CN115100271A - Method and device for detecting goods taking height, computer equipment and storage medium - Google Patents
Method and device for detecting goods taking height, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a method and a device for detecting the goods picking height, computer equipment and a storage medium. The method comprises the following steps: acquiring original point cloud data of a target goods taking area; the target goods taking area comprises a tray for storing and taking goods; carrying out picture conversion based on the original point cloud data to obtain a target picture; the target picture comprises a tray object used for representing the tray; determining a positioning contour edge of the tray object from the target picture; the positioning contour edge is an edge used for positioning the goods taking height in the contour of the tray object; determining target point cloud data matched with the positioning contour edge in the original point cloud data; and determining the target goods taking height based on the target point cloud data. According to the method and the device, the target goods taking height can still be normally calculated under the condition that original point cloud data are incomplete, and therefore the accuracy of the target goods taking height is improved.
Description
Technical Field
The application relates to the technical field of logistics application, in particular to a method and a device for detecting a goods picking height, computer equipment and a storage medium.
Background
With the development of the logistics industry, the quantity of goods is continuously increased, and the goods taking demand is gradually increased. Currently, the picking is mainly performed by a handling device, for example, by an unmanned forklift, according to a predetermined picking height.
In general, the pickup height is directly calculated from the point cloud data on the pallet. However, when the tray is damaged or blocked, the point cloud data on the tray is incomplete, and the accuracy of the goods picking height is affected. Therefore, how to improve the accuracy of the picking height becomes a technical problem that needs to be solved urgently by those skilled in the art.
Disclosure of Invention
In view of the above, it is necessary to provide a method, an apparatus, a computer device, and a storage medium for detecting a pickup height, which can improve the accuracy of the pickup height.
In a first aspect, the application provides a method for detecting a pickup height. The method comprises the following steps:
acquiring original point cloud data of a target goods taking area; the target goods taking area comprises a tray for storing and taking goods;
performing picture conversion based on the original point cloud data to obtain a target picture; the target picture comprises a tray object used for representing the tray;
determining a positioning contour edge of the tray object from the target picture; the locating profile edge is an edge in the profile of the pallet object for locating a pickup height;
determining target point cloud data matched with the positioning contour edge in the original point cloud data;
and determining a target goods taking height based on the target point cloud data.
In a second aspect, the application further provides a device for detecting the pickup height. The device comprises:
the data acquisition module is used for acquiring original point cloud data of the target goods taking area; the target goods taking area comprises a tray for storing and taking goods;
the picture conversion module is used for carrying out picture conversion based on the original point cloud data to obtain a target picture; the target picture comprises a tray object used for representing the tray;
an edge determination module for determining a positioning contour edge of the tray object from the target picture; the locating profile edge is an edge in the profile of the pallet object for locating a pickup height;
the data matching module is used for determining target point cloud data matched with the positioning contour edge in the original point cloud data;
and the height determining module is used for determining the target goods picking height based on the target point cloud data.
In some embodiments, the picture conversion module includes a filtering unit and a projection unit. The screening unit is used for screening point cloud data on a vertical section under a laser coordinate system from the original point cloud data to obtain reference point cloud data. The projection unit is used for performing projection processing on the reference point cloud data to obtain the target picture. The data matching module is further used for determining target point cloud data matched with the positioning contour edge from the reference point cloud data.
In some embodiments, the screening unit is further configured to preliminarily screen, from the raw point cloud data, preliminary point cloud data meeting a preset condition; dividing a voxel grid for the preliminary point cloud data; and respectively screening target points on the vertical section under the laser coordinate system from each voxel grid to obtain the reference point cloud data.
In some embodiments, the edge determination module is further configured to pre-process the target picture to obtain a pre-processed picture; carrying out contour recognition on the preprocessed picture to obtain a recognition contour; determining the locating profile edge of the tray object from the identified profile.
In some embodiments, each of the voxel grids corresponds to a voxel index in a voxel coordinate system, the voxel index being used for characterizing whether the corresponding voxel grid has some cloud data, and the edge determination module is further used for determining, for each pixel coordinate point in a pixel coordinate system of the target picture, a voxel index corresponding to the pixel coordinate point; the voxel index corresponding to the pixel coordinate point is the voxel index of the voxel grid corresponding to the pixel coordinate point in the voxel coordinate system; performing gray level processing on the target picture according to the voxel index corresponding to the pixel coordinate point to obtain a gray level image; and performing expansion corrosion operation on the gray level image to obtain the preprocessed picture.
In some embodiments, the data matching module is further configured to determine a target voxel index corresponding to a pixel coordinate point of the edge of the localization profile; and determining points in a target voxel grid pointed by the target voxel index from the reference point cloud data to obtain the target point cloud data.
In some embodiments, the height determining module is further configured to perform straight line fitting on the target point cloud data to obtain a fitted line segment; and determining the target goods taking height based on the line segment midpoint value of the fitted line segment.
In a third aspect, the present application also provides a computer device. The computer equipment comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps in the goods picking height detection method when executing the computer program.
In a fourth aspect, the present application further provides a computer-readable storage medium. The computer readable storage medium has a computer program stored thereon, and the computer program is used for realizing the steps of the goods picking height detection method when being executed by a processor.
In a fifth aspect, the present application further provides a computer program product. The computer program product comprises a computer program, and the computer program realizes the steps of the goods picking height detection method when being executed by a processor.
The method, the device, the computer equipment and the storage medium for detecting the goods picking height acquire the original point cloud data of the target goods picking area; the target goods taking area comprises a tray for storing and taking goods; carrying out picture conversion based on the original point cloud data to obtain a target picture; the target picture comprises a tray object used for representing the tray; determining a positioning contour edge of the tray object from the target picture; the positioning contour edge is an edge used for positioning the goods taking height in the contour of the tray object; determining target point cloud data matched with the positioning contour edge in the original point cloud data; and determining the target goods taking height based on the target point cloud data. According to the method and the device, the original point cloud data of the target goods taking area are obtained, the original point cloud data are subjected to picture conversion to obtain the target picture, the target point cloud data matched with the positioning outline edge in the original point cloud data are determined only aiming at the positioning outline edge in the target picture, the target goods taking height is determined based on the target point cloud data, the target goods taking height can still be normally calculated under the condition that the original point cloud data are not complete enough, and therefore the accuracy of the target goods taking height is improved.
Drawings
FIG. 1 is a schematic flow chart of a method for detecting pickup height according to some embodiments;
FIG. 2 is a schematic view of a tray in some embodiments;
FIG. 3 is a schematic diagram of a laser coordinate system in some embodiments;
FIG. 4 is a schematic diagram of a voxel coordinate system in some embodiments;
FIG. 5 is a schematic diagram of a pixel coordinate system in some embodiments;
FIG. 6 is a block diagram of a pickup height detection device according to some embodiments;
FIG. 7 is a diagram of the internal structure of a computer device in some embodiments.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In some embodiments, as shown in fig. 1, a method for detecting a pickup height is provided, and this embodiment is illustrated by applying the method to a server, and it is understood that the method may also be applied to a handling apparatus, and may also be applied to a system including a handling apparatus and a server, and is implemented by interaction between the handling apparatus and the server. Wherein, the handling device refers to a transportation device for handling goods, and the handling device may be, but is not limited to, at least one of an automatic Guided Vehicle (AGV cart) and a forklift, wherein the forklift may be, but is not limited to, an unmanned forklift. In this embodiment, the method includes the steps of:
102, acquiring original point cloud data of a target goods taking area.
Wherein, the target goods taking area comprises one or more trays for storing and taking goods.
A pallet is a cargo vehicle for transporting goods in groups.
Point cloud data refers to a collection of vectors in a three-dimensional coordinate system, usually represented in the form of X, Y, Z three-dimensional coordinates, that are typically used to represent the shape of the outer surface of an object.
Specifically, laser scanning is performed on a target goods picking area through laser equipment to obtain corresponding original point cloud data, wherein the laser equipment refers to a device capable of emitting laser. The server acquires original point cloud data obtained by laser scanning of the laser equipment and is used for calculating the target goods taking height of the carrying equipment subsequently.
In some embodiments, the laser device may be fixed to the handling device, and the specific fixing position of the laser device is not limited as long as the fixed laser device can accurately scan the point cloud data.
In some embodiments, can fix laser equipment two in handling equipment and be used for pressing from both sides or ask to get the middle point position between the arm lock root of goods on, can guarantee like this that laser equipment can not sheltered from by the goods when carrying out laser scanning to improve laser scanning accuracy. The root of the clamping arm refers to one end, facing the body of the carrying equipment, of the clamping arm in the carrying equipment.
In some embodiments, after the laser device is fixed on the carrying device, the server controls the carrying device to travel to the position to be picked, and the laser device performs laser scanning on the target picking area to obtain the original point cloud data. The goods waiting position is a preset approximate goods taking position of the carrying equipment.
In other embodiments, after the server controls the carrying device to travel to the position to be picked, the carrying device feeds back the pre-target position of the tray to be picked to the perception algorithm module. The perception algorithm module acquires point cloud data in front of the carrying equipment through the laser equipment fixed on the carrying equipment, and acquires partial point cloud data associated with a pre-target position from the point cloud data in front of the carrying equipment as original point cloud data according to the pre-target position sent by the carrying equipment.
The tray to be taken refers to a tray which is specified by the object goods taking area and needs to be carried by the carrying equipment to take goods. The perception algorithm module is a module used for extracting point cloud data on the server.
The pre-target position of the tray to be taken refers to a coordinate pose of the tray to be taken relative to a coordinate system where the carrying device is located, and specifically includes but is not limited to at least one of a longitudinal width of the tray to be taken relative to the coordinate system where the carrying device is located in an X-axis direction, a transverse length of the tray to be taken relative to the coordinate system where the carrying device is located in a Y-axis direction, a vertical height of the tray to be taken relative to the coordinate system where the carrying device is located in a Z-axis direction, and an included angle of a tray plane of the tray to be taken relative to the coordinate system where the carrying device is located in the Y-axis direction.
The point cloud data in front of the carrying equipment comprises point cloud data of the tray to be taken and point cloud data of the surrounding environment of the tray to be taken.
In some embodiments, the process of extracting the point of interest cloud data from the point cloud data in front of the handling equipment by the perception algorithm module according to the pre-target position is as follows: and the perception algorithm module extracts partial point cloud data matched with the pre-target position of the tray to be taken from the point cloud data in front of the carrying equipment as original point cloud data according to the laser external parameters calibrated in advance and the tray size parameters of the tray to be taken configured in advance.
The laser external reference refers to a coordinate system conversion relation between the laser device and other coordinate systems, such as a coordinate system where the carrying device is located. Tray size parameters including, but not limited to, at least one of tray length, tray width, and tray height.
In some embodiments, the perception algorithm module may further extract, as the original point cloud data, point cloud data within a preset size range and matching the tray to be taken according to the tray size parameter. Wherein the preset size range is set according to the tray size parameter and a preset numerical value. Specifically, the difference between the tray size parameter and the preset value is taken as the minimum value of the preset size range, and the sum of the tray size parameter and the preset value is taken as the maximum value of the preset size range. The preset value may be set according to actual requirements, for example, the preset value may be set to 0.3 cm.
In other embodiments, the laser device may be separated from the carrying device, and specifically, the laser device may be fixedly installed at a position where laser scanning may be performed on the target pickup area, and when the original point cloud data of the target pickup area needs to be acquired, the laser scanning may be directly performed through the laser device.
And 104, performing picture conversion based on the original point cloud data to obtain a target picture.
And the target picture comprises a tray object for representing the tray.
In some embodiments, the server may directly convert the original point cloud data into a picture, resulting in a target picture. In other embodiments, the server may further screen the original point cloud data, and convert the screened point cloud data into a picture to obtain a target picture. According to the method and the device, part of original point cloud data is screened out before the picture is converted, and the precision of the target picture can be improved.
And 106, determining the positioning contour edge of the tray object from the target picture.
Wherein, the positioning contour edge is an edge used for positioning the goods taking height in the positioning contour of the tray object. In practical applications, the contour of the tray object includes an inner contour and an outer contour, the positioning contour of the tray object is the inner contour of the tray object, and the positioning contour edge of the tray object includes but is not limited to at least one of an upper edge line of the inner contour of the tray object and a lower edge line of the inner contour of the tray object.
The positions of the inner contour of the pallet object, the outer contour of the pallet object, and the upper edge line of the inner contour of the pallet object and the lower edge line of the inner contour of the pallet object can be specifically referred to as 2.
In some embodiments, the server may directly extract the positioning profile, i.e. the inner profile, of the pallet object from the target picture and extract the corresponding positioning profile edge, i.e. the upper edge line of the inner profile of the pallet object or the lower edge line of the inner profile of the pallet object, based on the inner profile of the pallet object.
In other embodiments, the server may further extract an outer contour of the tray object from the target picture, identify a positioning contour within the contour range of the outer contour, i.e., an inner contour, and then extract a corresponding positioning contour edge based on the inner contour of the tray object, i.e., an upper edge line of the inner contour of the tray object or a lower edge line of the inner contour of the tray object.
And step 108, determining target point cloud data matched with the positioning contour edge in the original point cloud data.
Specifically, the server determines point cloud data matched with the positioning contour edge from the original point cloud data according to the positioning contour edge determined in the target picture, and the point cloud data is used as target point cloud data.
In some embodiments, the server may further screen the original point cloud data, and determine point cloud data matching the edge of the positioning contour from the screened point cloud data as target point cloud data.
And step 110, determining a target goods taking height based on the target point cloud data.
The target goods taking height refers to the height of the carrying equipment for taking goods from the tray to be taken.
Specifically, after acquiring target point cloud data matched with the positioning contour edge, the server fits the target point cloud data, and determines a target goods picking height according to a fitting result.
In some embodiments, the server may also determine a pickup plane of the tray to be picked for the target point cloud data and determine a target pickup height according to the pickup plane. The goods taking plane refers to a plane on which the carrying equipment needs to take goods for the tray to be taken.
In the method for detecting the goods picking height, original point cloud data of a target goods picking area are obtained; the target goods taking area comprises a tray for storing and taking goods; performing picture conversion based on the original point cloud data to obtain a target picture; the target picture comprises a tray object used for representing the tray; determining a positioning contour edge of the tray object from the target picture; the positioning contour edge is an edge used for positioning the goods taking height in the contour of the tray object; determining target point cloud data matched with the positioning contour edge in the original point cloud data; and determining the target goods taking height based on the target point cloud data. According to the method and the device, the original point cloud data of the target goods taking area are obtained, the original point cloud data are subjected to picture conversion to obtain the target picture, the target point cloud data matched with the positioning outline edge in the original point cloud data are determined only aiming at the positioning outline edge in the target picture, the target goods taking height is determined based on the target point cloud data, the target goods taking height can still be normally calculated under the condition that the original point cloud data are not complete enough, and therefore the accuracy of the target goods taking height is improved.
In some embodiments, step 104 specifically includes, but is not limited to: screening point cloud data on a vertical section of a laser coordinate system where laser equipment is located from the original point cloud data to obtain reference point cloud data; and performing projection processing on the reference point cloud data to obtain a target picture.
The laser coordinate system refers to a three-dimensional coordinate system constructed based on a laser device, and the vertical cross section located under the laser coordinate system refers to a two-dimensional plane located in the vertical direction of the laser coordinate system, and may specifically be an xoy plane, that is, a plane where z is 0 under the laser coordinate system. A schematic diagram of the laser coordinate system can be referred to in fig. 3.
Specifically, the server screens out point cloud data of a two-dimensional plane located on a vertical section under a laser coordinate system, namely an XOY plane with Z being 0 under the laser coordinate system, from the original point cloud data, and obtains reference point cloud data. And projecting the reference point cloud data into a picture to obtain a target picture.
In some embodiments, the server may further screen the point cloud data of the two-dimensional plane in the vertical direction under the laser coordinate system from the original point cloud data, and then screen the point cloud data of the two-dimensional plane in the vertical direction under the laser coordinate system again to obtain the reference point cloud data. And projecting the reference point cloud data into a picture to obtain a target picture.
In some embodiments, step 108 specifically includes, but is not limited to: from the reference point cloud data, target point cloud data matching the edge of the localization profile is determined.
Specifically, the server determines point cloud data matching the edge of the localization profile from the reference point cloud data as target point cloud data.
In some embodiments, the step of "screening point cloud data located on a vertical section under the laser coordinate system from the raw point cloud data to obtain the reference point cloud data" includes but is not limited to: preliminarily screening preliminary point cloud data which accord with preset conditions from the original point cloud data; dividing a voxel grid for the preliminary point cloud data; and respectively screening target points on the vertical section under the laser coordinate system from each voxel grid to obtain reference point cloud data.
The preset condition refers to each point after performing plane fitting on original point cloud data of an XOY plane (i.e., a plane with Z equal to 0 in the laser coordinate system) located in the laser coordinate system where the laser device is located, and a preset distance range from the fitting plane, where the fitting plane is a plane obtained after performing plane fitting on the original point cloud data, and the preset distance range may be set to 2 centimeters to 3 centimeters.
In practical applications, a Random Sample Consensus (RANSAC) algorithm may be used to perform plane fitting on the original point cloud data. The random sampling consistency algorithm is an algorithm for calculating mathematical model parameters of data according to a group of sample data sets containing abnormal data to obtain effective sample data.
Specifically, after the server performs plane fitting on the original point cloud data and obtains a fitting plane, the point cloud data meeting a preset distance range is screened out, for example, points within a range of 2 cm to 3 cm from the fitting plane are screened out, so as to form preliminary point cloud data. Then, the server performs voxel filtering on the preliminary point cloud data, specifically: and dividing voxel grids for the preliminary point cloud data, screening target points on a vertical section under a laser coordinate system from each voxel grid, and replacing all points in the corresponding voxel grids with the screened target points in each voxel grid to obtain reference point cloud data.
The voxel grid may be a cubic grid, specifically, a cube, and the edge length of the voxel grid may be 1 cm. The voxel grid may also be a planar grid, specifically a square, on a vertical section of the voxel grid in the laser coordinate system, and the corresponding side length may also be 1 cm.
Wherein the target points are points respectively screened from each voxel grid according to a preset standard. In general, a point in each voxel grid where the Y coordinate in the laser coordinate system (i.e., the ordinate in the laser coordinate system) is largest may be taken as the target point. For convenience of description, the ordinate in the laser coordinate system will be simply referred to as Y coordinate hereinafter.
In other embodiments, the server may further screen point cloud data on a vertical cross section under the laser coordinate system from the preliminary point cloud data, divide voxel grids for the point cloud data on the vertical cross section under the laser coordinate system, directly screen a target point from each voxel grid, and replace all points in the corresponding voxel grid with the target point screened in each voxel grid to obtain reference point cloud data.
In some embodiments, step 106 specifically includes, but is not limited to: preprocessing a target picture to obtain a preprocessed picture; carrying out contour recognition on the preprocessed picture to obtain a recognition contour; the locating profile edge of the pallet object is determined from the identified profile.
Specifically, the server needs to perform preprocessing on the target picture, for example, gray processing, erosion-dilation operation, and the like on the target picture, so as to improve picture accuracy. Then, the server identifies the outline of the tray object from the preprocessed picture, compares the outline of the tray object with the size of the actual tray, extracts the outline which is in accordance with the size of the actual tray, and takes the extracted outline as an alternative outline, and identifies the positioning outline edge of the tray object from the alternative outline.
Wherein the size of the actual tray includes, but is not limited to, at least one of the length and width of the actual tray.
In some embodiments, the specific process of identifying the locating contour edge of the pallet object from the alternative contours is: for each alternative contour, searching for a pixel point with a preset gray value from bottom to top or from bottom to top, for example, searching for a pixel point with a gray value of 255, so as to find out edges of all positioning contours on the tray object.
In some embodiments, after the step "replace all points in the corresponding voxel grid with the target point screened in each voxel grid to obtain the reference point cloud data", a voxel coordinate system may be established according to the reference point cloud data, and an index array corresponding to the reference point cloud data may be set according to the established voxel coordinate system.
Wherein, each pair of voxel grid index arrays is the index array of the target point in each voxel grid. That is, the value of the array of [ voxel index for each voxel grid ] (the voxel index for each target point with the largest Y coordinate in the voxel grid) is set to a fixed value, for example, minus 1, if there is no point in the voxel grid.
A specific form of the voxel coordinate system may be referred to fig. 4, and 0, 1, 2, and the like shown in the coordinate system of fig. 4 are voxel indexes. In fig. 4, (X _ min, Y _ min) indicates a coordinate point where the X coordinate is minimum and the Y coordinate is minimum in the laser coordinate system, which is also the origin of the voxel coordinate system. In fig. 4, (X _ max, Y _ min) indicates a coordinate point where the X coordinate is maximum and the Y coordinate is minimum in the laser coordinate system, (X _ min, Y _ max) indicates a coordinate point where the X coordinate is minimum and the Y coordinate is maximum in the laser coordinate system, and (X _ max, Y _ max) indicates a coordinate point where the X coordinate is maximum and the Y coordinate is maximum in the laser coordinate system. For convenience of description, the abscissa in the laser coordinate system is also referred to as the X coordinate, and the ordinate in the laser coordinate system is also referred to as the Y coordinate.
It should be noted that (x _ min, y _ min) is used as the origin of the voxel coordinate system for convenience of calculation. In practical application, points at other positions in the reference point cloud data may also be taken as the origin of the voxel coordinate system, which is not limited in the present application.
In some embodiments, each voxel grid corresponds to a voxel index in a voxel coordinate system, and the voxel index is used to represent whether there is some cloud data in the corresponding voxel grid, specifically, whether there is some cloud data in the corresponding voxel grid is determined according to a value corresponding to the voxel index, if a value of a number group corresponding to the voxel index is negative 1, it indicates that there is no point cloud data in the corresponding voxel grid, and if the value of the number group corresponding to the voxel index is not negative 1, it indicates that there is some cloud data in the corresponding voxel grid.
In some embodiments, the points in the voxel grid corresponding to all voxel indexes in the voxel coordinate system may also be projected to obtain the target picture. Specifically, the points in the voxel grid are all located on the XOY plane of the laser coordinate system where the laser device is located, and therefore, the points in the voxel grid need to be projected at a position parallel to the XOY plane to obtain the target picture.
In some embodiments, after the target picture is obtained, the pixel coordinate point corresponding to the voxel index in the pixel coordinate system established based on the target picture can be further determined according to the position of the point in the voxel grid corresponding to the voxel index in the voxel coordinate system.
In some embodiments, the step of "preprocessing the target picture to obtain a preprocessed picture" includes, but is not limited to: determining a voxel index corresponding to a pixel coordinate point aiming at each pixel coordinate point in a pixel coordinate system of a target picture; performing gray processing on the target picture according to the voxel index corresponding to the pixel coordinate point in the voxel coordinate system to obtain a gray image; and carrying out expansion corrosion operation on the gray level image to obtain a preprocessed picture.
The pixel coordinate system refers to a pixel coordinate system established based on the target picture, and an origin of the pixel coordinate system needs to be consistent with an origin of the voxel coordinate system. The voxel index corresponding to the pixel coordinate point is a voxel index of the voxel grid corresponding to the pixel coordinate point in the voxel coordinate system.
In some embodiments, referring to fig. 5, the origin of the pixel coordinate system where the target picture is located is set as the origin of the voxel coordinate system, (X _ min, Y _ min), where (X _ min, Y _ min) represents the pixel point with the smallest X coordinate and the smallest Y coordinate in the pixel coordinate system. In fig. 5, (X _ max, Y _ min) indicates a pixel having the largest X coordinate and the smallest Y coordinate in the pixel coordinate system, (X _ min, Y _ max) indicates a pixel having the smallest X coordinate and the largest Y coordinate in the pixel coordinate system, and (X _ max, Y _ max) indicates a pixel having the largest X coordinate and the largest Y coordinate in the pixel coordinate system.
Specifically, the server determines a voxel index in the voxel coordinate system corresponding to the pixel coordinate point in a one-to-one manner for each pixel coordinate point in the pixel coordinate system of the target picture. And the server acquires corresponding array values from the array of voxel grid indexes according to the voxel indexes, and sets pixel coordinate points corresponding to the voxel indexes with different array values as different gray values so as to obtain a gray image. And then, the server performs expansion corrosion operation on the gray level image, specifically, closing operation can be performed on the gray level image, so that the small holes in the gray level image are filled and leveled, and the cracks in the gray level image are fitted, so that the total position and shape in the gray level image are ensured to be unchanged. The closed operation is carried out on the gray level image, so that cavities generated by point cloud data obtained by scanning the tray to be taken due to the fact that a shielding object is attached to the tray to be taken or other factors can be prevented.
In practical applications, the step "obtaining a gray-scale image by setting pixel coordinate points corresponding to voxel indexes of different array values to different gray-scale values" includes, but is not limited to: if the array value corresponding to the voxel index is greater than the fixed value (the fixed value may be set to be minus 1), the gray value of the pixel coordinate point corresponding to the voxel index whose array value is greater than the fixed value is set to be a preset value, for example, 255. If the array value corresponding to the voxel index is equal to or less than the fixed value, the gray value of the pixel coordinate point corresponding to the voxel index with the array value equal to or less than minus 1 is set to another preset value, for example, to 0.
In some embodiments, the step of determining target point cloud data matching the edge of the localization profile from the reference point cloud data specifically includes, but is not limited to: determining a target voxel index corresponding to a pixel coordinate point of the edge of the positioning contour; and determining points in a target voxel grid pointed by the target voxel index from the reference point cloud data to obtain target point cloud data.
Specifically, the server determines a pixel coordinate point of the locating contour edge in a pixel coordinate system in the target picture, and determines a voxel index corresponding to the pixel coordinate point of the locating contour edge from the voxel indexes as a target voxel index. Then, the server determines a voxel grid pointed by the target voxel index from the reference point cloud data, namely a target voxel grid, and extracts a corresponding target point from the target voxel grid to obtain target point cloud data.
In some embodiments, step 110 specifically includes, but is not limited to: performing linear fitting on the target point cloud data to obtain a fitting line segment; and determining the target goods taking height based on the line segment midpoint value of the fitted line segment.
Specifically, the server performs straight line fitting on the target point cloud data, for example, performs least square straight line fitting on the target point cloud data to obtain a fitting straight line. The server obtains a fitting line segment formed by the target point cloud data on the fitting straight line, and determines the target goods taking height of the carrying equipment according to a line segment midpoint value corresponding to the midpoint of the fitting line segment. And the line segment midpoint value refers to the Y value of the midpoint of the fitted line segment under the laser coordinate system.
It should be noted that, for any straight line, the equation can be expressed as a straight line, that is, y is in the form of bx + a, where b is the slope and a is the intercept. For N points in the target point cloud data, countless straight lines can be used for fitting, the target function is fitted by a least square method to obtain the optimal solution of the target function, namely the optimal slope and the optimal intercept in the linear equation, and the fitting straight line can be determined according to the optimal slope and the optimal intercept in the linear equation.
In some embodiments, the step of using a least squares line fit to find the optimal slope and the optimal intercept in the line equation is as follows, and the involved equations include equations (1) through (7).
The first step is as follows: an objective function is established based on a linear equation.
The second step: and (3) carrying out derivation on the target function of the formula (1) to obtain a linear equation expression.
The third step: and (4) sorting the linear equation expressions obtained by the formula (2) and the formula (3) to obtain a sorted linear equation expression.
aN+b∑x i ＝∑y i (4)
a∑x i +b∑x i 2 ＝∑x i y i (5)
The fourth step: and solving the sorted linear equation expression to obtain the optimal estimation values of the linear parameters a and b so as to obtain a fitting linear.
Wherein x is i And y i Namely the X coordinate value (i.e. the abscissa value of each point in the laser coordinate system) and the Y coordinate value (i.e. the ordinate value of each point in the laser coordinate system) of each point cloud data of the target point, a and b respectively represent the slope and intercept in the linear equation,
and
represents the optimal slope and the optimal intercept in the linear equation, and N represents the number of points in the target point cloud data.
In other embodiments, in order to prevent the transportation equipment from colliding during the picking process, the target picking height of the present application can also be determined according to the Y value (i.e. the midpoint value of the line segment fitted to the line segment) and the height of the tray foot pier arranged at the bottom of the tray. Specifically, the target pickup height is Y value + half of the pallet footer height. The tray foot piers are arranged at the bottom of the tray, are also called tray foot pads, tray cushion blocks, tray square piers and the like, and are used for protecting goods from being extruded and collided by carrying equipment in the transportation process.
In some embodiments, the method for detecting pickup height of the present application further includes, but is not limited to, the following steps:
firstly, fixing a laser device on a carrying device, and acquiring original point cloud data of a target area through the laser device after controlling the carrying device to travel to a position to be picked.
Secondly, the server acquires original point cloud data acquired by the laser equipment, preliminarily screens preliminary point cloud data meeting preset conditions from the original point cloud data, divides voxel grids for the preliminary point cloud data, and screens target points on a vertical section under a laser coordinate system from each voxel grid respectively to obtain reference point cloud data.
Then, the server establishes a voxel coordinate system for the reference point cloud data and determines a voxel index corresponding to the voxel coordinate system. And the server performs projection processing on the reference point cloud data to obtain a target picture, and performs preprocessing on the target picture to obtain a preprocessed picture.
In some embodiments, the process of preprocessing the target picture is as follows: establishing a pixel coordinate system for a target picture, determining a voxel index corresponding to the pixel coordinate system for each pixel coordinate point under the pixel coordinate system of the target picture, and performing gray processing on the target picture according to the voxel index corresponding to the pixel coordinate system to obtain a gray image; and carrying out expansion corrosion operation on the gray level image to obtain a preprocessed picture. Each voxel grid corresponds to a voxel index in a voxel coordinate system, the voxel index is used for representing whether the corresponding voxel grid has point cloud data or not, and the voxel index corresponding to a pixel coordinate point is the voxel index of the voxel grid corresponding to the pixel coordinate point in the voxel coordinate system.
Subsequently, the server carries out outline identification on the preprocessed picture to obtain an identification outline; the locating profile edge of the pallet object is determined from the identified profile. Specifically, the server identifies all contours in the preprocessed picture to obtain identified contours, and then determines the positioning contour edge of the tray object from the identified contours.
Then, the server determines a target voxel index corresponding to a pixel coordinate point of the edge of the positioning contour, and determines a point in a target voxel grid pointed by the target voxel index from the reference point cloud data to obtain target point cloud data.
And finally, the server performs straight line fitting on the target point cloud data to obtain a fitting line segment, and the target goods taking height is determined based on the line segment midpoint value of the fitting line segment.
It should be understood that, although the steps in the flowcharts related to the embodiments as described above are sequentially displayed as indicated by arrows, the steps are not necessarily performed sequentially as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be rotated or alternated with other steps or at least a part of the steps or stages in other steps.
Based on the same inventive concept, the embodiment of the application also provides a goods picking height detection device for realizing the above mentioned goods picking height detection method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the method, so the specific limitations in the following embodiments of one or more pickup height detection devices can be referred to the limitations on the pickup height detection method in the foregoing, and details are not described herein again.
In some embodiments, as shown in fig. 6, there is provided a pickup height detecting device, including: a data acquisition module 602, a picture conversion module 604, an edge determination module 606, a data matching module 608, and a height determination module 610, wherein:
a data obtaining module 602, configured to obtain original point cloud data of a target pickup area; the target goods taking area comprises a tray for storing and taking goods;
the picture conversion module 604 is configured to perform picture conversion based on the original point cloud data to obtain a target picture; the target picture comprises a tray object used for representing the tray;
an edge determination module 606, configured to determine a location contour edge of the tray object from the target picture; the positioning contour edge is an edge used for positioning the goods taking height in the contour of the tray object;
a data matching module 608, configured to determine target point cloud data that matches the edge of the positioning contour in the original point cloud data;
and the height determining module 610 is used for determining the target goods picking height based on the target point cloud data.
According to the goods taking height detection device, original point cloud data of a target goods taking area are obtained; the target goods taking area comprises a tray for storing and taking goods; carrying out picture conversion based on the original point cloud data to obtain a target picture; the target picture comprises a tray object used for representing the tray; determining a positioning contour edge of the tray object from the target picture; the positioning contour edge is an edge used for positioning the goods taking height in the contour of the tray object; determining target point cloud data matched with the positioning contour edge in the original point cloud data; and determining the target goods taking height based on the target point cloud data. The method comprises the steps of obtaining original point cloud data of a target goods taking area, carrying out picture conversion on the original point cloud data to obtain a target picture, determining target point cloud data matched with the positioning outline edge in the original point cloud data only aiming at the positioning outline edge in the target picture, and determining a target goods taking height based on the target point cloud data.
In some embodiments, the picture conversion module 604 includes a filtering unit and a projection unit. The screening unit is used for screening point cloud data on a vertical section under a laser coordinate system from the original point cloud data to obtain reference point cloud data. The projection unit is used for carrying out projection processing on the reference point cloud data to obtain a target picture. The data matching module 608 is further configured to determine target point cloud data that matches the edge of the locator profile from the reference point cloud data.
In some embodiments, the screening unit is further configured to preliminarily screen, from the raw point cloud data, preliminary point cloud data that meets a preset condition; dividing a voxel grid for the preliminary point cloud data; and respectively screening target points on the vertical section under the laser coordinate system from each voxel grid to obtain reference point cloud data.
In some embodiments, the edge determining module 606 is further configured to pre-process the target picture to obtain a pre-processed picture; carrying out contour recognition on the preprocessed picture to obtain a recognition contour; the locating profile edge of the pallet object is determined from the identified profile.
In some embodiments, each voxel grid corresponds to a voxel index in the voxel coordinate system, the voxel index being used to characterize whether the corresponding voxel grid has some cloud data, and the edge determination module 606 is further used to determine, for each pixel coordinate point in the pixel coordinate system of the target picture, the voxel index corresponding to the pixel coordinate point; the voxel index corresponding to the pixel coordinate point is the voxel index of the voxel grid corresponding to the pixel coordinate point in a voxel coordinate system; performing gray processing on the target picture according to the voxel index corresponding to the pixel coordinate point to obtain a gray image; and performing expansion corrosion operation on the gray level image to obtain a preprocessed picture.
In some embodiments, the data matching module 608 is further configured to determine a target voxel index corresponding to a pixel coordinate point of the edge of the locating profile; and determining points in a target voxel grid pointed by the target voxel index from the reference point cloud data to obtain target point cloud data.
In some embodiments, the height determining module 610 is further configured to perform straight line fitting on the target point cloud data to obtain a fitted line segment; and determining the target goods taking height based on the line segment midpoint value of the fitted line segment.
All or part of the modules in the goods picking height detection device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent of a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In some embodiments, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing point cloud data and pictures. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a pick level detection method.
Those skilled in the art will appreciate that the architecture shown in fig. 7 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In some embodiments, a computer device is provided, comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the above-described method embodiments when executing the computer program.
In some embodiments, a computer-readable storage medium is provided, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.
In some embodiments, a computer program product is provided, comprising a computer program which, when executed by a processor, performs the steps in the above-described method embodiments.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware instructions of a computer program, which may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), Magnetic Random Access Memory (MRAM), Ferroelectric Random Access Memory (FRAM), Phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.
All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.
Claims (10)
1. A method for detecting pickup height, the method comprising:
acquiring original point cloud data of a target goods taking area; the target goods taking area comprises a tray for storing and taking goods;
performing picture conversion based on the original point cloud data to obtain a target picture; the target picture comprises a tray object used for representing the tray;
determining a positioning contour edge of the tray object from the target picture; the locating profile edge is an edge in the profile of the pallet object for locating a pickup height;
determining target point cloud data matched with the positioning contour edge in the original point cloud data;
and determining a target goods taking height based on the target point cloud data.
2. The method of claim 1, wherein the performing a picture conversion based on the original point cloud data to obtain a target picture comprises:
screening point cloud data on a vertical section under a laser coordinate system from the original point cloud data to obtain reference point cloud data;
performing projection processing on the reference point cloud data to obtain the target picture;
the determining the target point cloud data matched with the positioning contour edge in the original point cloud data comprises:
and determining target point cloud data matched with the positioning contour edge from the reference point cloud data.
3. The method of claim 2, wherein the step of filtering the point cloud data on a vertical section under a laser coordinate system from the original point cloud data to obtain reference point cloud data comprises:
preliminarily screening preliminary point cloud data which accord with preset conditions from the original point cloud data;
dividing a voxel grid for the preliminary point cloud data;
and respectively screening target points on the vertical section under the laser coordinate system from each voxel grid to obtain the reference point cloud data.
4. The method of claim 3, wherein said determining a locating profile edge of said tray object from said target picture comprises:
preprocessing the target picture to obtain a preprocessed picture;
carrying out contour recognition on the preprocessed picture to obtain a recognition contour;
determining the locating profile edge of the tray object from the identified profile.
5. The method according to claim 4, wherein each voxel grid corresponds to a voxel index in a voxel coordinate system, and the voxel index is used for characterizing whether the corresponding voxel grid has some cloud data;
the preprocessing the target picture to obtain a preprocessed picture comprises the following steps:
determining a voxel index corresponding to each pixel coordinate point in a pixel coordinate system of the target picture; the voxel index corresponding to the pixel coordinate point is the voxel index of the voxel grid corresponding to the pixel coordinate point in the voxel coordinate system;
performing gray processing on the target picture according to the voxel index corresponding to the pixel coordinate point to obtain a gray image;
and performing expansion corrosion operation on the gray level image to obtain the preprocessed picture.
6. The method of claim 3, wherein determining target point cloud data from the reference point cloud data that matches the locating profile edges comprises:
determining a target voxel index corresponding to a pixel coordinate point of the edge of the positioning contour;
and determining points in a target voxel grid pointed by the target voxel index from the reference point cloud data to obtain the target point cloud data.
7. The method of any one of claims 1 to 6, wherein determining a target pickup height based on the target point cloud data comprises:
performing linear fitting on the target point cloud data to obtain a fitting line segment;
and determining the target goods taking height based on the line segment midpoint value of the fitted line segment.
8. A device for detecting the height of a pick, the device comprising:
the data acquisition module is used for acquiring original point cloud data of a target goods taking area; the target goods taking area comprises a tray for storing and taking goods;
the picture conversion module is used for carrying out picture conversion based on the original point cloud data to obtain a target picture; the target picture comprises a tray object used for representing the tray;
an edge determination module for determining a positioning contour edge of the tray object from the target picture; the locating profile edge is an edge in the profile of the pallet object for locating a pickup height;
the data matching module is used for determining target point cloud data matched with the positioning contour edge in the original point cloud data;
and the height determining module is used for determining the target goods picking height based on the target point cloud data.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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| CN115839675B (en) * | 2023-02-20 | 2023-05-12 | 宜科(天津)电子有限公司 | Object contour line identification system |
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