CN111462017A - Denoising method for tunnel laser point cloud data - Google Patents
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Abstract
The invention discloses a denoising method of tunnel laser point cloud data, which comprises the following steps: A. constructing a topological structure of the tunnel laser point cloud data; B. filtering the tunnel laser point cloud data with the topological structure to filter noise; C. performing data compression processing on the filtered tunnel laser point cloud data; D. and extracting the characteristics of the compressed tunnel laser point cloud data. The invention can reduce the complexity of point cloud data and reduce the data processing difficulty; the method filters outlier noise points, reduces the data volume of the point cloud data, and accordingly reduces the calculated amount of subsequent processes.
Description
Technical Field
The invention relates to the field of tunnel construction, in particular to a denoising method of tunnel laser point cloud data.
Background
In mountain areas, a large number of tunnels need to be excavated for building railways or highways. And the drilling and blasting method is mainly used in tunnel construction, and the condition of over-short excavation cannot be avoided. The over-under excavation of the tunnel directly affects the cost and the period of construction and construction. Major safety hazards may also result if the overbreak and underrun condition is not monitored.
With the popularization of three-dimensional laser scanning systems and the development of scanning technologies, the cost of using point clouds to measure real space is lower and lower, and the efficiency and the precision are gradually improved. Therefore, if the three-dimensional laser technology is applied to tunnel construction to restore the tunnel excavation condition, the tunnel construction efficiency can be greatly improved, and the cost of tunnel construction can be reduced.
However, since excavation and blasting are needed for the excavated tunnel, and the air circulation in the environment is weak, a large amount of dust particles can be suspended on the site of the excavated tunnel, and if the data scanned on the site is directly utilized, a large error can be brought to subsequent calculation. In addition, data after three-dimensional laser scanning belongs to disordered point data, and can be directly calculated only by designing a complex algorithm. Moreover, the data volume of the point cloud data after three-dimensional laser scanning is large, and with the increase of the scanning area, the subsequent processing of the data consumes a large amount of calculation.
Disclosure of Invention
The invention aims to: aiming at the existing problems, the method for denoising the tunnel laser point cloud data is provided. The method and the device reduce the complexity of the point cloud data, remove noise in the point cloud data and provide a basis for reducing the calculation amount in the subsequent calculation process.
The technical scheme adopted by the invention is as follows:
a denoising method of tunnel laser point cloud data comprises the following steps:
A. constructing a topological structure of the tunnel laser point cloud data;
B. filtering the tunnel laser point cloud data with the topological structure to filter noise;
C. and performing data compression processing on the filtered tunnel laser point cloud data.
The construction of the topological structure can fix the point cloud data structure and remove the disorder of the data set, thereby reducing the complexity of the point cloud data and facilitating the direct processing of the subsequent process. The filtering operation may filter out noise (e.g., dust particle data) in the point cloud data. Compressing the point cloud data can reduce the data size of the point cloud data, thereby reducing the amount of computation in the subsequent processing.
Further, the denoising method further includes:
D. and extracting the characteristics of the compressed tunnel laser point cloud data.
And the abnormal values in the point cloud data can be further filtered by extracting the characteristics of the point cloud data.
Further, the step D specifically includes: and fitting a mathematical model of the compressed tunnel laser point cloud data by using a random sampling consistency method.
No numerical relation exists between any two iterations of the random sampling consistency method, so that the random sampling consistency method can be parallelized, and the data processing efficiency is improved. Meanwhile, the robustness of feature fitting is strong by adopting a random sampling consistency method.
Further, in the step a, the tunnel laser point cloud data is constructed into a tree topology structure.
Further, in the step B, filtering processing is performed on the tunnel laser point cloud data with the topological structure by using a filtering algorithm based on statistics.
Further, in the step C, a body sampling method is adopted to compress the filtered tunnel laser point cloud data.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
the invention can reduce the complexity of the original tunnel laser point cloud data, thereby reducing the difficulty of the subsequent processing process. Meanwhile, the method can be used for carrying out targeted filtering on outlier noise points in the point cloud data, and the influence on effective components is reduced by a filtering method based on statistics. Furthermore, the point cloud data are compressed, so that the data volume of the data set is reduced, and a foundation is provided for reducing the calculation amount of the subsequent processing process. In addition, the invention also provides a characteristic extraction step to further filter abnormal values in the point cloud data. By randomly adopting a consistency algorithm to carry out the fitting of the mathematical model, the fitting process can be iterated in parallel, the efficiency is high, and meanwhile, the fitting result has high robustness.
Drawings
The invention will now be described, by way of example, with reference to the accompanying drawings, in which:
fig. 1 is two topologies of point cloud data.
Fig. 2 is a comparison graph of three smoothing filter effects.
FIG. 3 is a diagram illustrating the effect of filtering out outlier noise points in the point cloud data.
FIG. 4 is one embodiment of line fitting using a random sampling consensus algorithm.
Detailed Description
All of the features disclosed in this specification, or all of the steps in any method or process so disclosed, may be combined in any combination, except combinations of features and/or steps that are mutually exclusive.
Any feature disclosed in this specification (including any accompanying claims, abstract) may be replaced by alternative features serving equivalent or similar purposes, unless expressly stated otherwise. That is, unless expressly stated otherwise, each feature is only an example of a generic series of equivalent or similar features.
The method is carried out based on a three-dimensional scanning technology, and can obtain corresponding three-dimensional point cloud data by scanning a target through the three-dimensional laser scanning technology.
The three-dimensional point cloud data formats are various, and each instrument manufacturer generally has its own file format, which increases the difficulty of post data processing, and the current more common point cloud data formats mainly include P L Y, ST L, OBJ, X3D, L AS, PCD, and the like.
(a) P L Y is a polygonal file format designed and developed by Turk et al, university of Stanford;
(b) ST L is a model file format created by 3D Systems company, and is mainly applied to the fields of CAD and CAM;
(c) OBJ is a geometrically defined file format first developed by Wavefront Technologies;
(d) X3D is an ISO-compliant XM L-based file format representing 3D computer graphics data;
(e) l AS is the standard L IDAR data format published by the L IDAR Committee under the American photogrammetry and remote sensing (ASPRS) agency.
(f) The PCD is a Point Cloud data storage format proposed and used by an open source algorithm library Point Cloud library (PC L, Point Cloud L ibrary) for Point Cloud data processing.
Example one
The embodiment discloses a method for denoising tunnel laser point cloud data, wherein the tunnel laser point cloud data is obtained by scanning an excavated tunnel by adopting a three-dimensional laser scanning system. The denoising method comprises the following steps:
and constructing a topological structure of the tunnel laser point cloud data. Therefore, the complexity of processing unordered data in the subsequent calculation process is reduced.
And filtering the tunnel laser point cloud data with the topological structure. The filtering process may remove noise, such as outlier noise points, from the point cloud data.
And compressing the filtered tunnel laser point cloud data. Data compression can refine the characteristics of the data set, so that the data amount of point cloud is reduced, and further, the data calculation amount in the subsequent processing process is reduced.
In one embodiment, the denoising method includes:
and scanning the excavated tunnel by using a three-dimensional laser scanning system to obtain tunnel laser point cloud data, and omitting the step if the tunnel laser point cloud data is obtained. And constructing a topological structure of the tunnel laser point cloud data. And filtering the tunnel laser point cloud data with the topological structure. And compressing the filtered tunnel laser point cloud data.
Example two
The embodiment discloses a method for denoising tunnel laser point cloud data, wherein the tunnel laser point cloud data is obtained by scanning an excavated tunnel by adopting a three-dimensional laser scanning system; the denoising method comprises the following steps:
firstly, the topological structure construction is carried out on the tunnel laser point cloud data to fix the topological structure of the disordered point cloud data, so that the calculation difficulty in the subsequent processing process is reduced.
The adjacent point of any data point in the point cloud is closely related to the property of the point, wherein K points adjacent to the data point are called K adjacent points. The point cloud data obtained by the three-dimensional laser scanner is generally disordered point cloud, and the disordered point cloud data is different from the two-dimensional image data, and is mainly distinguished in that no inherent data adjacency relation exists. In many point cloud algorithms, K neighboring points need to be calculated, and when the direct method is used for calculation in the disordered point cloud, the calculation time is exponentially increased along with the increase of the number of points. The topology needs to be established before data processing to reduce the complexity of subsequent calculations.
Aiming at the application scene, namely tunnel break-in-break analysis, the method builds the tree-shaped topological structure for the tunnel laser point cloud data.
The tree topology relationship comprises two structures: KD-tree and octree (octree), the corresponding structures are shown in fig. 1.
The KD-tree is a binary search tree proposed by Bentley in 1975, which uses a hyperplane to divide a high-dimensional space into two parts cyclically, the division being based on a dimensional midpoint where search points are sparse, and finally a binary search tree structure is formed. When the adjacent point is searched, only the father node and the child node are required to search, so that the search range of the adjacent point is greatly reduced, and the speed is improved. The KD-tree is characterized by being fast and suitable for laser scanning data.
The octree is similar to a quadtree which divides a two-dimensional space into four quadrants in the two-dimensional space for management, and the octree divides the point cloud into eight octagrams according to space circulation to form an octree structure. When the adjacent point needs to be searched, the method is only used in the adjacent divinatory limits, so that the search range is reduced. Octree is easy to understand relative to KD tree, and can be used in a point cloud model of a specific object, such as a point cloud model of a part. Therefore, an octree topology is generally adopted.
And then, filtering the tunnel laser point cloud data. The purpose of the filtering is to remove noise from the data. In the tunnel excavation, because of reasons such as mechanical construction, blasting, and the like, in addition to the difficult circulation of air, a large amount of dust particles can suspend in the air, and because the precision of laser scanning is higher, these suspended dust particles can also be collected when data acquisition (scanning) is carried out, and for the subsequent calculation process, these dust data are unnecessary, even influence the subsequent calculation, therefore, need to filter these data and remove.
Because the excavation amount is not too large between two adjacent collected point cloud data, namely the obtained original tunnel laser point cloud data amount is not too large, the original data can be directly subjected to denoising treatment.
For denoising of point cloud data, there are various methods such as a smoothing filtering method and a statistical-based filtering method.
The smoothing filtering is mainly used for smoothing the surface of the point cloud, and the smoothing does not remove noise points, and common methods include mean filtering, median filtering, gaussian filtering and bilateral filtering. The methods firstly need to establish topological relation to accelerate the search of the adjacent points, then analyze the adjacent point set and estimate the search points by using the relation of the adjacent points. The Gaussian filtering uses a Gaussian function to carry out smoothing, each point is weighted according to the distance from the search point, and the closer the point is to the search point, the higher the weight is. And the mean filtering directly calculates the gravity center of the point set to replace the original search point as a filtering result. And (4) sorting the distances by median filtering, and taking the median as a result to replace the original search point. The filter effect pair is shown in fig. 2.
Bilateral filtering is improved by a bilateral filtering algorithm in two-dimensional image filtering, a normal vector relation is used as a first weight of a feature, a distance is used as a second weight, and filtering is performed by simultaneously using the two weights, so that excessive reduction of features like corners can be avoided.
For outlier noise points, a grid algorithm is generally used, the point cloud data is firstly rasterized, and then each grid is analyzed. If there are data points in a certain grid and there are no data points in the surrounding grid, the data points in the grid are marked as discrete points and removed. The method is sensitive to network division of the grid, and the outlier noise point only comprises one point in the space and also forms a small block, and grid parameters are difficult to set. Most of outlier noise points can be filtered by using a statistical analysis method, the algorithm needs to firstly obtain K neighbor points of a search point, then the distance from the search point to each neighbor point is calculated, the distribution of the distance meets Gaussian distribution, the shape of the function is determined by a distance mean value and a standard deviation, and if the average distance is out of a standard range, the outlier noise points are removed.
In combination with the scene-tunnel break-in-break analysis applied by the invention, outlier noise points in the collected point cloud data are the key points to be filtered, so that a filtering method based on statistical analysis is adopted to filter the tunnel laser point cloud data, and the corresponding filtering effect is shown in fig. 3.
In consideration that the dispersibility of the point cloud data is not beneficial to the calculation of the data, in order to improve the calculation efficiency of the data, in one embodiment, data compression processing is also performed on the filtered point cloud data.
Compression of point cloud data (or other data) is typically lossy compression, also known as down-sampling. In addition, there are different down-sampling methods for ordered and unordered point clouds, where the sampling method for unordered point clouds is also common for ordered point clouds, and thus, several methods of compressing unordered point cloud data are mainly introduced herein: random sampling, voxel body sampling and curvature weighted sampling methods.
The random sampling method is a method which is easy to understand and realize, only a random number generating function is used for generating a random number which is less than the total number of points, and then the data points are removed through the indexes of the points until the sampling requirement is met. Due to the fact that the randomness of random sampling is too strong, the sampling effect is not very stable, and the obtained point cloud is possibly not uniform. And the characteristics are not protected, so that the details of the characteristics are easily lost, but the operation speed is the fastest.
The body sampling method covers all point clouds by using a cuboid bounding box, and then divides the bounding box according to the set size of the body. And replacing all points in the voxel body by using the center point of the voxel body or the gravity center of each point in the voxel body so as to realize down-sampling. The method is high in speed, the sampled point cloud is uniform in spatial distribution, and the problem of loss of detail characteristics is also solved.
Curvature is an intrinsic property describing the relationship between three-dimensional points and their surrounding data points, and the accuracy of curvature feature regions can be guaranteed by weighting the samples with curvature features. Firstly, a point cloud is segmented by using a body sampling dividing method, and meanwhile, the number of points needing to be reserved is judged according to the curvature value of each body internal data point, and finally, random extraction is carried out. The obtained sampling point cloud is uniform, more data points can be extracted in the curvature characteristic area, and the attenuation of the characteristic is reduced.
Similarly, aiming at the application scene aimed by the invention, a voxel downsampling algorithm is selected to compress the filtered data, the running speed of subsequent calculation and analysis is improved by reducing the data volume of the point cloud, and the uniformity of the point cloud data is ensured as much as possible, so that the characteristic distribution of the point cloud data is not influenced.
EXAMPLE III
The embodiment discloses a denoising method of tunnel laser point cloud data, which comprises the following steps:
and scanning the excavated tunnel by using a three-dimensional laser scanning system to obtain tunnel laser point cloud data, and omitting the step if the tunnel laser point cloud data is obtained. And constructing a topological structure of the tunnel laser point cloud data. And filtering the tunnel laser point cloud data with the topological structure. And compressing the filtered tunnel laser point cloud data. And (4) carrying out feature extraction on the compressed tunnel laser point cloud data so as to fit a mathematical model of the data set.
In an embodiment, the denoising method specifically includes:
and constructing original tunnel laser point cloud data into a tree topology structure.
And filtering the tunnel laser point cloud data by adopting a filtering method based on statistical analysis.
And compressing the filtered tunnel laser point cloud data by adopting a body sampling method.
And (4) carrying out feature extraction on the compressed tunnel laser point cloud data so as to fit a mathematical model of the data set.
In one embodiment, the feature extraction process is performed using a Random sample consensus (RANSAC) algorithm. The RANSAC algorithm estimates parameters of a mathematical model from a set of data sets including local interior points in an iterative manner, and is an uncertain algorithm because it has a certain probability of obtaining a reasonable result, and the number of iterations must be increased in order to improve the fitting accuracy. Unlike a general iteration, there is no numerical connection between any two iterations of the algorithm, so parallelization is possible.
The RANSAC algorithm randomly extracts a group of data points from the data set to calculate model parameters, and then evaluates the calculation effect. This is cycled through until the evaluation score reaches a threshold, or exceeds a maximum number of iterations.
Assume the number of local points is ninThe number of data lump points is nallThe probability of each extraction of an inlier point from the dataset is denoted by w:
let it be assumed that the determination of the parameters requires t points, wtIs the probability that the t points are all local points, then the model calculated from the extracted data should be optimal. 1-wtIs the probability that at least one point is an outlier, the estimated model is not optimal. If iterated k times, (1-w)t)kIf the probability that t points are local interior points cannot be selected for k times, and p is set as the probability that at least one point t in the iteration is local interior point, then the following steps are carried out:
1-p＝(1-wt)k(2)
p＝1-(1-wt)k(3)
so when k is large enough, the value of p approaches 1, i.e., there must be at least one local point chosen.
As shown in fig. 4, a RANSAC algorithm can be used to fit a straight line equation in a group of data sets with a large amount of interference data, and it can be seen that the robustness of straight line fitting is good, and the algorithm design is simple and can be parallelized, and these advantages are not comparable to the least square method, for example. Therefore, by setting the characteristic fitting step after filtering, abnormal data can be further eliminated, and the accuracy of the point cloud data is improved.
The invention is not limited to the foregoing embodiments. The invention extends to any novel feature or any novel combination of features disclosed in this specification and any novel method or process steps or any novel combination of features disclosed.
Claims (6)
1. A method for denoising tunnel laser point cloud data is characterized by comprising the following steps:
A. constructing a topological structure of the tunnel laser point cloud data;
B. filtering the tunnel laser point cloud data with the topological structure to filter noise;
C. and performing data compression processing on the filtered tunnel laser point cloud data.
2. The method for denoising tunnel laser point cloud data according to claim 1, further comprising:
D. and extracting the characteristics of the compressed tunnel laser point cloud data.
3. The method for denoising tunnel laser point cloud data according to claim 2, wherein the step D specifically comprises: and fitting a mathematical model of the compressed tunnel laser point cloud data by using a random sampling consistency method.
4. The method for denoising tunnel laser point cloud data according to claim 1, wherein in the step a, the tunnel laser point cloud data is constructed into a tree topology.
5. The method for denoising tunnel laser point cloud data according to claim 1, wherein in the step B, the tunnel laser point cloud data having a topological structure is filtered using a statistical-based filtering algorithm.
6. The method for denoising tunnel laser point cloud data according to claim 1, wherein in the step C, a volume sampling method is adopted to compress the filtered tunnel laser point cloud data.
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