CN113344983B - Multi-point cloud registration method based on Ping Miandian cloud segmentation - Google Patents
Multi-point cloud registration method based on Ping Miandian cloud segmentation Download PDFInfo
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Abstract
The invention discloses a multi-point cloud registration method based on Ping Miandian cloud segmentation, which comprises the following steps: grouping the acquired initial point clouds according to a combination method, and preprocessing the first initial point cloud and the second initial point cloud in each group to obtain an intermediate point cloud; grouping the point cloud planes of the intermediate point clouds to obtain a point cloud plane combination; performing dichotomy segmentation on the point cloud plane to obtain a sub-plane combination; obtaining a matching relation of effective sub-planes in the sub-plane combination according to the matching relation of the point cloud planes; acquiring registration relations of the first initial point cloud and the second initial point cloud in each group according to the matching relation; inputting the registration relationship into the observation diagram, and acquiring the registration relationship between the non-reference point cloud and the reference point cloud in all initial point clouds so as to acquire the mobile measurement point clouds. The point cloud plane is divided by a dichotomy method, the sub-planes are obtained, the number of points participating in registration calculation is reduced, the accuracy of registration relation is improved, and the accuracy of multi-point cloud registration is further improved.
Description
Technical Field
The invention relates to the technical field of mobile mapping, in particular to a multi-point cloud registration method based on Ping Miandian cloud segmentation.
Background
The mobile measurement based on the multi-beam laser scanner is an important means for mobile geometric information acquisition, has wide application potential in the aspect of rapid mobile measurement, and has a plurality of methods based on sensor fusion to realize registration of mobile point cloud and generation of scene point cloud. In the process of mobile mapping, a method based on planar characteristics is widely adopted at present to finish mobile mapping of manual or hybrid environments, and geometric information is provided for geographic information data acquisition, measurement and model generation.
Because of the particularity of the multi-wire-harness mobile scanner, the acquired plane characteristics of the multi-wire-harness mobile scanner have the characteristics of low coverage and low resolution, the use of the traditional point cloud registration method is not facilitated, the precision of gesture generation in the mobile mapping process is reduced, the texture effect and the geometric precision of the point cloud generated by images are improved, and the difficulty of registering the images and the point cloud in the data production process is increased.
Therefore, a new point cloud registration method is needed to improve the accuracy of the point cloud gesture generated in the mobile mapping process.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a Ping Miandian cloud segmentation-based multi-point cloud registration method, which aims to solve the problem that the precision of gesture generation in the mobile mapping process in the prior art is not high.
The technical scheme adopted by the invention for solving the problems is as follows:
in a first aspect, an embodiment of the present invention provides a multi-point cloud registration method based on Ping Miandian cloud segmentation, including:
grouping the acquired initial point clouds according to a combination method, wherein each group comprises a first initial point cloud and a second initial point cloud to be registered;
preprocessing the first initial point cloud and the second initial point cloud to obtain an intermediate point cloud;
grouping the point cloud planes of the intermediate point clouds to obtain a point cloud plane combination;
performing dichotomy segmentation on the point cloud planes in the point cloud plane combination to obtain a sub-plane combination;
obtaining a matching relationship of effective sub-planes in the sub-plane combination according to the matching relationship of the point cloud planes in the intermediate point cloud;
acquiring registration relations of the first initial point cloud and the second initial point cloud in each group according to the matching relations of the effective sub-planes in the sub-plane combinations in each group;
inputting the registration relation of each group into an observation diagram, carrying out adjustment, and obtaining the registration relation between non-reference point clouds and reference point clouds in all initial point clouds after adjustment so as to obtain mobile measurement point clouds.
In an embodiment, in the step of preprocessing the first initial point cloud and the second initial point cloud to obtain the intermediate point cloud, the preprocessing method includes respectively performing serialization processing, precision correction processing or intensity correction processing on the first initial point cloud and the second initial point cloud, and extracting a matching relationship between a point cloud plane and an acquired point cloud plane in the initial point clouds.
In one embodiment, the step of grouping the point cloud planes of the intermediate point cloud to obtain the point cloud plane combination includes:
acquiring the number of points contained in each point cloud plane in the intermediate point cloud, and determining a point cloud plane corresponding to the maximum value of the number of points as a main point cloud plane;
determining the normal direction of the main point cloud plane as a first grouping direction;
calculating a first included angle between the normal direction of the point cloud plane except the main point cloud plane in the middle point cloud and the first grouping direction;
determining the normal direction corresponding to the first included angle with the largest value as a second grouping direction;
determining a direction perpendicular to the first grouping direction and the second grouping direction as a third grouping direction;
and grouping the point cloud planes according to the first grouping direction, the second grouping direction and the third grouping direction to obtain a point cloud plane combination.
In one embodiment, the step of grouping the point cloud planes according to the first grouping direction, the second grouping direction and the third grouping direction to obtain a point cloud plane combination includes:
acquiring the normal direction of each point cloud plane in the intermediate point cloud;
calculating a first included angle alpha between the normal direction and the first grouping direction 1 A second angle alpha with the second packet direction 2 A third included angle alpha with the third grouping direction 3 ；
If alpha is 1 <α 2 And alpha is 1 <α 3 The point cloud plane corresponding to the normal direction is classified into a first point cloud plane combination;
if alpha is 2 <α 1 And alpha is 2 <α 3 The point cloud plane corresponding to the normal direction is classified into a second point cloud plane combination;
if alpha is 3 <α 1 And alpha is 3 <α 2 And classifying the point cloud plane corresponding to the normal direction into a third point cloud plane combination.
In one embodiment, the step of performing dichotomy segmentation on the point cloud planes in the point cloud plane combination to obtain a sub-plane combination includes:
acquiring the number S of the point cloud planes in the first point cloud plane combination 1 The number S of the point cloud planes in the second point cloud plane combination 2 And the number S of point cloud planes in the third point cloud plane combination 3 ；
Calculating the S 1 The S is 2 And said S 3 And determining the quotient S divided by S 1 The value of (1) is a first sub-plane limit value of a first point cloud plane combination, and S quotient is determined to divide S 2 Is a second sub-plane limit value for a second point cloud plane combination, and determines a quotient S divided by S 3 The value of (2) is a third sub-plane limit value of a third point cloud plane combination;
performing dichotomy segmentation on the point cloud planes in the first point cloud plane combination according to the first sub-plane limiting value to obtain a first sub-plane combination;
performing dichotomy segmentation on the point cloud planes in the second point cloud plane combination according to the second sub-plane limiting value to obtain a second sub-plane combination;
and performing dichotomy segmentation on the point cloud plane in the third point cloud plane combination according to the third sub-plane limiting value to obtain a third sub-plane combination.
In one embodiment, the step of performing a dichotomy segmentation on the point cloud planes in the first point cloud plane combination according to the first sub-plane limit value, to obtain a first sub-plane combination includes:
obtaining geometric information of the point cloud plane in the first point cloud plane combination, wherein the geometric information comprises a plane centroid point and a width direction of the point cloud plane;
Performing dichotomy segmentation on the point cloud plane through the plane centroid point and along the width direction to obtain a plurality of layers of effective sub-planes until an ineffective point set is obtained by segmentation;
determining that the division of the effective sub-plane of the one layer is one-time effective division, and determining that the effective sub-plane obtained by N-th effective division is an N-th sub-plane, wherein the total number of effective division is N, and N is less than or equal to N;
determining an effective sub-plane set consisting of the first layer sub-plane and the Nth layer sub-plane as a first sub-plane combination;
and determining whether to acquire enough effective sub-planes for the effective sub-planes according to the total number of the effective sub-planes in the first sub-plane combination and the first sub-plane limit value.
In one embodiment, the step of determining whether to perform sufficient acquisition of the valid sub-plane based on the total number of valid sub-planes in the first sub-plane combination and the first sub-plane limit value includes:
if the total number of the effective sub-planes in the first sub-plane combination is smaller than the first sub-plane limiting value, removing the point of each effective sub-plane in the nth layer sub-plane according to a first preset proportion;
Counting the effective sub-plane formed by the points which are not removed in the effective sub-plane into the first sub-plane combination;
if the total number of the effective sub-planes in the first sub-plane combination is smaller than the first sub-plane limiting value, removing the point of each effective sub-plane in the nth layer sub-plane according to a second preset proportion; or (b)
And removing the point of each effective sub-plane in the n-1 layer sub-plane according to a third preset proportion until the total number of the effective sub-planes in the first sub-plane combination is greater than or equal to a first sub-plane limit value, wherein n is E [1, N ].
In one embodiment, the intermediate point cloud includes a first intermediate point cloud and a second intermediate point cloud, and the step of obtaining the matching relationship of the effective sub-planes in the sub-plane combination according to the matching relationship of the point cloud planes in the intermediate point cloud includes:
according to the first intermediate point cloud planeAnd a point cloud plane of the second intermediate point cloud +.>Is used for determining the matching relation of the +.>Is +.>And said->Is an effective sub-plane set of (2)Is a matching relationship, wherein a is a point cloud plane identifier, and k is the +.>The number of the effective sub-plane of (2), said l being said +. >The number of the active sub-planes of (2);
calculating the saidChinese medicine "Zhongxiaozi PingFace->Is in contact with the->Middle effective sub-plane->Distance set of centroid lines between +.>
According to the describedCalculate and said->Corresponding centroid line +.>In the->Middle effective sub-plane->Plane projection length set d of (2) k-l ；
If d is k-l Wherein one or more distance values are smaller than saidAnd then determining the d k-l Is corresponding to the minimum distance value +.>Is in contact with the->In a matching relationship.
In one embodiment, the step of obtaining the registration relationship between the first initial point cloud and the second initial point cloud in each group according to the matching relationship of the effective sub-planes in the sub-plane combinations in each group includes:
determining the matching relation with the effective sub-planes in the sub-plane combinations in each groupCorresponding centroid line +.>In the->Middle effective sub-plane->Is a normal projection length set of (1);
and inputting the plane projection length set into a nonlinear optimization model for optimization to obtain the registration relationship between the first initial point cloud and the second initial point cloud in each group.
In one embodiment, the step of inputting the registration relationship of each group into an observation map, performing adjustment, and obtaining registration relationships between non-reference point clouds and the reference point clouds in all the initial point clouds after adjustment, so as to obtain a mobile measurement point cloud includes:
Inputting the registration relation of each group into an observation chart, carrying out adjustment on the registration relation, removing the registration relation with residual errors larger than a preset threshold value, and repeating the adjustment step until all residual errors are smaller than the preset threshold value;
and selecting a datum point cloud and a non-datum point cloud from all the initial point clouds, and calculating the registration relation between the datum point clouds and the non-datum point clouds according to the registration relation of each group after adjustment so as to realize registration of the datum point clouds and the non-datum point clouds and obtain a mobile measurement point cloud.
The invention has the beneficial effects that: the method comprises the steps of grouping acquired initial point clouds according to a combination method, preprocessing the first initial point clouds and the second initial point clouds which are to be registered, obtaining intermediate point clouds, grouping point cloud planes of the intermediate point clouds, obtaining point cloud plane combinations, respectively carrying out dichotomy segmentation on the point cloud planes in the point cloud plane combinations, obtaining sub-plane combinations, obtaining the matching relation of effective sub-planes in the sub-plane combinations according to the matching relation of the point cloud planes in the intermediate point clouds, obtaining the matching relation of the first initial point clouds and the second initial point clouds in each group according to the matching relation of the effective sub-planes in each group of sub-plane combinations, inputting the matching relation of each group into an observation diagram, carrying out adjustment, and obtaining the matching relation of non-reference point clouds and reference point clouds in all the initial point clouds after adjustment so as to obtain the moving measurement point clouds. The point cloud plane is divided by a dichotomy method, the sub-planes are obtained, the number of points participating in registration calculation is reduced, the accuracy of registration relation is improved, and the accuracy of multi-point cloud registration is further improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings may be obtained according to the drawings without inventive effort to those skilled in the art.
FIG. 1 is a schematic diagram of a first process according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a second process according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a third flow chart according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a fifth flow chart according to an embodiment of the invention.
Fig. 5 is a schematic block diagram of an internal structure of an intelligent terminal according to an embodiment of the present invention.
Detailed Description
The invention discloses a Ping Miandian cloud segmentation-based multipoint cloud registration method and a computer-readable storage medium, and in order to make the purposes, technical schemes and effects of the invention clearer and more definite, the invention is further described in detail below by referring to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. The term "and/or" as used herein includes all or any element and all combination of one or more of the associated listed items.
It will be understood by those skilled in the art that all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs unless defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Because of the particularity of the multi-beam mobile scanner in the prior art, the acquired plane characteristics of the multi-beam mobile scanner have the characteristics of low coverage and low resolution, so that the registration accuracy of the traditional method is not high, and in order to solve the problems in the prior art, the embodiment provides a multi-point cloud registration method based on Ping Miandian cloud segmentation.
The embodiment provides a multi-point cloud registration method based on Ping Miandian cloud segmentation, which can be applied to intelligent terminals (with data security protection). As shown in fig. 1, the method includes:
step S10, grouping the acquired initial point clouds according to a combination method, wherein each group comprises a first initial point cloud and a second initial point cloud to be registered;
the initial point cloud may be two, three, four or more, and the registration of multiple point clouds is achieved in this embodiment. The initial point cloud can be the point cloud collected at different moments, and also can be the point cloud at different positions collected by different equipment, and the equipment for collecting the point cloud can be a multi-beam mobile three-dimensional laser scanner, a depth camera or a single-beam fixed three-dimensional laser scanner. The present embodiment is described by taking a multi-beam mobile three-dimensional laser scanner as an example.
After a plurality of initial point clouds are obtained, each initial point cloud is grouped according to a combination method. For example, the initial point clouds include an initial point cloud 1, an initial point cloud 2, an initial point cloud 3, and an initial point cloud 4, and the 4 initial point clouds are grouped according to a combination method to obtain 6 groups of point clouds: initial point cloud 1 and 2, initial point cloud 1 and initial point cloud 3, initial point cloud 1 and initial point cloud 4, initial point cloud 2 and initial point cloud 3, initial point cloud 2 and initial point cloud 4, initial point cloud 3 and initial point cloud 4, determine the initial point cloud in each group as first initial point cloud and second initial point cloud, first handle each group of initial point cloud respectively.
Step S20, preprocessing the first initial point cloud and the second initial point cloud to obtain an intermediate point cloud;
the method for preprocessing the first initial point cloud and the second initial point cloud includes, but is not limited to, serialization processing, precision correction processing, intensity correction processing, planar point cloud extraction processing, planar matching relation establishment and the like. The first initial point cloud after pretreatment is regarded as a first intermediate point cloud, the second initial point cloud after pretreatment is regarded as a second intermediate point cloud, and the first intermediate point cloud and the second intermediate point cloud are collectively referred to as intermediate point clouds.
Step S30, grouping the point cloud planes of the intermediate point clouds to obtain a point cloud plane combination;
the intermediate point cloud is composed of a larger number of points, but many points are not on the same plane due to the difference in spatial positions of the points, so many point cloud planes appear in the intermediate point cloud. Generally, when the surface of the object is a plane, the point clouds corresponding to the surface are on the same plane, and exist in the form of planes in different point clouds. The point cloud registration method provided by the embodiment is realized based on the segmentation of the point cloud plane. Firstly, grouping point cloud planes in the intermediate point cloud, and grouping the point cloud planes into the same group to form a point cloud plane combination.
Referring to fig. 2, in some specific embodiments, step S30 includes:
step S31, obtaining the number of points contained in each point cloud plane in the intermediate point cloud, and determining the point cloud plane corresponding to the maximum value of the number of points as a main point cloud plane;
step S32, determining the normal direction of the main point cloud plane as a first grouping direction;
step S33, calculating a first included angle between the normal direction of the point cloud plane except the main point cloud plane in the middle point cloud and the first grouping direction;
step S34, determining the normal direction corresponding to the largest first included angle as a second grouping direction;
step S35, determining a direction perpendicular to the first grouping direction and the second grouping direction as a third grouping direction;
step S36, grouping the point cloud planes according to the first grouping direction, the second grouping direction and the third grouping direction, so as to obtain a point cloud plane combination.
The embodiment provides a method for grouping point cloud planes, which divides the point cloud planes into three groups by comparing normal directions of different point cloud planes. First three packet directions are determined, a first packet direction, a second packet direction and a third packet direction, respectively. Regarding the determination of the first grouping direction, counting the number of points contained in each point cloud plane, determining the point cloud plane containing the largest number of points as a main point cloud plane, and determining the normal direction of the main point cloud plane as the first grouping direction; regarding the determination of the second grouping direction, calculating the included angle between the normal direction of the rest point cloud plane and the first grouping direction except the main point cloud plane, and determining the normal direction with the largest included angle with the first grouping direction as the second grouping direction; regarding the determination of the third grouping direction, a direction perpendicular to both the first grouping direction and the second grouping direction is determined as the third grouping direction. It is understood that the first grouping direction, the second grouping direction, and the third grouping direction approximately express a spatial coordinate system. The three point cloud planes are included in the three point cloud plane combinations based on the three grouping directions.
In some specific embodiments, step S36 includes:
step a, acquiring the normal direction of each point cloud plane in the intermediate point cloud;
step b, calculating a first included angle alpha between the normal direction and the first grouping direction 1 A second angle alpha with the second packet direction 2 A third included angle alpha with the third grouping direction 3 ；
Step c, if alpha 1 <α 2 And alpha is 1 <α 3 The point cloud plane corresponding to the normal direction is classified into a first point cloud plane combination;
step d, if alpha 2 <α 1 And alpha is 2 <α 3 The point cloud plane corresponding to the normal direction is classified into a second point cloud plane combination;
step e, if alpha 3 <α 1 And alpha is 3 <α 2 And classifying the point cloud plane corresponding to the normal direction into a third point cloud plane combination.
And classifying the point cloud planes into three groups according to the principle that the included angle between the normal direction of each point cloud plane and different grouping directions is minimum. Obtaining clamps of the normal direction of each point cloud plane and the first grouping direction, the second grouping direction and the third grouping direction respectivelyThe angle is the first included angle alpha between the normal direction of the point cloud plane and the first grouping direction 1 A second included angle alpha between the normal direction of the point cloud plane and the second grouping direction 2 Third included angle alpha between normal direction of point cloud plane and third grouping direction 3 If min { alpha } 1 ，α 2 ，α 3 }＝α 1 The point cloud plane is classified into a first point cloud plane combination; if min { alpha } 1 ，α 2 ，α 3 }＝α 2 The point cloud plane is classified into a second point cloud plane combination; if min { alpha } 1 ，α 2 ，α 3 }＝α 3 And classifying the point cloud plane into a third point cloud plane combination. It is understood that each point cloud plane is incorporated into a point cloud plane combination corresponding to the grouping direction whose normal direction included angle is smallest.
Step S40, respectively carrying out dichotomy segmentation on the point cloud planes in the point cloud plane combination to obtain a sub-plane combination;
it should be noted that, the point cloud plane is an approximate description, and is limited by the accuracy of the sensor, the points in the point cloud plane are not strictly in the same plane, and the point cloud plane is a plane with a thickness. In order to achieve accuracy of registration to as many points as possible, the embodiment provides a method for dividing a point cloud plane, dividing the point cloud plane into a plurality of sub-planes, and achieving fine registration of the point cloud by fine division of points in the point cloud plane. The segmentation method provided by the embodiment is bisection segmentation, the point cloud plane in each point cloud plane combination is subjected to bisection segmentation, and the sub-planes segmented by the point cloud planes in the same point cloud plane combination form a sub-plane combination. It should be noted that, other methods for obtaining the sub-plane from the point cloud plane through segmentation all belong to the protection scope of the embodiment.
Referring to fig. 3, in some specific embodiments, step S40 includes:
step S41, obtaining the number S of the point cloud planes in the first point cloud plane combination 1 The number S of the point cloud planes in the second point cloud plane combination 2 And the describedNumber S of point cloud planes in third point cloud plane combination 3 ；
Step S42, calculating the S 1 The S is 2 And said S 3 And determining the quotient S divided by S 1 The value of (1) is a first sub-plane limit value of a first point cloud plane combination, and S quotient is determined to divide S 2 Is a second sub-plane limit value for a second point cloud plane combination, and determines a quotient S divided by S 3 The value of (2) is a third sub-plane limit value of a third point cloud plane combination;
step S43, performing dichotomy segmentation on the point cloud planes in the first point cloud plane combination according to the first subplane limit value to obtain a first subplane combination;
step S44, performing dichotomy segmentation on the point cloud planes in the second point cloud plane combination according to the second sub-plane limiting value to obtain a second sub-plane combination;
and step S45, performing dichotomy segmentation on the point cloud plane in the third point cloud plane combination according to the third sub-plane limiting value to obtain a third sub-plane combination.
Dividing a point cloud plane in a first point cloud plane combination, and forming a first sub-plane combination by the obtained sub-planes; dividing the point cloud plane in the second point cloud plane combination, and forming a second sub-plane combination by the obtained sub-planes; and dividing the point cloud plane in the third point cloud plane combination, and forming a third sub-plane combination by the obtained sub-planes. Setting a lower limit value on the number of sub-planes obtained by dividing the point cloud plane, otherwise, obtaining too few sub-planes, and affecting the accuracy of point cloud registration.
Regarding the method for obtaining the lower limit value of the number of sub-planes obtained by segmentation, firstly, the number S of the point cloud planes in the first point cloud plane combination is respectively obtained 1 Number S of point cloud planes in second point cloud plane combination 2 And the number S of the third point cloud plane combination point cloud planes 3 Calculate S 1 、S 2 And S is 3 S/S is calculated by the least common multiple S of 1 As a lower limit value for point cloud plane segmentation for a first point cloud plane combinationI.e. a first sub-plane limit value; will S/S 2 The value of (2) is taken as a lower limit value for the point cloud plane segmentation of the second point cloud plane combination, namely a second sub-plane limit value; will S/S 3 As a lower limit value for the point cloud plane segmentation of the third point cloud plane combination, i.e. a third sub-plane constraint value.
Further, the point cloud plane in the first point cloud plane combination is divided in a dichotomy mode according to the first subplane limiting value, the point cloud plane in the second point cloud plane combination is divided in a dichotomy mode according to the second subplane limiting value, and the point cloud plane in the third point cloud plane combination is divided in a dichotomy mode according to the third subplane limiting value, so that the first subplane combination, the second subplane combination and the third subplane combination are obtained respectively.
In some specific embodiments, step S43 further comprises:
f, performing dichotomy segmentation on the point cloud plane through the plane centroid point and along the width direction to obtain the effective sub-plane of multiple layers until an ineffective point set is obtained by segmentation;
step g, determining that the division of the effective sub-plane to obtain one layer is one-time effective division, and determining that the effective sub-plane obtained by N-th effective division is an N-th layer sub-plane, wherein the total number of effective division is N, and N is less than or equal to N;
step h, determining an effective sub-plane set consisting of the first layer sub-plane to the Nth layer sub-plane as a first sub-plane combination;
and i, determining whether to acquire the effective sub-plane according to the total number of the effective sub-planes in the first sub-plane combination and the first sub-plane limiting value.
Since the point cloud plane is a plane having a thickness, it is understood that the geometric information of the point cloud plane includes a length direction, a width direction, and a thickness direction, and a plane centroid point. The method comprises the steps of obtaining a plane centroid point and a width direction of a point cloud plane of a first sub-plane combination, dividing the point cloud plane along the width direction through the plane centroid point, and judging whether the two sub-planes are effective sub-planes or not at the moment, if so, continuing the division by a dichotomy, obtaining the plane centroid point and the width direction of the two sub-planes respectively, and dividing until an ineffective point set is obtained, and stopping the division until a plurality of layers of effective sub-planes are obtained. The division of the effective sub-plane is obtained as an effective division, and the division of the set of invalid points is obtained as an invalid division, that is, the last division is an invalid division. The invalid point set is a set formed by dividing an effective sub-plane but not determining any one plane, the point sets except the invalid point set can form the effective sub-plane, the division of one layer of effective sub-plane is remembered to be one-time effective division, the effective sub-plane obtained by N-th effective division is determined to be the N-th layer of sub-plane, the total number of effective divisions is N, then N layers of effective sub-planes are obtained by N-th dichotomy, and the first sub-plane is combined into the effective sub-plane set formed by the effective sub-planes in the first layer of sub-plane, the second layer of sub-plane and the N-th layer of sub-plane.
Further, whether to perform second effective sub-plane acquisition on the obtained effective sub-planes is determined according to the size relation between the total number of the effective sub-planes in the first sub-plane combination and the first plane limit value. When the total number of the effective sub-planes in the first sub-plane combination is greater than or equal to the first sub-plane limit value, the effective sub-planes do not need to be acquired for the second time, because the number of the effective sub-planes obtained through the first effective segmentation already meets the requirement of registration accuracy, and if the segmentation is continued, the calculated amount is increased.
It should be noted that, in actual operation, the total number of effective sub-planes in the one or more sub-plane combinations is smaller than the sub-plane limiting value, in order to satisfy the registration accuracy, taking the case that the total number of effective sub-planes in the first sub-plane combination is smaller than the first sub-plane limiting value, the following processing steps are proposed:
step k, if the total number of the effective sub-planes in the first sub-plane combination is smaller than the first sub-plane limiting value, removing the point of each effective sub-plane in the nth layer sub-plane according to a first preset proportion;
step l, counting the effective sub-plane formed by the points which are not removed in the effective sub-plane into the first sub-plane combination;
M, if the total number of the effective sub-planes in the first sub-plane combination is smaller than the first sub-plane limiting value, removing the point of each effective sub-plane in the nth layer sub-plane according to a second preset proportion; or (b)
And n, removing the point of each effective sub-plane in the n-1 layer sub-plane according to a third preset proportion until the total number of the effective sub-planes in the first sub-plane combination is greater than or equal to a first sub-plane limit value, wherein n is E [1, N ].
When the total number of the effective sub-planes in the first sub-plane combination is smaller than a first sub-plane limit value, acquiring the effective sub-planes again on the basis of the generated effective sub-planes, selecting an nth layer sub-plane (n=n at the moment) from the first sub-plane combination, removing the points from each effective sub-plane of the nth layer sub-plane according to the proportion of the number of the set removal points, namely a first preset proportion, judging whether the rest points form the effective sub-plane, if yes, classifying the formed effective sub-planes into the first sub-plane combination, judging whether the total number of the effective sub-planes in the first sub-plane combination is larger than or equal to the first sub-plane limit value, and if yes, not generating the effective sub-plane any more; if not, the points can be removed from each effective sub-plane of the nth layer sub-plane according to another set number of removing points, namely a second preset proportion (the size relation between the second preset proportion and the first preset proportion is not limited, a user can flexibly adjust the values of the first preset proportion and the second preset proportion according to actual conditions), and the operation is the same as the above, and it can be understood that when the total number of the effective sub-planes in the first sub-plane combination is still smaller than the limit value of the first sub-plane, the proportion of the removing points is continuously changed to obtain the effective sub-plane; the embodiment also provides a method for acquiring the effective sub-plane again, which can acquire the effective sub-plane from the n-1 (n e [1, n ], when n-1=0), this layer plane is the point cloud plane in the first point cloud plane combination according to the above method. The two methods for acquiring the effective sub-planes again can be used separately or in combination, and the total number of the effective sub-planes in the first sub-plane combination can be generally larger than or equal to the first sub-plane limit value. If the two methods cannot reach the first sub-plane limiting value after being combined, the first sub-plane combination with the largest number of sub-planes is taken as a segmentation result.
For example, the first preset ratio is one-fourth, when the third layer subplane has an effective subplane containing 8 points, 2 points are randomly removed from the subplane, and whether the remaining 6 points can determine an effective subplane is determined, if so, the determined effective subplane is included in the first subplane combination, and the effective subplane is combined by different random combinations (sharing in this exampleSeed combinations) to generate different sub-planes; after all the effective sub-planes generated after the random combination is removed are listed, judging whether the total number of the effective sub-planes in the first sub-plane combination is larger than or equal to a first sub-plane limit value; if the limit value cannot be met, 1 point is removed from the sub-plane, and the steps are repeated; or the active sub-plane is obtained from the second sub-plane combination.
It should be noted that the process of executing step S44 and step S45 is the same as that described above, and will not be repeated here.
Step S50, according to the matching relationship of the point cloud planes in the intermediate point cloud, obtaining the matching relationship of the effective sub-planes in the sub-plane combination;
the intermediate point cloud comprises a first intermediate point cloud and a second intermediate point cloud, and the first intermediate point cloud and the second intermediate point cloud determine the matching relationship of the sub-plane combined sub-planes according to the matching relationship of the first intermediate point cloud and the second intermediate point cloud. The first intermediate point cloud is set to comprise a point cloud plane Then in the second intermediate point cloud +.>The corresponding point cloud plane is +.>The matching relationship is used for registration between the initial point clouds.
In some specific embodiments, step S50 further comprises:
step S51, according to the first intermediate point cloud planeAnd the second intermediate point cloud planeIs used for determining the matching relation of the +.>Is +.>And said->Is +.>Is a matching relationship, wherein a is a point cloud plane identifier, and k is the +.>The number of the effective sub-plane of (2), said l being said +.>The number of the active sub-planes of (2);
step S52, calculating theMiddle effective sub-plane->Is in contact with the->Middle effective sub-plane->Distance set of centroid lines between +.>
Step S53, according to theCalculate and said->Corresponding centroid line +.>In the->Middle effective sub-plane->Plane projection length set d of (2) k-l ；
Step S54, if d k-l Wherein one or more distance values are smaller than saidAnd then determining the d k-l Is corresponding to the minimum distance value +.>Is in contact with the->In a matching relationship.
If the point cloud planeAnd Point cloud plane->In a matching relationship, then based on the point cloud plane +.>Dividing the obtained effective sub-plane set +. >And based on the point cloud plane->The segmented effective sub-plane setAlso in a matching relationship, the present embodiment is from +.>Find out in (a)The effective sub-planes in (a) form the effective sub-plane of the matching relation, k represents the point cloud plane +.>Is the number of the effective sub-plane of (1) and l represents the point cloud plane +.>Is a number of the active sub-planes of (c).
First draw upIs associated with +.>The centroid connecting line between each effective sub-plane, and calculating the distance of each centroid connecting line to obtain a distance set +.>When k=1, l=1, distance set +.>Is->Representing a point cloud plane->Effective sub-plane numbered 1 and point cloud plane +.>The distance of the centroid line of the effective sub-plane numbered 1; when k=1, l=2, distance set +.>Is->Representing a point cloud planeEffective sub-plane numbered 1 and point cloud plane +.>Is numbered 2, the distance of the centroid line of the active sub-plane. Let point cloud plane->The center point coordinate of the plane of the effective sub-plane numbered k is (x k1 ，y k1 ，z k1 ) Let point cloud plane->The center point coordinate of the plane of the effective sub-plane numbered l is (x l2 ，y l2 ，z l2 ) Then-> Distance according to centroid line->Calculating centroid linesAt->Plane projection length d of the middle effective sub-plane k k-l ，Wherein (a, b, c) is +. >The unit normal vector of the effective sub-plane k in (x), and ax+by+cz+p=0 is the unit normal vector representing said +.>General equation for the effective sub-plane k. Further according toCalculate and->Corresponding centroid line->At->Middle effective sub-plane->Plane projection length set d of (2) k-l 。
Finally if d k-l Wherein one or more distance values are smaller than saidAnd then determining the d k-l Is corresponding to the minimum distance value +.>Is in contact with the->In a matching relationship. For example, if d 1-1 Less than the effective sub-plane->Point cloud resolution of d 1-2 Less than the effective sub-plane->And d 1-1 <d 1-2 Then the effective sub-plane->And the effective sub-plane->Has a matching relationship. Further, after all the matching relations are obtained, a matching relation set M is formed k 。
Step S60, according to the matching relation of the effective sub-planes in the sub-plane combinations in each group, acquiring the registration relation of the first initial point cloud and the second initial point cloud in each group;
it can be understood that each initial point cloud corresponds to a matching relation set M k Obtaining a matching relation set M of each group according to the method k Further, according to M k And obtaining the registration relation between the first initial point cloud and the second initial point cloud in each group.
In some specific embodiments, step S60 further comprises:
step S61, determining the matching relation with the effective sub-plane in the sub-plane combination in each groupCorresponding centroid line +.>In the->Middle effective sub-plane->Is a normal projection length set of (1);
and step S62, inputting the normal projection length set into a nonlinear optimization model for optimization to obtain the registration relationship between the first initial point cloud and the second initial point cloud in each group.
According to the matching relation of the effective sub-planes in each group of sub-plane combinations, the registration relation of the first initial point cloud and the second initial point cloud in each group is obtained, firstly, M is used for obtaining the registration relation of the effective sub-planes in each group k Acquiring two effective sub-planes with matching relation, and calculating the centroid connecting line of the two effective sub-planes in one of the effective sub-planesThe length of projection on normal, i.e. the length of projection of normal d n Further obtain the normal projection length set d n . Establishing a nonlinear optimization model, and collecting the normal projection length set d n Substituting into nonlinear optimization model to obtain min R，T ∑ i ‖R[d n ]-T‖ 2 And acquiring an optimal solution, namely acquiring the registration relationship between the first initial point cloud and the second initial point cloud in each group.
Step S70, inputting the registration relation of each group into an observation diagram, carrying out adjustment, and obtaining the registration relation of non-reference point clouds and the reference point clouds in all initial point clouds after adjustment so as to obtain a mobile measurement point cloud.
Using registration relation of each group of initial point cloudsFor convenience of description, numbering is performed on the initial point cloud, where i, j is the number of the initial point cloud, +.>And representing a registration relation of a coordinate system for registering the initial point cloud j to the initial point cloud i, wherein the initial point cloud i is a first initial point cloud, and the initial point cloud j is a second initial point cloud. In the above method, only the registration relationship between the first initial point cloud and the second initial point cloud in each group can be obtained, but the multi-point cloud is registered, and the coordinate system in which one of the point clouds is located is used as the reference coordinate system, and the other point clouds are rotationally translated into the reference coordinate system. And inputting the registration relation of each group into an observation chart, wherein the observation chart is used for realizing registration of the initial point cloud, and finally obtaining a registration result, namely a mobile measurement point cloud.
For example, the initial point cloud includes initial point cloud 1, initial point cloud 2, initial point cloud 3, and initial point cloud 4, and grouping 4 initial point clouds results in 6 sets of point cloud combinations: initial point cloud 1 and initial point cloud 2 (the first initial point cloud in the group is initial point cloud 1, the second initial point cloud is initial point cloud 2), initial point cloud 1 and initial point cloud 3, initial point cloud 1 and initial point cloud 4, initial point cloud 2 and initial point cloud 3, initial point cloud 2 and initial point cloud 4, initial point cloud 3 and initial point cloud 4, then 6 groups of point clouds The registration relationships of (a) are respectivelyIf the initial point cloud 1 is determined to be the reference point cloud, the initial point cloud 2, the initial point cloud 3 and the initial point cloud 4 are all non-reference point clouds, the registration relations of all groups are input into the observation diagram, adjustment is carried out on the registration relations of all groups in the observation diagram, and the mobile measurement point cloud is output based on the registration relations of the non-reference point clouds and the reference point clouds obtained after adjustment.
Referring to fig. 4, in some specific embodiments, step S70 further includes:
step S71, inputting the registration relation of each group into an observation chart, carrying out adjustment on the registration relation, removing the registration relation with residual errors larger than a preset threshold value, and repeating the adjustment step until all residual errors are smaller than the preset threshold value;
step S72, selecting a reference point cloud and a non-reference point cloud from all the initial point clouds, and calculating a registration relationship between the reference point cloud and the non-reference point cloud according to the registration relationship of each group after adjustment, so as to realize registration between the reference point cloud and the non-reference point cloud, thereby obtaining a mobile measurement point cloud.
Inputting each group of registration relations into an observation diagram, carrying out adjustment on the registration relations in the observation diagram, generally least square adjustment, removing observations which are not in accordance with the registration relations in the observation diagram, namely removing the registration relations with residual errors far larger than a preset threshold, wherein the preset threshold can be set according to experience, and the common residual errors larger than the preset threshold are residual errors with larger difference values with other residual errors.
After the reference point cloud and the non-reference point cloud are selected from the initial point cloud, calculating the registration relation of the reference point Yun Yufei reference point clouds, rotationally translating each non-reference point cloud according to the registration relation of the reference point Yun Yufei reference point clouds, registering the non-reference point clouds to the coordinate system where the reference point clouds are located, and completing multi-point cloud registration to obtain the mobile measurement point clouds.
For example, it willInput of an observation diagram toThe initial point cloud 1 is determined to be a reference point cloud, the initial point cloud 2, the initial point cloud 3 and the initial point cloud 4 are non-reference point clouds, and a registration relationship is theoretically existed +.>There is no need to continue calculating the registration relationship of the reference point cloud and the non-reference point cloud, but in actual operation, cases where the registration relationship of part is missing, for exampleIn order to achieve registration of the initial point cloud 1 and the initial point cloud 3, the deletion is also needed to calculate +.>The embodiment also provides a registration relation calculating method. If the initial point cloud a is used as the reference point cloud, the initial point cloud b is used as the non-reference point cloud, and the registration relationship between the initial point cloud a and the initial point cloud b is +.>Missing, then the registration relationship between the initial point cloud c and the initial point cloud b can be realized(initial point cloud c is non-reference point cloud) obtain +.> Therefore, the registration relationship in the input observation map should include the registration relationship between the non-reference point Yun Yufei reference point clouds in addition to the registration relationship between the reference point Yun Yufei reference point clouds, and besides being applicable to the adjustment, the registration relationship between each non-reference point cloud and the reference point clouds can be further obtained, so as to realize the registration of the reference point clouds and a plurality of non-reference point clouds, and obtain the mobile measurement point clouds.
According to the embodiment, the acquired initial point clouds are grouped according to a combination method, each group comprises a first initial point cloud and a second initial point cloud to be registered, the first initial point cloud and the second initial point cloud are preprocessed to obtain middle point clouds, point cloud planes of the middle point clouds are grouped to obtain point cloud plane combinations, point cloud planes in the point cloud plane combinations are divided by a dichotomy to obtain sub-plane combinations, the matching relationship of effective sub-planes in the sub-plane combinations is obtained according to the matching relationship of the point cloud planes in the middle point clouds, the matching relationship of the first initial point clouds and the second initial point clouds in each group is obtained according to the matching relationship of the effective sub-planes in each group of sub-plane combinations, the matching relationship of the first initial point clouds and the second initial point clouds in each group is input into an observation diagram, and the matching relationship of non-reference point clouds and reference point clouds in all the initial point clouds is obtained after adjustment, so that the mobile measurement point clouds are obtained. The point cloud plane is divided by a dichotomy method, the sub-planes are obtained, the number of points participating in registration calculation is reduced, the accuracy of registration relation is improved, and the accuracy of multi-point cloud registration is further improved.
Based on the above embodiment, the present invention further provides an intelligent terminal, and a functional block diagram thereof may be shown in fig. 5. The intelligent terminal comprises a processor, a memory, a network interface, a display screen and a temperature sensor which are connected through a system bus. The processor of the intelligent terminal is used for providing computing and control capabilities. The memory of the intelligent terminal comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the intelligent terminal is used for communicating with an external terminal through network connection. The computer program, when executed by a processor, implements a method of multi-point cloud registration based on Ping Miandian cloud segmentation. The display screen of the intelligent terminal can be a liquid crystal display screen or an electronic ink display screen, and a temperature sensor of the intelligent terminal is arranged in the intelligent terminal in advance and used for detecting the running temperature of internal equipment.
It will be appreciated by those skilled in the art that the schematic diagram in fig. 5 is merely a block diagram of a portion of the structure related to the present invention and is not limiting of the smart terminal to which the present invention is applied, and that a specific smart terminal may include more or less components than those shown in the drawings, or may combine some components, or have a different arrangement of components.
In one embodiment, a smart terminal is provided that includes a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by one or more processors, the one or more programs comprising instructions for:
grouping the acquired initial point clouds according to a combination method, wherein each group comprises a first initial point cloud and a second initial point cloud to be registered;
preprocessing the first initial point cloud and the second initial point cloud to obtain an intermediate point cloud;
grouping the point cloud planes of the intermediate point clouds to obtain a point cloud plane combination;
performing dichotomy segmentation on the point cloud planes in the point cloud plane combination to obtain a sub-plane combination;
Obtaining a matching relationship of effective sub-planes in the sub-plane combination according to the matching relationship of the point cloud planes in the intermediate point cloud;
acquiring registration relations of the first initial point cloud and the second initial point cloud in each group according to the matching relations of the effective sub-planes in the sub-plane combinations in each group;
inputting the registration relation of each group into an observation diagram, carrying out adjustment, and obtaining the registration relation between non-reference point clouds and reference point clouds in all initial point clouds after adjustment so as to obtain mobile measurement point clouds.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink), DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
In summary, the invention discloses a Ping Miandian cloud segmentation-based multipoint cloud registration method, an intelligent terminal and a storage medium, wherein the method comprises the following steps:
grouping the acquired initial point clouds according to a combination method, wherein each group comprises a first initial point cloud and a second initial point cloud to be registered;
preprocessing the first initial point cloud and the second initial point cloud to obtain an intermediate point cloud;
grouping the point cloud planes of the intermediate point clouds to obtain a point cloud plane combination;
performing dichotomy segmentation on the point cloud planes in the point cloud plane combination to obtain a sub-plane combination;
obtaining a matching relationship of effective sub-planes in the sub-plane combination according to the matching relationship of the point cloud planes in the intermediate point cloud;
acquiring registration relations of the first initial point cloud and the second initial point cloud in each group according to the matching relations of the effective sub-planes in the sub-plane combinations in each group;
inputting the registration relation of each group into an observation diagram, carrying out adjustment, and obtaining the registration relation between non-reference point clouds and reference point clouds in all initial point clouds after adjustment so as to obtain mobile measurement point clouds.
Based on the above embodiments, the present invention discloses a multi-point cloud registration method based on Ping Miandian cloud segmentation, it should be understood that the application of the present invention is not limited to the above examples, and those skilled in the art can make modifications or changes according to the above description, and all such modifications and changes should fall within the scope of the appended claims.
Claims (8)
1. A method of multi-point cloud registration based on Ping Miandian cloud segmentation, the method comprising:
grouping the acquired initial point clouds according to a combination method, wherein each group comprises a first initial point cloud and a second initial point cloud to be registered;
preprocessing the first initial point cloud and the second initial point cloud to obtain an intermediate point cloud;
grouping the point cloud planes of the intermediate point clouds to obtain a point cloud plane combination;
performing dichotomy segmentation on the point cloud planes in the point cloud plane combination to obtain a sub-plane combination;
obtaining a matching relationship of effective sub-planes in the sub-plane combination according to the matching relationship of the point cloud planes in the intermediate point cloud;
acquiring registration relations of the first initial point cloud and the second initial point cloud in each group according to the matching relations of the effective sub-planes in the sub-plane combinations in each group;
Inputting the registration relation of each group into an observation chart, and carrying out adjustment to obtain registration relation between non-reference point clouds and reference point clouds in all initial point clouds after adjustment so as to obtain mobile measurement point clouds;
the intermediate point cloud comprises a first intermediate point cloud and a second intermediate point cloud, and the step of obtaining the matching relationship of the effective sub-planes in the sub-plane combination according to the matching relationship of the point cloud planes in the intermediate point cloud comprises the following steps:
according to the first intermediate point cloud planeAnd a point cloud plane of the second intermediate point cloud +.>Is used for determining the matching relation of the +.>Is +.>And said->Is an effective sub-plane set of (2)Is a matching relationship, wherein a is a point cloud plane identifier, and k is the +.>The number of the effective sub-plane of (2), said l being said +.>The number of the active sub-planes of (2);
calculating the saidMiddle effective sub-plane->Is in contact with the->Middle effective sub-plane->Distance set of centroid lines between
According to the describedCalculate and said->Corresponding centroid line +.>In the->Middle effective sub-plane->Plane projection length set d of (2) k-l ；
If d is k-l Wherein one or more distance values are smaller than said And then determining the d k-l Is corresponding to the minimum distance value +.>Is in contact with the->Forming a matching relationship;
the step of obtaining the registration relationship between the first initial point cloud and the second initial point cloud in each group according to the matching relationship of the effective sub-planes in the sub-plane combinations in each group comprises the following steps:
determining the matching relation with the effective sub-planes in the sub-plane combinations in each groupCorresponding centroid line +.>In the->Middle effective sub-plane->Is a normal projection length set of (1);
and inputting the plane projection length set into a nonlinear optimization model for optimization to obtain the registration relationship between the first initial point cloud and the second initial point cloud in each group.
2. The method for registering multiple point clouds based on Ping Miandian cloud segmentation according to claim 1, wherein in the step of preprocessing the first initial point cloud and the second initial point cloud to obtain an intermediate point cloud, the preprocessing method comprises performing serialization processing, precision correction processing or intensity correction processing on the first initial point cloud and the second initial point cloud, respectively, and extracting a matching relationship between a point cloud plane and an acquired point cloud plane in the initial point clouds.
3. The method for multi-point cloud registration based on Ping Miandian cloud segmentation of claim 1, wherein the step of grouping the point cloud planes of the intermediate point clouds to obtain a point cloud plane combination comprises:
acquiring the number of points contained in each point cloud plane in the intermediate point cloud, and determining a point cloud plane corresponding to the maximum value of the number of points as a main point cloud plane;
determining the normal direction of the main point cloud plane as a first grouping direction;
calculating a first included angle between the normal direction of the point cloud plane except the main point cloud plane in the middle point cloud and the first grouping direction;
determining the normal direction corresponding to the first included angle with the largest value as a second grouping direction;
determining a direction perpendicular to the first grouping direction and the second grouping direction as a third grouping direction;
and grouping the point cloud planes according to the first grouping direction, the second grouping direction and the third grouping direction to obtain a point cloud plane combination.
4. The Ping Miandian cloud segmentation based multipoint cloud registration method as claimed in claim 3, wherein the step of grouping the point cloud planes according to the first grouping direction, the second grouping direction and the third grouping direction to obtain a point cloud plane combination comprises:
Acquiring the normal direction of each point cloud plane in the intermediate point cloud;
calculating a first included angle alpha between the normal direction and the first grouping direction 1 A second angle alpha with the second packet direction 2 A third included angle alpha with the third grouping direction 3 ；
If alpha is 1 <α 2 And alpha is 1 <α 3 The point cloud plane corresponding to the normal direction is classified into a first point cloud plane combination;
if alpha is 2 <α 1 And alpha is 2 <α 3 The point cloud plane corresponding to the normal direction is classified into a second point cloud plane combination;
if alpha is 3 <α 1 And alpha is 3 <α 2 And classifying the point cloud plane corresponding to the normal direction into a third point cloud plane combination.
5. The Ping Miandian cloud segmentation-based multi-point cloud registration method of claim 4, wherein the step of performing dichotomous segmentation on the point cloud planes in the point cloud plane combination to obtain a sub-plane combination comprises:
acquiring the number S of the point cloud planes in the first point cloud plane combination 1 The number S of the point cloud planes in the second point cloud plane combination 2 And the number S of point cloud planes in the third point cloud plane combination 3 ；
Calculating the S 1 The S is 2 And said S 3 And determining the quotient S divided by S 1 The value of (1) is a first sub-plane limit value of a first point cloud plane combination, and S quotient is determined to divide S 2 Is a second sub-plane limit value for a second point cloud plane combination, and determines a quotient S divided by S 3 The value of (2) is a third sub-plane limit value of a third point cloud plane combination;
performing dichotomy segmentation on the point cloud planes in the first point cloud plane combination according to the first sub-plane limiting value to obtain a first sub-plane combination;
performing dichotomy segmentation on the point cloud planes in the second point cloud plane combination according to the second sub-plane limiting value to obtain a second sub-plane combination;
and performing dichotomy segmentation on the point cloud plane in the third point cloud plane combination according to the third sub-plane limiting value to obtain a third sub-plane combination.
6. The Ping Miandian cloud segmentation-based multi-point cloud registration method of claim 5, wherein the step of performing a dichotomous segmentation on the point cloud planes in the first point cloud plane combination according to the first subplane constraint value to obtain a first subplane combination comprises:
obtaining geometric information of the point cloud plane in the first point cloud plane combination, wherein the geometric information comprises a plane centroid point and a width direction of the point cloud plane;
Performing dichotomy segmentation on the point cloud plane through the plane centroid point and along the width direction to obtain a plurality of layers of effective sub-planes until an ineffective point set is obtained by segmentation;
determining that the division of the effective sub-plane of the one layer is one-time effective division, and determining that the effective sub-plane obtained by N-th effective division is an N-th sub-plane, wherein the total number of effective division is N, and N is less than or equal to N;
determining an effective sub-plane set consisting of the first layer sub-plane and the Nth layer sub-plane as a first sub-plane combination;
and determining whether to acquire enough effective sub-planes for the effective sub-planes according to the total number of the effective sub-planes in the first sub-plane combination and the first sub-plane limit value.
7. The Ping Miandian cloud segmentation based multipoint cloud registration method as claimed in claim 6, wherein said step of determining whether to perform sufficient acquisition of the effective sub-plane based on the total number of effective sub-planes in the first sub-plane combination and the first sub-plane limit value comprises:
if the total number of the effective sub-planes in the first sub-plane combination is smaller than the first sub-plane limiting value, removing the point of each effective sub-plane in the nth layer sub-plane according to a first preset proportion;
Counting the effective sub-plane formed by the points which are not removed in the effective sub-plane into the first sub-plane combination;
if the total number of the effective sub-planes in the first sub-plane combination is smaller than the first sub-plane limiting value, removing the point of each effective sub-plane in the nth layer sub-plane according to a second preset proportion; or (b)
And removing the point of each effective sub-plane in the n-1 layer sub-plane according to a third preset proportion until the total number of the effective sub-planes in the first sub-plane combination is greater than or equal to a first sub-plane limit value, wherein n is E [1, N ].
8. The method for registering a plurality of point clouds based on Ping Miandian cloud segmentation according to claim 1, wherein the step of inputting the registration relation of each group into an observation map and performing a adjustment to obtain registration relation between non-reference point clouds and reference point clouds in all the initial point clouds after adjustment to obtain a mobile measurement point cloud comprises:
inputting the registration relation of each group into an observation chart, carrying out adjustment on the registration relation, removing the registration relation with residual errors larger than a preset threshold value, and repeating the adjustment step until all residual errors are smaller than the preset threshold value;
And selecting a datum point cloud and a non-datum point cloud from all the initial point clouds, and calculating the registration relation between the datum point clouds and the non-datum point clouds according to the registration relation of each group after adjustment so as to realize registration of the datum point clouds and the non-datum point clouds and obtain a mobile measurement point cloud.
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| CN106296693A (en) * | 2016-08-12 | 2017-01-04 | 浙江工业大学 | Based on 3D point cloud FPFH feature real-time three-dimensional space-location method |
| CN109325998A (en) * | 2018-10-08 | 2019-02-12 | 香港理工大学 | A kind of indoor 3D modeling method, system and relevant apparatus based on point cloud data |
| CN112489099A (en) * | 2020-12-11 | 2021-03-12 | 北京航空航天大学 | Point cloud registration method and device, storage medium and electronic equipment |
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| CN109325998A (en) * | 2018-10-08 | 2019-02-12 | 香港理工大学 | A kind of indoor 3D modeling method, system and relevant apparatus based on point cloud data |
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