CN113610916A - Irregular object volume determination method and system based on point cloud data - Google Patents
Irregular object volume determination method and system based on point cloud data Download PDFInfo
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Abstract
The invention provides a method and a system for measuring the volume of an irregular object based on point cloud data, which project the information of the point cloud data of the irregular object to be measured to a Z axis of a space system, determine a probability density function on the Z axis, further determine a curvature function on the projection of the Z axis, and calculate the curvature of each point on the projection of the Z axis by using the curvature function; determining a preferred length list of the spacing between adjacent slices on the z axis according to the curvature of each point, the set reference spacing, the reference curvature and the relative spacing coefficient; slicing the point cloud according to the list to sequentially obtain a point cloud platform body in the z-axis direction; carrying out boundary detection on the cross section of each slice and calculating the cross section area of each point cloud slice; and determining the volume of each table body according to the cross section area of each point cloud slice and the height of each table body, and summing all the table bodies to obtain the final volume of the measured irregular object. The invention has simple measuring process, reliable result, high efficiency and controllable precision.
Description
Technical Field
The invention relates to the technical field of volume calculation, in particular to a volume calculation algorithm and system for irregular objects such as solid waste and the like based on point cloud data.
Background
The solid waste refers to solid or muddy substances discarded in production and living activities by human beings, and is a large pollution source of the environment. In the field of buildings, due to the increase of the urbanization speed in recent years, more and more buildings are dismantled, and a large amount of building wastes such as plastics, wood, gypsum boards, red bricks, concrete blocks, tile stones, braided fabrics and the like are generated along with the dismantling of the buildings, because the building wastes are large in quantity and multiple in types, the sorting difficulty is large and the cost is high only by manual sorting, in order to avoid resource waste and environmental pollution, an intelligent robot sorting system has been researched at home and abroad, the gripping angle of the robot to the solid wastes determines whether the robot can stably deliver the building wastes into boxes of corresponding types, and the parameters such as the volume, the mass center, the weight and the like of the objects are important factors for determining the gripping angle of the robot.
The solid construction waste is characterized in that the solid construction waste is different in material and shape, and the volume of the solid construction waste cannot be calculated according to certain characteristics of the material or the shape, so that a volume measuring method aiming at irregular characteristics of an object is needed to be designed. In the same way, in the volume calculation module, according to the cylinder volume calculation mode, the area difference of the upper cross section and the lower cross section is not considered, so that the calculation accuracy of the point cloud volume is further reduced.
Disclosure of Invention
The invention aims to make up the defects of the existing point cloud volume measuring method, provides the irregular object volume measuring method and system based on point cloud data, has simple calculation process, controllable calculation precision and reliable calculation result, and improves the accuracy of solid waste volume calculation.
The invention provides an irregular object volume determination method based on point cloud data, which comprises the following steps: scanning the detected irregular object to obtain point cloud data of the detected irregular object, and optionally, performing filtering and denoising pretreatment on the obtained point cloud data; the point cloud filtering method comprises methods such as Gaussian filtering, uniform sampling filtering, voxel filtering, statistical filtering and the like, wherein the voxel filtering method is adopted for filtering the original point cloud, and the voxel grid filter can achieve the function of down-sampling without damaging the geometrical structure of the point cloud. The filtered point cloud data is relatively uniform and smooth and is used for preparing for calculating the point cloud volume.
Projecting the information of the point cloud data to a Z axis of a space system according to the data of the three-dimensional coordinates in the point cloud data, determining a probability density function on the Z axis, further determining a curvature function on the Z axis projection according to the probability density function on the Z axis projection of the point cloud, and calculating the curvature of each point on the Z axis projection by using the curvature function; determining a preferred length list of the spacing between adjacent slices on the z axis according to the curvature of each point, the set reference spacing, the reference curvature and the relative spacing coefficient; slicing the point clouds according to the list of the optimal lengths of the adjacent slicing intervals to sequentially obtain point cloud table bodies in the z-axis direction;
carrying out boundary detection on the cross section of each slice and calculating the cross section area of each point cloud slice; and determining the volume of each table body according to the cross section area of each point cloud slice and the height of each table body, and summing the volumes of all the table bodies to obtain the final volume of the measured irregular object.
Further, the probability density function on the z-axis is expressed as follows:
using projection method to obtain distribution function d (z) on z-axis, z is ∈ [ z [ ]min，zmax]，
Wherein z isminIs the minimum coordinate value of the point cloud on the z-axis projection，zmaxThe maximum coordinate value of the point cloud on the z-axis projection is obtained;
the calculated probability density function is expressed as follows:
wherein D is the total number of points, satisfying:
further, the curvature function is expressed as follows:
juqueshi:
Δpk(z)＝p(z+k·Δz)-p(z+(k-1)Δz)，
c (z) is a curvature function, Δ z is a unit step size symbol "x" of the variable z of the probability density function p (z) represents a cross product, "· represents a point multiplication, and" | | | "represents a modular length; wherein k is 1, 2, 3, Δ pk(z) represents the difference between the probability density function value p (z + (k-1) Δ z) and p (z + k · Δ z).
Further, the determination method of the length list with the better spacing between adjacent slices on the z-axis is as follows:
the pitch function in the z-direction is obtained according to the following equation:
h(z)＝hb+(c(z)-cb)s
wherein c (z) is a curvature function, hbIs a reference pitch, cbIs the reference curvature and s is the relative spacing coefficient;
the method for determining the optimal length list of the adjacent slice intervals comprises the following steps:
step s 21: let z be zmin；
Step s 22: adding a z-axis coordinate value z at the tail of the length list l with the better spacing between adjacent slices;
step s 23: let z ═ z + h (z);
step s 24: if z < zmaxReturning to step s22, otherwise continuing;
step s 25: adding z-coordinate z at the end of list lmaxAnd ending;
zminis the minimum coordinate value of the point cloud on the z-axis projection,
the z-axis coordinate of the kth slice from bottom to top is expressed as follows:
wherein liThe ith element of the length list l with the better spacing between adjacent slices is used;
wherein K is the number of slices and satisfies
HK＝zmax
Further, the coordinate is H on the z-axisKThe method for calculating the cross section area of each point cloud slice obtained by point interception comprises the following steps:
and mapping the point cloud slice to an XOY coordinate system perpendicular to a Z axis to obtain a two-dimensional plane graph and a contour point set:
{(xi，yi)，1≤i≤n}，
calculating the area of the polygonal outline as the area of the cross section of the corresponding point cloud slice according to the following formula;
wherein i is the sign coefficient of the cross section vertex of the point cloud slice, n is the number of the vertex of the polygon, and
xn+1＝x1，yn+1＝y1。
further, the volume of the ith stage from bottom to top is calculated according to the following formula
Wherein h isiIs the height of the ith platform body from bottom to top, SiThe area of the ith slice from bottom to top; and:
hi＝Hk-Hk-1
the volume of the point cloud is calculated as follows:
wherein n is the number of cross sections and the number of the table bodies is n-1.
The invention also provides an irregular object volume measuring system based on point cloud data, which comprises the following components: the device comprises a data acquisition module, a slicing module, a boundary detection module and a volume calculation module;
the data acquisition module is used for scanning the detected irregular object to acquire point cloud data of the detected irregular object;
the slicing module is used for projecting the information of the point cloud data to a Z axis of a space system according to the data of the three-dimensional coordinates in the point cloud data, determining a probability density function on the Z axis, further determining a curvature function on the Z axis projection according to the probability density function on the Z axis projection of the obtained point cloud, and calculating the curvature of each point on the Z axis projection by using the curvature function; determining a preferred length list of the spacing between adjacent slices on the z axis according to the curvature of each point, the set reference spacing, the reference curvature and the relative spacing coefficient; slicing the point clouds according to the list of the optimal lengths of the adjacent slicing intervals to sequentially obtain point cloud table bodies in the z-axis direction;
the boundary detection module is used for carrying out boundary detection on the cross section of each slice and calculating the cross section area of each point cloud slice;
and the volume calculation module is used for determining the volume of each table body according to the cross section area of each point cloud slice and the height of each table body, and summing all the table bodies to obtain the final volume of the measured irregular object.
The invention has the following beneficial technical effects: the invention provides an improved slicing method, which comprises the steps of firstly obtaining point cloud information of an irregular object, projecting the point cloud information in the z-axis direction, and carrying out continuous slicing at unequal intervals according to the curvature of a probability density function so as to obtain a series of discrete point cloud slices corresponding to a point cloud main body. The problem that the slicing mode of equal spacing can cause that the part with large shape and volume change has few slices, and the part with small change reversely cuts more slices is solved. On the other hand, the contour boundaries of the point cloud slices are searched one by one according to the slice sequence and the polygonal area, so that the area of the point cloud section of a single slice is calculated, volume information of parts between adjacent slices is obtained by using a table body calculation formula, and the calculated volumes of the parts between the slices are added by the method, so that the volume information of the whole point cloud is finally obtained. The whole algorithm process is simple, the result is reliable, the calculation method is efficient, the precision is controllable, the defects of the point cloud equidistant segmentation method are effectively overcome, the calculation precision of the original slicing method is improved, and a good solution is provided for the volume calculation of irregular objects.
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FIG. 1 is point cloud data of a sofa commonly used in the home;
FIG. 2 is a schematic diagram of the steps of the irregular object volume determination method based on point cloud data according to an embodiment of the present invention;
FIG. 3 is a probability density function of z-axis direction point cloud data of irregularities in an embodiment of the present invention;
fig. 4 is a schematic view of an embodiment of the present invention, in which an irregularity is divided into a plurality of mesas from bottom to top.
Detailed Description
The irregular object volume measuring method based on the point cloud data can be applied to the scene of an intelligent sorting system for construction waste, the object volume calculation is an important step in the intelligent sorting system, and the effect of the method is explained by combining the attached legend, so that a reader can clearly understand the essence of the method.
Example (b): the flow schematic diagram of the irregular object volume measuring method based on the point cloud data is shown in fig. 2, and specifically comprises the following steps:
and step s1, preprocessing the collected original point cloud data containing a large number of hash points and isolated points. Optionally, in a specific embodiment, step s1 specifically includes: and removing burrs and obvious outliers in the original point cloud by adopting a voxel filtering method to obtain uniform and smooth point cloud data. The present embodiment uses point cloud data of a sofa commonly used in a family, as shown in fig. 1.
And step s2, projecting the information of the filtered point cloud data to a Z axis of a space system according to the data of the three-dimensional coordinates in the filtered point cloud to obtain a distribution function of points on the Z axis, and further calculating a probability density function on the Z axis.
In this embodiment, a projection method is used to obtain a distribution function on the z-axis: d (z), z ∈ [ z ]min，zmax]Let the total number of points be D, the probability density function is expressed as follows:
satisfies the following conditions:
according to the probability density function (as shown in fig. 3) of the z-axis projection of the point cloud, the curvature change function of the point on the probability density function in the z-axis direction is calculated.
The curvature of each point on the z-axis projection is calculated according to the following formula:
wherein:
Δpk(z)＝p(z+k·Δz)-p(z+(k-1)Δz)，
c (z) is the resulting curvature function, Δ z is the unit step size of the variable z of the probability density function p (z), the symbol "x" represents the cross product, "· represents the point multiplication, and" | | "represents the modular length. .
And setting a reference distance, a reference curvature and a relative distance coefficient, and calculating a distance function in the z-axis direction according to a designed distance calculation formula.
Setting a reference pitch hbReference curvature cbAnd a relative spacing coefficient s, the spacing function in the z-axis direction being obtained according to:
h(z)＝hb+(c(z)-cb)s
the significance of this function is: if z is on the z-axis0The height of a slice is the preferred distance h (z) between the slice and the slice above the adjacent slice0)。
And (4) starting from the lowest point of the z axis, running a designed interval learning algorithm to obtain a list of the optimal length of the interval between the adjacent slices. Obtaining a list l of the optimal length of the adjacent slice spacing according to the following spacing learning algorithm:
step s 21: let z be zmin。
Step s 22: the z-axis coordinate value z is added at the end of the list l.
Step s 23: let z be z + h (z).
Step s 24: if z < zmaxReturn to step s22, otherwise continue.
Step s 25: adding z-coordinate z at the end of list lmaxAnd then, the process is ended.
And further, carrying out unequal-interval segmentation according to the segmentation step length of the obtained list l with the optimal interval between adjacent slices to obtain a plurality of cross sections perpendicular to the z axis. The z-axis coordinate of the kth slice from bottom to top is expressed as follows:
wherein K is the number of slices and satisfies
HK＝zmax。
And according to the obtained optimal length list of the adjacent slice intervals, starting from the lowest point of the z axis, and taking the direction vertical to the z axis as a cross section, slicing the measured object to obtain a plurality of point sets vertical to the z axis. The algorithm may obtain the segmentation position, i.e., the set of z-axis coordinates, of the entire point cloud according to steps s21-s 25.
And (4) slicing the point cloud according to the adjacent slice spacing optimal length list obtained in the step s2, and cutting the point cloud into a plurality of platforms as a whole, as shown in fig. 4.
And step s3, obtaining the outline boundary of the cross section of the point cloud slice by using an alpha-shape boundary detection algorithm.
Step S4, after the segmentation z-axis coordinate set is obtained, another key parameter in the volume calculation formula, namely the cross-sectional area S of the point cloud slice, is also needed. Determining boundary points of the cross section outline of the point cloud slice; and calculating the area of the cross section according to the cross section outline boundary.
The method specifically comprises the following steps: and sequentially connecting boundary points of the point cloud slices in sequence, and taking outline boundaries of the point cloud slices to obtain polygons, wherein the outlines of different point cloud slices are different, and the outline boundaries are arbitrary polygons.
Solving the area of any polygon, comprising the steps of:
and mapping the point cloud slice to an XOY coordinate system perpendicular to a Z axis to obtain a two-dimensional plane graph and a contour point set:
{(xi，yi)，1≤i≤n}
in general, since the point cloud image obtained by the object is irregular, the polygon obtained by the contour boundary of different point cloud slices is an arbitrary polygon. To solve the area of an arbitrary polygon, it is first necessary to know the coordinate information of the polygon vertices, i.e., X, Y information of the three-dimensional coordinates in the point cloud, where the cross-section of the point cloud is projected perpendicular to the z-axis to the XOY plane.
And calculating the area of the polygonal outline as the area of the cross section of the corresponding point cloud slice according to the following formula.
Wherein i is the sign coefficient of the cross section vertex of the point cloud slice, and n is the vertex number of the polygon.
And step s5, decomposing the measured object into a plurality of platforms from bottom to top according to the cross section, obtaining the volume of each platform by using a platform volume calculation formula, and then adding all the volumes to obtain the final volume of the measured object.
Using the cross section to integrally divide the point cloud into a plurality of table bodies according to the dividing step length of the adjacent slice spacing optimal length list l, and calculating the volume of the table bodies according to the area of the cross section, wherein the step comprises the following steps:
the volume of the ith platform from bottom to top is calculated according to the following formula
Wherein hi is the height of the ith platform from bottom to top, SiThe area of the ith slice from bottom to top.
Calculating the volume of the point cloud according to the following formula:
wherein n is the number of cross sections, i.e. the number of stages is n-1.
Calculating the volume DeltaV of the table bodyiWhere i is the volume of the ith table from bottom to top, e.g. S in the figure1To S10Is a first stage body, S10To S11Is the second stage. . . And so on.
The point cloud volume is the addition result of the table volume, and the calculation formula is as follows:
wherein n is the number of cross sections and the number of the table bodies is n-1.
The method provided by the invention effectively overcomes the defects of the original point cloud segmentation method, improves the calculation precision of the original slicing method, and provides a good solution for calculating the volume of the irregular solid waste of the construction waste.
In correspondence with the irregular object volume measuring method based on point cloud data provided in the above embodiment, the present embodiment provides an irregular object volume measuring system based on point cloud data, including: the device comprises a data acquisition module, a slicing module, a boundary detection module and a volume calculation module;
the data acquisition module is used for scanning the detected irregular object to acquire point cloud data of the detected irregular object;
the slicing module is used for projecting the information of the point cloud data to a Z axis of a space system according to the data of the three-dimensional coordinates in the point cloud data, determining a probability density function on the Z axis, further determining a curvature function on the Z axis projection according to the probability density function on the Z axis projection of the point cloud, and calculating the curvature of each point on the Z axis projection by using the curvature function; determining a preferred length list of the spacing between adjacent slices on the z axis according to the curvature of each point, the set reference spacing, the reference curvature and the relative spacing coefficient; slicing the point clouds according to the list of the optimal lengths of the adjacent slicing intervals to sequentially obtain point cloud table bodies in the z-axis direction;
the boundary detection module is used for carrying out boundary detection on the cross section of each slice and calculating the cross section area of each point cloud slice;
and the volume calculation module is used for determining the volume of each table body according to the cross section area of each point cloud slice and the height of each table body, and summing all the table bodies to obtain the final volume of the measured irregular object.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (8)
1. The irregular object volume determination method based on point cloud data is characterized by comprising the following steps:
scanning an irregular object to be detected to obtain point cloud data of the irregular object;
projecting the information of the point cloud data to a Z axis of a space system according to three-dimensional coordinate data in the point cloud data, determining a probability density function on the Z axis, further determining a curvature function on the projection of the Z axis, and calculating the curvature of each point on the projection of the Z axis by using the curvature function; determining a preferred length list of the spacing between adjacent slices on the z axis according to the curvature of each point, the set reference spacing, the reference curvature and the relative spacing coefficient; slicing the point clouds according to the list of the optimal lengths of the adjacent slicing intervals to sequentially obtain point cloud table bodies in the z-axis direction;
carrying out boundary detection on the cross section of each slice and calculating the cross section area of each point cloud slice; and determining the volume of each table body according to the cross section area of each point cloud slice and the height of each table body, and summing the volumes of all the table bodies to obtain the final volume of the measured irregular object.
2. The method of claim 1, wherein the probability density function on the z-axis is expressed as follows:
using projection method to obtain distribution function d (z) on z-axis, z is ∈ [ z [ ]min，zmax]Wherein z isminIs the minimum coordinate value of the point cloud in the z-axis projection, zmaxThe maximum coordinate value of the point cloud on the z-axis projection is obtained;
the calculated probability density function is expressed as follows:
wherein D is the total number of points, satisfying:
3. the method of claim 1, wherein the curvature function is expressed as follows:
wherein: Δ pk(z)＝p(z+k·Δz)-p(z+(k-1)Δz)，
c (z) is a curvature function, Δ z is a unit step size symbol "x" of the variable z of the probability density function p (z) represents a cross product, "· represents a point multiplication, and" | | | "represents a modular length; k is 1, 2, 3, Δ pk(z) represents the difference between the probability density function value p (z + (k-1) Δ z) and p (z + k · Δ z).
4. The method for determining the volume of an irregular object based on point cloud data as claimed in claim 1, wherein the method for determining the optimal length list of the spacing between adjacent slices in the z-axis is as follows:
the pitch function in the z-direction is obtained according to the following equation:
h(z)＝hb+(c(z)-cb)s
wherein c (z) is a curvature function, hbIs a reference pitch, cbIs the reference curvature and s is the relative spacing coefficient;
the method for determining the optimal length list of the adjacent slice intervals comprises the following steps:
step s 21: let z be zmin；
Step s 22: adding a z-axis coordinate value z at the tail of the length list l with the better spacing between adjacent slices;
step s 23: let z ═ z + h (z);
step s 24: if z < zmaxReturn stepStep s22, otherwise continue;
step s 25: adding z-coordinate z at the end of list lmaxAnd ending; z is a radical ofminIs the minimum coordinate value of the point cloud on the z-axis projection.
5. The method of claim 4, wherein the z-axis coordinate of the kth slice from bottom to top is expressed as follows:
wherein liThe ith element of the length list l with the better spacing between adjacent slices is used; k is the number of slices and satisfies
HK＝zmax。
6. The method of claim 1, wherein the coordinate on z-axis is HKThe method for calculating the cross section area of each point cloud slice obtained by point interception comprises the following steps:
and mapping the point cloud slice to an XOY coordinate system perpendicular to a Z axis to obtain a two-dimensional plane graph and a contour point set:
{(xi，yi)，1≤i≤n}，
calculating the area of the polygonal outline as the area of the cross section of the corresponding point cloud slice according to the following formula;
wherein i is the sign coefficient of the cross section vertex of the point cloud slice, n is the number of the vertex of the polygon, and
xn+1＝x1，yn+1＝y1。
7. the method of claim 1, wherein the volume of the ith stage from bottom to top is calculated according to the following formula
Wherein h isiIs the height of the ith platform body from bottom to top, SiThe area of the ith slice from bottom to top; and: h isi＝Hk-Hk-1，HkIs the z-axis coordinate of the k-th slice, Hk-1Is the z-axis coordinate of the k-th slice;
the volume of the point cloud is calculated as follows:
wherein n is the number of cross sections and the number of the table bodies is n-1.
8. Irregular object volumetric measurement system based on point cloud data, characterized by comprising:
the device comprises a data acquisition module, a slicing module, a boundary detection module and a volume calculation module;
the data acquisition module is used for scanning the detected irregular object to acquire point cloud data of the detected irregular object;
the slicing module is used for projecting the information of the point cloud data to a Z axis of a space system according to the data of the three-dimensional coordinates in the point cloud data, determining a probability density function on the Z axis, further determining a curvature function on the Z axis projection according to the probability density function on the Z axis projection of the point cloud, and calculating the curvature of each point on the Z axis projection by using the curvature function; determining a preferred length list of the spacing between adjacent slices on the z axis according to the curvature of each point, the set reference spacing, the reference curvature and the relative spacing coefficient; slicing the point clouds according to the list of the optimal lengths of the adjacent slicing intervals to sequentially obtain point cloud table bodies in the z-axis direction;
the boundary detection module is used for carrying out boundary detection on the cross section of each slice and calculating the cross section area of each point cloud slice;
and the volume calculation module is used for determining the volume of each table body according to the cross section area of each point cloud slice and the height of each table body, and summing the volumes of all the table bodies to obtain the final volume of the measured irregular object.
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