CN116188803B - Polar coordinate-based boundary point cloud extraction method, system, equipment and medium - Google Patents
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Abstract
The invention discloses a boundary point cloud extraction method, a system, equipment and a medium based on polar coordinates, and relates to the technical field of point cloud processing, wherein the method comprises the following steps: acquiring a target point cloud data set aiming at a target object, wherein the target point cloud data set comprises a plurality of target point cloud data; performing dimension reduction processing on the target point cloud data to obtain planar point cloud data corresponding to the target point cloud data, wherein the planar point cloud data has corresponding two-dimensional position information in a pre-established planar rectangular coordinate system; determining a polar origin according to two-dimensional position information corresponding to each of the plurality of planar point cloud data, wherein the polar origin has corresponding origin position information in the planar rectangular coordinate system; and determining boundary point cloud data corresponding to the target object according to the origin position information corresponding to the polar origin and the two-dimensional position information corresponding to each planar point cloud data.
Description
Technical Field
The invention relates to the technical field of point cloud processing, in particular to a boundary point cloud extraction method, a system, equipment and a medium based on polar coordinates.
Background
Currently, humans have produced a large amount of salts from salt lakes, as well as industrial materials such as potassium, magnesium, boron, bromine, and the like. Salt is not only a necessity for daily life of people, but also an important raw material in industry, so that the reasonable planning and utilization of salt in salt producing areas of salt lakes are necessary.
The precondition of reasonably planning and utilizing the salt in the salt producing area is to master each specific data of a salt pile, a salt pond and a water body in the salt producing area, a huge salt producing area comprises a large salt pond and a small salt pond, and how to digitize the area shape of the salt pond accurately is an important breakthrough point, which is particularly important for the subsequent production technology.
The point cloud can express the spatial outline and specific position of the object, and the point cloud boundary extraction is a computer vision technology, which can extract the object boundary from a large amount of three-dimensional point cloud data with high accuracy. Aiming at a water body target similar to a salt pond, the boundary point of the target object is extracted mainly by a point cloud boundary rapid extraction method combining K-means clustering and quadrant recognition, specifically, the point cloud is divided into a plurality of sub-clusters by the K-means clustering, the boundary clusters are detected according to the plurality of sub-clusters, and the boundary point is extracted from the boundary clusters by the quadrant recognition. However, the method requires a large amount of data (multiple data sets are needed to verify the extraction effect) for acquiring the point cloud boundary, and has the advantages of complex process, long time consumption, low accuracy and low fault tolerance.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: the existing method has low efficiency of determining the boundary point cloud data of the target object, low accuracy and low fault tolerance. In order to solve the technical problem, the invention provides a boundary point cloud extraction method, a system, equipment and a medium based on polar coordinates.
The technical scheme for solving the technical problems is as follows:
a boundary point cloud extraction method based on polar coordinates comprises the following steps:
step S1, acquiring a target point cloud data set aiming at a target object, wherein the target point cloud data set comprises a plurality of target point cloud data;
step S2, performing dimension reduction processing on the target point cloud data to obtain planar point cloud data corresponding to the target point cloud data, wherein the planar point cloud data has corresponding two-dimensional position information in a pre-established planar rectangular coordinate system;
step S3, determining a polar origin according to two-dimensional position information corresponding to each of the plurality of planar point cloud data, wherein the polar origin has corresponding origin position information in the planar rectangular coordinate system;
and S4, determining boundary point cloud data corresponding to the target object according to the original point position information corresponding to the polar coordinate original point and the two-dimensional position information corresponding to each planar point cloud data, wherein the boundary point cloud data are the planar point cloud data.
The beneficial effects of the invention are as follows: the dimension reduction processing is carried out on the target point cloud data to obtain the plane point cloud data, the polar coordinate origin and the origin position information corresponding to the polar coordinate origin are determined according to the two-dimensional position information corresponding to the plane point cloud data, and the boundary point cloud data corresponding to the target object can be rapidly determined according to the position relationship and the distance between the two-dimensional position information corresponding to each plane point cloud data and the origin position information in the same plane, so that the accuracy rate is high and the fault tolerance rate is high; for the same target object, the boundary point cloud data corresponding to the same target object in different periods is determined by the method, so that relevant personnel can evaluate the change of the target object conveniently, and data support is provided for subsequent work.
On the basis of the technical scheme, the invention can be improved as follows.
Further, the step S1 includes:
step S1.1, acquiring an original point cloud data set aiming at the target object, wherein the original point cloud data set comprises a plurality of original point cloud data;
and S1.2, preprocessing the original point cloud data set to obtain the target point cloud data set.
The beneficial effects of adopting the further scheme are as follows: the obtained original point cloud data is preprocessed, so that the point cloud data irrelevant to the target object is prevented from influencing the extraction of the boundary point cloud data of the target object.
Further, the step S1.2 includes:
and for each original point cloud data, performing downsampling processing on the original point cloud data through a VoxelGrid filter to obtain target point cloud data corresponding to the original point cloud data.
The beneficial effects of adopting the further scheme are as follows: the data volume in the original point cloud data set is large, each original point cloud data in the original point cloud data set is subjected to downsampling, the data volume is simplified under the condition that the characteristics of the original point cloud data are reserved, the processing efficiency of the subsequent steps is improved, and a data foundation is laid for improving the extraction accuracy and the fault tolerance of the boundary point cloud data.
Further, in the step S2, for each target point cloud data, the performing a dimension reduction process on the target point cloud data to obtain planar point cloud data corresponding to the target point cloud data includes:
and for each target point cloud data, taking the bottom of the target object as a projection direction, and vertically projecting the target point cloud data to obtain planar point cloud data corresponding to the target point cloud data, wherein the planar point cloud data is the point cloud data corresponding to the projection of the corresponding target point cloud data to the bottom of the target object.
The beneficial effects of adopting the further scheme are as follows: the dimension reduction processing is carried out on the target point cloud data to obtain corresponding two-dimensional point cloud data (namely plane point cloud data), so that the complexity of the subsequent determination of the polar coordinate origin and the boundary point cloud data is simplified, and the efficiency of determining the boundary point cloud data is improved.
Further, in the step S3, the determining a polar origin according to two-dimensional position information corresponding to each of the plurality of planar point cloud data includes:
determining a plurality of vertex point cloud data for forming a quadrilateral from a plurality of plane point cloud data according to two-dimensional position information corresponding to each of the plane point cloud data;
and determining the polar coordinate origin according to a quadrilateral formed by the plurality of vertex point cloud data.
The beneficial effects of adopting the further scheme are as follows: and determining a polar origin according to the center of a quadrilateral formed by the plurality of vertex point cloud data, wherein the polar origin can represent the center position of the target object, and the boundary point cloud data can be conveniently and subsequently determined by determining the polar origin, so that the accuracy of determining the boundary point cloud data is improved.
Further, in the step S4, determining boundary point cloud data according to origin position information corresponding to the origin of polar coordinates and two-dimensional position information corresponding to each of the planar point cloud data, includes:
Establishing a target two-dimensional coordinate system by taking the polar coordinate origin as an origin;
for each plane point cloud data, determining target two-dimensional position information corresponding to the plane point cloud data in the target two-dimensional coordinate system according to the position relation of the two-dimensional position information corresponding to the plane point cloud data and the origin position information corresponding to the polar origin in the plane rectangular coordinate system;
for each planar point cloud data, determining a polar angle corresponding to the planar point cloud data according to target two-dimensional position information corresponding to the planar point cloud data and a preset rotation direction, wherein the polar angle represents an angle relation between a connecting line of the target two-dimensional position information corresponding to the planar point cloud data and an origin of the target two-dimensional coordinate system and a target coordinate axis, and the target coordinate axis is an X axis of the target two-dimensional coordinate system or a Y axis of the target two-dimensional coordinate system;
for each polar angle, taking the origin of the target two-dimensional coordinate system as a starting point, sending out rays with angles between the two-dimensional coordinate system and the target coordinate axes as the polar angles according to the rotation direction, and taking each plane point cloud data positioned on the rays as coincident point cloud data corresponding to the polar angles;
And for each polar angle, determining the polar diameter corresponding to each coincident point cloud data corresponding to the polar angle, and determining the boundary point cloud data corresponding to the polar angle according to the polar diameter corresponding to each coincident point cloud data corresponding to the polar angle, wherein the boundary point cloud data corresponding to the target object comprises the boundary point cloud data corresponding to each polar angle.
The beneficial effects of adopting the further scheme are as follows: determining target two-dimensional position information corresponding to each planar point cloud data according to origin position information corresponding to the origin of polar coordinates and two-dimensional position information corresponding to each planar point cloud data, determining polar angles corresponding to each planar point cloud data according to target two-dimensional position information corresponding to each planar point cloud data, determining polar diameters corresponding to each coincident point cloud data according to coincident point cloud data corresponding to each polar angle, and determining the relationship among the planar point cloud data, the polar angles, the polar diameters and a target two-dimensional coordinate system; and determining the boundary point cloud data corresponding to each polar angle based on the polar diameter, so as to determine the boundary point cloud data corresponding to the target object, and improve the efficiency, accuracy and fault tolerance of determining the boundary point cloud data.
Further, for each planar point cloud data, the target two-dimensional position information corresponding to the planar point cloud data includes an abscissa value and an ordinate value;
for each planar point cloud data, determining a polar angle corresponding to the planar point cloud data according to the target two-dimensional position information corresponding to the planar point cloud data includes:
according to the target two-dimensional position information corresponding to the plane point cloud data, determining a polar angle corresponding to the plane point cloud data through a first formula, wherein the first formula is as follows:
；
wherein ,representing a polar angle corresponding to the planar point cloud data,>ordinate value representing the correspondence of the plane point cloud data,/->An ordinate value representing the origin of said target two-dimensional coordinate system,/->An abscissa value, < >/representing the plane point cloud data>An abscissa value representing an origin of the target two-dimensional coordinate system;
for each polar angle, determining a respective polar diameter of each coincident point cloud data corresponding to the polar angle includes:
for each coincidence point cloud data, determining a polar diameter corresponding to the coincidence point cloud data according to a second formula, wherein the second formula is as follows:
；
wherein ,Representing the polar diameter corresponding to the coincident point cloud data, < >>An abscissa value, < > -representing the coincidence point cloud data>And representing an ordinate value corresponding to the coincident point cloud data.
The beneficial effects of adopting the further scheme are as follows: the polar angle corresponding to each plane point cloud data and the polar diameter corresponding to each coincident point cloud data are low in calculation complexity, and the efficiency of determining the boundary point cloud data is improved.
In order to solve the technical problem, the invention also provides a boundary point cloud extraction system based on polar coordinates, which comprises:
the target point cloud data acquisition module is used for acquiring a target point cloud data set aiming at a target object, wherein the target point cloud data set comprises a plurality of target point cloud data;
the point cloud dimension reduction processing module is used for carrying out dimension reduction processing on the target point cloud data for each target point cloud data to obtain planar point cloud data corresponding to the target point cloud data, wherein the planar point cloud data has corresponding two-dimensional position information in a pre-established planar rectangular coordinate system;
the polar coordinate origin determining module is used for determining a polar coordinate origin according to two-dimensional position information corresponding to each of the plurality of plane point cloud data, wherein the polar coordinate origin has corresponding origin position information in the plane rectangular coordinate system;
The boundary point cloud data determining module is used for determining boundary point cloud data corresponding to the target object according to the original point position information corresponding to the polar coordinate origin and the two-dimensional position information corresponding to each planar point cloud data, wherein the boundary point cloud data are the planar point cloud data.
In order to solve the technical problem, the invention also provides electronic equipment, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the polar coordinate-based boundary point cloud extraction method when executing the computer program.
To solve the above technical problem, the present invention further provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the polar coordinate-based boundary point cloud extraction method as described above.
Drawings
FIG. 1 is a schematic flow chart of a polar coordinate-based boundary point cloud extraction method in the invention;
fig. 2 is a schematic structural diagram of a polar coordinate-based boundary point cloud extraction system according to the present invention.
Detailed Description
The principles and features of the present invention are described below with examples given for the purpose of illustration only and are not intended to limit the scope of the invention.
In order to facilitate understanding of the scheme of the present application, the nouns and principles involved in the present application are explained, in the present application, the target object may be a salt pond, a lake (such as a barrier lake), a pond, a river segment (refer to a segment of a river with a length of several tens of meters, for example) or the like, where the point cloud data can be obtained, and the bottom surface of the entity has a certain area, and the entity is formed by a plurality of closed packages of surfaces, and each surface is composed of a plurality of points. Each point composing the target object corresponds to position information in a multidimensional space coordinate system, namely, each target point cloud data contained in the target point cloud data set corresponds to multidimensional position information (including a point cloud abscissa, a point cloud ordinate and a point cloud ordinate) in the multidimensional space coordinate system, the bottom surface of the target object represents the surface of the target point cloud data, on which the target point cloud data corresponding to the minimum point cloud ordinate is located, in the target point cloud data, and the bottom surface of the target object is the bottom of the target object.
Example 1
As shown in fig. 1, the present embodiment provides a boundary point cloud extraction method based on polar coordinates, including:
Step S1, acquiring a target point cloud data set aiming at a target object, wherein the target point cloud data set comprises a plurality of target point cloud data;
step S2, performing dimension reduction processing on the target point cloud data to obtain planar point cloud data corresponding to the target point cloud data, wherein the planar point cloud data has corresponding two-dimensional position information in a pre-established planar rectangular coordinate system;
step S3, determining a polar origin according to two-dimensional position information corresponding to each of the plurality of planar point cloud data, wherein the polar origin has corresponding origin position information in the planar rectangular coordinate system;
and S4, determining boundary point cloud data corresponding to the target object according to the original point position information corresponding to the polar coordinate original point and the two-dimensional position information corresponding to each planar point cloud data, wherein the boundary point cloud data are the planar point cloud data.
In this embodiment, if the salt pool is taken as the target object, the target point cloud data set for the salt pool can be acquired by the vehicle radar.
Wherein, the step S1 includes:
step S1.1, acquiring an original point cloud data set aiming at the target object, wherein the original point cloud data set comprises a plurality of original point cloud data;
And S1.2, preprocessing the original point cloud data set to obtain the target point cloud data set.
In this embodiment, the specific process of obtaining the original point cloud data set is: measuring around a salt producing area by a vehicle-mounted radar at a preset inclination angle, recording point cloud data of objects except for a water body in real time, and obtaining point clouds corresponding to a plurality of salt pools after the vehicle-mounted radar runs around the salt producing area for one circle, wherein each point cloud contains a plurality of point cloud data, and selecting a point cloud with a neat outer boundary from the plurality of point clouds as a target point cloud; and regarding the target point clouds, taking the point cloud data of the target point clouds positioned above the water surface as the original point cloud data.
Wherein, the step S1.2 includes:
and for each original point cloud data, performing downsampling processing on the original point cloud data through a VoxelGrid filter to obtain target point cloud data corresponding to the original point cloud data.
In the step S2, for each target point cloud data, the performing dimension reduction processing on the target point cloud data to obtain planar point cloud data corresponding to the target point cloud data includes:
And for each target point cloud data, taking the bottom of the target object as a projection direction, and vertically projecting the target point cloud data to obtain planar point cloud data corresponding to the target point cloud data, wherein the planar point cloud data is the point cloud data corresponding to the projection of the corresponding target point cloud data to the bottom of the target object.
In this embodiment, if the salt pond is used as the target object, for each target point cloud data, the specific process of performing the dimension reduction processing on the target point cloud data to obtain the planar point cloud data corresponding to the target point cloud data is as follows: taking the direction vertical to the water surface as the vertical axis direction, and performing vertical projection on the target point cloud data to obtain planar point cloud data corresponding to the target point cloud data, wherein all the planar point cloud data are on the same plane; for each planar point cloud data, the two-dimensional position information corresponding to the planar point cloud data includes an original abscissa value and an original ordinate value, the original abscissa value corresponding to the planar point cloud data is a point cloud abscissa corresponding to the target point cloud data corresponding to the planar point cloud data, and the original ordinate value corresponding to the planar point cloud data is a point cloud ordinate corresponding to the target point cloud data corresponding to the planar point cloud data.
In the step S3, determining the origin of polar coordinates according to the two-dimensional position information corresponding to each of the plurality of planar point cloud data includes:
determining a plurality of vertex point cloud data for forming a quadrilateral from a plurality of plane point cloud data according to two-dimensional position information corresponding to each of the plane point cloud data;
and determining the polar coordinate origin according to a quadrilateral formed by the plurality of vertex point cloud data.
In this embodiment, the specific process of determining the origin of polar coordinates according to the two-dimensional position information corresponding to each of the plurality of planar point cloud data is: according to the two-dimensional position information corresponding to each planar point cloud data, a plurality of vertex point cloud data for forming a quadrilateral can be determined from the planar point cloud data in a program preset by a machine or in an artificial mode, and according to the two-dimensional position information corresponding to each vertex point cloud data, a center point of the quadrilateral is determined, and the center point of the quadrilateral is used as a polar coordinate origin. For example, four vertex point cloud data for forming a rectangle are artificially determined from a plurality of plane point cloud data, a center point of the rectangle is determined according to two-dimensional position information corresponding to each vertex point cloud data, the center point of the rectangle is taken as the polar coordinate origin, origin position information corresponding to the polar coordinate origin comprises a new origin abscissa value and a new origin ordinate value, and origin position information corresponding to the polar coordinate origin is two-dimensional position information corresponding to the center point of the rectangle in the plane rectangular coordinate system.
The present invention may also determine the origin of the polar coordinates by:
according to the two-dimensional position information corresponding to the plane point cloud data, determining an abscissa minimum value, an abscissa maximum value, an ordinate minimum value and an ordinate maximum value; the minimum value of the abscissa is the minimum value in the original abscissa value corresponding to each planar point cloud data, the maximum value of the abscissa is the maximum value in the original abscissa value corresponding to each planar point cloud data, the minimum value of the ordinate is the minimum value in the original ordinate value corresponding to each planar point cloud data, and the maximum value of the ordinate is the maximum value in the original ordinate value corresponding to each planar point cloud data;
determining a first average value according to the abscissa minimum value and the abscissa maximum value;
determining a second average value according to the ordinate minimum value and the ordinate maximum value;
and taking a point with an original abscissa value as the first average value and an original ordinate value as the second average value as the polar coordinate origin, wherein a new origin abscissa value corresponding to the polar coordinate origin is the first average value, and a new origin ordinate value corresponding to the polar coordinate origin is the second average value.
In the step S4, determining boundary point cloud data according to origin position information corresponding to the origin of polar coordinates and two-dimensional position information corresponding to each of the planar point cloud data, includes:
establishing a target two-dimensional coordinate system by taking the polar coordinate origin as an origin; in this embodiment, the two-dimensional coordinate system of the target is established by translating the origin of the rectangular coordinate system of the plane to the origin position information corresponding to the origin of the polar coordinate;
for each plane point cloud data, determining target two-dimensional position information corresponding to the plane point cloud data in the target two-dimensional coordinate system according to the position relation of the two-dimensional position information corresponding to the plane point cloud data and the origin position information corresponding to the polar origin in the plane rectangular coordinate system;
for each planar point cloud data, determining a polar angle corresponding to the planar point cloud data according to target two-dimensional position information corresponding to the planar point cloud data and a preset rotation direction, wherein the polar angle represents an angle relation between a connecting line of the target two-dimensional position information corresponding to the planar point cloud data and an origin of the target two-dimensional coordinate system and a target coordinate axis, and the target coordinate axis is an X axis of the target two-dimensional coordinate system or a Y axis of the target two-dimensional coordinate system; in this embodiment, the rotation direction is clockwise or counterclockwise;
For each polar angle, taking the origin of the target two-dimensional coordinate system as a starting point, sending out rays with angles between the two-dimensional coordinate system and the target coordinate axes as the polar angles according to the rotation direction, and taking each plane point cloud data positioned on the rays as coincident point cloud data corresponding to the polar angles;
and for each polar angle, determining the polar diameter corresponding to each coincident point cloud data corresponding to the polar angle, and determining the boundary point cloud data corresponding to the polar angle according to the polar diameter corresponding to each coincident point cloud data corresponding to the polar angle, wherein the boundary point cloud data corresponding to the target object comprises the boundary point cloud data corresponding to each polar angle. In this embodiment, for each polar angle, overlapping point cloud data corresponding to a minimum value in a polar path corresponding to each overlapping point cloud data corresponding to the polar angle is used as boundary point cloud data corresponding to the polar angle.
For each planar point cloud data, determining a polar angle corresponding to the planar point cloud data according to the target two-dimensional position information corresponding to the planar point cloud data includes:
according to the target two-dimensional position information corresponding to the plane point cloud data, determining a polar angle corresponding to the plane point cloud data through a first formula, wherein the first formula is as follows:
；
wherein ,representing the polar angle corresponding to the planar point cloud data, wherein the polar angle is the arctangent value of the difference value of the planar point cloud data and the polar origin point on the X axis and the difference value on the Y axis, +.>Ordinate value representing the correspondence of the plane point cloud data,/->An ordinate value representing the origin of said target two-dimensional coordinate system,/->An abscissa value, < >/representing the plane point cloud data>An abscissa value representing an origin of the target two-dimensional coordinate system;
for each polar angle, determining a respective polar diameter of each coincident point cloud data corresponding to the polar angle includes:
for each coincidence point cloud data, determining a polar diameter corresponding to the coincidence point cloud data according to a second formula, wherein the second formula is as follows:
；
wherein ,representing the polar diameter corresponding to the coincident point cloud data, wherein the value of the polar diameter is the sum of squares of the difference value of the coincident point cloud data and the polar origin on the X axis and the difference value of the coincident point cloud data and the polar origin on the Y axis, and the value of the polar diameter is>An abscissa value, < > -representing the coincidence point cloud data>And representing an ordinate value corresponding to the coincident point cloud data.
And drawing the obtained boundary point cloud data in sequence to obtain the water boundary (namely boundary point cloud data) of the salt pool.
According to the method, the collected salt pond point cloud is subjected to dimension reduction and detection, and the water body and the dry beach of the salt pond are distinguished, so that boundary division is performed, and salt pond workers can evaluate important indexes such as salt pond change, water level, channel planning and the like. Specifically, for the same salt producing area, by implementing the method, the change of the boundary (namely the water boundary and the outer boundary) of the salt pond can be detected, a calculated change range is generated for precipitation or dissolution of salt stacks, the change trend of the salt pond can be analyzed and known based on the change range, and similarly, the relative change of a dry beach and a water body on a two-dimensional plane can be clearly obtained through the determined boundary of the salt pond, so that the fluctuation of the water level in a period of time can be determined; based on the polar coordinate origin and the polar diameter determined by the polar angle, the water boundary of the salt pond is determined, the accuracy is high, and the accurate water boundary can enable the channel of the operation ship to be more accurate, so that the safety and the operation efficiency of operators are ensured.
Example two
Based on the same principle as the polar coordinate-based boundary point cloud extraction method described in the first embodiment, the present embodiment provides a polar coordinate-based boundary point cloud extraction system, as shown in fig. 2, including:
The target point cloud data acquisition module is used for acquiring a target point cloud data set aiming at a target object, wherein the target point cloud data set comprises a plurality of target point cloud data;
the point cloud dimension reduction processing module is used for carrying out dimension reduction processing on the target point cloud data for each target point cloud data to obtain planar point cloud data corresponding to the target point cloud data, wherein the planar point cloud data has corresponding two-dimensional position information in a pre-established planar rectangular coordinate system;
the polar coordinate origin determining module is used for determining a polar coordinate origin according to two-dimensional position information corresponding to each of the plurality of plane point cloud data, wherein the polar coordinate origin has corresponding origin position information in the plane rectangular coordinate system;
the boundary point cloud data determining module is used for determining boundary point cloud data corresponding to the target object according to the original point position information corresponding to the polar coordinate origin and the two-dimensional position information corresponding to each planar point cloud data, wherein the boundary point cloud data are the planar point cloud data.
The target point cloud data acquisition module comprises:
an original data acquisition unit configured to acquire an original point cloud data set for the target object, the original point cloud data set including a plurality of original point cloud data;
And the data preprocessing unit is used for preprocessing the original point cloud data set to obtain the target point cloud data set.
The data preprocessing unit is specifically configured to:
and for each original point cloud data, performing downsampling processing on the original point cloud data through a VoxelGrid filter to obtain target point cloud data corresponding to the original point cloud data.
The point cloud dimension reduction processing module is configured to, for each target point cloud data, perform dimension reduction processing on the target point cloud data, and when obtaining planar point cloud data corresponding to the target point cloud data, specifically configured to:
and for each target point cloud data, taking the bottom of the target object as a projection direction, and vertically projecting the target point cloud data to obtain planar point cloud data corresponding to the target point cloud data, wherein the planar point cloud data is the point cloud data corresponding to the projection of the corresponding target point cloud data to the bottom of the target object.
The polar origin determining module is configured to determine a polar origin according to two-dimensional position information corresponding to each of the plurality of planar point cloud data, where the polar origin determining module is specifically configured to:
Determining a plurality of vertex point cloud data for forming a quadrilateral from each of the planar point cloud data according to the two-dimensional position information corresponding to each of the planar point cloud data;
and determining the polar coordinate origin according to a quadrilateral formed by the plurality of vertex point cloud data.
The boundary point cloud data determining module comprises:
the two-dimensional coordinate system establishing unit is used for establishing a target two-dimensional coordinate system by taking the polar coordinate origin as an origin;
a two-dimensional position information determining unit, configured to determine, for each planar point cloud data, target two-dimensional position information corresponding to the planar point cloud data in the target two-dimensional coordinate system according to a positional relationship between two-dimensional position information corresponding to the planar point cloud data and origin position information corresponding to the polar origin in the planar rectangular coordinate system; for each planar point cloud data, the target two-dimensional position information corresponding to the planar point cloud data comprises an abscissa value and an ordinate value;
the polar angle determining unit is used for determining a polar angle corresponding to the plane point cloud data according to the target two-dimensional position information corresponding to the plane point cloud data and a preset rotation direction, wherein the polar angle represents an angle relation between a connecting line of the target two-dimensional position information corresponding to the plane point cloud data and an origin of the target two-dimensional coordinate system and a target coordinate axis, and the target coordinate axis is an X axis of the target two-dimensional coordinate system or a Y axis of the target two-dimensional coordinate system;
The coincident point cloud data determining unit is used for sending out rays with angles between the two-dimensional coordinate system of the target and the target coordinate axes as polar angles according to the rotation directions by taking the origin of the two-dimensional coordinate system of the target as a starting point for each polar angle, and taking each plane point cloud data positioned on the rays as coincident point cloud data corresponding to the polar angles;
the boundary point cloud data determining unit is configured to determine, for each polar angle, a polar diameter corresponding to each overlapping point cloud data corresponding to the polar angle, determine boundary point cloud data corresponding to the polar angle according to the polar diameter corresponding to each overlapping point cloud data corresponding to the polar angle, where the boundary point cloud data corresponding to the target object includes boundary point cloud data corresponding to each polar angle.
The polar angle determining unit is configured to determine, for each planar point cloud data, a polar angle corresponding to the planar point cloud data according to target two-dimensional position information corresponding to the planar point cloud data and a preset rotation direction, where the polar angle determining unit is specifically configured to:
according to the target two-dimensional position information corresponding to the plane point cloud data, determining a polar angle corresponding to the plane point cloud data through a first formula, wherein the first formula is as follows:
；
wherein ,representing a polar angle corresponding to the planar point cloud data,>ordinate value representing the correspondence of the plane point cloud data,/->An ordinate value representing the origin of said target two-dimensional coordinate system,/->An abscissa value, < >/representing the plane point cloud data>An abscissa value representing an origin of the target two-dimensional coordinate system.
The boundary point cloud data determining unit is configured to, for each polar angle, determine a polar diameter corresponding to each overlapping point cloud data corresponding to the polar angle, where the determining unit is specifically configured to:
for each coincidence point cloud data, determining a polar diameter corresponding to the coincidence point cloud data according to a second formula, wherein the second formula is as follows:
；
wherein ,representing the polar diameter corresponding to the coincident point cloud data, < >>An abscissa value, < > -representing the coincidence point cloud data>And representing an ordinate value corresponding to the coincident point cloud data.
Example III
In order to solve the above technical problem, the present embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the polar coordinate-based boundary point cloud extraction method according to the first embodiment when executing the computer program.
Example IV
To solve the above-mentioned technical problem, the present embodiment provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the polar coordinate-based boundary point cloud extraction method according to the first embodiment.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly, through intermediaries, or both, may be in communication with each other or in interaction with each other, unless expressly defined otherwise. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In the present invention, unless expressly stated or limited otherwise, a first feature "up" or "down" a second feature may be the first and second features in direct contact, or the first and second features in indirect contact via an intervening medium. Moreover, a first feature being "above," "over" and "on" a second feature may be a first feature being directly above or obliquely above the second feature, or simply indicating that the first feature is level higher than the second feature. The first feature being "under", "below" and "beneath" the second feature may be the first feature being directly under or obliquely below the second feature, or simply indicating that the first feature is less level than the second feature.
In the description of the present specification, a description referring to the terms "one embodiment," "some embodiments," "examples," "particular examples," "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (9)
1. The boundary point cloud extraction method based on the polar coordinates is characterized by comprising the following steps of:
Step S1, acquiring a target point cloud data set aiming at a target object, wherein the target point cloud data set comprises a plurality of target point cloud data;
step S2, performing dimension reduction processing on the target point cloud data to obtain planar point cloud data corresponding to the target point cloud data, wherein the planar point cloud data has corresponding two-dimensional position information in a pre-established planar rectangular coordinate system;
step S3, determining a polar origin according to two-dimensional position information corresponding to each of the plurality of planar point cloud data, wherein the polar origin has corresponding origin position information in the planar rectangular coordinate system;
step S4, determining boundary point cloud data corresponding to the target object according to origin position information corresponding to the polar origin and two-dimensional position information corresponding to each planar point cloud data, wherein the boundary point cloud data are the planar point cloud data;
in the step S4, determining boundary point cloud data corresponding to the target object according to origin position information corresponding to the origin of polar coordinates and two-dimensional position information corresponding to each of the planar point cloud data, includes:
Establishing a target two-dimensional coordinate system by taking the polar coordinate origin as an origin;
for each plane point cloud data, determining target two-dimensional position information corresponding to the plane point cloud data in the target two-dimensional coordinate system according to the position relation of the two-dimensional position information corresponding to the plane point cloud data and the origin position information corresponding to the polar origin in the plane rectangular coordinate system;
for each planar point cloud data, determining a polar angle corresponding to the planar point cloud data according to target two-dimensional position information corresponding to the planar point cloud data and a preset rotation direction, wherein the polar angle represents an angle relation between a connecting line of the target two-dimensional position information corresponding to the planar point cloud data and an origin of the target two-dimensional coordinate system and a target coordinate axis, and the target coordinate axis is an X axis of the target two-dimensional coordinate system or a Y axis of the target two-dimensional coordinate system;
for each polar angle, taking the origin of the target two-dimensional coordinate system as a starting point, sending out rays with angles between the two-dimensional coordinate system and the target coordinate axes as the polar angles according to the rotation direction, and taking each plane point cloud data positioned on the rays as coincident point cloud data corresponding to the polar angles;
And for each polar angle, determining the polar diameter corresponding to each coincident point cloud data corresponding to the polar angle, and determining the boundary point cloud data corresponding to the polar angle according to the polar diameter corresponding to each coincident point cloud data corresponding to the polar angle, wherein the boundary point cloud data corresponding to the target object comprises the boundary point cloud data corresponding to each polar angle.
2. The method according to claim 1, wherein the step S1 comprises:
step S1.1, acquiring an original point cloud data set aiming at the target object, wherein the original point cloud data set comprises a plurality of original point cloud data;
and S1.2, preprocessing the original point cloud data set to obtain the target point cloud data set.
3. The method according to claim 2, wherein the step S1.2 comprises:
and for each original point cloud data, performing downsampling processing on the original point cloud data through a VoxelGrid filter to obtain target point cloud data corresponding to the original point cloud data.
4. The method according to claim 1, wherein in the step S2, for each target point cloud data, the performing a dimension reduction process on the target point cloud data to obtain planar point cloud data corresponding to the target point cloud data includes:
And for each target point cloud data, taking the bottom of the target object as a projection direction, and vertically projecting the target point cloud data to obtain planar point cloud data corresponding to the target point cloud data, wherein the planar point cloud data is the point cloud data corresponding to the projection of the corresponding target point cloud data to the bottom of the target object.
5. The method according to claim 1, wherein in the step S3, the determining a polar origin according to two-dimensional position information corresponding to each of the plurality of planar point cloud data includes:
determining a plurality of vertex point cloud data for forming a quadrilateral from a plurality of plane point cloud data according to two-dimensional position information corresponding to each of the plane point cloud data;
and determining the polar coordinate origin according to a quadrilateral formed by the plurality of vertex point cloud data.
6. The method according to claim 1, wherein for each of the planar point cloud data, the target two-dimensional position information corresponding to the planar point cloud data includes an abscissa value and an ordinate value;
for each planar point cloud data, determining a polar angle corresponding to the planar point cloud data according to the target two-dimensional position information corresponding to the planar point cloud data includes:
According to the target two-dimensional position information corresponding to the plane point cloud data, determining a polar angle corresponding to the plane point cloud data through a first formula, wherein the first formula is as follows:
；
wherein ,representing a polar angle corresponding to the planar point cloud data,>ordinate value representing the correspondence of the plane point cloud data,/->An ordinate value representing the origin of said target two-dimensional coordinate system,/->An abscissa value, < >/representing the plane point cloud data>An abscissa value representing an origin of the target two-dimensional coordinate system;
for each polar angle, determining a respective polar diameter of each coincident point cloud data corresponding to the polar angle includes:
for each coincidence point cloud data, determining a polar diameter corresponding to the coincidence point cloud data according to a second formula, wherein the second formula is as follows:
；
wherein ,representing the polar diameter corresponding to the coincident point cloud data, < >>An abscissa value, < > -representing the coincidence point cloud data>And representing an ordinate value corresponding to the coincident point cloud data.
7. A polar coordinate-based boundary point cloud extraction system, comprising:
the target point cloud data acquisition module is used for acquiring a target point cloud data set aiming at a target object, wherein the target point cloud data set comprises a plurality of target point cloud data;
The point cloud dimension reduction processing module is used for carrying out dimension reduction processing on the target point cloud data for each target point cloud data to obtain planar point cloud data corresponding to the target point cloud data, wherein the planar point cloud data has corresponding two-dimensional position information in a pre-established planar rectangular coordinate system;
the polar coordinate origin determining module is used for determining a polar coordinate origin according to two-dimensional position information corresponding to each of the plurality of plane point cloud data, wherein the polar coordinate origin has corresponding origin position information in the plane rectangular coordinate system;
the boundary point cloud data determining module is used for determining boundary point cloud data corresponding to the target object according to origin position information corresponding to the polar origin and two-dimensional position information corresponding to each planar point cloud data, wherein the boundary point cloud data are the planar point cloud data;
the boundary point cloud data determining module comprises:
the two-dimensional coordinate system establishing unit is used for establishing a target two-dimensional coordinate system by taking the polar coordinate origin as an origin;
a two-dimensional position information determining unit, configured to determine, for each planar point cloud data, target two-dimensional position information corresponding to the planar point cloud data in the target two-dimensional coordinate system according to a positional relationship between two-dimensional position information corresponding to the planar point cloud data and origin position information corresponding to the polar origin in the planar rectangular coordinate system; for each planar point cloud data, the target two-dimensional position information corresponding to the planar point cloud data comprises an abscissa value and an ordinate value;
The polar angle determining unit is used for determining a polar angle corresponding to the plane point cloud data according to the target two-dimensional position information corresponding to the plane point cloud data and a preset rotation direction, wherein the polar angle represents an angle relation between a connecting line of the target two-dimensional position information corresponding to the plane point cloud data and an origin of the target two-dimensional coordinate system and a target coordinate axis, and the target coordinate axis is an X axis of the target two-dimensional coordinate system or a Y axis of the target two-dimensional coordinate system;
the coincident point cloud data determining unit is used for sending out rays with angles between the two-dimensional coordinate system of the target and the target coordinate axes as polar angles according to the rotation directions by taking the origin of the two-dimensional coordinate system of the target as a starting point for each polar angle, and taking each plane point cloud data positioned on the rays as coincident point cloud data corresponding to the polar angles;
the boundary point cloud data determining unit is configured to determine, for each polar angle, a polar diameter corresponding to each overlapping point cloud data corresponding to the polar angle, determine boundary point cloud data corresponding to the polar angle according to the polar diameter corresponding to each overlapping point cloud data corresponding to the polar angle, where the boundary point cloud data corresponding to the target object includes boundary point cloud data corresponding to each polar angle.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the polar coordinate-based boundary point cloud extraction method of any of claims 1 to 6 when the computer program is executed.
9. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed by a processor, implements the polar coordinate based boundary point cloud extraction method according to any of claims 1 to 6.
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Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105404844A (en) * | 2014-09-12 | 2016-03-16 | 广州汽车集团股份有限公司 | Road boundary detection method based on multi-line laser radar |
| CN107796363A (en) * | 2017-10-13 | 2018-03-13 | 北京工业大学 | A kind of method based on the extraction of continental rise LiDAR radians tunnel cross-section |
| CN110163871A (en) * | 2019-05-07 | 2019-08-23 | 北京易控智驾科技有限公司 | A kind of ground dividing method of multi-line laser radar |
| JP2019200760A (en) * | 2018-05-16 | 2019-11-21 | アイコンヤマト株式会社 | Automatic drawing method from point group, and point group processing program |
| US10713841B1 (en) * | 2019-01-18 | 2020-07-14 | Unkie Oy | System for generating point cloud map and method therefor |
| CN111639626A (en) * | 2020-06-11 | 2020-09-08 | 深圳市泰沃德自动化技术有限公司 | Three-dimensional point cloud data processing method and device, computer equipment and storage medium |
| CN113050993A (en) * | 2019-12-27 | 2021-06-29 | 中兴通讯股份有限公司 | Laser radar-based detection method and device and computer-readable storage medium |
| CN114255252A (en) * | 2022-02-28 | 2022-03-29 | 新石器慧通（北京）科技有限公司 | Obstacle contour acquisition method, device, equipment and computer-readable storage medium |
| CN115482211A (en) * | 2022-09-13 | 2022-12-16 | 中国地质大学（武汉） | Tunnel overbreak and underexcavation detection method and device, electronic equipment and storage medium |
| CN115564820A (en) * | 2022-11-10 | 2023-01-03 | 武汉工程大学 | Volume determination method, system, device and medium based on greedy projection triangularization |
| CN115908541A (en) * | 2022-09-02 | 2023-04-04 | 中兵智能创新研究院有限公司 | Curvature information-based vehicle-mounted laser radar point cloud clustering algorithm for indoor environment |
| CN115908742A (en) * | 2022-11-30 | 2023-04-04 | 河海大学 | Mudflat sand particle random generation and fine modeling method based on generation of countermeasure neural network |
-
2023
- 2023-04-23 CN CN202310437369.8A patent/CN116188803B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105404844A (en) * | 2014-09-12 | 2016-03-16 | 广州汽车集团股份有限公司 | Road boundary detection method based on multi-line laser radar |
| CN107796363A (en) * | 2017-10-13 | 2018-03-13 | 北京工业大学 | A kind of method based on the extraction of continental rise LiDAR radians tunnel cross-section |
| JP2019200760A (en) * | 2018-05-16 | 2019-11-21 | アイコンヤマト株式会社 | Automatic drawing method from point group, and point group processing program |
| US10713841B1 (en) * | 2019-01-18 | 2020-07-14 | Unkie Oy | System for generating point cloud map and method therefor |
| CN110163871A (en) * | 2019-05-07 | 2019-08-23 | 北京易控智驾科技有限公司 | A kind of ground dividing method of multi-line laser radar |
| CN113050993A (en) * | 2019-12-27 | 2021-06-29 | 中兴通讯股份有限公司 | Laser radar-based detection method and device and computer-readable storage medium |
| CN111639626A (en) * | 2020-06-11 | 2020-09-08 | 深圳市泰沃德自动化技术有限公司 | Three-dimensional point cloud data processing method and device, computer equipment and storage medium |
| CN114255252A (en) * | 2022-02-28 | 2022-03-29 | 新石器慧通（北京）科技有限公司 | Obstacle contour acquisition method, device, equipment and computer-readable storage medium |
| CN115908541A (en) * | 2022-09-02 | 2023-04-04 | 中兵智能创新研究院有限公司 | Curvature information-based vehicle-mounted laser radar point cloud clustering algorithm for indoor environment |
| CN115482211A (en) * | 2022-09-13 | 2022-12-16 | 中国地质大学（武汉） | Tunnel overbreak and underexcavation detection method and device, electronic equipment and storage medium |
| CN115564820A (en) * | 2022-11-10 | 2023-01-03 | 武汉工程大学 | Volume determination method, system, device and medium based on greedy projection triangularization |
| CN115908742A (en) * | 2022-11-30 | 2023-04-04 | 河海大学 | Mudflat sand particle random generation and fine modeling method based on generation of countermeasure neural network |
Non-Patent Citations (2)
| Title |
|---|
| A Method of D-Type Weld Seam Extraction Based on Point Clouds;Jie Gao 等;《IEEE Access》;全文 * |
| 基于激光雷达的稻麦收获边界检测与自动对齐系统设计;尚业华 等;《农业机械学报》;全文 * |
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