CN113376651A - Three-dimensional laser-based method and device for detecting lifting prevention of container truck and computer equipment - Google Patents

Three-dimensional laser-based method and device for detecting lifting prevention of container truck and computer equipment Download PDF

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Publication number
CN113376651A
CN113376651A CN202010157795.2A CN202010157795A CN113376651A CN 113376651 A CN113376651 A CN 113376651A CN 202010157795 A CN202010157795 A CN 202010157795A CN 113376651 A CN113376651 A CN 113376651A
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point cloud
container
dimensional
laser radar
image
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CN113376651B (en
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胡荣东
文驰
彭清
李雅盟
李敏
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Changsha Intelligent Driving Research Institute Co Ltd
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Changsha Intelligent Driving Research Institute Co Ltd
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Priority to PCT/CN2021/079043 priority patent/WO2021179983A1/en
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
    • B65G67/00—Loading or unloading vehicles
    • B65G67/02—Loading or unloading land vehicles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
    • B66C13/00—Other constructional features or details
    • B66C13/18—Control systems or devices
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B66—HOISTING; LIFTING; HAULING
    • B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
    • B66C15/00—Safety gear
    • B66C15/06—Arrangements or use of warning devices
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00—Geometric image transformation in the plane of the image
    • G06T3/06—

Abstract

The application relates to a method and a device for detecting the lifting prevention of a container truck based on three-dimensional laser, computer equipment and a storage medium. The method comprises the following steps: acquiring three-dimensional point cloud of container operation acquired by a laser radar; acquiring an attitude angle of the laser radar; converting the three-dimensional point cloud according to the attitude angle; projecting the converted three-dimensional point cloud into a two-dimensional image; determining the position range of a gap between the container truck and the container in the two-dimensional image; and carrying out image detection on the two-dimensional image according to the position range to obtain a detection result of the container truck for preventing lifting. The method has high data source precision, and the detection method is not influenced by the installation position and the installation angle of the laser radar, so that the precision of the anti-hoisting detection is greatly improved.

Description

Three-dimensional laser-based method and device for detecting lifting prevention of container truck and computer equipment
Technical Field
The application relates to the technical field of laser radars, in particular to a method and a device for detecting the lifting prevention of a container truck based on three-dimensional laser and computer equipment.
Background
In the process of unloading the container from the container truck, because the container truck lock pin is not completely unlocked, the container is lifted or half of the container is lifted by the lifting appliance together with the container truck, so that the accident of lifting the container truck can be caused.
In order to avoid the occurrence of a truck accident, in the process of unloading the truck from the container, whether the truck is separated from the container or not needs to be detected, namely, the truck is detected to prevent lifting. In the traditional detection method, a 2D laser scanner is used for obtaining the profile of the container truck, and whether the container truck is separated from the container or not is judged according to the profile. The method needs to depend on the installation position of the laser scanner and the parking position of the collecting card, is influenced by data precision, and has low detection precision of preventing lifting.
Disclosure of Invention
In view of the above, it is necessary to provide a three-dimensional laser-based method, an apparatus, a computer device, and a storage medium for detecting a card-collecting lift, which can improve detection accuracy.
A three-dimensional laser-based method for detecting the lifting prevention of a container truck, comprising the following steps:
acquiring three-dimensional point cloud of container operation acquired by a laser radar;
acquiring an attitude angle of the laser radar;
converting the three-dimensional point cloud according to the attitude angle;
projecting the converted three-dimensional point cloud into a two-dimensional image;
determining the position range of the gap between the container and the container in the two-dimensional image;
and carrying out image detection on the position range in the two-dimensional image to obtain a detection result of the container truck for preventing lifting.
In one embodiment, the manner of obtaining the attitude angle of the lidar includes:
acquiring a three-dimensional calibration point cloud of container operation acquired by the laser radar in a calibration state;
determining ground point cloud from the three-dimensional calibration point cloud according to the installation height of the laser radar;
calculating a plane normal vector of the ground point cloud;
calculating a roll angle and a pitch angle of the laser radar according to the plane normal vector of the ground point cloud;
determining container side point cloud from the three-dimensional point cloud according to the installation height of the laser radar, the height of a container truck bracket, the height of a container and the distance between the container and the laser radar;
calculating a plane normal vector of the point cloud on the side surface of the container;
and calculating the yaw angle of the laser radar according to the plane normal vector of the point cloud on the side surface of the container, wherein the attitude angle comprises the roll angle, the pitch angle and the yaw angle.
In one embodiment, the converting the three-dimensional point cloud according to the attitude angle includes:
converting the three-dimensional point cloud according to the roll angle and the pitch angle of the laser radar, wherein the ground point cloud in the three-dimensional point cloud is parallel to the bottom plane of a laser radar coordinate system after conversion;
and converting the converted three-dimensional point cloud according to the yaw angle of the laser radar, wherein the container side point cloud in the converted three-dimensional point cloud is parallel to the side plane of the laser radar coordinate system.
In one embodiment, the projecting the converted three-dimensional point cloud into a two-dimensional image includes:
calculating two-dimensional coordinates of the three-dimensional point clouds after conversion;
converting the point clouds into pixel points according to the two-dimensional coordinates of the three-dimensional point clouds;
carrying out binarization processing on the pixel points of the point cloud and the pixel points of the non-point cloud to obtain a binary image;
and carrying out image preprocessing on the binary image to obtain a two-dimensional image.
In one embodiment, determining a range of positions of a gap between a container and a container in the two-dimensional image comprises:
and determining the position range of the gap between the container and the container in the two-dimensional image according to the height of the laser radar, the height of the container lifted and the height of the container pallet.
In one embodiment, the image detection according to the position range to obtain the detection result of the anti-lifting of the container truck includes:
traversing each row in the position range, and counting the number of point cloud pixel points in each row;
if the number of the current row point cloud pixel points is larger than a first threshold value, a counter increases a preset value;
after traversing of each row in the position range is finished, comparing the statistical value of the counter with a second threshold value;
and if the statistic value of the counter is greater than the second threshold value, obtaining the detection result that the container truck is lifted.
In one embodiment, the image detection according to the position range to obtain the detection result of the anti-lifting of the container truck includes:
performing boundary extraction on the two-dimensional image to obtain a boundary line image;
performing linear detection on the boundary line image, and reserving a straight line with a slope in a preset range;
and determining the intersection point of the straight line, and if the intersection point is in the position range, obtaining the detection result that the container truck is lifted.
A detection device is prevented lifting by collection card based on three-dimensional laser includes:
the point cloud acquisition module is used for acquiring three-dimensional point cloud of container operation acquired by a laser radar;
the attitude angle acquisition module is used for acquiring the attitude angle of the laser radar;
the conversion module is used for converting the three-dimensional point cloud according to the attitude angle;
the projection module is used for projecting the converted three-dimensional point cloud into a two-dimensional image;
the gap position determining module is used for determining the position range of the gap between the container and the container in the two-dimensional image;
and the detection module is used for carrying out image detection according to the position range in the two-dimensional image to obtain a detection result of the anti-lifting of the container truck.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method of any of the above embodiments when the computer program is executed.
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 of the above embodiments.
According to the detection method, device, computer equipment and storage medium for preventing the container from being lifted based on the three-dimensional laser, the three-dimensional data of container operation is collected by the aid of the laser radar, data accuracy is high, three-dimensional point cloud is converted according to attitude angles on the basis of high-accuracy three-dimensional point cloud data and is not affected by the installation position and installation angle of the laser radar, the point cloud data is projected to a two-dimensional image, and gaps between the container and the container are detected in the two-dimensional image, so that whether the container and the container are effectively separated is judged. The method has high data source precision, and the detection method is not influenced by the installation position and the installation angle of the laser radar, so that the precision of the anti-hoisting detection is greatly improved.
Drawings
FIG. 1 is an application environment diagram of a three-dimensional laser-based method for detecting the lifting of a container truck in one embodiment;
FIG. 2 is a schematic view showing a mounting position of the radar in one embodiment;
FIG. 3 is a schematic flow chart illustrating a three-dimensional laser-based method for detecting pick-up prevention in one embodiment;
FIG. 4 is a schematic flow chart illustrating the steps for obtaining an attitude angle of a lidar in one embodiment;
FIG. 5 is a schematic flowchart of the steps of converting the three-dimensional point cloud according to the pose angle in another embodiment;
FIG. 6 is a flowchart illustrating the steps of projecting the converted three-dimensional point cloud into a two-dimensional image in one embodiment;
FIG. 7 is a two-dimensional image of a three-dimensional point cloud of a container operation with a pallet un-hoisted during the container operation in one embodiment;
FIG. 8 is a schematic illustration of the range of positions of a pallet to container gap in one embodiment;
FIG. 9 is a flowchart illustrating the steps of performing image detection on a position range in a two-dimensional image to obtain a pick-up detection result in one embodiment;
FIG. 10 is a two-dimensional image of a three-dimensional point cloud of a container operation with a pallet hoisted during the container operation in one embodiment;
FIG. 11 is a flowchart illustrating the steps of performing image detection on a position range in a two-dimensional image to obtain a detection result of the truck lifting prevention in another embodiment;
FIG. 12 is a block diagram of the structure of a three-dimensional laser-based pick-up anti-lift detection device in one embodiment;
FIG. 13 is a diagram illustrating an internal structure of a computer device according to an embodiment.
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.
The three-dimensional laser-based method for detecting the lifting of the container truck can be applied to the application environment shown in fig. 1. The laser radar 101 is installed on one side of an operation lane of the container operation gantry crane 102 and collects laser point clouds. The installation position of the laser radar is set according to the height of the container truck. Master control device 103 is communicatively coupled to lidar 101. The main control equipment is also connected with the gantry crane control equipment 104. When the gantry crane control equipment 104 controls the lifting appliance 105 to lift the container 106 on the container collecting truck 105, a control signal is sent to the main control equipment 103, the main control equipment 103 sends an acquisition signal to the laser radar 101, the laser radar 101 acquires three-dimensional point cloud of container operation according to acquisition training and sends the acquired three-dimensional point cloud to the main control equipment 103, and the main control equipment 103 acquires the three-dimensional point cloud of the container operation acquired by the laser radar; acquiring an attitude angle of the laser radar; converting the three-dimensional point cloud according to the attitude angle; projecting the converted three-dimensional point cloud into a two-dimensional image; determining the position range of a gap between the container truck and the container in the two-dimensional image; and carrying out image detection on the position range in the two-dimensional image to obtain a detection result of the container truck for preventing lifting.
The laser radar installation position is shown in fig. 2, wherein a coordinate system is a laser radar coordinate system, an origin O represents the position of the laser radar, an X-axis direction represents the front of the laser radar, a Y-axis direction represents the front left of the laser radar, and a Z-axis direction represents the front of the laser radar. The squares in the figure represent the container and pallet positions.
In an embodiment, as shown in fig. 3, a three-dimensional laser-based method for detecting a raised position of a truck is provided, which is described by taking the method as an example of being applied to the master control device in fig. 1, and includes the following steps:
and S302, acquiring three-dimensional point cloud of container operation acquired by the laser radar.
Specifically, the laser radar collects three-dimensional laser point clouds of a container operation field. When the container on the collection truck is hoisted by the hanger, the master control equipment sends an acquisition signal to the laser radar and controls the laser radar to scan to obtain the three-dimensional point cloud of the container operation.
And S304, acquiring the attitude angle of the laser radar.
The attitude angle of the laser radar refers to an installation angle of the laser radar relative to a reference object, and includes but is not limited to a roll angle, a pitch angle and a yaw angle. The attitude angle of the laser radar can be determined according to the three-dimensional point cloud of the container operation. In practical application, because the position of the container loaded by the container truck is basically fixed, the attitude angle of the laser radar only needs to be calculated once, the first attitude angle can be used for point cloud calibration subsequently, and each detection can be calibrated in real time, so that the point cloud calibrated can be more accurate.
In one embodiment, the attitude angles include a roll angle, a pitch angle, and a yaw angle. As shown in fig. 4, the step of acquiring the attitude angle of the lidar includes:
s402, acquiring three-dimensional calibration point cloud of container operation acquired by the laser radar in a calibration state.
The container operation for carrying out the anti-hoisting detection by adopting the method can be used as a calibration state for the first time. In order to ensure the accuracy of the attitude angle data, calibration can be performed at regular time, for example, the container operation which is firstly performed with the method for preventing lifting detection every week is used as a calibration state.
And S404, determining ground point cloud from the three-dimensional calibration point cloud according to the installation height of the laser radar.
The ground point cloud refers to the point cloud on the ground in the collected three-dimensional fixed point cloud of the container operation site. Specifically, according to the installation height a of the laser radar, the point cloud with the Z coordinate value smaller than-a in the three-dimensional calibration point cloud is taken as the ground point cloud of the three-dimensional container operation. The container operation is carried out on the ground, the ground point cloud is taken, the three-dimensional point cloud related to a non-operation scene in the three-dimensional point cloud can be removed, and the influence of the three-dimensional point cloud related to the non-operation scene on the follow-up hoisting prevention detection is reduced.
S406, calculating a plane normal vector of the ground point cloud.
The normal vector is a concept of a space analytic geometry, and a vector represented by a straight line perpendicular to a plane is a normal vector of the plane.
The method for calculating the normal vector comprises the steps of firstly calculating a covariance matrix of ground point cloud, then carrying out singular value decomposition on the covariance matrix, describing three main directions of point cloud data by singular vectors obtained by singular value decomposition, representing the direction with the minimum variance by the normal vector vertical to a plane, representing the minimum singular value by the minimum variance, and finally selecting the vector with the minimum singular value as the normal vector of the plane.
Figure BDA0002404706130000061
Where C is the covariance matrix, siAre the points in the point cloud and,
Figure BDA0002404706130000062
the mean of the point cloud is represented.
And S408, calculating the roll angle and the pitch angle of the laser radar according to the plane normal vector of the ground point cloud.
The pitch angle is an included angle between an X axis of a laser radar coordinate system and a horizontal plane, and the roll angle is an included angle between a Y axis of the laser radar coordinate system and a vertical plane of the laser radar.
Specifically, the formula for calculating the roll angle and the pitch angle is:
T1＝(a1,b1,c1)
Figure BDA0002404706130000063
wherein, T1Is the normal vector of the ground, alpha is the roll angle and beta is the pitch angle.
And S410, determining container side point cloud from the container operation three-dimensional point cloud according to the installation height of the laser radar, the height of the container truck bracket, the height of the container and the distance between the container and the laser radar.
The container side point cloud is the point cloud which represents the side part of the container in the collected three-dimensional laser point cloud of the container operation site. The method can be specifically determined according to the height of the point cloud and the distance between the point cloud and the laser radar.
Specifically, the container side point cloud is obtained by taking the point cloud with the known laser radar height of a, the height of the container truck bracket of b and the height of the container of c, and the z-coordinate range of [ -a + b, -a + b + c ] as the point cloud after primary filtering. Because the side face of the container is close to the laser radar, the distance threshold value t is set, and on the basis of the point cloud after primary filtering, the point cloud with the distance from the laser radar smaller than t is taken as the point cloud of the side face of the container.
And S412, calculating a plane normal vector of the point cloud on the side surface of the container.
The calculation method of the plane normal vector of the container side point cloud is the same as that in step S406, and is not described herein again.
And S414, calculating the yaw angle of the laser radar according to the plane normal vector of the point cloud on the side surface of the container.
And the yaw angle is an included angle between the Z axis of the laser radar coordinate system and the side surface of the container.
Specifically, the calculation formula for calculating the yaw angle is as follows:
T2＝(a2,b2,c2)
Figure BDA0002404706130000071
wherein, T2The normal vector of the plane of the point cloud on the side surface of the container is shown, and gamma is a yaw angle.
In this embodiment, the roll angle, the pitch angle, and the yaw angle of the laser radar are calculated by a plane normal vector method.
After step S304, the method further includes:
and S306, converting the three-dimensional point cloud according to the attitude angle.
As mentioned above, the attitude angle includes a roll angle, a pitch angle, and a yaw angle, wherein the roll angle and the pitch angle are obtained from a normal plane vector of the ground point cloud in the three-dimensional point cloud, and the yaw angle is obtained from a normal plane vector of the container side point cloud in the three-dimensional point cloud. Therefore, in this embodiment, after the conversion, the ground point cloud in the three-dimensional point cloud is parallel to the bottom plane of the lidar coordinate system, and the converted container side point cloud is parallel to the side plane of the lidar coordinate system. After conversion, the obtained point cloud data is not influenced by the installation angle, the installation position and the collector card parking position of the laser radar, and the ground point cloud with the head-up angle in front can be obtained.
Specifically, as shown in fig. 5, the step of converting the three-dimensional point cloud according to the attitude angle includes:
and S502, converting the three-dimensional point cloud according to the roll angle and the pitch angle of the laser radar, wherein the ground point cloud in the converted three-dimensional point cloud is parallel to the bottom plane of the laser radar coordinate system.
Specifically, according to the pitch angle of the laser radar, the ground point cloud is rotated around the X axis of the laser radar coordinate system, according to the roll angle of the laser radar, the ground point cloud is rotated around the Y axis of the laser radar coordinate system, and the converted ground point cloud is parallel to the bottom plane of the laser radar coordinate system. As follows:
Figure BDA0002404706130000081
Figure BDA0002404706130000082
pg＝Ry·Rx·pc
wherein R isxAnd RyIs a matrix of rotations about the x-axis and about the y-axis, pgIs a ground point cloud p parallel to the XOY plane of the laser radar coordinate system after conversioncIs the original ground point cloud.
And S504, converting the converted three-dimensional point cloud according to the yaw angle of the laser radar, wherein the container side point cloud in the converted three-dimensional point cloud is parallel to the side plane of the laser radar coordinate system.
Specifically, according to the yaw angle of the laser radar, the converted three-dimensional point cloud rotates around the Z axis of the laser radar coordinate system, and the container side point cloud in the converted three-dimensional point cloud is parallel to the side plane of the laser radar coordinate system. As follows:
Figure BDA0002404706130000083
p＝Rz·pg
wherein R iszAs a matrix of rotation about the z-axis, pgThe converted ground point cloud is parallel to the XOY plane of the laser radar coordinate system, and p is the converted container side anda point cloud parallel to the XOZ plane of the lidar coordinate system.
After step S306, the method further includes:
and S308, projecting the converted three-dimensional point cloud into a two-dimensional image.
Specifically, for three-dimensional point cloud, a two-dimensional image is obtained by representing the three-dimensional point cloud by pixel points.
As shown in fig. 6, the step of projecting the converted three-dimensional point cloud into a two-dimensional image includes:
s602, calculating two-dimensional coordinates of the three-dimensional point clouds after conversion.
Specifically, for each three-dimensional point in the ground point cloud, the coordinates of its two-dimensional image may be calculated by the following formula.
u＝[(xi-xmin)/ur]
v＝[(zi-zmin)/vr]
Where u and v are the row and column coordinates of the two-dimensional image, xiAnd ziAs the x-axis and z-axis coordinates of the ith ground point cloud, xminAnd zminIs the minimum value, u, of the ground point cloud in the x-axis and the Z-axisrAnd vrThe accuracy of the ground point cloud projected onto the two-dimensional image represents the distance between each pixel on the two-dimensional image.
S604, converting the point clouds into pixel points according to the two-dimensional coordinates of the three-dimensional point clouds.
Specifically, a ground point cloud is represented by a pixel point, and the coordinate of the pixel point is the two-dimensional coordinate of the ground point cloud.
And S606, performing binarization processing on the pixel points of the point cloud and the pixel points of the non-point cloud to obtain a binary image.
Specifically, the binarization processing refers to a process of setting the gray value of a pixel point on an image to 0 or 255, that is, rendering the entire image to have an obvious black-and-white effect. In one embodiment, the gray value of the pixel point converted from the point cloud is 255, and the gray values of the other pixel points not converted from the point cloud are 0, thereby obtaining a binary image. Another mode may be that the gray value of the pixel point converted from the point cloud is set to 0, and the gray values of the other pixel points not converted from the point cloud are set to 255, so as to obtain a binary image.
And S608, carrying out image preprocessing on the binary image to obtain a two-dimensional image.
Wherein the preprocessing of the image comprises: firstly, performing median filtering and bilateral filtering preprocessing operations on a two-dimensional image, wherein the median filtering is used for protecting edge information, and the bilateral filtering is used for protecting edges and denoising; then the morphological dilation operation is performed. Due to the scanning mode of the laser sensor, the distance between some adjacent points is larger than the pixel distance of the image, so that holes appear in the image, if the pixel precision is increased, the resolution of the image is reduced, and the holes can be effectively reduced by performing expansion operation on the image.
The image preprocessing method is not limited to morphological dilation. Or performing morphological closing operation on the image to fill the black hole area, and then performing morphological opening operation to enhance the edge information and filter discrete interference pixel points. As shown in fig. 7, is a two-dimensional image of a three-dimensional point cloud of a container operation in which the container is not picked up during the container operation.
After step S306, the method further includes:
and S308, determining the position range of the gap between the container and the container in the two-dimensional image.
The container-to-container gap is the gap between the container and the container where the container is lifted off the container. It follows that the gap between a pallet and a container is related to the height at which the container is lifted and the height of the pallet. Specifically, determining the position range of the container and the container gap in the two-dimensional image comprises the following steps: and determining the position range of the gap between the container and the container in the two-dimensional image according to the height of the laser radar, the lifted height of the container and the height of the container bracket. As shown in fig. 8, given a lidar mounting height of a, a pallet height of b, and a container lifting height of d, the slot position is in the z-coordinate range of the laser sensor coordinate system [ b-a, b-a + d ]. And obtaining a coordinate value range relative to a laser radar coordinate system, and determining the position range of the gap on the two-dimensional image according to the coordinate formula of converting the three-dimensional point cloud into the two-dimensional image.
S310, carrying out image detection on the position range in the two-dimensional image to obtain a detection result of the container truck for preventing lifting.
Specifically, image detection is performed on pixel points within the position range, and a detection result of the container truck for preventing lifting is obtained.
In one embodiment, as shown in fig. 9, the step of performing image detection on a position range in a two-dimensional image to obtain a detection result of preventing a truck from being lifted includes:
s902, traversing each row in the position range in the two-dimensional image, and counting the number of point cloud pixel points in each row.
The point cloud pixel points refer to pixel points converted from point clouds. According to the binarization rule, the gray value of the point cloud pixel point can be 255, and the gray value of the non-point cloud pixel point is 0. The gray value of the point cloud pixel point may be 0, and the gray value of the non-point cloud pixel point is 255. Specifically, the number of pixels in each row in the position range in the two-dimensional image is counted according to the gray value of the point cloud pixel, wherein the gray value of the pixel is the number of the pixels with corresponding numerical values. For example, if the gray value of the point cloud pixel is 255, counting the number of pixels with the gray value of 255 in each row of the pixel in the position range in the two-dimensional image, that is, counting the number of pixels with the pixel value of 255 in each row in the position range in the two-dimensional image, thereby obtaining the number of point cloud pixels in each row in the position range in the two-dimensional image.
And S904, comparing the number of the current row point cloud pixel points with a first threshold value.
If the number of the current row point cloud pixel points is greater than the first threshold, step S906 is executed, and if the number of the current row point cloud pixel points is less than the first threshold, step S902 is returned.
S906, the counter increases the preset value.
Specifically, the preset value is 1, and if the number of the current row point cloud pixel points is greater than a first threshold, the counter is incremented by 1. Step S908 is performed after step S906.
S908, judging whether each row in the position range is traversed.
If yes, go to step S910, otherwise return to step S902.
S910, comparing the statistic value of the counter with a second threshold value.
If the statistical value of the counter is greater than the second threshold, step S912 is executed, and if the statistical value of the counter is less than the second threshold, step S914 is executed.
And S912, obtaining the detection result of the lifted truck.
S914, obtaining the detection result that the container truck is not lifted.
As shown in fig. 7, when the container is completely detached and the container is not lifted, there is a gap at any position between the container and the container, and the corresponding gap position should not collect the three-dimensional point cloud, and correspondingly, the number of the point cloud pixel points in each row in the position range is 0. As shown in fig. 10, when the container and the container are lifted, the number of pixels in each row of the image of the container and container gap is greater than 0, and the number of rows exceeding the threshold T1, that is, the statistical value of the counter is greater than T2, it can be determined that the container is lifted. The first threshold and the second threshold may be set according to accuracy requirements and empirical values.
In another embodiment, as shown in fig. 11, the step of performing image detection on the position range in the two-dimensional image to obtain the detection result of the anti-lifting of the container truck includes:
s1101, boundary extraction is carried out on the two-dimensional image, and a boundary line image is obtained.
The boundary extraction can adopt an edge extraction method of the image, such as canny edge detection, sobel edge detection, and the like. Or using a connected domain method to find the outline boundaries of the image, such as findcontours.
S1104, performing line detection on the boundary line image, and keeping a line with a slope within a preset range.
Specifically, only the pixel points in the boundary line image within the position range can be retained according to the position range of the container truck and the container gap in the two-dimensional image, and the image to be detected is obtained. And (4) carrying out Hough line detection on the image to be detected, and reserving a straight line with a slope in a certain range. In other embodiments, the whole boundary line image may also be subjected to line detection, and a line with a slope within a certain range is retained, that is, pixel points of a line with a slope not within a range are removed.
S1106, the intersection of the straight lines is determined.
Specifically, the intersection point of straight lines with the reserved slope within a preset range is calculated.
S1108, judging whether the intersection point is in the position range;
if yes, step S1110 is executed, that is, the intersection point is within the position range, so as to obtain the detection result that the truck is lifted. If not, step S1112 is executed, that is, if the intersection is not within the position range, a detection result that the truck is not lifted is obtained.
However, as shown in fig. 7, when the container is completely detached and the container is not lifted, the container and the container are judged to be not lifted without a straight intersection point in the position range. When the container is lifted, the container is not completely separated, the container is still contacted with the container, and the other parts are lifted upwards by the lifting appliance, so that the container and the container have intersecting parts. As shown in fig. 10, when the container is lifted, the container and the container intersect each other within the range of the position of the gap, and it can be determined that the container is lifted.
In the embodiment, the image detection is performed according to the position range by using the projection image of the three-dimensional point cloud on the two-dimensional plane, so that the detection result of the collecting card for preventing lifting is obtained, the detection accuracy is ensured, and the calculation amount is reduced.
According to the method for detecting the lifting prevention of the container truck based on the three-dimensional laser, the three-dimensional data of container operation is acquired by the aid of the laser radar, the data accuracy is high, the three-dimensional point cloud is converted according to the attitude angle on the basis of the high-accuracy three-dimensional point cloud data and is not influenced by the installation position and the installation angle of the laser radar, the point cloud data are projected to the two-dimensional image, and gaps between the container truck and the container are detected in the two-dimensional image, so that whether the container truck and the container are effectively separated is judged. The method has high data source precision, and the detection method is not influenced by the installation position and the installation angle of the laser radar, so that the precision of the anti-hoisting detection is greatly improved.
It should be understood that, although the steps in the flowcharts are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence 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 each flowchart may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a part of the steps or stages in other steps.
In one embodiment, as shown in fig. 12, there is provided a three-dimensional laser-based pick-up prevention detection apparatus including:
and the point cloud obtaining module 1202 is used for obtaining the three-dimensional point cloud of the container operation collected by the laser radar.
And an attitude angle obtaining module 1204, configured to obtain an attitude angle of the laser radar.
And the conversion module 1206 is used for converting the three-dimensional point cloud according to the attitude angle.
And a projection module 1208, configured to project the converted three-dimensional point cloud into a two-dimensional image.
A gap location determination module 1210 for determining a location range of a gap between a container and a container in the two-dimensional image.
And the detection module 1212 is configured to perform image detection on the position range in the two-dimensional image to obtain a detection result of the container truck for preventing lifting.
The container truck anti-lifting detection device based on the three-dimensional laser has the advantages that the laser radar is used for collecting three-dimensional data of container operation, the data precision is high, the three-dimensional point cloud is converted according to the attitude angle on the basis of the high-precision three-dimensional point cloud data, the influence of the installation position and the installation angle of the laser radar is avoided, and the point cloud data are projected to a two-dimensional image; and detecting a gap between the container and the container in the two-dimensional image so as to judge whether the container and the container are effectively separated. The method has high data source precision, and the detection method is not influenced by the installation position and the installation angle of the laser radar, so that the precision of the anti-hoisting detection is greatly improved.
In one embodiment, the three-dimensional laser-based container truck anti-lifting detection device further comprises:
the calibration point cloud acquisition module is used for acquiring three-dimensional calibration point cloud of container operation acquired by the laser radar in a calibration state;
and the ground point cloud determining module is used for determining ground point cloud from the three-dimensional calibration point cloud according to the installation height of the laser radar.
The first normal vector calculation module is used for calculating a plane normal vector of the ground point cloud.
The first angle calculation module is used for calculating the roll angle and the pitch angle of the laser radar according to the plane normal vector of the ground point cloud;
the side point cloud determining module is used for determining container side point cloud from the three-dimensional point cloud according to the installation height of the laser radar, the height of the container truck bracket, the height of the container and the distance between the container and the laser radar;
the second normal vector calculation module is used for calculating a plane normal vector of the point cloud on the side surface of the container;
and the second angle calculation module is used for calculating the yaw angle of the laser radar according to the plane normal vector of the point cloud on the side surface of the container, wherein the attitude angle comprises a roll angle, a pitch angle and a yaw angle.
In another implementation, the conversion module is used for converting the three-dimensional point cloud according to the roll angle and the pitch angle of the laser radar, and the ground point cloud in the converted three-dimensional point cloud is parallel to the bottom plane of the laser radar coordinate system; and converting the converted three-dimensional point cloud according to the yaw angle of the laser radar, wherein the container side point cloud in the converted three-dimensional point cloud is parallel to the side plane of the laser radar coordinate system.
In another embodiment, a projection module, comprises:
and the coordinate calculation module is used for calculating the two-dimensional coordinates of the three-dimensional point clouds after conversion.
And the pixel point conversion module is used for converting the point clouds into pixel points according to the two-dimensional coordinates of the three-dimensional point clouds.
The binarization processing module is used for carrying out binarization processing on the pixel points of the point cloud and the pixel points of the non-point cloud to obtain a binary image;
and the preprocessing module is used for preprocessing the binary image to obtain a two-dimensional image.
In another embodiment, the gap position determination module is configured to determine a range of positions of a gap between the container and the container in the two-dimensional image based on a height of the lidar, a height at which the container is hoisted, and a height of the pallet.
In another embodiment, a detection module includes:
and the traversing module is used for traversing each row in the position range in the two-dimensional image and counting the number of point cloud pixel points in each row.
The calculator module is used for increasing a preset value by the counter if the number of the current row point cloud pixel points is larger than a first threshold value;
the comparison module is used for comparing the statistical value of the counter with a second threshold value after the traversal of each row in the position range is finished;
and the detection analysis module is used for obtaining the detection result that the collecting card is lifted if the statistic value of the counter is greater than a second threshold value.
In another embodiment, a detection module includes:
the edge detection module is used for extracting the boundary of the two-dimensional image to obtain a boundary line image;
the straight line detection module is used for carrying out straight line detection on the boundary line image and keeping a straight line with a slope within a preset range;
and the detection analysis module is used for determining the intersection point of the straight line, and obtaining the detection result of the lifted collection card if the intersection point is within the position range.
For specific limitations of the three-dimensional laser-based container truck anti-lifting detection device, reference may be made to the above limitations of the three-dimensional laser-based container truck anti-lifting detection method, and details thereof are not repeated here. All or part of each module in the three-dimensional laser-based container truck anti-lifting detection device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from 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 one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 13. The computer device includes a processor, a memory communication 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 comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program is executed by a processor to realize a three-dimensional laser-based method for detecting the lifting prevention of the container truck.
Those skilled in the art will appreciate that the architecture shown in fig. 13 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 one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring three-dimensional point cloud of container operation acquired by a laser radar;
acquiring an attitude angle of the laser radar;
converting the three-dimensional point cloud according to the attitude angle;
projecting the converted three-dimensional point cloud into a two-dimensional image;
determining the position range of a gap between the container truck and the container in the two-dimensional image;
and carrying out image detection on the position range in the two-dimensional image to obtain a detection result of the container truck for preventing lifting.
In one embodiment, the method for obtaining the attitude angle of the laser radar comprises the following steps:
acquiring a three-dimensional calibration point cloud of container operation acquired by a laser radar in a calibration state;
determining ground point cloud from the three-dimensional calibration point cloud according to the installation height of the laser radar;
calculating a plane normal vector of the ground point cloud;
calculating the roll angle and the pitch angle of the laser radar according to the plane normal vector of the ground point cloud;
determining a container side point cloud from the three-dimensional point cloud according to the installation height of the laser radar, the height of the container truck bracket, the height of the container and the distance between the container and the laser radar;
calculating a plane normal vector of a point cloud on the side surface of the container;
and calculating the yaw angle of the laser radar according to the plane normal vector of the point cloud on the side surface of the container, wherein the attitude angle comprises a roll angle, a pitch angle and a yaw angle.
In one embodiment, converting the three-dimensional point cloud according to the pose angle comprises:
converting the three-dimensional point cloud according to the roll angle and the pitch angle of the laser radar, wherein the ground point cloud in the converted three-dimensional point cloud is parallel to the bottom plane of a laser radar coordinate system;
and converting the converted three-dimensional point cloud according to the yaw angle of the laser radar, wherein the container side point cloud in the converted three-dimensional point cloud is parallel to the side plane of the laser radar coordinate system.
In one embodiment, projecting the converted three-dimensional point cloud into a two-dimensional image comprises:
calculating two-dimensional coordinates of the three-dimensional point clouds after conversion;
converting the point clouds into pixel points according to the two-dimensional coordinates of the three-dimensional point clouds;
carrying out binarization processing on the pixel points of the point cloud and the pixel points of the non-point cloud to obtain a binary image;
and carrying out image preprocessing on the binary image to obtain a two-dimensional image.
In one embodiment, determining a range of positions of a gap between a container and a container in a two-dimensional image comprises:
and determining the position range of the gap between the container and the container in the two-dimensional image according to the height of the laser radar, the height of the container lifted and the height of the container bracket.
In one embodiment, the image detection of the position range in the two-dimensional image to obtain the detection result of the container truck for preventing lifting comprises:
traversing each row in the position range in the two-dimensional image, and counting the number of point cloud pixel points in each row;
if the number of the current row point cloud pixel points is larger than a first threshold value, a counter increases a preset value;
after traversing of each row in the position range is completed, comparing the statistical value of the counter with a second threshold value;
and if the statistic value of the counter is greater than the second threshold value, obtaining the detection result that the container truck is lifted.
In one embodiment, the image detection is performed according to the position range to obtain the detection result of the anti-lifting of the container truck, and the method comprises the following steps:
performing boundary extraction on the two-dimensional image to obtain a boundary line image;
performing straight line detection on the boundary line image, and keeping a straight line with a slope within a preset range;
and determining the intersection point of the straight lines, and if the intersection point is within the position range, obtaining the detection result that the container truck is lifted.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring three-dimensional point cloud of container operation acquired by a laser radar;
acquiring an attitude angle of the laser radar;
converting the three-dimensional point cloud according to the attitude angle;
projecting the converted three-dimensional point cloud into a two-dimensional image;
determining the position range of a gap between the container truck and the container in the two-dimensional image;
and carrying out image detection on the position range in the two-dimensional image to obtain a detection result of the container truck for preventing lifting.
In one embodiment, the method for obtaining the attitude angle of the laser radar comprises the following steps:
acquiring a three-dimensional calibration point cloud of container operation acquired by a laser radar in a calibration state;
determining ground point cloud from the three-dimensional calibration point cloud according to the installation height of the laser radar;
calculating a plane normal vector of the ground point cloud;
calculating the roll angle and the pitch angle of the laser radar according to the plane normal vector of the ground point cloud;
determining a container side point cloud from the three-dimensional point cloud according to the installation height of the laser radar, the height of the container truck bracket, the height of the container and the distance between the container and the laser radar;
calculating a plane normal vector of a point cloud on the side surface of the container;
and calculating the yaw angle of the laser radar according to the plane normal vector of the point cloud on the side surface of the container, wherein the attitude angle comprises a roll angle, a pitch angle and a yaw angle.
In one embodiment, converting the three-dimensional points according to the attitude angles includes:
converting the three-dimensional point cloud according to the roll angle and the pitch angle of the laser radar, wherein the ground point cloud in the converted three-dimensional point cloud is parallel to the bottom plane of a laser radar coordinate system;
and converting the converted three-dimensional point cloud according to the yaw angle of the laser radar, wherein the container side point cloud in the converted three-dimensional point cloud is parallel to the side plane of the laser radar coordinate system.
In one embodiment, projecting the converted ground point cloud into a two-dimensional image comprises:
calculating two-dimensional coordinates of the three-dimensional point clouds after conversion;
converting the point clouds into pixel points according to the two-dimensional coordinates of the three-dimensional point clouds;
carrying out binarization processing on the pixel points of the point cloud and the pixel points of the non-point cloud to obtain a binary image;
and carrying out image preprocessing on the binary image to obtain a two-dimensional image.
In one embodiment, determining a range of positions of the container and the container gap in the two-dimensional image comprises:
and determining the position range of the gap between the container and the container in the two-dimensional image according to the height of the laser radar, the height of the container lifted and the height of the container bracket.
In one embodiment, the image detection of the position range in the two-dimensional image to obtain the detection result of the container truck for preventing lifting comprises:
traversing each row in a position range in the two-dimensional image, counting the number of point cloud pixel points in each row, and if the number of point cloud pixel points in the current row is greater than a first threshold value, increasing a preset value by a counter;
after traversing of each row in the position range is completed, comparing the statistical value of the counter with a second threshold value;
and if the statistic value of the counter is greater than the second threshold value, obtaining the detection result that the container truck is lifted.
In one embodiment, the image detection is performed according to the position range to obtain the detection result of the anti-lifting of the container truck, and the method comprises the following steps:
performing boundary extraction on the two-dimensional image to obtain a boundary line image;
performing straight line detection on the boundary line image, and keeping a straight line with a slope within a preset range;
and determining the intersection point of the straight lines, and if the intersection point is within the position range, obtaining the detection result that the container truck is lifted.
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 can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. 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 technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification 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 invention. 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 patent shall be subject to the appended claims.

Claims (10)

1. A three-dimensional laser-based method for detecting the lifting prevention of a container truck, comprising the following steps:
acquiring three-dimensional point cloud of container operation acquired by a laser radar;
acquiring an attitude angle of the laser radar;
converting the three-dimensional point cloud according to the attitude angle;
projecting the converted three-dimensional point cloud into a two-dimensional image;
determining the position range of the gap between the container and the container in the two-dimensional image;
and carrying out image detection on the position range in the two-dimensional image to obtain a detection result of the container truck for preventing lifting.
2. The method of claim 1, wherein the obtaining the attitude angle of the lidar comprises:
acquiring a three-dimensional calibration point cloud of container operation acquired by the laser radar in a calibration state;
determining ground point cloud from the three-dimensional calibration point cloud according to the installation height of the laser radar;
calculating a plane normal vector of the ground point cloud;
calculating a roll angle and a pitch angle of the laser radar according to the plane normal vector of the ground point cloud;
determining container side point cloud from the three-dimensional point cloud according to the installation height of the laser radar, the height of a container truck bracket, the height of a container and the distance between the container and the laser radar;
calculating a plane normal vector of the point cloud on the side surface of the container;
and calculating the yaw angle of the laser radar according to the plane normal vector of the point cloud on the side surface of the container, wherein the attitude angle comprises the roll angle, the pitch angle and the yaw angle.
3. The method of claim 2, wherein said converting the three-dimensional point cloud according to the pose angle comprises:
converting the three-dimensional point cloud according to the roll angle and the pitch angle of the laser radar, wherein the ground point cloud in the three-dimensional point cloud is parallel to the bottom plane of a laser radar coordinate system after conversion;
and converting the converted three-dimensional point cloud according to the yaw angle of the laser radar, wherein the container side point cloud in the converted three-dimensional point cloud is parallel to the side plane of the laser radar coordinate system.
4. The method of claim 1, wherein the projecting the converted three-dimensional point cloud into a two-dimensional image comprises:
calculating two-dimensional coordinates of the three-dimensional point clouds after conversion;
converting the point clouds into pixel points according to the two-dimensional coordinates of the three-dimensional point clouds;
carrying out binarization processing on the pixel points of the point cloud and the pixel points of the non-point cloud to obtain a binary image;
and carrying out image preprocessing on the binary image to obtain a two-dimensional image.
5. The method of claim 1, wherein determining a range of positions of a gap between a hub and a container in the two-dimensional image comprises:
and determining the position range of the gap between the container and the container in the two-dimensional image according to the height of the laser radar, the height of the container lifted and the height of the container pallet.
6. The method of claim 1, wherein performing image detection on the position range in the two-dimensional image to obtain a detection result of anti-lifting of the container truck comprises:
traversing each row in the position range in the two-dimensional image, and counting the number of point cloud pixel points in each row;
if the number of the current row point cloud pixel points is larger than a first threshold value, a counter increases a preset value;
after traversing of each row in the position range is finished, comparing the statistical value of the counter with a second threshold value;
and if the statistic value of the counter is greater than the second threshold value, obtaining the detection result that the container truck is lifted.
7. The method of claim 1, wherein detecting images according to the position range to obtain a detection result of the anti-lifting of the container truck comprises:
performing boundary extraction on the two-dimensional image to obtain a boundary line image;
performing linear detection on the boundary line image, and reserving a straight line with a slope in a preset range;
and determining the intersection point of the straight line, and if the intersection point is in the position range, obtaining the detection result that the container truck is lifted.
8. A detection device is prevented lifting by collection card based on three-dimensional laser includes:
the point cloud acquisition module is used for acquiring three-dimensional point cloud of container operation acquired by a laser radar;
the attitude angle acquisition module is used for acquiring the attitude angle of the laser radar;
the conversion module is used for converting the three-dimensional points according to the attitude angles;
the projection module is used for projecting the converted three-dimensional point cloud into a two-dimensional image;
the gap position determining module is used for determining the position range of the gap between the container and the container in the two-dimensional image;
and the detection module is used for carrying out image detection on the position range in the two-dimensional image to obtain a detection result of the anti-lifting of the container truck.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
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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