CN112650790A - Target point cloud plane determining method and device, electronic equipment and storage medium - Google Patents
Target point cloud plane determining method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The present disclosure relates to a method, an apparatus, an electronic device and a storage medium for determining a target point cloud plane, including: selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane; acquiring second point cloud data, and calculating the distance between the second point cloud data and the point cloud plane; when the distance is larger than the preset distance, returning to execute the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane; and when the distance is smaller than or equal to the preset distance, determining the target point cloud plane according to the point cloud plane and the residual point cloud data. And eliminating a part of point cloud planes with lower probability as target point cloud planes through the distance between the second point cloud data and the estimated point cloud plane, and not calculating the distance between the residual point cloud data and the point cloud plane any more so as to reduce the calculation amount and improve the overall calculation efficiency of the algorithm.
Description
Technical Field
The present disclosure relates to the field of point cloud data processing technologies, and in particular, to a method and an apparatus for determining a target point cloud plane, an electronic device, and a storage medium.
Background
The random sampling consistency algorithm, namely the RANSAC algorithm, can be used for estimating a point cloud plane, when the RANSAC algorithm is used for estimating the point cloud plane, point cloud data are generally extracted randomly to estimate one point cloud plane, the distance between other point cloud data and the point cloud plane is calculated to determine whether the point cloud data are on the point cloud plane, the process is iterated continuously, and the plane with the most point cloud data is found to be a final plane. Then, in the algorithm, the distances between all the point cloud data and the point cloud planes need to be calculated, so that the calculation amount of the whole algorithm is large, and the efficiency is not high.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
In a first aspect, the present disclosure provides a method for determining a target point cloud plane, the method comprising: selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane; acquiring second point cloud data, and calculating the distance between the second point cloud data and the point cloud plane, wherein the second point cloud data is one point cloud data randomly selected from point cloud data except the first point cloud data in the point cloud data set; when the distance is larger than the preset distance, returning to execute the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane; and when the distance is smaller than or equal to the preset distance, determining the target point cloud plane according to the point cloud plane and residual point cloud data, wherein the residual point cloud data is point cloud data in the point cloud data set except the first point cloud data and the second point cloud data.
In a second aspect, the present disclosure provides an apparatus for determining a target point cloud plane, the apparatus comprising: the estimation module is used for selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane; the acquisition module is used for acquiring second point cloud data and calculating the distance between the second point cloud data and the point cloud plane, wherein the second point cloud data is one point cloud data randomly selected from point cloud data except the first point cloud data in the point cloud data set; the iteration module is used for returning to the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane when the distance is greater than the preset distance; and the determining module is used for determining the target point cloud plane according to the point cloud plane and residual point cloud data when the distance is less than or equal to the preset distance, wherein the residual point cloud data is point cloud data in the point cloud data set except the first point cloud data and the second point cloud data.
In a third aspect, the present disclosure provides a non-transitory computer-readable storage medium having stored thereon a computer program, wherein the program, when executed by a processing apparatus, implements the above-described target point cloud plane determination method.
In a fourth aspect, the present disclosure provides an electronic device, which includes a storage device and a processing device, wherein the storage device stores a computer program thereon, and the processing device implements the above target point cloud plane determining method when executing the computer program in the storage device.
According to the technical scheme, a preset number of first point cloud data are selected from a point cloud data set to estimate a point cloud plane; randomly extracting one point cloud data from the point cloud data except the first point cloud data in the point cloud data set to serve as second point cloud data, and calculating the distance between the second point cloud data and the point cloud plane; and when the distance is less than or equal to the preset distance, determining the target point cloud plane according to the point cloud plane and the residual point cloud data. And eliminating a part of point cloud planes with lower probability as target point cloud planes through the distance between the second point cloud data and the estimated point cloud plane, and not calculating the distance between the residual point cloud data and the point cloud plane any more so as to reduce the calculation amount and improve the overall calculation efficiency of the algorithm.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale. In the drawings:
fig. 1 is a flowchart of a target point cloud plane determination method according to an embodiment of the present application.
Fig. 2 is a flowchart of a target point cloud plane determination method according to another embodiment of the present application.
Fig. 3 is a flowchart of a target point cloud plane determination method according to another embodiment of the present application.
Fig. 4 is a functional block diagram of a target point cloud plane determining apparatus according to an embodiment of the present application.
Fig. 5 shows a schematic structural diagram of an electronic device suitable for implementing embodiments of the present application.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
Referring to fig. 1, an embodiment of the present application provides a method for determining a target point cloud plane, which may be applied to an electronic device, where the electronic device may be an intelligent device, or a local service, a cloud server, and the like.
Step S101: and selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane.
The point cloud data set is a set including a plurality of point cloud data. In the point cloud data set, a preset number of point cloud data can be randomly selected as first point cloud data, and a point cloud plane is estimated by using the randomly selected first point cloud data.
The randomly selecting the preset number of first point cloud data may be randomly selecting 3 first point cloud data from the point cloud data set, and a point cloud plane may be estimated by using the 3 first point cloud data.
Specifically, the point cloud plane is estimated by using the 3 point cloud data, which may be establishing a reference coordinate system, determining the coordinates of the first point cloud data based on the reference coordinates, and determining a corresponding plane equation according to the coordinates of the first point cloud data.
Step S102: and acquiring second point cloud data, and calculating the distance between the second point cloud data and the point cloud plane.
After one point cloud plane is estimated using the randomly extracted 3 point cloud data, the acquisition of the second point cloud data may be continued. The second point cloud data is randomly selected from the point cloud data except the first point cloud data in the point cloud data set. That is, the second point cloud data is different point cloud data from the first point cloud data.
After the second point cloud data is acquired, the distance between the second point cloud data and the point cloud plane may be calculated, and it may be further determined whether the second point cloud data belongs to the point cloud plane according to the distance. The calculating of the distance between the second point cloud data and the point cloud plane may be determining a coordinate of the second point cloud data in the reference coordinate system, and calculating the distance between the second point cloud data and the point cloud plane according to the coordinate of the second point cloud data and a plane equation and the coordinate of the second point cloud data and the plane equation.
Determining whether the second point cloud data belongs to the point cloud plane, which may be comparing the distance with a preset distance, if the distance is greater than the preset distance, executing step S101, and if the distance is less than or equal to the preset distance, executing step S103.
Step S103: and when the distance is greater than the preset distance, returning to execute the step of selecting a preset number of first point cloud data from the point cloud data set to estimate the point cloud plane.
After the distance between the second point cloud data and the point cloud plane is calculated, the distance may be compared with a preset distance. The preset distance is a preset critical value representing the distance of the point cloud data belonging to the point cloud plane, namely, if the distance between the point cloud data and the point cloud plane is greater than the preset distance, the point cloud data belonging to the point cloud plane is indicated; and if the distance between the point cloud data and the point cloud plane is less than or equal to the preset distance, indicating that the point cloud data does not belong to the point cloud plane.
For example, if the preset distance is X, the distance between the point cloud data a and the point cloud plane is X1, and the distance between the point cloud data B and the point cloud plane is X2, where X1< X2, then the point cloud data a and the point cloud data B may be considered to belong to the point cloud plane, respectively.
Therefore, when the distance between the second point cloud data and the point cloud plane is calculated to be greater than the preset distance, the second point cloud data does not belong to the point cloud plane, and the target point cloud plane to be determined is the point cloud plane containing the most point cloud data, so that the probability that the point cloud plane is the target point cloud plane is considered to be very small, the point cloud plane can be directly rejected, namely, the subsequent steps are not executed by using the point cloud plane, but the step S101 is returned to be executed, and the preset number of first point cloud data are selected from the point cloud data set again to estimate the point cloud plane.
Step S104: and when the distance is smaller than or equal to the preset distance, determining the target point cloud plane according to the point cloud plane and the residual point cloud data.
The target point cloud plane is the point cloud plane containing the most point cloud data in the point cloud data set. If the distance is smaller than or equal to the preset distance, the second point cloud data can be considered to belong to the point cloud plane, the probability that the plane is the target point cloud plane is high, and the point cloud plane can be continuously utilized to execute subsequent steps so as to determine the target point cloud plane.
In determining the target point cloud plane, the determination may be based on the point cloud plane and the remaining point cloud data. And the residual point cloud data is point cloud data in the point cloud data set except the first point cloud data and the second point cloud data.
It is understood that a preset number of first point cloud data and one second point cloud data are randomly extracted from the point cloud data set, and then point cloud data other than the first point cloud data and the second point cloud data in the point cloud data set are residual point cloud data. For example, the point cloud data set includes six point cloud data of point cloud data a, B, C, D, E, F, and G, where the first point cloud data extracted randomly is a, C, and G, and the second point cloud data extracted randomly is E, and then the remaining point cloud data is B, D, and F.
The step of determining the target point cloud plane by using the point cloud plane and the residual point cloud data may be calculating a distance between each residual point cloud data and the point cloud plane, counting the number of the distances smaller than or equal to a preset distance, namely, taking the number of the residual point cloud data belonging to the point cloud plane as a target number, and performing the steps in a recycling manner, namely, returning to perform the steps of randomly extracting a preset number of first point cloud data from the point cloud data set to estimate the point cloud plane, randomly extracting second point cloud data, calculating a distance between the second point cloud data and the point cloud plane, and calculating a distance between each residual point cloud data and the point cloud plane when the distance is smaller than or equal to the preset distance. And performing multiple iterations to obtain a plurality of point cloud planes and the number of targets corresponding to the point cloud planes, and determining the point cloud plane corresponding to the maximum value of the number of targets as the target point cloud plane.
The preset distance is a value that can be set by a person skilled in the art according to actual needs, and is not specifically limited herein.
The method for determining the target point cloud plane comprises the steps of selecting a preset number of first point cloud data from a point cloud data set to estimate the point cloud plane; acquiring second point cloud data, and calculating the distance between the second point cloud data and the point cloud plane; when the distance is larger than the preset distance, returning to execute the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane; and when the distance is smaller than or equal to the preset distance, determining the target point cloud plane according to the point cloud plane and the residual point cloud data. The second point cloud data is randomly extracted, the distance between the second point cloud data and the point cloud plane is calculated, the plane with lower probability of the target point cloud plane is eliminated, and a large amount of calculation is avoided, so that the iterative process can be accelerated, and the overall efficiency of the algorithm is improved.
Referring to fig. 2, another embodiment of the present application provides a method for determining a target point cloud plane, and on the basis of the above embodiment, a process of determining a target point cloud plane according to the point cloud plane and the remaining point cloud data is described in detail. Specifically, the method may include the following steps.
Step S201: and selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane.
Step S202: and acquiring second point cloud data, and calculating the distance between the second point cloud data and the point cloud plane.
Step S203: and when the distance is greater than the preset distance, returning to execute the step of selecting a preset number of first point cloud data from the point cloud data set to estimate the point cloud plane.
The steps S201 to S203 refer to corresponding parts of the foregoing embodiments, and are not described herein again.
Step S204: and when the distance is smaller than or equal to the preset distance, acquiring the distance between each residual point cloud data and the point cloud plane, and acquiring the target number of the residual point cloud data of which the distance is smaller than or equal to the preset distance.
When the distance between the second point cloud data and the point cloud plane is smaller than the preset distance, a quantity obtaining operation may be performed on the point cloud plane. Specifically, the distance between each remaining point cloud data and the point cloud plane may be obtained, that is, the distance between each remaining point cloud data and the point cloud plane is calculated respectively. After the distance between each residual point cloud data and the point cloud plane is obtained through calculation, each obtained distance is compared with the preset distance, and the number of the distances smaller than or equal to the preset distance is obtained to serve as the number of targets corresponding to the point cloud plane.
As in the foregoing example, the point cloud data set includes six point cloud data, i.e., point cloud data a, B, C, D, E, F, and G, where the first point cloud data extracted randomly is a, C, and G, and the second point cloud data extracted randomly is E, and then the remaining point cloud data is B, D, and F. Assuming that the point cloud plane determined according to the first point cloud data is SACGRespectively calculating point cloud data B, D and F and the point cloud plane SACGIs X1, X2, X1, whichMiddle X1<X<X2, indicating that the number of point cloud data having the distance less than the preset distance is 2, and thus the point cloud plane SACGThe corresponding target number is 2.
Step S205: and determining a target point cloud plane according to the target quantity.
After the number of targets corresponding to the point cloud planes is obtained, the step of selecting a preset number of first point cloud data from the point cloud data set to estimate the point cloud planes can be executed for multiple times to obtain a plurality of point cloud planes, and for each point cloud plane with a distance from the second point cloud data being smaller than or equal to the preset distance, a number obtaining operation is executed respectively to obtain the number of targets corresponding to each point cloud plane.
That is, after the number of targets corresponding to the current point cloud plane is obtained, the method may return to execute selecting a preset number of first point cloud data from the point cloud data set, and estimate a point cloud plane by using the selected first point cloud data. And after the point cloud plane is obtained, continuously acquiring second point cloud data, calculating the distance between the second point cloud data and the point cloud plane, and acquiring the number of targets corresponding to the point cloud plane when the distance is smaller than or equal to the preset distance. The above processes are continuously and circularly executed, a plurality of point cloud planes and the number of targets corresponding to the point cloud planes one by one can be obtained, the maximum number of targets can be determined in the number of the targets, and the point cloud plane corresponding to the maximum number of targets is determined as the target plane.
In some embodiments, the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane may be performed in parallel, and the number obtaining operation may be performed separately for each of the point cloud planes having a distance to the second point cloud data less than or equal to the preset distance. That is, when calculating the distance between each remaining point and the point cloud plane and obtaining the target number of the point cloud points with the distance less than or equal to the preset distance, a preset number of first point cloud data selected from the point cloud data set may be simultaneously executed to estimate the point cloud plane so as to obtain a plurality of point cloud planes, and the number obtaining operation may be executed for each point cloud plane satisfying the condition that the distance is less than or equal to the preset distance.
After the number of targets corresponding to each point cloud plane is obtained, target point cloud planes can be determined according to the number of targets, for example, the corresponding relationship between the point cloud planes and the number of targets is shown in table 1.
TABLE 1
| Point cloud plane | Point cloud plane 1 | Point cloud plane 2 | Point cloud plane 3 | Point cloud plane 4 |
| Target number | Y1 | Y2 | Y3 | Y4 |
In table 1, the number of targets corresponding to the point cloud plane 1 is Y1, the number of targets corresponding to the point cloud plane 2 is Y2, the number of targets corresponding to the point cloud plane 3 is Y3, the number of targets corresponding to the point cloud plane 4 is Y4, assuming that Y2> Y1> Y4> Y3, that is, the maximum number of targets is Y2, it can be known from table 1 that the point cloud plane corresponding to the number of targets Y2 is the point cloud plane 2, and therefore, it can be determined that the point cloud plane 2 is the target point cloud plane.
In the above step, after the step S202 is executed, if the distance is greater than the preset distance, the step S203 is executed, and if the distance is less than or equal to the preset distance, the steps S204 and S205 are executed.
According to the method for determining the target point cloud plane, the distance between the second point cloud data and the point cloud plane estimated by the first point cloud data is calculated, and when the distance is greater than the preset distance, the point cloud plane is not used for subsequent calculation, so that the calculation amount is reduced; and when the distance is smaller than or equal to the preset distance, calculating the distance between the point cloud plane and each residual point cloud data, acquiring the number of targets corresponding to the point cloud plane, returning to the step of selecting the first point cloud data to estimate the point cloud plane so as to acquire a plurality of point cloud planes, further acquiring the number of targets corresponding to each point cloud plane, and determining the point cloud plane corresponding to the maximum number of targets as the target point cloud plane. And a plane with lower probability of the target point cloud plane is eliminated through the randomly extracted distance between the second point cloud data and the point cloud plane, so that a large amount of calculation is avoided, the iterative process can be accelerated, and the overall efficiency of the algorithm is improved.
Referring to fig. 3, another embodiment of the present application provides a method for determining a target point cloud plane, and the overall process of determining the target point cloud plane is described in detail based on the foregoing embodiments. Specifically, the method may include the following steps.
Step S301: and selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane.
Step S302: and acquiring second point cloud data, and calculating the distance between the second point cloud data and the point cloud plane.
The steps S301 to S302 refer to corresponding parts of the foregoing embodiments, and are not described herein again.
Step S303: judging whether the distance is greater than a preset distance; if yes, go to step S301; if not, go to step S304.
When the distance between the second point cloud data and the point cloud plane is obtained, it may be determined whether the distance is greater than a preset distance, if the distance is greater than the preset distance, step S301 is performed, and if the distance is less than or equal to the preset distance, step S304 is performed. The relevant contents of this step can be referred to the corresponding parts of the previous embodiments.
Step S304: and acquiring the distance between each residual point cloud data and the point cloud plane, and acquiring the target number of the residual point cloud data of which the distance is less than or equal to the preset distance.
Step S305: and acquiring the target quantity corresponding to each point cloud plane.
And obtaining the distance between each point cloud data and the point cloud plane to obtain a plurality of distances, comparing each distance in the plurality of obtained distances with the preset distance, and obtaining the number of the distances which are less than or equal to the preset distance in the plurality of distances as the target number.
After step S304 is executed, step S301 is executed again, and when step S301 is executed again, a plurality of point cloud planes satisfying that the distance is smaller than or equal to the preset distance may be obtained, and for each point cloud plane, the number of targets corresponding to the point cloud plane may be obtained. Therefore, the target number corresponding to each point cloud plane can be obtained.
Step S306: judging whether an iteration condition is met; if yes, go to step S307; if not, go to step S301.
When the target number corresponding to each point cloud plane is obtained, it may be determined whether an iteration condition is satisfied, that is, whether the loop may be ended, if it is determined that the iteration condition is satisfied, it indicates that the loop may be ended, and step S307 is performed. If the iteration condition is not satisfied, indicating that the loop needs to be continued, step S301 is executed.
In some embodiments, the predetermined number of times may be preset, that is, the number of times of performing the step of selecting the predetermined number of first point cloud data from the point cloud data set to estimate the point cloud plane. That is, the number of times of executing the step of selecting a preset number of first point cloud data from the point cloud data set to estimate the point cloud plane is recorded, and if the recorded number of times is equal to the preset number of times, indicating that the iteration condition is satisfied, the loop may be ended, and step S307 is executed; if the recorded times are less than or equal to the preset times, the iteration condition is not met, and the step of selecting a preset number of first point cloud data from the point cloud data set to estimate the point cloud plane still needs to be executed.
In other embodiments, it may be determined whether the number of the acquired point cloud planes meeting the condition that the distance is less than or equal to the preset distance is greater than or equal to a preset threshold. That is, a preset threshold is preset, the number of point cloud planes with the distance between the second point cloud data and the point cloud plane being less than or equal to the preset distance is recorded, and if the number is greater than or equal to the preset threshold, it indicates that the iteration condition is met, step S307 may be executed; if the number is smaller than the preset threshold value, the iteration condition is not met, and the step of selecting a preset number of first point cloud data from the point cloud data set to estimate the point cloud plane still needs to be executed.
It should be noted that the preset number of times and the preset threshold may be empirical values commonly used by those skilled in the art, or may be values determined according to actual needs, and are not specifically limited in the embodiments of the present application.
Step S307: and determining the point cloud plane corresponding to the maximum target number as the target point cloud plane.
When a plurality of point cloud planes and corresponding target numbers are obtained, the point cloud plane corresponding to the maximum value in the target numbers can be determined as the target point cloud plane.
According to the target point cloud plane determining method provided by the embodiment of the application, a point cloud plane is estimated by utilizing first point cloud data, the distance between second point cloud data and the point cloud plane is calculated, when the distance is smaller than or equal to a preset distance, the distance between each residual point cloud data and the point cloud plane is obtained, and the target number of the residual point cloud data of which the distance is smaller than or equal to the preset distance is obtained; returning to the estimation of the point cloud planes by using the first point cloud data to obtain a plurality of point cloud planes with the distance to the second point cloud data smaller than or equal to the preset distance; acquiring the target quantity corresponding to each point cloud plane; and when the iteration condition is met, determining the point cloud plane corresponding to the maximum target quantity as the target point cloud plane. And a plane with lower probability of the target point cloud plane is eliminated through the randomly extracted distance between the second point cloud data and the point cloud plane, so that a large amount of calculation is avoided, the iterative process can be accelerated, the overall efficiency of the algorithm is improved, and the accuracy of the determined target point cloud plane is also ensured by determining the target point cloud plane when the iterative condition is met.
Referring to fig. 4, a target point cloud plane determining apparatus 400 according to an embodiment of the present application is shown, where the target point cloud plane determining apparatus 400 includes an estimating module 401, an obtaining module 402, an iterating module 403, and a determining module 404.
The estimation module 401 is configured to select a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane; the obtaining module 402 is configured to obtain second point cloud data, and calculate a distance between the second point cloud data and the point cloud plane, where the second point cloud data is one point cloud data randomly selected from point cloud data in the point cloud data set except for the first point cloud data; the iteration module 403 is configured to return to perform the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane when the distance is greater than a preset distance; the determining module 404 is configured to determine the target point cloud plane according to the point cloud plane and remaining point cloud data when the distance is less than or equal to the preset distance, where the remaining point cloud data is point cloud data in the point cloud data set except for the first point cloud data and the second point cloud data.
Further, the determining module 404 is further configured to perform the following number obtaining operations for the point cloud plane: acquiring the distance between each residual point cloud data and the point cloud plane, and acquiring the target number of the residual point cloud data of which the distance is less than or equal to the preset distance; and determining a target point cloud plane according to the target quantity.
Further, the determining module 404 is further configured to return to performing the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane for multiple times; respectively executing the quantity obtaining operation aiming at each point cloud plane with the distance to the second point cloud data being smaller than or equal to the preset distance to obtain the target quantity corresponding to each point cloud plane; and determining the point cloud plane corresponding to the maximum target number as the target point cloud plane.
Further, the determining module 404 is further configured to return to execute the step of selecting a preset number of first point cloud data from the point cloud data set to estimate the point cloud planes for a preset number of times, or return to execute the step of selecting a preset number of first point cloud data from the point cloud data set to estimate the point cloud planes for a plurality of times until the number of point cloud planes with the distance from the second point cloud data being smaller than or equal to the preset distance is greater than or equal to a preset threshold.
Further, the determining module 404 is further configured to execute the step of returning to select a preset number of first point cloud data from the point cloud data set to estimate point cloud planes for multiple times in parallel, and execute the number obtaining operation for each point cloud plane having a distance to the second point cloud data that is smaller than or equal to the preset distance.
Further, the estimation module 401 is further configured to establish a reference coordinate system, and determine coordinates of the first point cloud data based on the reference coordinate system; and determining a corresponding plane equation according to the coordinates of the first point cloud data.
Further, the obtaining module 402 is further configured to determine coordinates of the second point cloud data in the reference coordinate system; and calculating the distance between the second point cloud data and the point cloud plane according to the coordinates of the second point cloud data and the plane equation.
It should be noted that, as will be clear to those skilled in the art, for convenience and brevity of description, the specific working processes of the above-described apparatuses and modules may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Referring now to FIG. 5, a block diagram of an electronic device 500 suitable for use in implementing embodiments of the present disclosure is shown. The terminal device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 5, electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Generally, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage devices 508 including, for example, magnetic tape, hard disk, etc.; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data. While fig. 5 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program performs the above-described functions defined in the methods of the embodiments of the present disclosure when executed by the processing device 501.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some implementations, the electronic devices may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane; acquiring second point cloud data, and calculating the distance between the second point cloud data and the point cloud plane, wherein the second point cloud data is one point cloud data randomly selected from point cloud data except the first point cloud data in the point cloud data set; when the distance is larger than the preset distance, returning to execute the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane; and when the distance is smaller than or equal to the preset distance, determining the target point cloud plane according to the point cloud plane and residual point cloud data, wherein the residual point cloud data is point cloud data in the point cloud data set except the first point cloud data and the second point cloud data.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present disclosure may be implemented by software or hardware. Wherein the name of a module in some cases does not constitute a limitation on the module itself.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Example 1 provides a target point cloud plane determination method including selecting a preset number of first point cloud data from a point cloud data set to estimate a point cloud plane, according to one or more embodiments of the present disclosure; acquiring second point cloud data, and calculating the distance between the second point cloud data and the point cloud plane, wherein the second point cloud data is one point cloud data randomly selected from point cloud data except the first point cloud data in the point cloud data set; when the distance is larger than the preset distance, returning to execute the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane; and when the distance is smaller than or equal to the preset distance, determining the target point cloud plane according to the point cloud plane and residual point cloud data, wherein the residual point cloud data is point cloud data in the point cloud data set except the first point cloud data and the second point cloud data.
Example 2 provides the method of example 1, in accordance with one or more embodiments of the present disclosure, comprising: for the point cloud plane, performing the following number acquisition operations: acquiring the distance between each residual point cloud data and the point cloud plane, and acquiring the target number of the residual point cloud data of which the distance is less than or equal to the preset distance; and determining a target point cloud plane according to the target quantity.
Example 3 provides the method of example 2, in accordance with one or more embodiments of the present disclosure, comprising: returning to the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane for multiple times; the performing, for the point cloud plane, the following number acquisition operations includes: respectively executing the quantity obtaining operation aiming at each point cloud plane with the distance to the second point cloud data being smaller than or equal to the preset distance to obtain the target quantity corresponding to each point cloud plane; the determining a target point cloud plane according to the target number comprises: and determining the point cloud plane corresponding to the maximum target number as the target point cloud plane.
Example 4 provides the method of example 3, comprising, in accordance with one or more embodiments of the present disclosure: and returning to execute the step of selecting the preset number of first point cloud data from the point cloud data set to estimate the point cloud planes for the preset times, or returning to execute the step of selecting the preset number of first point cloud data from the point cloud data set to estimate the point cloud planes for the multiple times until the number of the point cloud planes with the distance to the second point cloud data smaller than or equal to the preset distance is larger than or equal to a preset threshold value.
Example 5 provides the method of example 3, in accordance with one or more embodiments of the present disclosure, comprising: and executing the step of returning to the step of selecting a preset number of first point cloud data from the point cloud data set to estimate point cloud planes for multiple times in parallel, and executing the number obtaining operation respectively for each point cloud plane with the distance from the second point cloud data being smaller than or equal to the preset distance.
Example 6 provides the method of example 1, comprising, in accordance with one or more embodiments of the present disclosure: establishing a reference coordinate system, and determining the coordinates of the first point cloud data based on the reference coordinate system; and determining a corresponding plane equation according to the coordinates of the first point cloud data.
Example 7 provides the method of example 6, in accordance with one or more embodiments of the present disclosure, comprising: determining coordinates of the second point cloud data in the reference coordinate system; and calculating the distance between the second point cloud data and the point cloud plane according to the coordinates of the second point cloud data and the plane equation.
Example 8 provides a target point cloud plane determination apparatus, the apparatus comprising: the estimation module is used for selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane; the acquisition module is used for acquiring second point cloud data and calculating the distance between the second point cloud data and the point cloud plane, wherein the second point cloud data is one point cloud data randomly selected from point cloud data except the first point cloud data in the point cloud data set; the iteration module is used for returning to the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane when the distance is greater than the preset distance; and the determining module is used for determining the target point cloud plane according to the point cloud plane and residual point cloud data when the distance is less than or equal to the preset distance, wherein the residual point cloud data is point cloud data in the point cloud data set except the first point cloud data and the second point cloud data.
Example 9 provides, in accordance with one or more embodiments of the present disclosure, a non-transitory computer readable storage medium having a computer program stored thereon, wherein the program, when executed by a processing device, implements the steps of the method of any one of examples 1-7.
Example 10 provides, in accordance with one or more embodiments of the present disclosure, an electronic device comprising: a storage device having a computer program stored thereon; processing means for executing the computer program in the storage means to carry out the steps of the method of any of examples 1-7.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims. With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Claims (10)
1. A method for determining a target point cloud plane, the method comprising:
selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane;
acquiring second point cloud data, and calculating the distance between the second point cloud data and the point cloud plane, wherein the second point cloud data is one point cloud data randomly selected from point cloud data except the first point cloud data in the point cloud data set;
when the distance is larger than the preset distance, returning to execute the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane;
and when the distance is smaller than or equal to the preset distance, determining the target point cloud plane according to the point cloud plane and residual point cloud data, wherein the residual point cloud data is point cloud data in the point cloud data set except the first point cloud data and the second point cloud data.
2. The method of claim 1, wherein determining the target point cloud plane from the point cloud plane and remaining point cloud data comprises:
for the point cloud plane, performing the following number acquisition operations: acquiring the distance between each residual point cloud data and the point cloud plane, and acquiring the target number of the residual point cloud data of which the distance is less than or equal to the preset distance;
and determining a target point cloud plane according to the target quantity.
3. The method of claim 2, wherein determining the target point cloud planes from the target number specifically comprises:
returning to the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane for multiple times;
the performing, for the point cloud plane, the following number acquisition operations includes: respectively executing the quantity obtaining operation aiming at each point cloud plane with the distance to the second point cloud data being smaller than or equal to the preset distance to obtain the target quantity corresponding to each point cloud plane;
the determining a target point cloud plane according to the target number comprises: and determining the point cloud plane corresponding to the maximum target number as the target point cloud plane.
4. The method of claim 3, wherein said returning for a plurality of times performs said selecting a predetermined number of first point cloud data from the point cloud data set to estimate the point cloud plane comprises:
returning to the step of selecting a preset number of first point cloud data from the point cloud data set and estimating the point cloud plane for a preset number of times, or,
and returning to the step of selecting a preset number of first point cloud data from the point cloud data set to estimate point cloud planes for multiple times until the number of the point cloud planes with the distance from the second point cloud data smaller than or equal to the preset distance is larger than or equal to a preset threshold value.
5. The method of claim 3, wherein said returning is performed a plurality of times in parallel with said selecting a predetermined number of first point cloud data from the point cloud data set to estimate a point cloud plane, and wherein said number obtaining is performed separately for each of said point cloud planes having a distance from said second point cloud data less than or equal to said predetermined distance.
6. The method of claim 1, wherein selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane comprises:
establishing a reference coordinate system, and determining the coordinates of the first point cloud data based on the reference coordinate system;
and determining a corresponding plane equation according to the coordinates of the first point cloud data.
7. The method of claim 6, wherein the calculating the distance of the second point cloud data from the point cloud plane comprises:
determining coordinates of the second point cloud data in the reference coordinate system;
and calculating the distance between the second point cloud data and the point cloud plane according to the coordinates of the second point cloud data and the plane equation.
8. An apparatus for determining a target point cloud plane, the apparatus comprising:
the estimation module is used for selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane;
the acquisition module is used for acquiring second point cloud data and calculating the distance between the second point cloud data and the point cloud plane, wherein the second point cloud data is one point cloud data randomly selected from point cloud data except the first point cloud data in the point cloud data set;
the iteration module is used for returning to the step of selecting a preset number of first point cloud data from the point cloud data set to estimate a point cloud plane when the distance is greater than the preset distance;
and the determining module is used for determining the target point cloud plane according to the point cloud plane and residual point cloud data when the distance is less than or equal to the preset distance, wherein the residual point cloud data is point cloud data in the point cloud data set except the first point cloud data and the second point cloud data.
9. A non-transitory computer readable storage medium, having stored thereon a computer program, characterized in that the program, when executed by a processing device, implements the steps of the method of any one of claims 1-7.
10. An electronic device, comprising:
a storage device having a computer program stored thereon;
processing means for executing the computer program in the storage means to carry out the steps of the method according to any one of claims 1 to 7.
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