CN108801171B - Tunnel section deformation analysis method and device - Google Patents
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Abstract
The invention discloses a tunnel section deformation analysis method, which comprises the following steps: acquiring tunnel section information to obtain a point cloud data set; and analyzing and comparing the point cloud data sets acquired by the tunnel section to be detected at different periods to determine the deformation trend of the tunnel section to be detected. Therefore, the cross section information of the tunnel in different periods can be visually displayed, the deformation condition of the cross section is determined through the comparison of the point cloud data sets, the trend of the cross section of the tunnel along with the change of time is integrally and comprehensively detected, the measuring precision is high, and the measuring method is simple and convenient to realize.
Description
Technical Field
The invention relates to the technical field of tunnel detection, in particular to a tunnel section deformation analysis method and device.
Background
With the continuous improvement of the urbanization level, the urban population is rapidly increased, and a series of problems such as traffic congestion and environmental pollution are caused. Urban subways are rapidly developed as a tool for relieving traffic congestion pressure of cities.
Because urban subway lines generally pass through main roads and central areas with a large number of people, underground tunnels, pipelines and surrounding buildings deform in the construction process of the subway. Meanwhile, in the operation process of the subway, deformation of the subway in some sections may be obvious due to the nature of soil bodies, underground water and deformation of vertical displacement, horizontal displacement, cracks and the like caused by the load of ground buildings on the tunnel, and serious consequences which are difficult to imagine can be caused if deformation monitoring is not carried out in time and monitoring data are analyzed.
The deformation monitoring scheme is a precondition for deformation prediction, is an important link of informatization in the whole process, has a great influence on the deformation prediction, and the reasonable monitoring scheme is an important condition for normal and orderly construction of subway construction, provides experience for the construction of similar projects, and avoids risks and accidents. Due to the fact that the subway operation stage is large in time span, complex in influencing factors and large in disaster social influence, deformation monitoring on subway construction needs to be long-term and continuous.
The monitoring scheme is designed to be the basic content in the whole monitoring process, the monitoring content mainly comprises single data indexes of vertical settlement, horizontal displacement, section convergence deformation and the like of a tunnel, and the global tunnel deformation condition is difficult to reflect. The existing method mostly adopts a total station to select a limited number of discrete points on the section for measurement, the measurement precision of the total station is high, but the measured discrete points are difficult to reflect the integral deformation condition of the tunnel section, and the analysis and processing of the monitoring data and the analysis method of the deformation data by the existing monitoring method are not perfect.
At present, no effective solution is provided for the problem that the whole and local change conditions of the tunnel section and the trend of the tunnel section changing along with time cannot be reflected simultaneously in the prior art.
Disclosure of Invention
The embodiment of the invention provides a tunnel section deformation analysis method and device, which can solve the problems that the whole and local change conditions of a tunnel section and the deformation trend of the tunnel section along with time cannot be reflected simultaneously in the prior art.
In order to solve the above technical problem, in a first aspect, an embodiment of the present invention provides a method for analyzing a deformation of a tunnel section, where the method includes:
step 1, collecting tunnel section information to obtain a point cloud data set;
and 2, analyzing and comparing point cloud data sets acquired by the tunnel section to be detected at different periods to determine the deformation trend of the tunnel section to be detected.
Further, step 1 comprises:
step 11, acquiring tunnel section information in a mode of scanning the same tunnel section to be detected at different periods by a three-dimensional laser scanner; acquiring point cloud data sets P of two periods according to the tunnel section information1And P2。
Further, step 2 comprises:
step 22, placing the two point cloud data sets under the same coordinate system, and then overlapping the circle centers to perform first registration on the two point cloud data sets;
step 23, performing second registration on the two point cloud data sets after the first registration according to an ICP algorithm;
step 24, randomly selecting one period of point cloud data set as a reference point cloud data set, and respectively determining corresponding points of each reference point in the other period of point cloud data set by using an ANN algorithm;
step 25, analyzing the relation between each pair of reference points and the corresponding points to determine the deformation trend of the section of the tunnel to be detected;
wherein the reference points are points in the reference point cloud data set.
Further, the step 21 specifically includes: and respectively performing circle fitting on the point cloud data sets of the two periods in respective coordinate systems according to a least square method.
Further, step 23 comprises:
step 231, calculating a point cloud data set P2At each point in the point cloud dataset P1To obtain a first set of N corresponding point pairs;
step 232, determine the translation parameter that minimizes the distance sum of the first set of N corresponding point pairs
And rotation parameter
Wherein the translation parameter
And rotation parameter
Parameters in rigid body transformation;
step 233, when the sum of the distances of the first group of N corresponding point pairs is greater than or equal to the preset distance, according to the point cloud data set P2The translation parameter
And rotation parameter
Determining a point cloud dataset P'2(ii) a Calculating a Point cloud dataset P'2At each point in the point cloud dataset P1To obtain a second set of N corresponding point pairs;
and 234, when the sum of the distances of the second group of N corresponding point pairs is greater than or equal to the preset distance, re-determining a new point cloud data set and performing iterative computation until the distance of the corresponding point pair of the ith group is less than the preset distance.
Further, the step 231 specifically includes: according to the preset conditions and by using KD-tree structure in P1In determining P2Corresponding adjacent points of each point of (a) form a corresponding point pair set { (P)1，i，P2，i|i＝1，2，...，N)}；
Wherein the preset conditions are as follows: epsilon (P)1，P2)＝min(d2(P1，iP2，i))；P1，iAnd P2，iIs the ith group corresponding point pair, P1，iBelong to P1，P2，iBelong to P2。
Further, the step 232 specifically includes: step 2321, substituting the N corresponding point pairs into a target function; determination of rigid body transformation matrix by iterative algorithm
To determine translation parameters that minimize the sum of distances of corresponding point pairs
And rotation parameter
The objective function value is the sum of the distances of the N corresponding point pairs, and the formula is as follows:
wherein N is P1Number of midpoints, P1，iAnd P2，iIs the ith group corresponding point pair, P1，iBelong to P1，P2，iBelong to P2。
Is a translation matrix of 3 x 1,
a 3 x 3 rotation matrix.
Further, the objective function value is the sum of the distances of the N corresponding point pairs, and the formula is:
wherein N is P1Number of midpoints, P1，iAnd P2，iIs the ith group corresponding point pair, P1，iBelong to P1，P2，iBelong to P2。
Is a translation matrix of 3 x 1,
is a 3 × 3 rotation matrix, wiTo constrain the weight factors.
Further, the air conditioner is provided with a fan,
wherein, DDFk(p) is a deviation factor for the point p,
is the normalized standard deviation of point p.
Further, the step 231 further includes: and deleting the corresponding point pairs with the distance larger than the distance threshold.
Further, the distance of the corresponding point pair is the projection distance of the Euclidean distance between the two points on the extended line of the radius of the coincident circle center.
In a second aspect, an embodiment of the present invention provides a tunnel section deformation analysis apparatus, where the apparatus is applied to the method in the first aspect, and the apparatus includes:
the acquisition unit is used for acquiring tunnel section information to obtain a point cloud data set;
and the comparison and analysis unit is used for analyzing and comparing the point cloud data sets acquired by the tunnel section to be detected at different periods so as to determine the deformation trend of the tunnel section to be detected.
Furthermore, the acquisition unit is also used for acquiring tunnel section information in a mode of scanning the same tunnel section to be detected at different periods by the three-dimensional laser scanner; acquiring point cloud data sets P of two periods according to the tunnel section information1And P2。
Further, the comparison analysis unit is further configured to perform circle fitting on the point cloud data sets of the two periods in respective coordinate systems, so as to determine centers of the two point cloud data sets respectively; placing the two point cloud data sets under the same coordinate system, and then overlapping the circle centers to perform first registration on the two point cloud data sets; performing second registration on the two point cloud data sets after the first registration according to an ICP algorithm; randomly selecting one period of point cloud data set as a reference point cloud data set, and respectively determining corresponding points of each reference point in the other period of point cloud data set by using an ANN algorithm; analyzing the relation between each pair of reference points and the corresponding points to determine the deformation trend of the section of the tunnel to be detected; wherein the reference points are points in the reference point cloud data set.
By applying the technical scheme of the invention, the section information of the tunnel in different periods can be visually displayed, and the deformation condition of the section is determined by comparing the point cloud data sets, so that the integral and comprehensive detection of the time-varying trend of the tunnel section is realized.
Drawings
Fig. 1 is a flowchart of a tunnel section deformation analysis method according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for analyzing deformation of a tunnel cross section according to an embodiment of the present invention;
FIG. 3 is a flowchart of a method for analyzing deformation of a tunnel cross section according to an embodiment of the present invention;
FIG. 4 is a flowchart of a method for analyzing deformation of a tunnel section according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating a calculation of a distance between pairs of corresponding points according to an embodiment of the present invention;
FIG. 6 is a schematic illustration of a two-phase point cloud dataset after a first registration according to an embodiment of the invention;
FIG. 7 is a schematic illustration of a two-stage point cloud dataset after a second stage registration according to an embodiment of the invention;
FIG. 8 is a schematic diagram of a global deformation trend of a section of a tunnel to be detected according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of a global deformation result obtained by calculating a cross section of a real tunnel to be detected according to an embodiment of the present invention;
fig. 10 is a block diagram of a tunnel cross-section deformation analysis apparatus according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following drawings and specific embodiments, it being understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention.
In the following description, suffixes such as "module", "component", or "unit" used to denote elements are used only for facilitating the explanation of the present invention, and have no specific meaning in itself. Thus, "module", "component" or "unit" may be used mixedly.
In order to solve the problem that the whole and local change conditions of the tunnel section and the time-varying deformation trend of the tunnel section cannot be reflected simultaneously in the prior art, the embodiment of the invention provides a tunnel section deformation analysis method, which comprises the following steps:
step 1, collecting tunnel section information to obtain a point cloud data set;
and 2, analyzing and comparing the point cloud data sets acquired by the tunnel section to be detected at different periods to determine the deformation trend of the tunnel section to be detected.
By applying the technical scheme of the invention, the section information of the tunnel at different periods can be visually displayed, and the deformation condition of the section is determined by comparing the point cloud data sets, so that the whole and comprehensive detection of the time-varying trend of the tunnel section is realized. The measuring precision is high, and the measuring method is simple and convenient to achieve.
In one possible implementation, as shown in fig. 2, step 1 includes:
step 11, acquiring tunnel section information in a mode of scanning the same tunnel section to be detected at different periods by a three-dimensional laser scanner; acquiring point cloud data sets P of two periods according to tunnel section information1And P2。
It can be understood that the data that three-dimensional laser scanner gathered are more comprehensive and collection precision is higher, wait to detect the tunnel section and can be subway tunnel section, and accessible three-dimensional laser scanner scans same tunnel section in different periods. The present application is described by taking comparative analysis of the scanning results of two periods as an example.
In one possible implementation, as shown in fig. 3, step 2 includes:
wherein, step 21 specifically includes: and respectively performing circle fitting on the point cloud data sets of the two periods in respective coordinate systems according to a least square method.
It should be noted that the fitting method is not limited to the least square method, as long as the effect of fitting the point cloud data set into a curve can be achieved.
Step 22, placing the two point cloud data sets under the same coordinate system, and coinciding the circle centers to perform first registration on the two point cloud data sets;
the first registration may be understood as a preliminary registration, and a schematic diagram of the two-stage point cloud data set after the first registration may refer to fig. 6.
Step 23, performing second registration on the two Point cloud data sets after the first registration according to an ICP (Iterative Closest Point) algorithm; and a schematic diagram of the two-stage point cloud data set after the second registration can refer to fig. 7.
The second registration is more accurate than the first registration, and the optimal registration positions of the two point cloud data sets can be obtained in the same coordinate system by utilizing an ICP (inductively coupled plasma) algorithm, and the optimal registration positions can also be understood as optimal registration images.
Step 24, randomly selecting one period point cloud data set as a reference point cloud data set, and respectively determining corresponding points of each reference point in the other period point cloud data set by utilizing an ANN (Approximate Nearest Neighbor) algorithm;
step 25, analyzing the relation between each pair of reference points and the corresponding points to determine the deformation trend of the section of the tunnel to be detected; wherein, the reference point is a point in the reference point cloud data set.
The high-precision three-dimensional laser scanner is utilized, the two-stage data measured on the cross section of the tunnel are spliced and registered through an ICP (inductively coupled plasma) registration method, and then the two-stage point cloud is subjected to adjacent point correspondence, so that the cross section shapes of the tunnel at different stages can be placed in the same coordinate system for visual display, the deformation condition of the cross section is determined through the comparison between corresponding points, and the integral and comprehensive detection of the deformation condition of the cross section of the tunnel can be realized.
In one possible implementation, as shown in fig. 4, step 23 includes:
step 231, calculating a point cloud data set P2At each point in the point cloud dataset P1To obtain a first set of N corresponding point pairs;
step 231, specifically including: according to the preset conditions and by using KD-tree knotIs constructed at P1In determining P2Corresponding adjacent points of each point of (a) form a corresponding point pair set { (P)1，i，P2，i|i＝1，2，...，N)}；
Wherein the preset conditions are as follows: epsilon (P)1，P2)＝min(d2(P1，iP2，i))；P1，iAnd P2，iIs the ith group corresponding point pair, P1，iBelong to P1，P2，iBelong to P2。
That is, let ε (P)1，P2) Minimum P1，iP2，iIs the sought pair of corresponding points.
Step 231 further comprises: and deleting the corresponding point pairs with the distance larger than the distance threshold.
In the process of determining the corresponding point pairs, the point pairs with the distance between the point pairs larger than the threshold value are removed through distance calculation, the point pairs which do not meet the conditions are removed, the influence of the point pairs on the subsequent target function calculation can be eliminated, and therefore the registration accuracy is improved.
In one possible implementation manner, the distance of the corresponding point pair is a projection distance of a Euclidean distance between two points on a radius extension line of a coincident circle center. The method for calculating the distance of the corresponding point can be regarded as an improvement of an ICP algorithm, on the basis of the original algorithm, when an objective function is calculated, the original point-to-point error measurement mode is changed into the distance of the point along the radius direction, namely the error between the two points is the projection distance of the Euclidean distance on the radius extension line. As shown in fig. 5, the method performs distance calculation according to the characteristic that the point cloud of the tunnel section is substantially circular and subsequent deformation, so as to obtain an optimal point cloud registration result.
Step 232, determine the translation parameter that minimizes the distance sum of the first set of N corresponding point pairs
And rotation parameter
Wherein the translation parameter
And rotation parameter
Parameters in rigid body transformation;
wherein, step 232 specifically includes: step 2321, substituting the N corresponding point pairs into a target function; determination of rigid body transformation matrix by iterative algorithm
To determine translation parameters that minimize the sum of distances of corresponding point pairs
And rotation parameter
The objective function value is the sum of the distances of the N corresponding point pairs, and the formula is as follows:
wherein N is P1Number of midpoints, P1，iAnd P2，iIs the ith group corresponding point pair, P1，iBelong to P1，P2，iBelong to P2。
Is a translation matrix of 3 x 1,
a 3 x 3 rotation matrix. The objective function may also be denoted as an error function.
Step 233, when the sum of the distances of the first group of N corresponding point pairs is greater than or equal to the preset distance, according to the point cloud data set P2Translation parameter
And rotation parameter
Determining a point cloud dataset P'2(ii) a Calculating a Point cloud dataset P'2At each point in the point cloud dataset P1To obtain a second set of N corresponding point pairs;
and 234, when the sum of the distances of the second group of N corresponding point pairs is greater than or equal to the preset distance, re-determining a new point cloud data set and performing iterative computation until the distance of the ith group of corresponding point pairs is less than the preset distance.
Unlike the above formula for the objective function, in one possible implementation, the objective function value is the sum of the distances of the N corresponding point pairs, and the formula may be:
wherein N is P1Number of midpoints, P1，iAnd P2，iIs the ith group corresponding point pair, P1，iBelong to P1，P2，iBelong to P2。
Is a translation matrix of 3 x 1,
is a 3 × 3 rotation matrix, wiTo constrain the weight factors.
Wherein, DDFk(p) is a deviation factor for the point p,
is the normalized standard deviation of point p. In order to eliminate the influence of outliers and noise points on the calculation of the objective function, different points need to be assigned to each corresponding pairThe weight of (c). For example: and judging the corresponding point pairs as noise or outliers, and setting smaller weight for the corresponding point pairs, thereby reducing the influence of abnormal corresponding points on the registration precision and improving the registration precision.
The weighting factors introduced in this application are briefly introduced below.
In practical engineering applications, different regions of the registration model often have different importance. According to the method and the device, different weights are applied to corresponding point pairs, so that the weights are utilized to restrain and guarantee the registration accuracy of the important region of the model. In the existing research, the setting of the weight is mainly to distinguish whether the point set participates in the registration, and the weight is only 0 and 1. The method is easy to eliminate normal corresponding point pairs, and reduces the registration precision. Thereby introducing a weight factor wiThe original error function is improved, and the formula is as follows:
the constraint weight factor is defined as the degree of importance of the set of points involved in the registration, and the translation parameter is obtained by minimizing the error function
And rotation parameter
The method is shown in fig. 4. In order to reduce the influence of the abnormal corresponding point on the registration accuracy, the weighting factor corresponding to the point cloud data set should be inversely proportional to the magnitude of the abnormal point metric, i.e., the greater the possibility that the point is an abnormal point, the smaller the weighting factor corresponding to the point should be. Therefore, an abnormal point metric function N (p) is introduced, wherein p is the current measurement point, and the weighting factor is expressed as
In order for the metric function to correctly reflect the likelihood that point p is an outlier, the metric function is defined as:
wherein DDFk(p) is a deviation factor for the point p,
is the normalized standard deviation of point p. Both are represented as:
wherein N iskd(q) denotes k points, N, nearest to the point qkd(p) denotes k points nearest to the point p. That is, the probability of the point p belonging to the outlier is deduced through the correlation between the point p and the neighboring points. And brings this probability value into the final objective function as the distance calculation weight for point p. And solving the minimized weighted objective function by using an SVD algorithm, and calculating the rotational transformation R and the translational transformation T between the point clouds of the two cross sections to be registered.
And (3) introducing a weight into the objective function for constraint, and controlling the search direction of the optimization algorithm by using the weight, so that the registration precision of a normal region can be effectively ensured, and the influence of an abnormal point on the registration precision is reduced. When the subway tunnel is scanned at different periods, the position of the three-dimensional laser scanner cannot be fixed, and the two-period section point cloud data set at the same position in the subway tunnel is located in two different coordinate systems. Because the cross section of the subway tunnel is basically close to a circle, the circle center of the cross section point cloud can be preliminarily determined by least square fitting of a ring, and the two-stage cross section point cloud is circularly overlapped in the same coordinate system, so that the preliminary registration of the two-stage cross section point cloud can be realized. By utilizing an improved ICP algorithm, by introducing a weight constraint concept and giving higher weight to more important areas in the registered point cloud, high-precision registration of the point cloud of the two-stage section can be obtained. Finally, the comprehensive deformation of the tunnel cross section can be known through the distance between the corresponding point pairs of the two-stage point cloud data set (refer to fig. 8 and 9).
An embodiment of the present invention further provides a tunnel section deformation analysis apparatus, which is configured to execute the method shown in the foregoing embodiment, and as shown in fig. 10, the apparatus includes:
the acquisition unit 901 is used for acquiring tunnel section information to obtain a point cloud data set;
and the comparison and analysis unit 902 is configured to analyze and compare the point cloud data sets acquired by the tunnel section to be detected at different periods, so as to determine a deformation trend of the tunnel section to be detected.
The method can visually display the section information of the tunnel in different periods, and determine the deformation condition of the section through comparison of the point cloud data sets so as to realize overall and comprehensive detection of the time-varying trend of the tunnel section. The measuring precision is high, and the measuring method is simple and convenient to achieve.
In a possible implementation manner, the acquisition unit 901 is further configured to acquire tunnel section information in a manner that a three-dimensional laser scanner scans the same tunnel section to be detected at different periods; acquiring point cloud data sets P of two periods according to tunnel section information1And P2。
In a possible implementation manner, the comparison and analysis unit 902 is further configured to perform circle fitting on the point cloud data sets of the two periods in respective coordinate systems, so as to determine centers of circles of the two point cloud data sets respectively; placing the two point cloud data sets under the same coordinate system, and then overlapping the circle centers to perform first registration on the two point cloud data sets; performing second registration on the two point cloud data sets after the first registration according to an ICP algorithm; randomly selecting one period of point cloud data set as a reference point cloud data set, and respectively determining corresponding points of each reference point in the other period of point cloud data set by using an ANN algorithm; analyzing the relation between each pair of reference points and the corresponding points to determine the deformation trend of the section of the tunnel to be detected; wherein, the reference point is a point in the reference point cloud data set.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. The term "comprising" is used to specify the presence of stated features, integers, steps, operations, elements, components, operations.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a mobile terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
While the present invention has been described with reference to the embodiments illustrated in the drawings, the present invention is not limited to the embodiments, which are illustrative rather than restrictive, and it will be apparent to those skilled in the art that many more modifications and variations can be made without departing from the spirit of the invention and the scope of the appended claims.
Claims (12)
1. A tunnel section deformation analysis method is characterized by comprising the following steps:
step 1, collecting tunnel section information to obtain a point cloud data set;
step 2, analyzing and comparing point cloud data sets acquired by the section of the tunnel to be detected at different periods to determine the deformation trend of the section of the tunnel to be detected;
the step 2 comprises the following steps:
step 21, respectively performing circle fitting on the point cloud data sets in the two periods in respective coordinate systems to respectively determine the centers of circles of the two point cloud data sets;
step 22, placing the two point cloud data sets under the same coordinate system, and then overlapping the circle centers to perform first registration on the two point cloud data sets;
step 23, performing second registration on the two point cloud data sets after the first registration according to an iterative closest point ICP algorithm;
step 24, randomly selecting one period of point cloud data set as a reference point cloud data set, and respectively determining corresponding points of each reference point in the other period of point cloud data set by using an approximate nearest neighbor Algorithm (ANN);
step 25, analyzing the relation between each pair of reference points and the corresponding points to determine the deformation trend of the section of the tunnel to be detected;
wherein the reference points are points in the reference point cloud data set.
2. The method of claim 1, wherein step 1 comprises:
step 11, acquiring tunnel section information in a mode of scanning the same tunnel section to be detected at different periods by a three-dimensional laser scanner; acquiring two times according to the tunnel section informationPoint cloud dataset P of term1And P2。
3. The method of claim 1,
the step 21 specifically includes: and respectively performing circle fitting on the point cloud data sets of the two periods in respective coordinate systems according to a least square method.
4. The method of claim 1, wherein step 23 comprises:
step 231, calculating a point cloud data set P2At each point in the point cloud dataset P1To obtain a first set of N corresponding point pairs;
step 232, determine the translation parameter that minimizes the distance sum of the first set of N corresponding point pairs
And rotation parameter
Wherein the translation parameter
And the rotation parameter
Parameters in rigid body transformation;
step 233, when the sum of the distances of the first group of N corresponding point pairs is greater than or equal to the preset distance, according to the point cloud data set P2The translation parameter
And the rotation parameter
Determining a point cloud dataset P'2(ii) a Calculating a Point cloud dataset P'2At each point in the point cloud dataset P1To obtain a second set of N corresponding point pairs;
and 234, when the sum of the distances of the second group of N corresponding point pairs is greater than or equal to the preset distance, re-determining a new point cloud data set and performing iterative computation until the distance of the corresponding point pair of the ith group is less than the preset distance.
5. The method of claim 4,
the step 231 specifically includes: according to the preset conditions and by using KD-tree structure in P1In determining P2Corresponding adjacent points of each point of (a) form a corresponding point pair set { (P)1，i，P2，i|i＝1，2，...，N)}；
Wherein the preset conditions are as follows: epsilon (P)1，P2)＝min(d2(P1，iP2，i))；P1，iAnd P2，iIs the ith group corresponding point pair, P1，iBelong to P1，P2，iBelong to P2。
6. The method of claim 4,
the step 232 specifically includes: step 2321, substituting the N corresponding point pairs into a target function; determination of rigid body transformation matrix by iterative algorithm
To determine translation parameters that minimize the sum of distances of corresponding point pairs
And rotation parameter
The objective function value is the sum of the distances of the N corresponding point pairs, and the formula is as follows:
wherein N is P1Number of midpoints, P1，iAnd P2，iIs the ith group corresponding point pair, P1，iBelong to P1，P2，iBelong to P2，
Is a translation matrix of 3 x 1,
a 3 x 3 rotation matrix.
7. The method of claim 4,
the objective function value is the sum of the distances of the N corresponding point pairs, and the formula is as follows:
wherein N is P1Number of midpoints, P1，iAnd P2，iIs the ith group corresponding point pair, P1，iBelong to P1，P2，iBelong to P2，
Is a translation matrix of 3 x 1,
is a 3 × 3 rotation matrix, WiTo constrain the weight factors.
8. The method of claim 7,
wherein DDFk(p) is a deviation factor for the point p,
is the normalized standard deviation of point p.
9. The method of claim 4,
said step 231 further comprises: and deleting the corresponding point pairs with the distance larger than the distance threshold.
10. The method of claim 4,
the distance of the corresponding point pair is the projection distance of the Euclidean distance between the two points on the extended line of the radius of the coincident circle center.
11. A tunnel profile deformation analysis apparatus, characterized in that the apparatus is adapted to perform the method of any one of claims 1 to 10, the apparatus comprising:
the acquisition unit is used for acquiring tunnel section information to obtain a point cloud data set;
the comparison and analysis unit is used for analyzing and comparing point cloud data sets acquired by the section of the tunnel to be detected at different periods so as to determine the deformation trend of the section of the tunnel to be detected;
the comparison and analysis unit is also used for respectively performing circle fitting on the point cloud data sets in the two periods in respective coordinate systems so as to respectively determine the centers of circles of the two point cloud data sets; placing the two point cloud data sets under the same coordinate system, and then overlapping the circle centers to perform first registration on the two point cloud data sets; performing second registration on the two point cloud data sets after the first registration according to an iterative closest point ICP algorithm; randomly selecting one period point cloud data set as a reference point cloud data set, and respectively determining corresponding points of each reference point in the other period point cloud data set by using an approximate nearest neighbor Algorithm (ANN); analyzing the relation between each pair of reference points and the corresponding points to determine the deformation trend of the section of the tunnel to be detected; wherein the reference points are points in the reference point cloud data set.
12. The apparatus of claim 11,
the acquisition unit is also used for acquiring tunnel section information in a mode of scanning the same tunnel section to be detected at different periods through the three-dimensional laser scanner; acquiring point cloud data sets P of two periods according to the tunnel section information1And P2。
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| CN110411361B (en) * | 2019-05-15 | 2021-08-17 | 首都师范大学 | Laser detection data processing method for mobile tunnel |
| CN110426001A (en) * | 2019-08-30 | 2019-11-08 | 四川大学 | A kind of Dangerous Rock Body swing offset monitoring method based on 3 D laser scanning |
| CN110727660A (en) * | 2019-08-31 | 2020-01-24 | 广州海达安控智能科技有限公司 | Geological disaster monitoring data processing method and device |
| CN110986815A (en) * | 2020-03-05 | 2020-04-10 | 浙江交工集团股份有限公司 | Tunnel construction monitoring and measuring method based on three-dimensional laser point cloud |
| CN111089544B (en) * | 2020-03-19 | 2020-08-28 | 浙江交工集团股份有限公司 | Tunnel monitoring measurement data analysis method based on maximum entropy method reliability theory |
| CN111336990B (en) * | 2020-03-27 | 2021-04-13 | 南京航空航天大学 | Tunnel section convergence rapid analysis method and device |
| CN111862111A (en) * | 2020-07-01 | 2020-10-30 | 青岛九维华盾科技研究院有限公司 | Point cloud registration algorithm based on region segmentation and fusion |
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| CN115930800B (en) * | 2023-02-21 | 2023-05-05 | 西南石油大学 | Tunnel face displacement field monitoring method based on three-dimensional laser point cloud |
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| CN107289900A (en) * | 2017-06-22 | 2017-10-24 | 首都师范大学 | A kind of dynamic is without control tunnel cross-section detection means, analysis system and method |
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