A method and device for detecting the volume of material in a hopper

Abstract

This invention relates to the technical field of material volume detection, specifically disclosing a method and apparatus for detecting the volume of material in a hopper, comprising the following steps: S1 using a 3D camera to obliquely photograph the material in the hopper and generate a 3D point cloud map; S2 in the generated point cloud map, detecting areas with voids one by one, and then filling each void area to calibrate the 3D point cloud map; S3 starting material volume detection, obtaining the height difference between the surface height of the material and the bottom of the hopper, and then calculating the volume of the corresponding hopper material by integrating the bottom surface information of the calibrated 3D point cloud map.

Description

Technical Field

[0001] This invention relates to the field of real-time material volume detection technology, and specifically discloses a method and apparatus for detecting the volume of materials in a hopper. Background Technology

[0002] Hoppers are commonly used in production lines to store materials, such as raw materials or intermediate materials during the production process. They are widely used in production lines in industries such as building materials machinery and mining machinery. To monitor material usage on the production line in real time, it is essential to detect the amount of material in the hopper, i.e., to monitor its internal volume. In actual production lines, the hopper needs to be replenished periodically, requiring a replenishment device to be added directly above it. This makes it impossible to install a 3D camera directly above the hopper for material volume detection. Therefore, a 3D camera must be installed on the side of the hopper, and the material in the hopper is detected at an angle. This leads to the following problem: the light from the 3D camera is approximately parallel to the wall surface, making it difficult for light to be effectively reflected to the camera in certain areas. This affects the point cloud generated by the 3D camera for the corresponding areas, resulting in voids in the generated 3D point cloud (corresponding to the aforementioned areas), thus affecting the accuracy of material volume detection. Summary of the Invention

[0003] To address the issues of voids and low volume calculation accuracy in existing 3D camera-based material volume detection technologies, this invention provides a method for detecting the volume of materials in a hopper, comprising the following steps:

[0004] S1. Generate 3D point cloud map: Use a 3D camera to take oblique shots to obtain the first 3D point cloud map in the empty state of the hopper and the second 3D point cloud map in real time in the non-empty state.

[0005] S2. Calibration of 3D point cloud map: In the generated first 3D point cloud map and second 3D point cloud map, the regions with holes are detected one by one. Then, for each hole region, the spatial coordinate information of the surrounding point cloud is found. Based on the spatial coordinate information, the coordinate information of the hole region is interpolated to predict the point cloud coordinate information of the hole region. A new point cloud is then generated to fill the hole region, thereby realizing the calibration of the 3D point cloud map.

[0006] S3 performs material volume detection: Based on the calibrated first 3D point cloud map and the second 3D point cloud map, the depth value in the z-coordinate direction is subtracted according to the point cloud coordinate information to obtain the height difference between the surface height of the material and the bottom of the hopper. Then, the volume of the corresponding hopper material is calculated by integrating the bottom surface information of the calibrated first 3D point cloud map.

[0007] Preferably, step S2 specifically includes the following steps:

[0008] (1) Locate the empty points within the 3D point cloud map;

[0009] (2) Eliminating voids based on normal point information: After confirming the location of the void, find the nearest normal point to the left, right, up or down as the nearest surrounding normal point of the void. Interpolate the spatial coordinates of the void using interpolation fitting technology to predict the xyz coordinates of the void, thereby eliminating the case where the xyz coordinates of the nearest surrounding normal points are all 0, and finally eliminating the void.

[0010] (3) Eliminate void points based on normal points and already eliminated void point information: Using the normal point information around the next void point, predict the spatial coordinate information of the next void point through fitting interpolation technology;

[0011] (4) Repeat steps (1)-(3) until all void points are found, thereby achieving calibration of the 3D point cloud map.

[0012] Preferably, step S1 specifically includes the following steps:

[0013] A 3D camera is used to take M photos of the inner surface of the hopper under the empty chamber to obtain the corresponding cloud image information, where h=1~M, where h is the number of photos taken and M is the maximum number of photos taken.

[0014] Preferably, step S3 specifically includes the following steps:

[0015] (a) The spatial coordinate values ​​of the sub-elements of the cloud image information from multiple photographs are averaged and saved into a matrix. The specific expression is as follows:

[0016] (1)

[0017] Where i is the pixel coordinate in the width direction, and j is the pixel coordinate in the width direction;

[0018] (b) Eliminate the matrix according to the void point elimination method in step S2. Hollow points with neutron elements of 0 yield a new matrix. ;

[0019] (c) Take multiple 3D camera photos of the hopper containing materials in the production line, and obtain the averaged cloud image information according to the method in step (a);

[0020] (d) Following the hole elimination method described above, eliminate the holes in the matrix whose child elements are 0 to obtain a new matrix. ;

[0021] (e) Calculate the height of the material surface inside the hopper from the bottom of the hopper, using the following expression:

[0022] (2);

[0023] (f) Calculate the volume of the material using the following set of formulas:

[0024] (3)

[0025] in and These represent the spatial sizes occupied by the point cloud in the x and y directions, respectively. This indicates the height distance of the material surface at this location from the bottom of the hopper. This indicates the distance from the material surface at that location to the side of the hopper. This indicates the width distance between the material surface at that location and the side of the hopper. This represents the volume of the cuboid corresponding to the point cloud. , and These represent the height, length, and width of the corresponding cuboid.

[0026] A hopper material volume detection device, comprising:

[0027] A 3D camera is used to take angled pictures of the material inside the hopper and generate a 3D point cloud map;

[0028] The 3D point cloud calibration module detects areas with holes one by one in the point cloud map generated by the 3D camera. Then, for each hole area, it finds the spatial coordinate information of the surrounding point cloud and interpolates the coordinate information of the hole area based on the spatial coordinate information to predict the point cloud coordinate information of the hole area. A new point cloud is then generated to fill the hole area, thereby achieving the calibration of the 3D point cloud map.

[0029] The hopper material volume detection module uses a 3D point cloud image captured by a 3D camera when the hopper is empty as the initial point cloud information. Then, it captures images of hoppers containing material at any time on the production line to obtain a 3D point cloud image containing the material as the point cloud information to be measured. Based on the above two point cloud information, the depth values ​​in the z-coordinate direction are subtracted to obtain the height difference between the surface height of the material and the bottom of the hopper. Finally, the volume of the material in the corresponding hopper is calculated by integrating the bottom surface information of the 3D point cloud image after calibration by the 3D point cloud image calibration module.

[0030] By adopting the above solution, the present invention has the following advantages and beneficial effects: The present invention provides a method and device for detecting the volume of material in a hopper, which can eliminate voids and improve the point cloud imaging quality of a structured light 3D camera. The present invention proposes a method for predicting the surface height of material in the hopper and a method for calculating the material volume. The above methods are not affected by the shape and size of the hopper, therefore, the method has strong applicability. The material volume calculation method provided by the present invention is suitable for integration into a production line and enables real-time measurement of the surface height and volume of material in the hopper. Attached Figure Description

[0031] Figure 1 This is a 3D point cloud image of an uncalibrated hopper with voids, taken in the Oxyz coordinate system (directly above the camera).

[0032] Figure 2 This is a 3D point cloud image of an uncalibrated hopper with voids, taken in the Ox'y'z' coordinate system (with the camera tilted).

[0033] Figure 3 A schematic diagram illustrating the difference between 3D cameras in the Oxyz coordinate system and the Ox'y'z' coordinate system.

[0034] Figure 4 This is a schematic diagram illustrating the present invention's method of eliminating void points based on normal point information surrounding voids.

[0035] Figure 5 This is a schematic diagram illustrating the present invention's method of eliminating void points based on information about normal points around the void and void points that have already been eliminated.

[0036] Figure 6 A 3D point cloud image of the calibrated hopper with eliminated voids.

[0037] Figure 7 This is a schematic diagram of the method for calculating the volume of material in the hopper according to the present invention. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to represent selected embodiments of the invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0040] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0041] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0042] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby providing a clearer and more explicit definition of the scope of protection of the present invention.

[0043] Reference manual attached Figure 1-2 In existing technologies, the hopper volume measuring device consists of a 3D camera supported by a camera bracket above the mechanical device carrying the hopper. The 3D camera is tilted to capture images of the material being conveyed from the mixing device into the hopper via a conveyor. However, in these existing technologies, the 3D camera cannot take pictures directly above the hopper; it can only take pictures at an angle from the right side. Because the side wall is approximately parallel to the camera's shooting direction, some of the light emitted by the 3D camera is difficult to reflect back to the camera, thus failing to effectively generate a side 3D point cloud. In other words, the generated 3D point cloud contains voids.

[0044] As per the instruction manual Figure 1-2 As shown, Figure 1 This is the point cloud diagram of the hopper in the Oxyz coordinate system. Through coordinate transformation, we can obtain... Figure 2 The image shown is a point cloud diagram of the hopper in the Ox'y'z' coordinate system. See the attached instruction manual for details. Figure 2 The arrow points to a void. This problem exists in all 3D camera applications for material volume measurement. Regardless of whether the camera is tilted, some parts of the object being photographed are approximately parallel to the direction of light from the camera. This results in some light not reflecting back to the 3D camera, affecting the image quality of the 3D point cloud of that surface; in other words, voids exist in the 3D point cloud. Therefore, this is a common problem.

[0045] As per the instruction manual Figure 3 The image shows the camera positioned on the left side of the hopper. A 3D camera is then used to take a picture, resulting in an image similar to the one shown in the Oxyz coordinate system. Figure 1 The 3D point cloud map. Through coordinate transformation, a similar map in the Ox'y'z' coordinate system can be obtained. Figure 2 The point cloud image shows that, due to the tilted camera angle, the light rays from the camera are approximately parallel to the hopper wall, resulting in insufficient light reflection to the camera in some areas, thus creating a similar effect. Figure 3 Hollows in the cavity. (Refer to the attached instruction manual.) Figure 3 , where θ is the angle between the 3D camera and the vertical direction, H1 is the height of the hopper, H2 is the height of the camera relative to the upper surface of the hopper (the highest surface of the hopper), L1 is the length of the lower surface of the hopper, L2 is the length of the upper surface of the hopper, W1 is the width of the lower surface of the hopper, W2 is the width of the upper surface of the hopper, and B is the distance between the origin of the Oxyz coordinate system and the origin of the Ox'y'z' coordinate system.

[0046] To address this issue, this application proposes a method for detecting the volume of material inside a hopper. This method can effectively reduce voids in the point cloud generated by the 3D camera on the material wall surface, thereby improving the quality of the generated wall surface. Simultaneously, it also improves the accuracy of subsequent volume detection algorithms.

[0047] This invention provides a method for detecting the volume of material in a hopper, the method comprising the following steps:

[0048] S1 uses a 3D camera to take a 3D angled shot of the material in the hopper and generates a 3D point cloud map;

[0049] In the generated point cloud map, S2 detects the regions with holes one by one. Then, for each hole region, it finds the spatial coordinate information of the surrounding point cloud and interpolates the coordinate information of the hole region based on the spatial information to predict the point cloud coordinate information of the hole region. A new point cloud is then generated to fill the hole region, thereby achieving the calibration of the 3D point cloud map.

[0050] S3 begins material volume detection. First, a 3D point cloud image of the empty hopper is captured as the initial point cloud information. Then, images are captured of hoppers containing material at any time on the production line to obtain a 3D point cloud image containing the material as the point cloud information to be measured. Next, based on the above two point cloud information, the depth values ​​in the z-coordinate direction are subtracted to obtain the height difference between the surface height of the material and the bottom of the hopper. Then, the volume of the material in the corresponding hopper is calculated by integrating the bottom surface information of the calibrated 3D point cloud image.

[0051] For step S2, see the attached manual. Figure 2 The diagram shown is a point cloud plot in the Ox'y'z' coordinate system. Hollow points represent voids. Gray points represent normal point clouds. The difference between void points and normal points is that their x, y, and z coordinates are all 0. (Refer to the instruction manual appendix.) Figure 4 Its point cloud consists of 13 rows and 15 columns; therefore, m=13 and n=15. (See attached instruction manual.) Figure 4 The point cloud information is stored in B13×15. The following refers to the appendix of the instruction manual. Figure 4 A method for eliminating voids is provided. The steps are as follows:

[0052] Step 1) Locate the void.

[0053] Traverse each cloud point one by one from left to right and top to bottom. When traversing to point B(i,j), if its x, y, and z coordinates are all 0, that is: B(i,j).x=0, ​​B(i,j).y=0, B(i,j).z=0, it means that the point is a hole, then proceed to step 2). Otherwise, proceed to step 1) and continue traversing to the next cloud point until all cloud points have been traversed.

[0054] Step 2) Eliminate void points based on normal point information

[0055] Included in the instruction manual Figure 4 Taking void point 1 as an example. Starting from void point 1, find the nearest normal point to it in the left, right, up, and down directions, and take these as the nearest surrounding normal points of void point 1. The nearest surrounding points of void point 1 are points 2 to 5. Refer to the appendix in the instruction manual. Figure 5 As can be seen, the child elements within B13×15 corresponding to points 1 to 5 are B(7,5), B(7,4), B(7,8), B(6,5), and B(10,5), respectively. Taking point 2 as an example, in B(7,5), 7 and 5 correspond to the pixel coordinates in the color image, that is, the pixels in the 7th row and 5th column of the color image.

[0056] B(7,5).x, B(7,5).y, and B(7,5).z represent the x, y, and z coordinates of point 1, respectively.

[0057] Combining the pixel coordinate information and spatial coordinate information of the corresponding sub-elements of points 2 to 5, the spatial coordinates of point 1 can be interpolated using mature interpolation fitting techniques (such as kriging model, response surface methodology, radial basis function neural network, etc.) to predict the xyz coordinates of point 1, thereby eliminating the case where the xyz coordinates of B(7,5) are all 0, and finally eliminating the hole point.

[0058] Step 3) Eliminate void points based on information about normal points and already eliminated void points.

[0059] Included in the instruction manual Figure 4 Taking void point 6 as an example. Following the procedure in step 2), we can find the normal points around void point 6, namely points 1 and points 7 to 9. Point 1 and the points before it are void points, which are transformed into normal points after processing in step 2). Further following the procedure in step 2), we can use the information of the normal points around void point 6 and predict the spatial coordinate information of void point 6 through fitting interpolation technology.

[0060] Step 4) Repeat step 1.

[0061] Follow steps 1) through 4) above to complete the process. Figure 4 Elimination of all voids. Following this method, for... Figure 3 After eliminating the voids in the cloud, the resulting new cloud map is shown in the attached instruction manual. Figure 6 As shown. (Comparison instruction manual attached) Figure 2 Included with instruction manual Figure 6 It can be seen that the effect of eliminating voids is very good.

[0062] For step S3, calculating the volume of material in the hopper, the following method for calculating the volume of material in the hopper is given based on the above void elimination method:

[0063] Step 1) Take M photos of the inner surface of the hopper under empty chamber using a 3D camera to obtain the corresponding cloud image information. Where h = 1 ~ M.

[0064] Step 2) Process cloud image information from multiple photographs The spatial coordinates of the sub-elements (h=1~M) are averaged and stored in a matrix. The calculation is performed according to formula 1).

[0065] (1)

[0066] Step 3) Eliminate the matrix using the hole elimination method described above. Hollow points with neutron elements of 0 yield a new matrix. .

[0067] Step 4) Take multiple 3D camera photos of the hopper containing materials generated on the production line, and obtain the averaged cloud image information by following the method in Step 2). .

[0068] Step 5) Eliminate the matrix using the hole elimination method described above. Hollow points with neutron elements of 0 yield a new matrix. .

[0069] Step 6) Calculate the height of the material surface in the hopper from the bottom of the hopper according to formula (2).

[0070] (2)

[0071] Among them, if If it is approximately 0, then it indicates that the position... This means that the cloud diagram of the hopper containing material and the cloud diagram of the empty hopper are at approximately the same height at that location. This situation only occurs on the wall surface submerged by the material. Therefore, it can be concluded that... If the z-value of the mid-coordinate is significantly greater than 0, it indicates the height distance of the material surface from the bottom of the hopper at that position.

[0072] Step 5) Calculate the volume of the material according to formula group (3).

[0073] (3)

[0074] In formula group (3) and These represent the size of the space occupied by the point cloud in the x and y directions, respectively. This indicates the height distance of the material surface at this location from the bottom of the hopper. The meaning is as shown in the instruction manual. Figure 7 As shown, this represents the volume of the cuboid corresponding to the point cloud. , and These represent the height, length, and width of the corresponding cuboid.

[0075] This invention also discloses a hopper material volume detection device, comprising:

[0076] A 3D camera is used to take angled pictures of the material inside the hopper and generate a 3D point cloud map;

[0077] The 3D point cloud calibration module detects areas with holes one by one in the point cloud map generated by the 3D camera. Then, for each hole area, it finds the spatial coordinate information of the surrounding point cloud and interpolates the coordinate information of the hole area based on the spatial information to predict the point cloud coordinate information of the hole area. A new point cloud is then generated to fill the hole area, thereby achieving the calibration of the 3D point cloud map.

[0078] The hopper material volume detection module uses a 3D point cloud image captured by a 3D camera when the hopper is empty as the initial point cloud information. Then, it captures images of hoppers containing material at any time on the production line to obtain a 3D point cloud image containing the material as the point cloud information to be measured. Based on the above two point cloud information, the depth values ​​in the z-coordinate direction are subtracted to obtain the height difference between the surface height of the material and the bottom of the hopper. Finally, the volume of the material in the corresponding hopper is calculated by integrating the bottom surface information of the 3D point cloud image after calibration by the 3D point cloud image calibration module.

[0079] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for detecting the volume of material in a hopper, characterized in that, The method includes the following steps: S1. Generate 3D point cloud map: Use a 3D camera to take oblique shots to obtain the first 3D point cloud map in the empty state of the hopper and the second 3D point cloud map in real time in the non-empty state. S2. Calibration of 3D Point Cloud Map: In the generated first and second 3D point cloud maps, regions with holes are detected one by one. Then, for each hole region, the spatial coordinate information of the surrounding point cloud is found. Based on this spatial coordinate information, the coordinate information of the hole region is interpolated to predict the point cloud coordinate information of the hole region. A new point cloud is then generated to fill the hole region, thereby achieving the calibration of the 3D point cloud map. Step S2 specifically includes the following steps: (1) Locate the empty points within the 3D point cloud map; (2) Eliminating voids based on normal point information: After confirming the location of the void, find the nearest normal point to the left, right, up or down as the nearest surrounding normal point of the void. Interpolate the spatial coordinates of the void using interpolation fitting technology to predict the xyz coordinates of the void, thereby eliminating the case where the xyz coordinates of the nearest surrounding normal points are all 0, and finally eliminating the void. (3) Eliminate void points based on normal points and already eliminated void point information: Using the normal point information around the next void point, predict the spatial coordinate information of the next void point through fitting interpolation technology; Repeat steps (1)-(3) until all void points are found, thereby calibrating the 3D point cloud map; S3 performs material volume detection: Based on the calibrated first 3D point cloud map and the second 3D point cloud map, the depth value in the z-coordinate direction is subtracted according to the point cloud coordinate information to obtain the height difference between the surface height of the material and the bottom of the hopper. Then, the volume of the corresponding hopper material is calculated by integrating the bottom surface information of the calibrated first 3D point cloud map.

2. The method for detecting the volume of material in a hopper according to claim 1, characterized in that, Step S1 specifically includes the following steps: The inner surface of the hopper under empty chamber was photographed M times using a 3D camera to obtain the corresponding cloud image information. , where h = 1 to M, where h is the number of times to take a photo and M is the maximum number of times to take a photo.

3. The method for detecting the volume of material in a hopper according to claim 2, characterized in that, Step S3 specifically includes the following steps: (a) The spatial coordinate values ​​of the sub-elements of the cloud image information from multiple photographs are averaged and saved into a matrix. The specific expression is as follows: （1） Where i is the pixel coordinate in the width direction, and j is the pixel coordinate in the width direction. It is a matrix composed of the spatial coordinates of the sub-elements of cloud image information from multiple averaged photographs. A matrix composed of the spatial coordinates of the sub-elements of cloud image information from a single photograph; (b) Eliminate the matrix according to the void point elimination method in step S2. Hollow points with neutron elements of 0 yield a new matrix. (c) Take multiple 3D camera photos of the hopper containing materials in the production line, and obtain the averaged cloud image information according to the method in step (a); (d) Following the hole elimination method described above, eliminate the holes in the matrix whose child elements are 0 to obtain a new matrix. ; (e) Calculate the height of the material surface inside the hopper from the bottom of the hopper, using the following expression: （2） This indicates the height of the material surface inside the hopper from the bottom of the hopper. This indicates the height of the material surface inside the hopper. Indicates the height of the bottom of the material in the hopper; (f) Calculate the volume of the material using the following set of formulas: （3） in and These represent the size of the target point cloud in the x and y directions, respectively. This indicates the height distance of the material surface at this location from the bottom of the hopper. This indicates the distance from the material surface at that location to the side of the hopper. This indicates the width distance of the material surface from the side of the hopper at the target point cloud location. This represents the volume of the cuboid corresponding to the point cloud. , and These represent the height, length, and width of the corresponding cuboid.

4. A hopper material volume detection device, characterized in that, include: A 3D camera is used to take angled pictures of the material inside the hopper and generate a 3D point cloud map; The 3D point cloud calibration module detects void areas one by one in the point cloud image generated by the 3D camera. For each void area, it finds the spatial coordinates of the surrounding point clouds and interpolates the coordinates of the void area based on these coordinates to predict its coordinates. A new point cloud is then generated to fill the void area, thus calibrating the 3D point cloud image. Specifically, it identifies void points within the 3D point cloud image and eliminates them based on normal point information. After confirming the location of the void points, it moves them to the left, right, and... The system searches upwards or downwards for the nearest normal point as the nearest surrounding normal point of the hole. Interpolation fitting techniques are then used to interpolate the spatial coordinates of the hole to predict its x, y, and z coordinates. This eliminates the case where the x, y, and z coordinates of the nearest surrounding normal points are all 0, ultimately eliminating the hole. Hole elimination is then based on the information of normal points and eliminated holes: using the information of the normal points surrounding the next hole, the spatial coordinates of the next hole are predicted using fitting interpolation techniques. After finding all hole points, the 3D point cloud map is calibrated. The hopper material volume detection module uses a 3D point cloud image captured by a 3D camera when the hopper is empty as the initial point cloud information. Then, it captures images of hoppers containing material at any time on the production line to obtain a 3D point cloud image containing the material as the point cloud information to be measured. Based on the above two point cloud information, the depth values ​​in the z-coordinate direction are subtracted to obtain the height difference between the surface height of the material and the bottom of the hopper. Finally, the volume of the material in the corresponding hopper is calculated by integrating the bottom surface information of the 3D point cloud image after calibration by the 3D point cloud image calibration module.

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