CN114782438B - Object point cloud correction method and device, electronic equipment and storage medium - Google Patents
Object point cloud correction method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN114782438B CN114782438B CN202210698046.XA CN202210698046A CN114782438B CN 114782438 B CN114782438 B CN 114782438B CN 202210698046 A CN202210698046 A CN 202210698046A CN 114782438 B CN114782438 B CN 114782438B
- Authority
- CN
- China
- Prior art keywords
- point cloud
- coordinate
- clouds
- line
- object point
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
- G06T7/75—Determining position or orientation of objects or cameras using feature-based methods involving models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/80—Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30164—Workpiece; Machine component
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30244—Camera pose
Landscapes
- Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Quality & Reliability (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The invention discloses an object point cloud correction method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining an object point cloud; determining the effective point cloud number and the effective point cloud set of each line in the object point cloud; when the number of the effective point clouds is larger than a preset threshold value, selecting a reference point cloud from the effective point cloud set; and correcting the row of object point clouds according to the effective point cloud set and the reference point cloud. By adopting the scheme provided by the invention, the deformation of the three-dimensional point cloud of the object in the vibration scene of the object can be corrected.
Description
Technical Field
The invention relates to the technical field of object detection, in particular to an object point cloud correction method, an object point cloud correction device, electronic equipment and a storage medium.
Background
Along with the progress of science and technology, more and more factory productions have used machine vision to replace the manual work to carry out the detection of product flaw, like size and defect detection in panel, the steel sheet production process, machine vision has the characteristics of stability, high accuracy and can be twenty-four hours uninterrupted duty, has promoted the production efficiency of enterprise simultaneously and has avoided missing the loss that examines the flaw piece and cause greatly.
However, the machine vision is usually accompanied with the transformation of the production line at present, the production line needs to be shut down during the transformation, the cost is high, if the transformation is not performed, the stability of the traditional production line is poor, for example, when the three-dimensional point cloud of a flat object is obtained through line laser, the collected point cloud is deformed to a certain degree because the product is in random up-and-down vibration caused by a conveyor belt, and the defect detection of the product is difficult.
Disclosure of Invention
At present, to under the object vibration scene, there is the technical problem of deformation in the object three-dimensional point cloud among the prior art, mainly adopt to reform transform producing the line, improve conveyer belt stability, fix leveling the object on the conveyer belt, reduce the mode of leveling the vibration of object and solve.
However, the above method has the following disadvantages: the production line needs to be transformed into huge economic cost, and production halt is needed during transformation, so that huge losses are brought to enterprises.
Based on this, in order to solve the technical problem that the three-dimensional point cloud of the object is deformed in the vibration scene of the object, embodiments of the present invention provide an object point cloud correction method, apparatus, electronic device and storage medium.
The technical scheme of the embodiment of the invention is realized as follows:
the embodiment of the invention provides an object point cloud correction method, which comprises the following steps:
acquiring an object point cloud;
determining the effective point cloud number and the effective point cloud set of each line in the object point cloud;
when the number of the effective point clouds is larger than a preset threshold value, selecting a reference point cloud from the effective point cloud set;
and correcting the row of object point clouds according to the effective point cloud set and the reference point cloud.
In the above scheme, the correcting the row of object point clouds according to the effective point cloud set and the reference point cloud includes:
determining a first coordinate included angle of the effective point cloud set relative to the reference point cloud according to the effective point cloud set and the reference point cloud;
carrying out angle leveling on the Z coordinate axis on the row of object point clouds by utilizing the first coordinate included angle;
determining parameters of line laser equipment;
and adjusting the angle of the line of object point clouds on the X coordinate axis according to the line laser equipment parameters and the reference point clouds.
In the above scheme, determining a first coordinate included angle of the effective point cloud set relative to the reference point cloud according to the effective point cloud set and the reference point cloud includes:
calculating included angles between coordinate vectors and Y coordinate axes, wherein the coordinate vectors are formed by all point clouds in the effective point cloud set and the reference point clouds;
determining the average value of the included angles;
and taking the average value as the first coordinate included angle.
In the above solution, the leveling of the angle on the Z coordinate axis of the line of object point clouds by using the first coordinate included angle includes:
calculating a first distance between each point cloud in the row of object point clouds and the reference point cloud;
calculating the adjustment height of each point cloud in the row of object point clouds on the Z coordinate axis according to the first distance and the first coordinate included angle;
and subtracting the adjustment height of each point cloud in the row of object point clouds on the Z coordinate axis on the basis of the Z coordinate value of each point cloud in the row of object point clouds, and performing angle leveling on the Z coordinate axis on each point cloud in the row of object point clouds.
In the foregoing solution, calculating the adjustment height of each point cloud in the line of object point clouds on the Z coordinate axis according to the first distance and the first coordinate included angle includes:
according to the first distance and the first coordinate included angle, calculating the adjustment height of each point cloud in the line of object point clouds on the Z coordinate axis by using the following formula (1):
Wherein the content of the first and second substances,
indicating the adjusted height of each point cloud in the row of object point clouds on the Z coordinate axis,
representing a first distance between each point cloud in the row of object point clouds and the reference point cloud,
representing a first coordinate angle.
In the above scheme, the adjusting the angle of the line of object point clouds on the X coordinate axis according to the line laser device parameters and the reference point clouds includes:
inputting the line laser equipment parameters and the reference point cloud coordinates into a preset first equation, and obtaining the adjustment height of the line of object point clouds on the X coordinate axis;
when the Z coordinate value of the reference point cloud is larger than 0, subtracting the adjustment height of the line of object point clouds on the X coordinate axis on the basis of the X coordinate value of each point cloud in the line of object point clouds, and adjusting the height of each point cloud in the line of object point clouds on the X coordinate axis; and when the Z coordinate value of the reference point cloud is less than or equal to 0, adding the adjustment height of the line of object point clouds on the X coordinate axis on the basis of the X coordinate value of each point cloud in the line of object point clouds, and performing adjustment height on the X coordinate axis on each point cloud in the line of object point clouds.
In the above scheme, the preset first equation is:
wherein the content of the first and second substances,
coordinate values on the Z coordinate axis of each point cloud in the row of object point clouds,
representing coordinate values of the reference point cloud on the Z coordinate axis,
and
the parameters of the line laser apparatus are shown,
indicating the adjusted height of the row of object point clouds on the X coordinate axis,
the column number of the object point cloud of the current row is represented,
representing the total number of columns of the object point cloud.
The embodiment of the invention also provides an object point cloud correction device, which comprises:
the acquisition module is used for acquiring an object point cloud;
the determining module is used for determining the effective point cloud number and the effective point cloud set of each line in the object point cloud;
the selecting module is used for selecting the reference point cloud from the effective point cloud set when the effective point cloud number is larger than a preset threshold value;
and the correcting module is used for correcting the line of object point clouds according to the effective point cloud set and the reference point cloud.
An embodiment of the present invention further provides an electronic device, including: a processor and a memory for storing a computer program capable of running on the processor; wherein the content of the first and second substances,
the processor is adapted to perform the steps of any of the above methods when running the computer program.
The embodiment of the invention also provides a storage medium, wherein a computer program is stored in the storage medium, and when the computer program is executed by a processor, the steps of any one of the methods are realized.
The embodiment of the invention provides an object point cloud correction method, an object point cloud correction device, electronic equipment and a storage medium, and the object point cloud is obtained;
determining the effective point cloud number and the effective point cloud set of each line in the object point cloud; when the number of the effective point clouds is larger than a preset threshold value, selecting a reference point cloud from the effective point cloud set; and correcting the row of object point clouds according to the effective point cloud set and the reference point cloud. By adopting the scheme provided by the invention, the deformation of the three-dimensional point cloud of the object in the vibration scene of the object can be corrected.
Drawings
FIG. 1 is a schematic flow chart of an object point cloud correction method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a calibration process according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of an object point cloud correction apparatus according to an embodiment of the present invention;
fig. 4 is an internal structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The embodiment of the invention provides an object point cloud correction method, as shown in fig. 1, the method comprises the following steps:
step 101: acquiring an object point cloud;
step 102: determining the effective point cloud number and the effective point cloud set of each line in the object point cloud;
step 103: when the number of the effective point clouds is larger than a preset threshold value, selecting a reference point cloud from the effective point cloud set;
step 104: and correcting the row of object point clouds according to the effective point cloud set and the reference point cloud.
The embodiment provides a method for correcting a three-dimensional point cloud of a flat object in a vibration scene with strong robustness, which takes a camera aperture imaging model into consideration to correct the point cloud in the x direction, so that flat and correct three-dimensional information of the surface of the flat object can be acquired in the vibration scene for subsequent analysis and use, and a production line does not need to be modified.
In addition, it should be noted that the solution of this embodiment is to correct the three-dimensional point cloud of the object into a correct shape, and unlike the smooth point cloud, the smooth point cloud makes the point cloud look smooth, but does not necessarily restore the point cloud into a correct shape.
In an embodiment, the correcting the row of object point clouds according to the effective point cloud set and the reference point cloud includes:
determining a first coordinate included angle of the effective point cloud set relative to the reference point cloud according to the effective point cloud set and the reference point cloud;
carrying out angle leveling on the Z coordinate axis on the row of object point clouds by utilizing the first coordinate included angle;
determining parameters of line laser equipment;
and adjusting the angle of the line of object point clouds on the X coordinate axis according to the line laser equipment parameters and the reference point clouds.
In one embodiment, the determining a first coordinate angle of the effective point cloud set relative to the reference point cloud according to the effective point cloud set and the reference point cloud includes:
calculating included angles between coordinate vectors and Y coordinate axes, wherein the coordinate vectors are formed by all point clouds in the effective point cloud set and the reference point clouds;
determining the average value of the included angles;
and taking the average value as the first coordinate included angle.
In an embodiment, the leveling the object point cloud on the Z coordinate axis by using the first coordinate angle includes:
calculating a first distance between each point cloud in the row of object point clouds and the reference point cloud;
calculating the adjustment height of each point cloud in the row of object point clouds on the Z coordinate axis according to the first distance and the first coordinate included angle;
and subtracting the adjustment height of each point cloud in the row of object point clouds on the Z coordinate axis on the basis of the Z coordinate value of each point cloud in the row of object point clouds, and performing angle leveling on the Z coordinate axis on each point cloud in the row of object point clouds.
In an embodiment, the calculating an adjusted height of each point cloud in the row of object point clouds on the Z coordinate axis according to the first distance and the first coordinate angle includes:
according to the first distance and the first coordinate included angle, calculating the adjustment height of each point cloud in the line of object point clouds on the Z coordinate axis by using the following formula (1):
Wherein the content of the first and second substances,
indicating the adjusted height of each point cloud in the row of object point clouds on the Z coordinate axis,
representing a first distance between each point cloud in the row of object point clouds and a reference point cloud,
representing a first coordinate angle.
In an embodiment, the performing, according to the line laser device parameter and the reference point cloud, an angle adjustment on an X coordinate axis of the line of object point clouds includes:
inputting the line laser equipment parameters and the reference point cloud coordinates into a preset first equation, and obtaining the adjustment height of the line of object point clouds on the X coordinate axis;
when the Z coordinate value of the reference point cloud is larger than 0, subtracting the adjustment height of the line of object point clouds on the X coordinate axis on the basis of the X coordinate value of each point cloud in the line of object point clouds, and adjusting the height of each point cloud in the line of object point clouds on the X coordinate axis; and when the Z coordinate value of the reference point cloud is less than or equal to 0, adding the adjustment height of the line of object point clouds on the X coordinate axis on the basis of the X coordinate value of each point cloud in the line of object point clouds, and performing adjustment height on the X coordinate axis on each point cloud in the line of object point clouds.
In one embodiment, the preset first equation is:
wherein the content of the first and second substances,
coordinate values on the Z coordinate axis of each point cloud in the row of object point clouds,
representing coordinate values of the reference point cloud on the Z coordinate axis,
and
the parameters of the line laser apparatus are shown,
indicating the adjusted height of the row of object point clouds on the X coordinate axis,
the column number of the object point cloud of the current row is represented,
representing the total number of columns of the object point cloud.
The object point cloud correction method provided by the embodiment of the invention obtains the object point cloud; determining the effective point cloud number and the effective point cloud set of each line in the object point cloud; when the number of the effective point clouds is larger than a preset threshold value, selecting a reference point cloud from the effective point cloud set; and correcting the row of object point clouds according to the effective point cloud set and the reference point cloud. By adopting the scheme provided by the invention, the deformation of the three-dimensional point cloud of the object in the vibration scene of the object can be corrected.
The present invention will be described in detail below with reference to application examples.
The application embodiment provides a method for correcting a three-dimensional point cloud of a flat object in a vibration scene with strong robustness, and the method considers a camera aperture imaging model to correct the point cloud in the x direction, so that flat and correct three-dimensional information of the surface of the flat object can be acquired in the vibration scene for subsequent analysis and use, and a production line does not need to be modified.
Specifically, the method comprises a calibration step part and a correction step part:
a calibration step part:
(1) as the internal reference of the line laser equipment cannot be given by the general equipment, but the method needs to be simply calibrated to obtain necessary information for subsequent calculation, referring to fig. 2, firstly, an object (such as a wood board) with edges and corners at the edge is taken, the edge of the object is placed at a position of about seventy percent in the line laser visual field, and the edge point of the current object is recorded as a position 1;
(2) acquiring a point cloud once by using a line laser to obtain a point cloud S1 (containing invalid point information, wherein the invalid point depth z value is-1000000000) at a position 1 and a corresponding camera image B1 thereof;
(3) transversely moving the object until the edge of the object is about to disappear and in a line laser view, recording the edge position of the current object as position 2, and acquiring a point cloud S2 and a camera image B2 corresponding to the point cloud S2, wherein the height of the position 1 is identical to that of the position 2 under the ideal condition;
(4) the main purpose of calibration is to obtain the distance d from the optical center of the line laser camera to the imaging plane, the half angle theta of the line laser view angle and the actual point distance dx on the imaging plane in fig. 1, and by respectively extracting the coordinates of the position 1 and the position 2 from the point cloud S1 and the point cloud S2, the height value of the position 1 is z, and the x-direction coordinate difference between the position 1 and the position 2 is delta x;
(5) the difference value delta u of the horizontal coordinate of the edge in the camera vision field can be obtained through images B1 and B2 when the object is at different positions, and the difference value delta u can be listed through the relation of similar triangles
Because two unknowns d and dx are included, the two unknowns d and dx cannot be directly solved, so that the steps 1 to 4 are repeated to obtain another set of similar triangular relationships at other positions, and the two equations are combined to obtain d and dx.
And a correction step part:
(1) acquiring a point cloud S of a flat object by a line laser sensor, wherein the point cloud S is H rows and W columns, and H multiplied by W points (including invalid points);
(2) establishing an array V with a size of H for recording the effective point number of each line (usually, when the line laser device collects the point cloud, a small z coordinate far beyond the measuring range is given to the ineffective point, such as-100000000), and counting the effective total point number of the z coordinate in the H-th line point cloud for each line of point cloud of the point cloud S
To make
；
(3) Establishing an array P with the size of H for recording the coordinates of the effective points of each line, and determining the method if V [ H ] is in the H-th line]Greater than the threshold of the number of the single row valid points
(empirically set to be not less than 0.1 XW), then from S [ h ]][0]Starting at Sh][W-1]Ending, sequentially arranging the row and column coordinates of the effective points
Store in P [ h ]]In this way P [ h ]]The first element and the last element of (a) are the starting row and the ending row of the effective points of the h row, and represent the point clouds of the flat objects in the row of point clouds;
(4) go through each row of point clouds of S, e.g., for h row of point clouds, if it is V [ h ]]Greater than the line significant point threshold
Then select the row point cloud valid point array P [ h ]]Medium number k =0.9 × V [ h ]](result rounded down) points
As a reference point of the line, the corresponding three-dimensional coordinates are expressed as
Is marked as
Then from P [ h ]][0]Starting up to p [ h ]][k-1]End, for each point p [ h ] in it][i]Obtaining corresponding three-dimensional coordinates from S
Calculating a vector
Angle with Y coordinate axis vector (0, 1, 0)
All of the row
Adding and dividing by the number k-1 to obtain the representative angle of the line
；
(5) Then through
The h-th line of point cloud of the point cloud S is subjected to angle leveling by leveling every point Sh in the line of point cloud][w]I.e. the point of the h-th line of the point cloud S, the distance between the point and the reference point of the line is calculated
Then pass through
Calculating the height Deltaz in the z direction required to be adjusted for leveling the angle of the steel plate,
finishing angle leveling;
(6) then, correcting the x coordinate of each point of the h-th line of point cloud based on the calibrated parameters by using an equation obtained by similar triangles
If only the equation is unknown, the equation can be directly solved
If the distance is more than 0, the point cloud of the row needs to be elongated after being adjusted to the 0 plane, and then
(7) For a single point cloud elongated in the step 6, the point cloud distance after elongation is too large to cause excessive sparseness, and the point density is improved by carrying out interpolation operation through averaging adjacent points;
(8) the z-direction height of each point in the final point cloud S
Subtract the datum height of the row
Namely, it is
And finishing the leveling of the whole point cloud S of the leveling object.
The embodiment provides a method for correcting a three-dimensional point cloud of a flat object in a vibration scene with strong robustness, which fully considers a camera pinhole imaging model, solves the problem of point cloud deformation of line laser acquisition caused by random shaking of a conveyor belt, can obtain three-dimensional information of the surface of the flat object with high accuracy, and can be used for various analyses such as size detection, defect identification and the like.
In order to implement the method according to the embodiment of the present invention, an embodiment of the present invention further provides an object point cloud correction apparatus, as shown in fig. 3, an object point cloud correction apparatus 300 includes: an acquisition module 301, a determination module 302, a selection module 303 and a correction module 304; wherein the content of the first and second substances,
an obtaining module 301, configured to obtain an object point cloud;
a determining module 302, configured to determine an effective point cloud number and an effective point cloud set of each line in the object point cloud;
a selecting module 303, configured to select a reference point cloud from the effective point cloud set when the number of effective point clouds is greater than a preset threshold;
and a correcting module 304, configured to correct the row of object point clouds according to the effective point cloud set and the reference point cloud.
In practical applications, the obtaining module 301, the determining module 302, the selecting module 303 and the correcting module 304 may be implemented by a processor in the object point cloud correcting apparatus.
It should be noted that: the above-mentioned apparatus provided in the above-mentioned embodiment is only exemplified by the division of the above-mentioned program modules when executing, and in practical application, the above-mentioned processing may be distributed to be completed by different program modules according to needs, that is, the internal structure of the terminal is divided into different program modules to complete all or part of the above-mentioned processing. In addition, the apparatus provided by the above embodiment and the method embodiment belong to the same concept, and the specific implementation process thereof is described in the method embodiment and is not described herein again.
To implement the method of the embodiment of the present invention, the embodiment of the present invention further provides a computer program product, where the computer program product includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. A processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the steps of the above-described method.
Based on the hardware implementation of the program module, in order to implement the method according to the embodiment of the present invention, an electronic device (computer device) is also provided in the embodiment of the present invention. Specifically, in one embodiment, the computer device may be a terminal, and its internal structure diagram may be as shown in fig. 4. The computer apparatus includes a processor a01, a network interface a02, a display screen a04, an input device a05, and a memory (not shown in the figure) connected through a system bus. Wherein processor a01 of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises an internal memory a03 and a non-volatile storage medium a 06. The nonvolatile storage medium a06 stores an operating system B01 and a computer program B02. The internal memory a03 provides an environment for the operation of the operating system B01 and the computer program B02 in the nonvolatile storage medium a 06. The network interface a02 of the computer device is used for communication with an external terminal through a network connection. The computer program is executed by the processor a01 to implement the method of any of the above embodiments. The display screen a04 of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device a05 of the computer device may be a touch layer covered on the display screen, a button, a trackball or a touch pad arranged on a casing of the computer device, or an external keyboard, a touch pad or a mouse.
Those skilled in the art will appreciate that the architecture shown in fig. 4 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
The device provided by the embodiment of the present invention includes a processor, a memory, and a program stored in the memory and capable of running on the processor, and when the processor executes the program, the method according to any one of the embodiments described above is implemented.
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.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include transitory computer readable media (transmyedia) such as modulated data signals and carrier waves.
It will be appreciated that the memory of embodiments of the invention may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. Among them, the nonvolatile Memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a magnetic random access Memory (FRAM), a Flash Memory (Flash Memory), a magnetic surface Memory, an optical disk, or a Compact Disc Read-Only Memory (CD-ROM); the magnetic surface storage may be disk storage or tape storage. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Enhanced Synchronous Dynamic Random Access Memory (Enhanced DRAM), Synchronous Dynamic Random Access Memory (SLDRAM), Direct Memory (DRmb Access), and Random Access Memory (DRAM). The memory described in connection with the embodiments of the invention is intended to comprise, without being limited to, these and any other suitable types of memory.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (8)
1. An object point cloud correction method, characterized in that the method comprises:
acquiring an object point cloud;
determining the effective point cloud number and the effective point cloud set of each line in the object point cloud;
when the number of the effective point clouds is larger than a preset threshold value, selecting a reference point cloud from the effective point cloud set;
correcting the row of object point clouds according to the effective point cloud set and the reference point cloud;
determining the effective point cloud number and the effective point cloud set of each row in the object point cloud, wherein the determining comprises the following steps:
determining an electric point of which the z coordinate does not exceed the measuring range in each row of point cloud of the object point cloud as an effective point cloud;
acquiring an effective point cloud number and an effective point cloud set of each row of effective point clouds;
wherein, according to the effective point cloud set and the reference point cloud, correcting the line of object point cloud, including:
calculating included angles between coordinate vectors and Y coordinate axes, wherein the coordinate vectors are formed by all point clouds in the effective point cloud set and the reference point clouds; determining the average value of the included angles; taking the average value as a first coordinate included angle, and leveling the angle of the line of object point clouds on a z coordinate axis by using the first coordinate included angle; determining parameters of line laser equipment; and adjusting the angle of the line of object point clouds on the x coordinate axis according to the line laser equipment parameters and the reference point clouds.
2. The method of claim 1, wherein the leveling the object point cloud with the first coordinate angle in the Z-coordinate axis comprises:
calculating a first distance between each point cloud in the row of object point clouds and the reference point cloud;
calculating the adjustment height of each point cloud in the row of object point clouds on the Z coordinate axis according to the first distance and the first coordinate included angle;
and subtracting the adjustment height of each point cloud in the row of object point clouds on the Z coordinate axis on the basis of the Z coordinate value of each point cloud in the row of object point clouds, and performing angle leveling on the Z coordinate axis on each point cloud in the row of object point clouds.
3. The method of claim 2, wherein calculating the adjusted height of each point cloud in the row of object point clouds along the Z-coordinate axis according to the first distance and the first coordinate angle comprises:
according to the first distance and the first coordinate included angle, calculating the adjustment height of each point cloud in the line of object point clouds on the Z coordinate axis by using the following formula (1):
Wherein the content of the first and second substances,
indicating the adjusted height of each point cloud in the row of object point clouds on the Z coordinate axis,
representing a first distance between each point cloud in the row of object point clouds and a reference point cloud,
representing a first coordinate angle.
4. The method of claim 1, wherein said adjusting the angle of the line of object point clouds in the X coordinate axis according to the line laser device parameters and the reference point clouds comprises:
inputting the line laser equipment parameters and the reference point cloud coordinates into a preset first equation, and obtaining the adjustment height of the line of object point clouds on the X coordinate axis;
when the Z coordinate value of the reference point cloud is larger than 0, subtracting the adjustment height of the line of object point clouds on the X coordinate axis on the basis of the X coordinate value of each point cloud in the line of object point clouds, and adjusting the height of each point cloud in the line of object point clouds on the X coordinate axis; and when the Z coordinate value of the reference point cloud is less than or equal to 0, adding the adjustment height of the line of object point clouds on the X coordinate axis on the basis of the X coordinate value of each point cloud in the line of object point clouds, and performing adjustment height on the X coordinate axis on each point cloud in the line of object point clouds.
5. The method of claim 4, wherein the predetermined first equation is:
wherein the content of the first and second substances,
coordinate values on the Z coordinate axis of each point cloud in the row of object point clouds,
representing coordinate values of the reference point cloud on the Z coordinate axis,
and
the parameters of the line laser apparatus are shown,
indicating the adjusted height of the row of object point clouds on the X coordinate axis,
the column number of the object point cloud of the current row is represented,
representing the total number of columns of the object point cloud.
6. An object point cloud correction apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring an object point cloud;
the determining module is used for determining the effective point cloud number and the effective point cloud set of each line in the object point cloud;
the selecting module is used for selecting the reference point cloud from the effective point cloud set when the effective point cloud number is larger than a preset threshold value;
the correction module is used for correcting the row of object point clouds according to the effective point cloud set and the reference point cloud;
the determining module is used for determining an electric point of which the z coordinate does not exceed the measuring range in each line of point cloud of the object point cloud as an effective point cloud; acquiring an effective point cloud number and an effective point cloud set of each row of effective point clouds;
the correction module is further used for calculating included angles between coordinate vectors and Y coordinate axes, wherein the coordinate vectors are formed by all point clouds in the effective point cloud set and the reference point clouds, and the point clouds are located in front of the reference point clouds; determining the average value of the included angles; taking the average value as a first coordinate included angle, and carrying out angle leveling on the z coordinate axis on the line of object point clouds by using the first coordinate included angle; determining parameters of line laser equipment; and adjusting the angle of the line of object point clouds on the x coordinate axis according to the line laser equipment parameters and the reference point clouds.
7. An electronic device, comprising: a processor and a memory for storing a computer program capable of running on the processor; wherein the content of the first and second substances,
the processor is adapted to perform the steps of the method of any one of claims 1 to 5 when running the computer program.
8. A storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, performs the steps of the method of any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210698046.XA CN114782438B (en) | 2022-06-20 | 2022-06-20 | Object point cloud correction method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210698046.XA CN114782438B (en) | 2022-06-20 | 2022-06-20 | Object point cloud correction method and device, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114782438A CN114782438A (en) | 2022-07-22 |
| CN114782438B true CN114782438B (en) | 2022-09-16 |
Family
ID=82420314
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210698046.XA Active CN114782438B (en) | 2022-06-20 | 2022-06-20 | Object point cloud correction method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114782438B (en) |
Citations (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9530225B1 (en) * | 2013-03-11 | 2016-12-27 | Exelis, Inc. | Point cloud data processing for scalable compression |
| GB201712922D0 (en) * | 2017-08-11 | 2017-09-27 | Canon Kk | Method and corresponding device for generating a point cloud representing a 3D object |
| WO2018072630A1 (en) * | 2016-10-17 | 2018-04-26 | 杭州海康威视数字技术股份有限公司 | Method and device for constructing 3d scene model |
| WO2019133922A1 (en) * | 2017-12-29 | 2019-07-04 | Flir Systems, Inc. | Point cloud denoising systems and methods |
| CN110942515A (en) * | 2019-11-26 | 2020-03-31 | 北京迈格威科技有限公司 | Point cloud-based target object three-dimensional computer modeling method and target identification method |
| CN111062255A (en) * | 2019-11-18 | 2020-04-24 | 苏州智加科技有限公司 | Three-dimensional point cloud labeling method, device, equipment and storage medium |
| WO2020093950A1 (en) * | 2018-11-06 | 2020-05-14 | 腾讯科技（深圳）有限公司 | Three-dimensional object segmentation method and device and medium |
| CN111210429A (en) * | 2020-04-17 | 2020-05-29 | 中联重科股份有限公司 | Point cloud data partitioning method and device and obstacle detection method and device |
| CN111553946A (en) * | 2020-04-17 | 2020-08-18 | 中联重科股份有限公司 | Method and device for removing ground point cloud and obstacle detection method and device |
| CN111582054A (en) * | 2020-04-17 | 2020-08-25 | 中联重科股份有限公司 | Point cloud data processing method and device and obstacle detection method and device |
| JP2020140697A (en) * | 2019-02-28 | 2020-09-03 | ベイジン バイドゥ ネットコム サイエンス アンド テクノロジー カンパニー リミテッド | Method and apparatus for determining rotation angle of construction machine plant |
| CN111699410A (en) * | 2019-05-29 | 2020-09-22 | 深圳市大疆创新科技有限公司 | Point cloud processing method, device and computer readable storage medium |
| WO2020206669A1 (en) * | 2019-04-09 | 2020-10-15 | 北京大学深圳研究生院 | Self-adaptive point cloud stripe division method |
| CN112365529A (en) * | 2021-01-11 | 2021-02-12 | 南京邮电大学 | Tunnel point cloud registration method and device based on gravity center deviation |
| WO2021082229A1 (en) * | 2019-10-31 | 2021-05-06 | 深圳市商汤科技有限公司 | Data processing method and related device |
| CN113362363A (en) * | 2021-06-18 | 2021-09-07 | 广东工业大学 | Automatic image annotation method and device based on visual SLAM and storage medium |
| WO2021179988A1 (en) * | 2020-03-09 | 2021-09-16 | 长沙智能驾驶研究院有限公司 | Three-dimensional laser-based container truck anti-smashing detection method and apparatus, and computer device |
| WO2021244363A1 (en) * | 2020-06-05 | 2021-12-09 | Oppo广东移动通信有限公司 | Point cloud compression method, encoder, decoder, and storage medium |
| WO2022001326A1 (en) * | 2020-06-30 | 2022-01-06 | 商汤集团有限公司 | Data processing method and apparatus, device, storage medium and program |
| WO2022062519A1 (en) * | 2020-09-22 | 2022-03-31 | 上海钛米机器人股份有限公司 | Ground detection method and apparatus, device, and storage medium |
| CN114440922A (en) * | 2020-10-30 | 2022-05-06 | 阿里巴巴集团控股有限公司 | Method and device for evaluating laser calibration, related equipment and storage medium |
| WO2022099530A1 (en) * | 2020-11-12 | 2022-05-19 | 深圳元戎启行科技有限公司 | Motion segmentation method and apparatus for point cloud data, computer device and storage medium |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106407408B (en) * | 2016-09-22 | 2019-08-16 | 北京数字绿土科技有限公司 | A kind of the spatial index construction method and device of mass cloud data |
| CN109285188B (en) * | 2017-07-21 | 2020-04-21 | 百度在线网络技术（北京）有限公司 | Method and apparatus for generating position information of target object |
| FR3093215B1 (en) * | 2019-02-22 | 2021-08-27 | Fogale Nanotech | Method and device for monitoring the environment of a robot |
| CN110335295B (en) * | 2019-06-06 | 2021-05-11 | 浙江大学 | Plant point cloud acquisition registration and optimization method based on TOF camera |
| US11682165B2 (en) * | 2020-01-21 | 2023-06-20 | Proprio, Inc. | Methods and systems for augmenting depth data from a depth sensor, such as with data from a multiview camera system |
| US11703577B2 (en) * | 2020-08-14 | 2023-07-18 | Baidu Usa Llc | Recalibration determination system for autonomous driving vehicles with multiple LiDAR sensors |
| CN113870358B (en) * | 2021-09-17 | 2024-05-24 | 聚好看科技股份有限公司 | Method and equipment for jointly calibrating multiple 3D cameras |
-
2022
- 2022-06-20 CN CN202210698046.XA patent/CN114782438B/en active Active
Patent Citations (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9530225B1 (en) * | 2013-03-11 | 2016-12-27 | Exelis, Inc. | Point cloud data processing for scalable compression |
| WO2018072630A1 (en) * | 2016-10-17 | 2018-04-26 | 杭州海康威视数字技术股份有限公司 | Method and device for constructing 3d scene model |
| GB201712922D0 (en) * | 2017-08-11 | 2017-09-27 | Canon Kk | Method and corresponding device for generating a point cloud representing a 3D object |
| WO2019133922A1 (en) * | 2017-12-29 | 2019-07-04 | Flir Systems, Inc. | Point cloud denoising systems and methods |
| WO2020093950A1 (en) * | 2018-11-06 | 2020-05-14 | 腾讯科技（深圳）有限公司 | Three-dimensional object segmentation method and device and medium |
| JP2020140697A (en) * | 2019-02-28 | 2020-09-03 | ベイジン バイドゥ ネットコム サイエンス アンド テクノロジー カンパニー リミテッド | Method and apparatus for determining rotation angle of construction machine plant |
| WO2020206669A1 (en) * | 2019-04-09 | 2020-10-15 | 北京大学深圳研究生院 | Self-adaptive point cloud stripe division method |
| CN111699410A (en) * | 2019-05-29 | 2020-09-22 | 深圳市大疆创新科技有限公司 | Point cloud processing method, device and computer readable storage medium |
| WO2021082229A1 (en) * | 2019-10-31 | 2021-05-06 | 深圳市商汤科技有限公司 | Data processing method and related device |
| CN111062255A (en) * | 2019-11-18 | 2020-04-24 | 苏州智加科技有限公司 | Three-dimensional point cloud labeling method, device, equipment and storage medium |
| CN110942515A (en) * | 2019-11-26 | 2020-03-31 | 北京迈格威科技有限公司 | Point cloud-based target object three-dimensional computer modeling method and target identification method |
| WO2021179988A1 (en) * | 2020-03-09 | 2021-09-16 | 长沙智能驾驶研究院有限公司 | Three-dimensional laser-based container truck anti-smashing detection method and apparatus, and computer device |
| CN111582054A (en) * | 2020-04-17 | 2020-08-25 | 中联重科股份有限公司 | Point cloud data processing method and device and obstacle detection method and device |
| CN111553946A (en) * | 2020-04-17 | 2020-08-18 | 中联重科股份有限公司 | Method and device for removing ground point cloud and obstacle detection method and device |
| CN111210429A (en) * | 2020-04-17 | 2020-05-29 | 中联重科股份有限公司 | Point cloud data partitioning method and device and obstacle detection method and device |
| WO2021244363A1 (en) * | 2020-06-05 | 2021-12-09 | Oppo广东移动通信有限公司 | Point cloud compression method, encoder, decoder, and storage medium |
| WO2022001326A1 (en) * | 2020-06-30 | 2022-01-06 | 商汤集团有限公司 | Data processing method and apparatus, device, storage medium and program |
| WO2022062519A1 (en) * | 2020-09-22 | 2022-03-31 | 上海钛米机器人股份有限公司 | Ground detection method and apparatus, device, and storage medium |
| CN114440922A (en) * | 2020-10-30 | 2022-05-06 | 阿里巴巴集团控股有限公司 | Method and device for evaluating laser calibration, related equipment and storage medium |
| WO2022099530A1 (en) * | 2020-11-12 | 2022-05-19 | 深圳元戎启行科技有限公司 | Motion segmentation method and apparatus for point cloud data, computer device and storage medium |
| CN112365529A (en) * | 2021-01-11 | 2021-02-12 | 南京邮电大学 | Tunnel point cloud registration method and device based on gravity center deviation |
| CN113362363A (en) * | 2021-06-18 | 2021-09-07 | 广东工业大学 | Automatic image annotation method and device based on visual SLAM and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| Rune Østergaard Pedersen等.Deriving individual tree competition indices from airborne laser scanning.《Forest Ecology and Management》.2012,第280卷第150-165页. * |
| 归一化互相关系数与迭代最近曲面片点云配准方法;张梅等;《计算机工程》;20161031;第42卷(第10期);第271-276页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114782438A (en) | 2022-07-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107633536B (en) | Camera calibration method and system based on two-dimensional plane template | |
| US8654193B2 (en) | Method for registering model data for optical recognition processing and optical sensor | |
| US11776217B2 (en) | Method for planning three-dimensional scanning viewpoint, device for planning three-dimensional scanning viewpoint, and computer readable storage medium | |
| KR20010101601A (en) | Software correction of image distortion in digital cameras | |
| CN113494893B (en) | Calibration method and device of three-dimensional laser scanning system and computer equipment | |
| JP2018511874A (en) | Three-dimensional modeling method and apparatus | |
| CN113470091B (en) | Hub point cloud registration method and device, electronic equipment and storage medium | |
| JPWO2020188799A1 (en) | Camera calibration device, camera calibration method, and program | |
| US20050053304A1 (en) | Method and device for the correction of a scanned image | |
| TWI510761B (en) | System and method for focusing multiple measurement points on a surface of an object | |
| CN114782438B (en) | Object point cloud correction method and device, electronic equipment and storage medium | |
| CN114529652B (en) | Point cloud compensation method, device, equipment and storage medium | |
| CN109029303B (en) | Method, device and system for measuring bottom area parameters of object and readable storage medium | |
| CN113379745B (en) | Product defect identification method and device, electronic equipment and storage medium | |
| US5199082A (en) | Method of detecting an amplitude transient in a field of elements having a multivalent amplitude distribution, device suitable for performing the method, and video system including the device | |
| CN111462321B (en) | Point cloud map processing method, processing device, electronic device and vehicle | |
| CN113421292A (en) | Three-dimensional modeling detail enhancement method and device | |
| CN115205129A (en) | Depth camera based on structured light and method of use | |
| CN112797917A (en) | High-precision digital speckle interference phase quantitative measurement method | |
| CN115775282B (en) | Method, device and storage medium for correcting image distortion at high speed on line | |
| CN111949925B (en) | Image relative orientation method and device based on Rodriger matrix and maximum convex hull | |
| CN117953038A (en) | Irregular volume measurement method, system, equipment and storage medium based on depth camera | |
| CN117953040A (en) | Minimum rectangular package determining method, system, equipment and storage medium based on depth camera | |
| CN117409065A (en) | Object measuring method, device, computer apparatus, readable storage medium and product | |
| CN117346656A (en) | Method, device, terminal and storage medium for measuring tread and out-of-roundness of wheel |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |