the first reflectivity threshold; and the coordinates of the first target laser reflection point in the laser coordinate system are acquired. Cluster the first target laser reflection points to obtain a first point cloud group; based on the target location, obtain the first point cloud group where the target feature is located, and record the first point cloud group where the target feature is located as the first target point cloud group.

4. The high-mounted forklift mast calibration method according to claim 1, characterized in that, When the forks of the high-mounted forklift are at the target height, the laser data is filtered and clustered to obtain second target laser data; based on the center coordinate algorithm, the second target laser data is processed to obtain the second center coordinates of the target feature in the laser coordinate system, including: When the forks of the high-mounted forklift are at the target height, the laser data is filtered and clustered to obtain a second target point cloud group. Based on the second target point cloud group, the second center coordinates of the target feature in the laser coordinate system are obtained. 。 5. The high-mounted forklift mast calibration method according to claim 4, characterized in that, The process of filtering and clustering the laser data to obtain a second target point cloud group includes: The laser data is processed to obtain second target data; a second reflectivity threshold is calculated based on the second target data; a second target laser reflection point is obtained based on the second target data and the second reflectivity threshold; and the coordinates of the second target laser reflection point in the laser coordinate system are acquired. Cluster the second target laser points to obtain a second point cloud group; based on the target location, obtain the second point cloud group where the target feature is located, and record the second point cloud group where the target feature is located as the second target point cloud group.

6. The high-mounted forklift mast calibration method according to claim 3, characterized in that, The step of obtaining the first point cloud group where the target feature is located based on the target location, and denoting the first point cloud group where the target feature is located as the first target point cloud group, includes: When the number of points in the first point cloud group is less than the number of target features, the target features and the target location are checked; the first point cloud group is reacquired; the first point cloud group where the target feature is located is reacquired based on the target location; the first point cloud group where the target feature is located is recorded as the first target point cloud group. When the number of points in the first point cloud group is equal to the number of target features, the first point cloud group is the first target point cloud group; obtain the first target point cloud group; When the number of the first point cloud clusters is greater than the number of the target features, the first target point cloud clusters are obtained based on the target location.

7. The high-mounted forklift mast calibration method according to claim 5, characterized in that, The step of obtaining the second point cloud group where the target feature is located based on the target location, and denoting the second point cloud group where the target feature is located as the second target point cloud group, includes: When the number of points in the second cloud cluster is less than the number of target features, the target features and the target location are checked; the second cloud cluster is reacquired; the second cloud cluster where the target feature is located is reacquired based on the target location; the second cloud cluster where the target feature is located is recorded as the second target cloud cluster. When the number of points in the second point cloud group is equal to the number of target features, the second point cloud group is the second target point cloud group; acquire the second target point cloud group; When the number of the second point cloud clusters is greater than the number of the target features, the second target point cloud clusters are obtained based on the target location.

8. The method for calibrating a high-mounted forklift mast according to claim 1, characterized in that, The lidar is mounted on the forks of the high-mounted forklift, including: The lidar is located at the exact center of the fork tooth; The LiDAR scanner is positioned downwards and perpendicular to the ground.

9. A high-mounted forklift mast calibration device, characterized in that, include: The radar setting module is used to obtain the initial position of the lidar relative to the mast of the high-mounted forklift; The lidar is mounted on the fork teeth of the high-mounted forklift; The lidar projects a laser line onto the target feature to generate corresponding laser data; the target feature is positioned at the target location. The coordinate acquisition module is used to establish a laser coordinate system with the lidar as the origin; when the forks of the high-mounted forklift are at a preset reference height, the laser data is filtered and clustered to obtain first target laser data; based on the center coordinate algorithm, the first target laser data is processed to obtain the first center coordinates of the target feature in the laser coordinate system; when the forks of the high-mounted forklift are at the target height, the laser data is filtered and clustered to obtain second target laser data; based on the center coordinate algorithm, the second target laser data is processed to obtain the second center coordinates of the target feature in the laser coordinate system. The calibration module is used to obtain the calibration value of the mast of the high-reach forklift based on the first center coordinates, the second center coordinates, and the initial position of the lidar relative to the mast of the high-reach forklift.

10. A high-mounted forklift mast calibration system, comprising a high-mounted forklift, a lidar, a target feature, a memory, and a processor, characterized in that, The memory and the processor are located on the high-lift forklift, the lidar is disposed on the fork teeth of the high-lift forklift, the target feature is located directly below the fork teeth of the high-lift forklift, the memory stores a computer program, and the processor is configured to run the computer program to perform the high-lift forklift mast calibration method according to any one of claims 1 to 8.

Images