CN116819561A - Point cloud data matching method, system, electronic equipment and storage medium - Google Patents
Point cloud data matching method, system, electronic equipment and storage medium Download PDFInfo
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Abstract
The application discloses a point cloud data matching method, a system, electronic equipment and a storage medium, wherein the point cloud data matching method is applied to laser radar point cloud data matching and comprises the following steps: acquiring a scene point cloud of a scene to be matched and a corresponding laser radar point cloud; acquiring a contribution degree reduction ratio of each point cloud in the laser radar point clouds based on the ranging distance of the laser radar point clouds; determining the reduction contribution degree of each point cloud according to the contribution degree reduction ratio; and sorting the size of the reduced contribution degree to sort the point cloud matching between the laser radar point cloud and the scene point cloud in the front. The method can determine the reduced contribution degree of the point cloud through the ranging distance of the laser radar point cloud, and reduce the influence of the ranging error on the matching precision of the point cloud.
Description
Technical Field
The present application relates to the field of three-dimensional scene modeling technologies, and in particular, to a method, a system, an electronic device, and a storage medium for matching point cloud data.
Background
With the increasing maturity and popularization of computer technology, in order to realize the positioning of the laser radar, the laser radar point cloud is usually adopted to perform point cloud matching with prior point cloud information or map information.
In the research and practice process of the current technology, the inventor discovers that in the point cloud matching process, if only the laser radar point cloud is coincident with the prior map information, the point cloud matching is successful; however, in the measuring process of the laser radar point cloud, because the distances are different, the distance measurement error of the laser radar point cloud exists, the measuring precision is reduced, and then the point cloud matching precision is affected.
Disclosure of Invention
The application mainly solves the technical problem of providing a point cloud data matching method, system electronic equipment and storage medium, which can determine the reduced contribution degree of point clouds through the ranging distance of laser radar point clouds and reduce the influence of ranging errors on the point cloud matching precision.
In order to solve the technical problems, the application adopts a technical scheme that: the point cloud data matching method is applied to laser radar point cloud data matching and comprises the following steps: acquiring a scene point cloud of a scene to be matched and a corresponding laser radar point cloud; acquiring a contribution degree reduction ratio of each point cloud in the laser radar point clouds based on the ranging distance of the laser radar point clouds; determining the reduction contribution degree of each point cloud according to the contribution degree reduction ratio; and sorting the size of the reduced contribution degree to sort the point cloud matching between the laser radar point cloud and the scene point cloud in the front.
In an embodiment of the application, the contribution reduction ratio comprises a first contribution reduction ratio; the obtaining the contribution degree reduction ratio of each point cloud in the laser radar point cloud based on the ranging distance of the laser radar point cloud comprises the following steps: acquiring the ranging distance of each point cloud in the point clouds of the laser radar, and acquiring the maximum ranging distance of the laser radar; acquiring a preset ranging error coefficient corresponding to the ranging distance; wherein the preset ranging error coefficient is positively correlated with the ranging distance; and determining the first contribution degree reduction ratio of each point cloud in the laser radar point clouds based on the ranging distance, the preset ranging error coefficient and the maximum ranging distance.
In an embodiment of the present application, the determining, according to the contribution reduction ratio, a reduced contribution of each point cloud includes: acquiring initial contribution of each point cloud in the laser radar point clouds, wherein the initial contribution of each point cloud in the laser radar point clouds is the same; and determining the reduction contribution degree of each point cloud according to the initial contribution degree and the first contribution degree reduction ratio.
In an embodiment of the present application, after the obtaining of the first contribution reduction ratio, the point cloud data matching method further includes: acquiring a distance change value of the distance measurement distance of each point cloud in the laser radar point clouds; determining a second contribution degree reduction ratio of each point cloud in the laser radar point clouds according to the distance variation value, the distance measurement distance and a preset suppression parameter; the determining the reduction contribution degree of each point cloud according to the contribution degree reduction ratio comprises the following steps: and determining the reduction contribution degree of each point cloud according to the first contribution degree reduction ratio and the second contribution degree reduction ratio.
In an embodiment of the present application, the obtaining the distance change value of the ranging distance for each point cloud in the laser radar point clouds includes: acquiring transverse point cloud changes and longitudinal point cloud changes of each point cloud in the laser radar point cloud in a preset angle interval; the distance change value is determined based on the lateral point cloud change and the longitudinal point cloud change.
In an embodiment of the present application, the determining the reduction contribution of each point cloud according to the first contribution reduction ratio and the second contribution reduction ratio includes: acquiring initial contribution degree of each point cloud in the laser radar point clouds; and determining the reduction contribution degree of each point cloud according to the initial contribution degree, the first contribution degree reduction ratio and the second contribution degree reduction ratio.
In an embodiment of the present application, the obtaining a scene point cloud of a scene to be matched and a corresponding lidar point cloud includes: acquiring priori map information of a scene to be matched; acquiring a scene point cloud of the scene to be matched based on the prior map information, and acquiring a laser radar point cloud of the scene to be matched based on the prior map information; wherein the scene point cloud is a standard point cloud.
In order to solve the technical problems, the application adopts another technical scheme that: the point cloud data matching system comprises a point cloud acquisition module, a contribution degree reduction ratio acquisition module, a reduction contribution degree determination module and a point cloud matching module, wherein the point cloud acquisition module is used for acquiring scene point clouds of a scene to be matched and corresponding laser radar point clouds; the contribution degree reduction ratio acquisition module acquires the contribution degree reduction ratio of each point cloud in the laser radar point clouds based on the distance measurement distance of the laser radar point clouds; the reduction contribution degree determining module determines the reduction contribution degree of each point cloud according to the contribution degree reduction ratio; and the point cloud matching module is used for sorting the size of the reduced contribution degree so as to sort the laser radar point cloud in the front and the scene point cloud for point cloud matching.
In order to solve the technical problems, the application adopts a further technical scheme that: there is provided an electronic device including: the computer program comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the point cloud data matching method when executing the computer program.
In order to solve the technical problems, the application adopts a further technical scheme that: there is provided a computer readable storage medium storing a computer program which when executed by a processor implements a point cloud data matching method as described above.
Unlike the prior art, the point cloud data matching method provided by the application is applied to laser radar point cloud data matching and comprises the following steps: acquiring a scene point cloud of a scene to be matched and a corresponding laser radar point cloud; acquiring a contribution degree reduction ratio of each point cloud in the laser radar point clouds based on the ranging distance of the laser radar point clouds; determining the reduction contribution degree of each point cloud according to the reduction ratio of the contribution degree; and sorting the size of the reduced contribution degree to sort the point cloud matching between the laser radar point cloud in the front and the scene point cloud. The method and the device can determine the reduced contribution degree of each point cloud through the ranging distance of the laser radar point cloud, reduce and inhibit the contribution degree of each point cloud in the point cloud matching based on the reduced contribution degree, so as to inhibit the negative influence caused by changing the point cloud matching contribution degree by the ranging error, reduce the influence of the ranging error on the point cloud matching precision, and improve the point cloud matching precision.
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FIG. 1 is a flow chart of a first embodiment of a point cloud data matching method of the present application;
FIG. 2 is a flow chart of an embodiment of the step S12 of the present application;
FIG. 3 is a flowchart illustrating an embodiment of the step S13 of the present application;
FIG. 4 is a flowchart of the step S14 according to an embodiment of the present application;
FIG. 5 is a flowchart of a second embodiment of a point cloud data matching method according to the present application;
FIG. 6 is a flowchart of step S23 in a second embodiment of the present application;
FIG. 7 is a flow chart of an embodiment of a point cloud data matching system of the present application;
FIG. 8 is a schematic diagram of an embodiment of an electronic device of the present application;
fig. 9 is a schematic diagram of a computer-readable storage medium according to an embodiment of the present application.
Detailed Description
The application is described in further detail below with reference to the drawings and examples. It is specifically noted that the following examples are only for illustrating the present application, but do not limit the scope of the present application. Likewise, the following examples are only some, but not all, of the examples of the present application, and all other examples, which a person of ordinary skill in the art would obtain without making any inventive effort, are within the scope of the present application.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
The current point cloud data matching method, especially the positioning of the laser radar, mainly matches the laser radar point cloud with the prior point cloud information or map information (point cloud matching), thereby realizing the positioning of the laser radar; however, in the measuring process of the laser radar point cloud, due to different distances, a ranging error of the laser radar point cloud exists, so that the final positioning accuracy is affected. Most of the prior art schemes process the errors by using other sensors to assist in restraining the errors, and new error influences are easy to bring.
Therefore, the point cloud data matching method is provided, the reduced contribution degree of the point cloud can be determined through the ranging distance of the laser radar point cloud, and the influence of the ranging error on the point cloud matching precision is reduced.
Referring to fig. 1, fig. 1 is a flow chart of a first embodiment of a point cloud data matching method according to the present application; it should be noted that, if there are substantially the same results, the method of the present application is not limited to the flow sequence shown in fig. 1, and as shown in fig. 1, the method includes the following steps:
s11, acquiring a scene point cloud of a scene to be matched and a corresponding laser radar point cloud.
The scene to be registered is an actual scene in reality, and the scene point cloud is standard point cloud data, namely, a set of vectors of the actual scene in a three-dimensional coordinate system contains geometric position information; the laser radar point cloud is a set of scanning points, the laser radar system scans the ground to obtain three-dimensional coordinates of ground reflection points, and each ground reflection point is distributed in a three-dimensional space in a point form according to the three-dimensional coordinates and is called a scanning point; each of the laser radar point clouds includes three-dimensional coordinate information, and sometimes includes color information, reflection intensity information, echo number information, and the like.
Specifically, three-dimensional modeling is carried out on an actual scene in reality through a three-dimensional modeling tool, so as to obtain a scene point cloud corresponding to the actual scene; and scanning the area through a laser radar system in the range of acquiring the scene point cloud to acquire the laser radar point cloud in the area.
S12, acquiring a contribution degree reduction ratio of each point cloud in the laser radar point cloud based on the distance measurement distance of the laser radar point cloud.
In the ranging process of the laser radar point clouds, in the same area, the laser radar point clouds of each frame and the laser radar point clouds of the other frame may have a ranging distance change, so that the laser radar point clouds of the different frames have different errors from scene point clouds of a real scene; therefore, the method can be based on the change of the ranging distance, so that the contribution of each point cloud in the laser radar point clouds to the point cloud matching is subjected to a reduction limitation, and the influence of the ranging error of the laser radar point clouds on the point cloud matching precision is reduced.
Specifically, in the process of acquiring the laser radar point clouds corresponding to the scene to be matched, acquiring the corresponding ranging distance of each point cloud in each frame, and determining the corresponding contribution degree reduction ratio of each point cloud in the laser radar point clouds based on the ranging distance of each point cloud in the laser radar point clouds and the error between the scene point clouds of the real scene; wherein, the larger the distance measurement distance is, the smaller the corresponding contribution degree reduction ratio is.
S13, determining the reduction contribution degree of each point cloud according to the reduction ratio of the contribution degree.
The contribution degree is related to the matching confidence degree in the point cloud matching process, namely the larger the contribution degree is, the higher the matching confidence degree in the point cloud matching process is; the contribution degree reduction ratio refers to a reduction limit on the contribution degree, so that the contribution degrees of the point clouds at different distances are limited to different degrees, and the larger the error is, the smaller the contribution degree of the point cloud matching is.
Specifically, after the distance measurement distance of the laser radar point clouds is obtained and the contribution degree reduction ratio of each point cloud in the laser radar point clouds is obtained, the contribution degree of each point cloud in the laser radar point clouds is reduced and limited through the corresponding contribution degree reduction ratio, and the corresponding reduction contribution degree of each point cloud in the laser radar point clouds is obtained.
S14, sorting the size of the reduced contribution degree to sort the laser radar point cloud in front and the scene point cloud for point cloud matching.
The sorting of the sizes refers to sorting from large to small according to the size of the reduced contribution degree, and the sorting front refers to sorting of the reduced contribution degree of a certain point cloud in different frames, so as to obtain the laser radar point cloud which is most matched with the scene point cloud and has the smallest error, namely, obtain the reduced contribution degree corresponding to each pose, the reduced contribution degree corresponds to the matching confidence degree, and determine the optimal pose based on the optimal matching confidence degree, namely, obtain the pose which is most matched with the laser radar point cloud and the scene point cloud.
Specifically, after the reduction contribution degree of each point cloud of the laser radar point cloud is obtained, the reduction contribution degree of the pose corresponding to the point cloud is calculated based on the reduction contribution degree of each point cloud, the reduction contribution degree corresponding to each pose is obtained, and then the sequence is carried out from large to small according to the reduction contribution degree corresponding to each pose, so that the target pose in the sequence is obtained, and then the point cloud matching is carried out on the laser radar point cloud corresponding to the target pose and the scene point cloud.
The point cloud matching refers to matching between point cloud data obtained by scanning the surrounding environment by the laser radar and a grid map or between point cloud maps. The method is characterized in that a pose point is found in the map, so that data of the laser radar fall into corresponding grids in the grid map as much as possible, and the pose point is a result of point cloud matching, namely the pose of the laser radar in the map.
For example, when the optical radar has 6 laser point clouds in total, the point cloud matching is to calculate and count the number of the point clouds overlapped with the black grid when the laser radar has different poses in the map, and the more the number is, the higher the confidence of the corresponding pose is represented. The pose with the largest number is the result of point cloud matching. If the result of determining the point cloud matching is screened by counting only the number of point clouds overlapping the corresponding grid, the contribution of each point cloud to the whole matching process is the same. In this case, if the lidar has the same number of point clouds overlapping the black grid in two different poses, no valid result can be screened out. Because of the influence of the ranging error coefficient, the error of each point cloud is different, namely, some errors are large, some errors are small, and when the errors are large, the errors of the point cloud matching result are large. Therefore, the contribution degree of the point cloud matching needs to be differentiated according to the distance measurement error of the point cloud, namely, the point cloud with large error has small contribution degree and the point cloud with small error has large contribution degree.
If there are 5 points falling within the corresponding grid, when the error is not considered, the matching confidence = (O5)/6 x 100%, when the error is considered, the matching confidence = (O1 x r1+ O2 x r2+ O3 x r3+ O4 x r4+ O5 x r 5)/6 x 100%, oi is the original confidence, typically 1, ri is the reduction ratio, and the matching confidence is related to the reduction contribution.
In the embodiment, a scene point cloud of a scene to be matched and a corresponding laser radar point cloud are obtained; acquiring a contribution degree reduction ratio of each point cloud in the laser radar point clouds based on the ranging distance of the laser radar point clouds; determining the reduction contribution degree of each point cloud according to the reduction ratio of the contribution degree; sorting the size of the reduced contribution degree to sort the point cloud of the laser radar point cloud of the front row and the point cloud of the scene to perform point cloud matching; in other words, in the embodiment, the contribution degree of the point cloud matching is reduced according to the ranging distance of each point cloud in the laser radar point clouds, so as to obtain the reduced contribution degree of each point cloud, and further, based on the reduced contribution degree, the laser radar point clouds in the front of the reduced contribution degree are screened out to perform the point cloud matching with the scene point clouds, so that the influence of the ranging error of the point cloud matching on the matching precision is reduced.
Referring to fig. 2, fig. 2 is a flow chart illustrating an embodiment of step S12 in the present application;
the contribution reduction ratio includes a first contribution reduction ratio;
as shown in fig. 2, the method comprises the following steps:
s121, acquiring a distance measurement distance of each point cloud in the point clouds of the laser radar, and acquiring a maximum distance measurement distance of the laser radar;
the distance measurement distance refers to a distance between the distance measurement device measured by the laser radar and a measured point, and the maximum distance measurement distance refers to a maximum distance which can be measured by the laser radar.
Specifically, in the process of acquiring the point clouds of the laser radar, acquiring the ranging distance of each point cloud in the point clouds of the laser radar, and acquiring the maximum ranging distance corresponding to the laser radar according to preset parameters or test data of the laser radar.
S122, acquiring a preset ranging error coefficient corresponding to the ranging distance; wherein, the preset ranging error coefficient is positively correlated with the ranging distance;
wherein the preset ranging error coefficient is related to the ranging distance; when the materials of the reflecting materials are different, the reflecting materials are also related to the materials of the reflecting materials; and when the environmental factors are different, the environmental factors are also related.
Specifically, after the ranging distance of each point cloud in the laser radar point clouds is obtained, a corresponding preset ranging error coefficient is obtained in a preset error ranging error database according to the current testing environment factors, the material quality of the reflecting material and the ranging distance.
For example, when the reflective material is black, the absolute error is 10mm or less, the relative average deviation is 8% or less, and the range of the relative average deviation is 13mm or less under normal temperature conditions (10 ℃ C. To 30 ℃ C., humidity is 93% or less, and no dew is formed), and the range of the distance is 0.15 to 0.3m, without being subjected to a reliability test; when the distance is 0.3-1, the absolute error is less than or equal to 20mm, the relative average deviation is less than or equal to 8%, and the range is less than or equal to 25mm; when the distance is 1-2, the absolute error is less than or equal to 4%, the relative average deviation is less than or equal to 0.7%, and the range is less than or equal to 4.5%; when the distance is 2 to 4, the absolute error is 5% or less, the relative average deviation is 1.2% or less, and the range is 5.5% or less. Outside normal temperature (-10 ℃, 30-40 ℃, humidity less than or equal to 93 percent, no condensation), or when the distance is 0.15-0.3 m through a reliability test, the absolute error is less than or equal to 13mm, the relative average deviation is less than or equal to 8 percent, and the range is less than or equal to 15mm; when the distance is 0.3-1, the absolute error is less than or equal to 30mm, the relative average deviation is less than or equal to 8%, and the range is less than or equal to 35mm; when the distance is 1-2, the absolute error is less than or equal to 4.5%, the relative average deviation is less than or equal to 0.7%, and the range is less than or equal to 5%; when the distance is 2 to 4, the absolute error is not more than 6%, the relative average deviation is not more than 1.2%, and the range is not more than 6.5%.
When the reflective material is white, under normal temperature (10-30 ℃, humidity is less than or equal to 93%, and no condensation) and without reliability test, when the distance is 0.15-0.3 m, the absolute error is less than or equal to 10mm, the relative average deviation is less than or equal to 8%, and the range is less than or equal to 10mm; when the distance is 0.3-1, the absolute error is less than or equal to 20mm, the relative average deviation is less than or equal to 0.5%, and the range is less than or equal to 25mm; when the distance is 1-2, the absolute error is less than or equal to 2.5%, the relative average deviation is less than or equal to 0.5%, and the range is less than or equal to 3.5%; when the distance is 2 to 4, the absolute error is not more than 3.5%, the relative average deviation is not more than 0.5%, and the range is not more than 4.5%. Outside normal temperature (-10 ℃, 30-40 ℃, humidity less than or equal to 93 percent, no condensation), or when the distance is 0.15-0.3 m through a reliability test, the absolute error is less than or equal to 10mm, the relative average deviation is less than or equal to 8 percent, and the range is less than or equal to 13mm; when the distance is 0.3-1, the absolute error is less than or equal to 25mm, the relative average deviation is less than or equal to 0.6%, and the range is less than or equal to 30mm; when the distance is 1-2, the absolute error is less than or equal to 3.5%, the relative average deviation is less than or equal to 0.6%, and the range is less than or equal to 4.5%; when the distance is 2 to 4, the absolute error is 5% or less, the relative average deviation is 0.6% or less, and the range is 5.5% or less.
S123, determining a first contribution degree reduction ratio of each point cloud in the laser radar point clouds based on the ranging distance, a preset ranging error coefficient and a maximum ranging distance.
Wherein the contribution reduction ratio comprises a first contribution reduction ratio;
specifically, after obtaining a ranging distance corresponding to each point cloud in the laser radar point clouds, a preset ranging error coefficient and a corresponding maximum ranging distance, determining a first contribution reduction ratio of each point cloud in the laser radar point clouds through a contribution reduction formula, as follows:
wherein r is a first contribution degree reduction ratio, d is a ranging distance of each point cloud in the laser radar point clouds, e is a ranging error coefficient, and L is a maximum ranging distance.
In this embodiment, the ranging distance corresponding to each point cloud in the laser radar point clouds, the ranging error coefficient and the corresponding maximum ranging distance are predicted, so that a first contribution degree reduction ratio corresponding to each point cloud in the laser radar point clouds can be determined, and further, the reduction contribution degree corresponding to each point cloud is determined according to the first contribution degree reduction ratio, so that the influence of the ranging error on the matching precision is reduced for subsequent point cloud matching.
Referring to fig. 3, fig. 3 is a flow chart illustrating an embodiment of step S13 in the present application;
as shown in fig. 3, the method comprises the following steps:
s131, acquiring initial contribution of each point cloud in the laser radar point clouds, wherein the initial contribution of each point cloud in the laser radar point clouds is the same;
the initial contribution degree is the contribution degree of each point cloud to the overall matching rate after the point cloud matching is successful, and in the current point cloud matching, the initial contribution degree corresponding to each point cloud is the same.
Specifically, for each point cloud in the lidar point clouds that can perform point cloud matching, a corresponding initial contribution is obtained, and it is understood that the initial contribution is the same here.
S132, determining the reduction contribution degree of each point cloud according to the initial contribution degree and the first contribution degree reduction ratio.
After the initial contribution degree of each point cloud in the laser radar point clouds is obtained, corresponding calculation is carried out on the initial contribution degree according to a first contribution degree reduction ratio so as to determine the reduction contribution degree corresponding to each point cloud; the calculation is as follows:
c＝r*o，
where c is the reduction contribution, r is the first contribution reduction ratio, and o is the initial contribution.
Referring to fig. 4, fig. 4 is a flow chart illustrating an embodiment of step S14 in the present application;
as shown in fig. 4, the method comprises the following steps:
s141, acquiring priori map information of a scene to be matched;
the prior map information may refer to obtaining three-dimensional model information corresponding to the scene to be matched through three-dimensional modeling.
S142, acquiring a scene point cloud of a scene to be matched based on prior map information, and acquiring a laser radar point cloud of the scene to be matched based on the prior map information;
wherein the scene point cloud is a standard point cloud. The laser radar point cloud is point cloud data obtained by performing laser radar scanning on a scene to be matched through a laser radar.
Specifically, three-dimensional modeling is carried out on a scene to be matched to obtain a three-dimensional model corresponding to the scene to be matched, and then a scene point cloud corresponding to the scene to be matched is obtained according to the three-dimensional model; the laser radar scans the scene to be matched, and the laser radar point cloud corresponding to the scene to be matched can be obtained.
In this embodiment, before acquiring corresponding point cloud data, a scene to be matched is determined, and then corresponding point cloud data acquisition is performed in the scene to be matched, so that measurement errors caused by environmental changes can be reduced, and subsequent point cloud matching accuracy is improved.
Referring to fig. 5, fig. 5 is a schematic flow chart of a second embodiment of a point cloud data matching method according to the present application;
as shown in fig. 5, the method comprises the following steps:
s21, acquiring a scene point cloud of a scene to be matched and a corresponding laser radar point cloud;
s22, determining a first contribution degree reduction ratio of each point cloud in the laser radar point clouds based on the ranging distance of the laser radar point clouds, a preset ranging error coefficient and a maximum ranging distance;
s23, determining a second contribution degree reduction ratio of each point cloud in the laser radar point clouds according to the distance variation value, the distance measurement distance and the preset suppression parameter;
wherein the range change value is determined based on the ranging distance of each of the lidar point clouds. The second contribution reduction ratio is a contribution reduction ratio formed by point cloud changes of the pointing cloud in different directions such as the transverse direction and the longitudinal direction.
Specifically, a distance change value between each point cloud in the laser radar point clouds and the scene point clouds is obtained, a corresponding preset suppression parameter is set for each distance change value, and a second contribution degree reduction ratio of each point cloud in the laser radar point clouds is calculated and determined through the distance change value, the distance measurement distance and the preset suppression parameter, and is calculated as follows:
wherein r is 2 And s is the standard deviation of the distance change value, d is the distance measurement distance, and w is the suppression parameter.
S24, determining the reduction contribution degree of each point cloud according to the first contribution degree reduction ratio and the second contribution degree reduction ratio.
Wherein, the liquid crystal display device comprises a liquid crystal display device,
specifically, after the first contribution degree reduction ratio and the second contribution degree reduction ratio are obtained, a reduction contribution degree corresponding to each point cloud in the laser radar point clouds may be determined according to the first contribution degree reduction ratio, the second contribution degree reduction ratio and the initial contribution degree, as follows:
wherein s is the standard deviation of the distance variation value, d is the distance measurement distance, w is the suppression parameter, e is the distance measurement error coefficient, and L is the maximum distance measurement distance.
S25, sorting the size of the reduced contribution degree to sort the laser radar point cloud in front and the scene point cloud for point cloud matching.
The size sorting is sorting according to the size of the reduction contribution degree.
Specifically, after the reduction contribution degree of each point cloud of the laser radar point cloud is obtained, the reduction contribution degree of the pose corresponding to the point cloud is calculated based on the reduction contribution degree of each point cloud, the reduction contribution degree corresponding to each pose is obtained, and then the sequence is carried out from large to small according to the reduction contribution degree corresponding to each pose, so that the target pose in the sequence is obtained, and then the point cloud matching is carried out on the laser radar point cloud corresponding to the target pose and the scene point cloud.
In this embodiment, the influence of the ranging error on the matching precision can be effectively reduced by obtaining the first contribution degree reduction ratio, and in order to further improve the matching precision, the influence of the ranging distance change on the matching precision of the point cloud matching can be effectively reduced by obtaining the second contribution degree reduction ratio corresponding to the point cloud-based distance change value.
Referring to fig. 6, fig. 6 is a flowchart illustrating an embodiment of step S23 in the present application;
as shown in fig. 6, the method comprises the following steps:
s231, acquiring transverse point cloud changes and longitudinal point cloud changes of each point cloud in the laser radar point clouds in a preset angle interval;
in order to reduce interference caused by scene change, a preset angle interval is set, and in the preset angle interval, the transverse point cloud change and the longitudinal point cloud change of each point cloud in the laser radar point cloud are acquired.
Specifically, the transverse point cloud change refers to the degree of change of the point cloud in the laser radar point cloud in the transverse direction based on the scene point cloud, and the longitudinal point cloud change refers to the degree of change of the point cloud in the laser radar point cloud in the longitudinal direction based on the scene point cloud.
In some embodiments, the degree of change in the point cloud may also be measured by means other than standard deviation, such as variance, covariance, and the like.
S232, determining a distance change value based on the transverse point cloud change and the longitudinal point cloud change.
Specifically, after the transverse point cloud change and the longitudinal point cloud change of the point clouds in the laser radar point clouds are acquired, the distance change value of the point clouds is determined based on the transverse point cloud change and the longitudinal point cloud change, and then the standard deviation of the corresponding distance change value is acquired based on the distance change value.
In this embodiment, the degree of change of the point clouds of each point cloud in the laser radar point clouds in a preset angle interval can be obtained through the change of the transverse point clouds and the change of the longitudinal point clouds of the point clouds, so that the contribution degree of the point clouds corresponding to the point clouds is increased according to the degree of change of the point clouds, and the contribution degree of the point clouds far away is further improved, so that the positioning precision in the longitudinal direction and the corresponding matching precision in the point cloud matching are ensured.
In some embodiments, the point clouds of the complex environment have a higher matching contribution than the point clouds in the simple environment, e.g., the point clouds of the polyline environment have a higher contribution than the point clouds of the straight line environment.
In some embodiments, the initial contribution of each of the laser radar point clouds is also acquired, where the initial contribution of each of the laser radar point clouds is the same; the initial contribution degree is the contribution degree of each point cloud to the overall matching rate after the point cloud matching is successful, and in the current point cloud matching, the initial contribution degree corresponding to each point cloud is the same. Specifically, for each point cloud in the lidar point clouds that can perform point cloud matching, a corresponding initial contribution is obtained, and it is understood that the initial contribution is the same here.
Referring to fig. 7, fig. 7 is a schematic structural diagram of an embodiment of a point cloud data matching system according to the present application;
as shown in fig. 7, the point cloud data matching system 300 includes: the system comprises a point cloud acquisition module 310, a contribution reduction ratio acquisition module 320, a reduction contribution determination module 330 and a point cloud matching module 340, wherein the point cloud acquisition module 310 is used for acquiring scene point clouds of a scene to be matched and corresponding laser radar point clouds; the contribution degree reduction ratio obtaining module 320 obtains a contribution degree reduction ratio of each point cloud in the laser radar point clouds based on the ranging distance of the laser radar point clouds; the reduced contribution determination module 330 determines a reduced contribution for each point cloud according to the contribution reduction ratio; the point cloud matching module 340 is configured to rank the reduced contribution degrees to rank the point cloud matching between the laser radar point cloud and the scene point cloud.
Referring to fig. 8, fig. 8 is a schematic structural diagram of an embodiment of an electronic device according to the present application. The electronic device can execute the steps executed by matching the point cloud data in the method.
The electronic device 400 includes a memory 420, a processor 410, and a computer program stored in the memory 420 and executable on the processor 410; the processor 410 may execute the steps of matching the cloud data in the above method when executing the computer program, and the related content is referred to in the above method and will not be described herein.
Referring to fig. 9, fig. 9 is a schematic structural diagram of an embodiment of a computer readable storage medium according to the present application. The computer readable storage medium 500 stores a computer program 510, which computer program 510 when executed by a processor implements the steps of matching execution of point cloud data in the method described above. For details, please refer to the above method, and detailed description is omitted herein.
According to the scheme, the scene point cloud of the scene to be matched and the corresponding laser radar point cloud are obtained; acquiring a contribution degree reduction ratio of each point cloud in the laser radar point clouds based on the ranging distance of the laser radar point clouds; determining the reduction contribution degree of each point cloud according to the reduction ratio of the contribution degree; sorting the size of the reduced contribution degree to sort the point cloud of the laser radar point cloud of the front row and the point cloud of the scene to perform point cloud matching; in other words, in the embodiment, the contribution degree of the point cloud matching is reduced according to the ranging distance of each point cloud in the laser radar point clouds, so as to obtain the reduced contribution degree of each point cloud, and further, based on the reduced contribution degree, the laser radar point clouds in the front of the reduced contribution degree are screened out to perform the point cloud matching with the scene point clouds, so that the influence of the ranging error of the point cloud matching on the matching precision is reduced.
In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing description is only of embodiments of the present application, and is not intended to limit the scope of the application, and all equivalent structures or equivalent processes using the descriptions and the drawings of the present application or directly or indirectly applied to other related technical fields are included in the scope of the present application.
Claims (10)
1. The point cloud data matching method is characterized by being applied to laser radar point cloud data matching and comprising the following steps of:
acquiring a scene point cloud of a scene to be matched and a corresponding laser radar point cloud;
acquiring a contribution degree reduction ratio of each point cloud in the laser radar point clouds based on the ranging distance of the laser radar point clouds;
determining the reduction contribution degree of each point cloud according to the contribution degree reduction ratio;
and sorting the size of the reduced contribution degree to sort the point cloud matching between the laser radar point cloud and the scene point cloud in the front.
2. The method for matching point cloud data as recited in claim 1, wherein,
the contribution reduction ratio includes a first contribution reduction ratio;
the obtaining the contribution degree reduction ratio of each point cloud in the laser radar point cloud based on the ranging distance of the laser radar point cloud comprises the following steps:
acquiring the ranging distance of each point cloud in the point clouds of the laser radar, and acquiring the maximum ranging distance of the laser radar;
acquiring a preset ranging error coefficient corresponding to the ranging distance; wherein the preset ranging error coefficient is positively correlated with the ranging distance;
and determining the first contribution degree reduction ratio of each point cloud in the laser radar point clouds based on the ranging distance, the preset ranging error coefficient and the maximum ranging distance.
3. The method for matching point cloud data as recited in claim 2, wherein,
the determining the reduction contribution degree of each point cloud according to the contribution degree reduction ratio comprises the following steps:
acquiring initial contribution of each point cloud in the laser radar point clouds, wherein the initial contribution of each point cloud in the laser radar point clouds is the same;
and determining the reduction contribution degree of each point cloud according to the initial contribution degree and the first contribution degree reduction ratio.
4. The method for matching point cloud data as recited in claim 2, wherein,
after the first contribution reduction ratio is obtained, the point cloud data matching method further comprises:
acquiring a distance change value of the distance measurement distance of each point cloud in the laser radar point clouds;
determining a second contribution degree reduction ratio of each point cloud in the laser radar point clouds according to the distance variation value, the distance measurement distance and a preset suppression parameter;
the determining the reduction contribution degree of each point cloud according to the contribution degree reduction ratio comprises the following steps:
and determining the reduction contribution degree of each point cloud according to the first contribution degree reduction ratio and the second contribution degree reduction ratio.
5. The method for matching point cloud data as recited in claim 4, wherein,
the obtaining the distance variation value of the ranging distance of each point cloud in the laser radar point clouds includes:
acquiring transverse point cloud changes and longitudinal point cloud changes of each point cloud in the laser radar point cloud in a preset angle interval;
the distance change value is determined based on the lateral point cloud change and the longitudinal point cloud change.
6. The method for matching point cloud data as recited in claim 4, wherein,
the determining the reduced contribution of each point cloud according to the first contribution reduction ratio and the second contribution reduction ratio comprises:
acquiring initial contribution degree of each point cloud in the laser radar point clouds;
and determining the reduction contribution degree of each point cloud according to the initial contribution degree, the first contribution degree reduction ratio and the second contribution degree reduction ratio.
7. The method for matching point cloud data as recited in claim 1, wherein,
the obtaining the scene point cloud of the scene to be matched and the corresponding laser radar point cloud comprises the following steps:
acquiring priori map information of a scene to be matched;
acquiring a scene point cloud of the scene to be matched based on the prior map information, and acquiring a laser radar point cloud of the scene to be matched based on the prior map information; wherein the scene point cloud is a standard point cloud.
8. A point cloud data matching system, comprising:
the point cloud acquisition module is used for acquiring scene point clouds of the scene to be matched and corresponding laser radar point clouds;
the contribution degree reduction ratio acquisition module is used for acquiring the contribution degree reduction ratio of each point cloud in the laser radar point clouds based on the ranging distance of the laser radar point clouds;
the reduction contribution degree determining module is used for determining the reduction contribution degree of each point cloud according to the contribution degree reduction ratio;
and the point cloud matching module is used for sorting the size of the reduced contribution degree so as to sort the laser radar point cloud in the front and the scene point cloud for point cloud matching.
9. An electronic device, comprising: a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the point cloud data matching method according to any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements the point cloud data matching method according to any of claims 1 to 7.
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