Granary environment three-dimensional modeling method based on three-dimensional point cloud data

Abstract

The embodiment of the invention discloses a granary environment three-dimensional modeling method based on three-dimensional point cloud data, and the method comprises the steps: carrying out the non-uniform weighted filtering of the point cloud data in a granary, and obtaining first point cloud data; performing raise dust noise elimination on the first point cloud data based on reflection intensity and dynamic filtering to obtain second point cloud data; identifying the position of the robot through a dynamic shielding detection method, and removing local point cloud data of the position of the robot in the second point cloud data to obtain third point cloud data; classifying hole regions in the third point cloud data, and repairing different types of hole regions; performing down-sampling on the repaired point cloud data by adopting a voxel grid method to generate fourth point cloud data with consistent point density; performing elevation map calculation and flatness analysis on the fourth point cloud data to obtain a granary analysis result; a high-precision granary environment three-dimensional model can be constructed, and environment key information can be extracted.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional point cloud data processing, and in particular to a three-dimensional modeling method for a granary environment based on three-dimensional point cloud data. Background Art

[0002] With the development of intelligent and digital technologies, the demand for granary environmental monitoring and management is increasing.

[0003] Traditional granary monitoring and management methods rely on manual operations, which have problems such as low efficiency and poor accuracy. The modeling method based on 3D point cloud data can achieve an accurate description of the internal environment of the granary, provide comprehensive data support for managers, and provide maps for robot operations.

[0004] However, in the application scenario of granary, the length and width of the granary are usually much larger than the distance from the grain surface to the installation position of the lidar, forming a flat collection environment. Point cloud data usually have problems such as noise, multiple holes, and large differences in point density. In addition, when there are robots operating in the granary, the robots and operations generate dust, which directly affects the accuracy of subsequent modeling and calculations. Summary of the invention

[0005] The main purpose of the present invention is to provide a three-dimensional modeling method for a granary environment based on three-dimensional point cloud data, solve the data quality problem, construct a high-precision three-dimensional model of the granary environment, and accurately extract key environmental information.

[0006] To achieve the above-mentioned purpose, the present application provides, in a first aspect, a method for three-dimensional modeling of a granary environment based on three-dimensional point cloud data, the method comprising: Perform non-uniform weighted filtering on the point cloud data in the granary to obtain the first point cloud data; Perform dust noise removal on the first point cloud data based on reflection intensity and dynamic filtering to obtain second point cloud data; Identify the location of the robot by a dynamic occlusion detection method, so as to remove the local point cloud data of the location of the robot in the second point cloud data, and obtain third point cloud data; Classifying the hole areas in the third point cloud data, and repairing different types of hole areas; The restored point cloud data is downsampled using the voxel grid method to generate the fourth point cloud data with consistent point density; Elevation map calculation and flatness analysis are performed on the fourth point cloud data to obtain a granary analysis result.

[0007] A second aspect of the present application provides a three-dimensional modeling device for a granary environment based on three-dimensional point cloud data, comprising: A filtering processing module is used to perform non-uniform weighted filtering on the point cloud data in the granary to obtain the first point cloud data; The filtering processing module is further used to remove dust noise from the first point cloud data based on reflection intensity and dynamic filtering to obtain second point cloud data; The filtering processing module is further used to identify the position of the robot through a dynamic occlusion detection method, so as to remove the local point cloud data of the position of the robot in the second point cloud data to obtain third point cloud data; A completion and repair module, used to classify hole areas in the third point cloud data and repair different types of hole areas; A rasterization module, used to downsample the repaired point cloud data using a voxel grid method to generate fourth point cloud data with consistent point density; The calculation and analysis module is used to perform elevation map calculation and flatness analysis on the fourth point cloud data to obtain a granary analysis result.

[0008] A third aspect of the present application provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the first aspect and any possible implementation thereof.

[0009] To achieve the above-mentioned purpose, the fourth aspect of the present application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the various steps in the method described in the first aspect.

[0010] The present application provides a three-dimensional modeling method for a granary environment based on three-dimensional point cloud data. The first point cloud data is obtained by performing non-uniform weighted filtering on the point cloud data in the granary; the dust noise is eliminated on the first point cloud data based on reflection intensity and dynamic filtering to obtain the second point cloud data; the position of the robot is identified by a dynamic occlusion detection method to eliminate the local point cloud data of the position of the robot in the second point cloud data to obtain the third point cloud data; the hole areas in the third point cloud data are classified, and different types of hole areas are repaired; the repaired point cloud data are down-sampled by using a voxel grid method to generate fourth point cloud data with consistent point density; the fourth point cloud data is subjected to elevation map calculation and flatness analysis to obtain the granary analysis result; a high-precision three-dimensional model of the granary environment can be constructed, and key environmental information can be extracted. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0012] in: Figure 1 A schematic diagram of a process of a three-dimensional modeling method of a granary environment based on three-dimensional point cloud data provided in an embodiment of the present application; Figure 2 A flowchart of a method for three-dimensional modeling of a granary environment based on three-dimensional point cloud data provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of a three-dimensional modeling device for a granary environment based on three-dimensional point cloud data provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0013] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in 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. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0014] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0015] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0016] See also Figure 1 , is a flow chart of a method for three-dimensional modeling of a granary environment based on three-dimensional point cloud data provided in an embodiment of the present application, Figure 1 The steps include: 101. Perform non-uniform weighted filtering on the point cloud data in the granary to obtain the first point cloud data.

[0017] The execution subject of the method in the embodiment of the present application may be a three-dimensional modeling of a granary environment based on three-dimensional point cloud data, and in a specific application may be a terminal device, such as a computer.

[0018] A three-dimensional modeling method for a granary environment based on three-dimensional point cloud data is provided in an embodiment of the present application, which mainly includes the steps of point cloud filtering processing, point cloud completion, point cloud rasterization, elevation map calculation, flatness analysis, etc. A high-precision three-dimensional model is constructed through a series of data processing and analysis technologies, and key environmental information is extracted.

[0019] In view of the uneven distribution density of point clouds in the granary, non-uniform weighted filtering is adopted. First, a local density estimation algorithm is used to obtain the sparsity of the point cloud.

[0020] In an optional implementation, the above step 101 includes: Calculate and obtain the local density of the point cloud data in the granary; The points are weighted according to the local density of the point cloud data in the granary; A statistical filtering algorithm is used to evaluate the outlier degree of each point in the point cloud using the mean and standard deviation to filter out outliers.

[0021] Specifically, the above local density can be calculated in the following way: Assume that the point cloud dataset is , for each point , calculate its local density:

[0022] in: express Neighborhood (such as Neighboring points); is the kernel function:

[0023] in, is the scale parameter.

[0024] In the embodiment of the present application, the points are weighted according to the sparsity of the point cloud, and the points in the lower density area receive lower filtering weights to avoid feature loss. Calculate weighting factors :

[0025] Make , points with lower density have smaller weights.

[0026] Then apply the statistical filtering algorithm. The point cloud is statistically filtered using the mean and standard deviation to define the point The degree of outlier:

[0027] in: Yes The value of a dimension (such as shaft height).

[0028] and are the mean and standard deviation of this dimension respectively.

[0029] According to the set threshold , filter out and Larger points are used to retain feature points in low-density areas.

[0030] 102. Remove dust noise from the first point cloud data based on reflection intensity and dynamic filtering to obtain second point cloud data.

[0031] In the embodiment of the present application, in order to address the possible dust interference in the granary, the reflection intensity caused by the dust is high but the duration is short, and dust noise is removed based on reflection intensity and dynamic filtering.

[0032] In an optional implementation, the above step 102 includes: Calculate the global mean and standard deviation of the reflection intensity of all points in the first point cloud data; According to the above global mean standard deviation and set threshold, outliers are determined; Perform dynamic filtering based on the scanning cycle; The frequency of occurrence of each point in the time window is calculated, and dust points are eliminated based on the frequency of occurrence of each point in the time window and the stability threshold.

[0033] Reflection intensity anomaly detection, assuming the point cloud dataset is ,in Yes The reflection intensity of . Calculate the global mean and standard deviation:

[0034] Setting Thresholds , determine the dust point:

[0035] in It can be an empirical parameter. For example, the value can be between 2 and 3.

[0036] Based on the dynamic filtering of the scanning cycle, the multi-frame point cloud data is assumed to be Indicates Frame point cloud, define a time window Contains recent frame:

[0037] For each point , calculate its frequency of occurrence in the time window :

[0038] in is the indicator function, indicating that the point Whether it appears in a certain frame of point cloud.

[0039] Setting stability thresholds ,like:

[0040] It is considered that It is caused by dust and should be removed.

[0041] 103. Identify the location of the robot by using a dynamic occlusion detection method, so as to eliminate the local point cloud data of the location of the robot in the second point cloud data, and obtain third point cloud data.

[0042] In the embodiment of the present application, a dynamic occlusion detection method is adopted to solve the problem that the robot operating in the granary affects the accuracy of the point cloud data. The position of the robot in the granary point cloud is identified through a deep learning object detection algorithm, and the local point cloud data at the robot's location is eliminated.

[0043] Specifically, the granary point cloud data can be set as , where each point is represented by The robot’s position in the granary is identified by a deep learning object detection algorithm and is denoted as ,in The detection frame The defined point set is:

[0044] Eliminate the point cloud data of the area where the robot is located, that is, calculate the point cloud after removing the influence of the robot :

[0045] By combining statistical methods and time series analysis, outliers caused by dust are effectively identified and eliminated, thereby improving the quality and reliability of point cloud data.

[0046] 104. Classify the hole areas in the third point cloud data, and repair different types of hole areas.

[0047] The larger holes in the granary point cloud data are mainly caused by the scanning shadow surface formed by the grain pile blocking the laser beam, or the area where the point cloud is located is eliminated. The smaller holes are caused by the local point cloud being too sparse.

[0048] In the embodiment of the present application, the hole areas in the point cloud can be classified according to their area or diameter: Small hole area: The area smaller than the set area threshold is defined as a small hole.

[0049] Large hole area: Holes with an area exceeding the set area threshold are defined as large holes. The specific area threshold can be dynamically adjusted according to the resolution and accuracy requirements of the granary point cloud data, such as 40 square centimeters.

[0050] In an optional implementation, the above step 104 includes: Classifying the hole regions in the third point cloud data according to the area or diameter of the hole regions to obtain first-class holes and second-class holes; For the first type of holes mentioned above, an interpolation-based reconstruction method is used to fill them with bilinear interpolation based on the surrounding data; For the second type of holes mentioned above, the Poisson surface reconstruction algorithm is used to complete the repair.

[0051] The area (or diameter) of the first type of holes is smaller than the area (or diameter) of the second type of holes.

[0052] Specifically, the hole area calculation step may include: Use the Delaunay triangulation algorithm to triangulate the point cloud into a series of triangles. The edges that are not filled in the triangulation process are the hole boundaries. Project the boundary points to the XY plane to construct a closed polygon. Given the boundary points : Calculate area

[0053] in , that is, it is applicable to closed polygons.

[0054] For smaller hole areas, an interpolation-based reconstruction method is used to perform bilinear interpolation filling based on the surrounding data.

[0055] Specifically, it can be interpolated based on the height values ​​of the nearest known points (four neighboring points) around it:

[0056] are the height values ​​of the four surrounding points respectively.

[0057] and is the normalized distance from the interpolation point to its neighboring point.

[0058] For larger hole areas, the Poisson surface reconstruction algorithm is used to complete the repair. The surface fitting is performed by setting a reasonable neighborhood range, such as three times the point spacing of the boundary points, to fill the missing area. Specific steps may include: Gradient field generation

[0059] Establishing the Poisson equation

[0060] in, represents the Laplace operator.

[0061] Establish a regularization objective to minimize energy

[0062] Zero isosurface extraction, i.e. extracting the surface

[0063] 105. The restored point cloud data is downsampled using a voxel grid method to generate fourth point cloud data with consistent point density.

[0064] After completion and repair, the point cloud still has a large point density distribution, and the overall data volume is large, which is not conducive to subsequent calculations. In the embodiment of the present application, the voxel grid method can be used to downsample the point cloud to generate point cloud data with consistent point density, thereby reducing the amount of calculation.

[0065] In an optional implementation, the above step 105 includes: Construct a 3D voxel grid and partition the above repaired point cloud data according to the voxel size; For each voxel, the center point or average point is calculated as the down-sampling point to generate a down-sampling point cloud.

[0066] Specifically, the above steps may include: Voxel partitioning: construct a 3D voxel grid to transform the point cloud By voxel size Partition each voxel Contains point set

[0067] Representative point selection: For each voxel, calculate the center point or average point as the downsampling point :

[0068] Generate downsampled point cloud: Get a new uniform point cloud:

[0069] in is the number of point clouds after downsampling the voxel grid.

[0070] 106. Perform elevation map calculation and flatness analysis on the fourth point cloud data to obtain the granary analysis result.

[0071] Specifically, the extracted grain surface point cloud data is mapped to a regular grid, such as the spacing , and calculate the average elevation of each grid by interpolation. Use the nearest neighbor interpolation method to interpolate the point cloud data in each grid to the center of the grid to generate a smoother elevation map. Perform Gaussian filtering on the elevation map to remove isolated noise points and smooth the elevation map. For example, the elevation map resolution can be 10cm and the convolution kernel size can be , standard deviation 15.

[0072] In an optional implementation, performing a flatness analysis on the fourth point cloud data includes: The neighborhood gradient regional average method is used for the fourth point cloud data to find the gradient of the neighborhood, and the average gradient is calculated according to the set rectangular area size.

[0073] In the embodiment of the present application, in order to characterize the overall flatness index of the grain surface and guide the operation of the grain surface flattening robot, the neighborhood gradient regional averaging method is adopted. First, the gradient of the neighborhood is calculated, and the flatness is calculated by regional averaging. Different calculation area sizes can be adjusted according to needs to achieve different granularities.

[0074] Specifically, the above steps can be expressed as follows: Calculate the gradient value of each point in the elevation map to reflect the changes in the grain pile morphology in the local area.

[0075] Elevation map For each point in , calculate the gradient

[0076]

[0077] The gradient magnitude matrix, each point represents the intensity of local terrain change at that location.

[0078]

[0079] Set the rectangular area, the size is determined according to the requirements, such as , calculate its center point The average gradient of

[0080] Where N represents Neighborhood of is the number of points in the neighborhood.

[0081] Figure 2 A flowchart of a method for three-dimensional modeling of a granary environment based on three-dimensional point cloud data provided in an embodiment of the present application is provided to more clearly illustrate the method flow in the embodiment of the present application; Figure 2 The steps involved can be referred to Figure 1 The relevant specific description in the illustrated embodiment will not be repeated here.

[0082] The three-dimensional modeling method of the granary environment based on three-dimensional point cloud data in the embodiment of the present application obtains the first point cloud data by performing non-uniform weighted filtering on the point cloud data in the granary; removes dust noise from the first point cloud data based on reflection intensity and dynamic filtering to obtain the second point cloud data; identifies the location of the robot by a dynamic occlusion detection method to remove the local point cloud data of the robot's location in the second point cloud data to obtain the third point cloud data; classifies the hole areas in the third point cloud data and repairs different types of hole areas; downsamples the repaired point cloud data using the voxel grid method to generate the fourth point cloud data with consistent point density; performs elevation map calculation and flatness analysis on the fourth point cloud data to obtain the granary analysis results; can construct a high-precision three-dimensional model of the granary environment and extract key environmental information. This method is suitable for application scenarios such as global map establishment of granary robots, internal space analysis of granaries, and evaluation of grain surface flatness.

[0083] Based on the description of the aforementioned method embodiment, the embodiment of the present application also provides a three-dimensional modeling device for a granary environment based on three-dimensional point cloud data.

[0084] Figure 3 A schematic structural diagram of a three-dimensional modeling device for a granary environment based on three-dimensional point cloud data provided in an embodiment of the present application. Figure 3The granary environment three-dimensional modeling device 300 based on three-dimensional point cloud data includes: The filtering processing module 310 is used to perform non-uniform weighted filtering on the point cloud data in the granary to obtain first point cloud data; The filtering processing module 310 is further used to remove dust noise from the first point cloud data based on reflection intensity and dynamic filtering to obtain second point cloud data; The filtering processing module 310 is further used to identify the location of the robot through a dynamic occlusion detection method, so as to remove the local point cloud data of the location of the robot in the second point cloud data to obtain third point cloud data; A completion and repair module 320 is used to classify hole areas in the third point cloud data and repair different types of hole areas; A rasterization module 330 is used to downsample the repaired point cloud data using a voxel grid method to generate fourth point cloud data with consistent point density; The calculation and analysis module 340 is used to perform elevation map calculation and flatness analysis on the fourth point cloud data to obtain a granary analysis result.

[0085] in, Figure 1 The steps in the illustrated embodiment can be executed by various modules of the granary environment three-dimensional modeling device 300 based on three-dimensional point cloud data, and will not be described in detail here.

[0086] In one embodiment of the present application, an electronic device is also provided. Figure 4 , Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 4 The electronic device 400 includes a processor 401 and a memory 402, wherein the memory 402 stores a computer program. When the computer program is executed by the processor 401, the following operations are performed: Figure 1 Any step in the method embodiment shown; the electronic device 400 can be used for model training and / or application of data enhancement network and point cloud classification network. The electronic device 400 can also include input / output devices, etc. In a specific implementation, the electronic device can be a terminal device, etc.

[0087] In one embodiment, a computer-readable storage medium is further provided. The computer-readable storage medium stores a computer program. When the computer program is executed by the processor 401, the processor 401 executes any step in the above method embodiment.

[0088] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0089] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0090] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A three-dimensional modeling method for a granary environment based on three-dimensional point cloud data, characterized in that: The method comprises: Perform non-uniform weighted filtering on the point cloud data in the granary to obtain the first point cloud data; Perform dust noise removal on the first point cloud data based on reflection intensity and dynamic filtering to obtain second point cloud data; Identify the location of the robot by a dynamic occlusion detection method, so as to remove the local point cloud data of the location of the robot in the second point cloud data, and obtain third point cloud data; Classifying the hole areas in the third point cloud data, and repairing different types of hole areas; The restored point cloud data is downsampled using the voxel grid method to generate the fourth point cloud data with consistent point density; Elevation map calculation and flatness analysis are performed on the fourth point cloud data to obtain a granary analysis result.

2. The method for three-dimensional modeling of a granary environment based on three-dimensional point cloud data according to claim 1 is characterized in that: The non-uniform weighted filtering of the point cloud data in the granary includes: Calculate and obtain the local density of the point cloud data in the granary; weighting the points according to the local density of the point cloud data in the granary; A statistical filtering algorithm is used to evaluate the outlier degree of each point in the point cloud using the mean and standard deviation to filter out outliers.

3. The method for three-dimensional modeling of a granary environment based on three-dimensional point cloud data according to claim 2 is characterized in that: The removing dust noise from the first point cloud data based on reflection intensity and dynamic filtering includes: Calculating the global mean and standard deviation of the reflection intensity of all points in the first point cloud data; Determine outliers based on the global mean standard deviation and a set threshold; Perform dynamic filtering based on the scanning cycle; The occurrence frequency of each point in the time window is calculated, and dust points are eliminated according to the occurrence frequency of each point in the time window and the stability threshold.

4. The method for three-dimensional modeling of a granary environment based on three-dimensional point cloud data according to claim 2 is characterized in that: The classifying the hole areas in the third point cloud data and repairing different types of hole areas includes: Classifying the hole regions in the third point cloud data according to the area or diameter of the hole regions to obtain first-class holes and second-class holes; For the first type of holes, an interpolation-based reconstruction method is used to perform bilinear interpolation filling based on surrounding data; For the second type of holes, the Poisson surface reconstruction algorithm is used to complete the repair.

5. The method for three-dimensional modeling of a granary environment based on three-dimensional point cloud data according to claim 4 is characterized in that: The method of downsampling the repaired point cloud data using the voxel grid method to generate fourth point cloud data with consistent point density includes: Constructing a 3D voxel grid, partitioning the repaired point cloud data according to voxel size; For each voxel, the center point or average point is calculated as the down-sampling point to generate a down-sampling point cloud.

6. The method for three-dimensional modeling of a granary environment based on three-dimensional point cloud data according to any one of claims 2 to 5, characterized in that: The step of calculating the elevation map of the fourth point cloud data includes: Mapping the fourth point cloud data to a regular grid, and calculating the average elevation of each grid by interpolation; interpolating the point cloud data in each grid to the center of the grid using a nearest neighbor interpolation method to generate a first elevation map; Gaussian filtering is performed on the first elevation map, and the elevation map is smoothed to obtain a target elevation map.

7. The method for three-dimensional modeling of a granary environment based on three-dimensional point cloud data according to any one of claims 2 to 5, characterized in that: Performing a flatness analysis on the fourth point cloud data includes: A neighborhood gradient regional averaging method is used for the fourth point cloud data to obtain the gradient of the neighborhood, and the average gradient is calculated according to the set rectangular area size.

8. A three-dimensional modeling device for a granary environment based on three-dimensional point cloud data, characterized in that: include: A filtering processing module is used to perform non-uniform weighted filtering on the point cloud data in the granary to obtain the first point cloud data; The filtering processing module is further used to remove dust noise from the first point cloud data based on reflection intensity and dynamic filtering to obtain second point cloud data; The filtering processing module is further used to identify the position of the robot through a dynamic occlusion detection method, so as to remove the local point cloud data of the position of the robot in the second point cloud data to obtain third point cloud data; A completion and repair module, used to classify hole areas in the third point cloud data and repair different types of hole areas; A rasterization module, used to downsample the repaired point cloud data using a voxel grid method to generate fourth point cloud data with consistent point density; The calculation and analysis module is used to perform elevation map calculation and flatness analysis on the fourth point cloud data to obtain a granary analysis result.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

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