CN112085843A - Method and device for extracting and measuring tunnel type target features in real time - Google Patents
Method and device for extracting and measuring tunnel type target features in real time Download PDFInfo
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Abstract
The invention provides a method and a device for extracting and measuring tunnel type target characteristics in real time, which solve the technical problem that the existing tunnel type target characteristics are difficult to extract and measure. The method comprises the following steps: eliminating interference point clouds to obtain effective point clouds; fitting the ground point cloud and calculating a normal vector of a fitting plane to solve a pitch angle; after ground point clouds are removed, the pitch angle of the residual effective point clouds is compensated; extracting characteristic point clouds in the effective point clouds, and projecting the characteristic point clouds on a projection plane to generate cross-section point clouds; identifying boundary point clouds in the cross-section point clouds, and distinguishing the cross-section point clouds by taking the boundary point clouds as boundary points; constructing a mathematical description model of the tunnel type target section according to the distinguished section point cloud; and calculating the height and width of the tunnel-type target according to the mathematical description model. The method can extract the tunnel type target characteristics in real time in the vehicle running process, simultaneously realize the mathematical description and the height and width measurement of the tunnel type target characteristics, meet the advanced prejudgment requirement and assist the vehicle driving.
Description
Technical Field
The invention relates to the technical field of intelligent driving, in particular to a method and a device for extracting and measuring tunnel type target characteristics in real time.
Background
With the continuous development of science and technology and the development of automobile manufacturing and information technology, the tools of riding instead of walk of people are gradually changed from a carriage to a bicycle, a fuel automobile and an electric automobile, and nowadays intelligent driving is promoted by a new schedule.
In the road condition of automobile driving, tunnel target height-limiting road infrastructures such as culverts, tunnels and bridges are important factors influencing vehicle traffic and are hot spots for research in the field of intelligent driving. The detection and extraction of the tunnel type target height limiting facility characteristics are realized, and the safety and the reliability of vehicle running can be effectively improved. However, the tunnel type target features are not easy to extract and measure, and the prior art is lack of a technology for extracting and measuring the tunnel type target features.
Disclosure of Invention
In view of the above problems, embodiments of the present invention provide a method for extracting and measuring a tunnel type target feature in real time, which solves the technical problem that the existing tunnel type target feature is not easy to extract and measure.
In a first aspect, the present invention provides a method for extracting and measuring target features of tunnel type in real time, including:
eliminating interference point clouds in the three-dimensional point clouds collected by the multi-line laser radar to obtain effective point clouds;
selecting the point cloud with the minimum height range in the effective point cloud to form ground point cloud, performing plane fitting on the ground point cloud, and calculating a normal vector of a fitting plane to solve a pitch angle;
after ground point clouds are removed, the pitch angle of the residual effective point clouds is compensated;
extracting point clouds with upward emission angles in the effective point clouds after pitch angle compensation to form a characteristic point cloud of a tunnel type target, and projecting the characteristic point cloud on a projection plane which is parallel to the axle and vertical to the advancing direction to generate a section point cloud;
identifying boundary point clouds in the section point clouds, and distinguishing the section point clouds into top boundary point clouds, left boundary point clouds and right boundary point clouds by taking the boundary point clouds as boundary points;
constructing a mathematical description model of the tunnel type target section according to the distinguished section point cloud;
and calculating the height and width of the tunnel-type target according to the mathematical description model.
In an embodiment, the removing the interference point cloud from the three-dimensional point cloud collected by the multiline laser radar to obtain the effective point cloud includes:
determining an area not in front of the vehicle as an redundant point cloud;
determining the point cloud with the data value not being NAF as invalid point cloud;
determining a point cloud without adjacent point clouds around as an isolated point cloud;
and removing the redundant point clouds, the invalid point clouds and the isolated point clouds to obtain the valid point clouds.
In one embodiment, the selecting the point cloud with the minimum height in the effective point cloud to form a ground point cloud, and performing plane fitting on the ground point cloud and calculating a normal vector of a fitting plane to solve a pitch angle includes:
rasterizing the effective point cloud data space, wherein each grid is a cuboid with the same bottom surface and the height changed according to the point cloud space distribution;
the point clouds in each grid are sorted according to the elevation, the point cloud with the lowest elevation in each grid is compared with the point cloud with the lowest elevation in the adjacent grid, the point cloud with the lowest elevation is obtained, and the point cloud with the lowest elevation and the distance smaller than the threshold range is used as the ground point cloud;
and performing plane fitting on the ground point cloud, solving a normal vector of a fitting plane, and solving the pitch angle according to the normal vector of the fitting plane.
In one embodiment, the extracting of the point cloud with the upward emission angle in the effective point cloud after the pitch angle compensation to form a feature point cloud of the tunnel-like target, and the projecting the feature point cloud on a projection plane parallel to the axle and perpendicular to the advancing direction to generate the cross-section point cloud includes:
determining an XOY plane of a multiline laser radar coordinate system as a radar plane;
calculating the included angle theta between the single-beam laser point cloud and the radar plane in the effective point cloud
Screening single-beam laser point clouds with an included angle theta of 10-15 degrees as characteristic point clouds according to the radar installation angle and the radar vertical field angle parameter;
and projecting the characteristic point cloud to a plane vertical to the axle to generate the cross-section point cloud of the tunnel type target.
In one embodiment, the identifying the boundary point cloud in the cross-section point cloud, and the dividing the cross-section point cloud into a top boundary point cloud, a left boundary point cloud, and a right boundary point cloud with the boundary point cloud as a boundary point comprises:
selecting upper, middle and lower adjacent point clouds from the cross-section point clouds to form a scanning triangle, and dividing the cross-section point clouds into a left part and a right part from the right front of the vehicle according to clockwise and anticlockwise directions for scanning; calculating the angle of the vertex of the scanning triangle by taking the intermediate point cloud as the vertex;
taking the point with the abrupt change of the angle value of the scanning triangle vertex as the boundary point cloud in the cross-section point cloud;
taking the point cloud before the boundary point as the top boundary point cloud of the tunnel type target, taking the point cloud after the boundary point cloud of the left half part as the left boundary point cloud, and taking the point cloud after the boundary point cloud of the right half part as the right boundary point cloud;
and respectively storing the top boundary point cloud, the left boundary point cloud and the right boundary point cloud in a top boundary container, a left boundary container and a right boundary container.
In one embodiment, the building of the mathematical description model of the tunnel-like target section according to the differentiated section point cloud includes:
performing quadratic curve fitting on the top boundary point cloud to obtain a top boundary curve parameter U (a)1,b1,c)，U(a1,b1And c) representing the coefficient of the top edge secondary curve, and constructing a top edge secondary curve model y as a1x2+b1x+c；
Performing straight line fitting on the left boundary point cloud and the right boundary point cloud to obtain a left boundary curve parameter L and a right boundary curve parameter L (a)2,b2) And R (a)3,b3) Wherein L (a)2,b2) Represents the left straight line coefficient; r (a)3,b3) And constructing a left straight line model y as a by using the right straight line coefficient2x+b2And the right straight line model y ═ a3x+b3。
In one embodiment, the calculating the height and the width of the tunnel-like object according to the mathematical description model includes:
calculating a target width according to a formula width, namely fabs (L _ b-R _ b) according to the left and right boundary straight line model;
in the formula: l _ b represents a left boundary linear constant term coefficient; and R _ b respectively represents the coefficients of the right boundary linear constant term.
According to the top boundary curve model and the formula:
calculating the target height;
in the formula: h is0The height of the multiline laser radar from the ground is adopted.
In a second aspect, the present invention provides a device for extracting and measuring target features of tunnel type in real time, the device comprising:
a point cloud preprocessing module: eliminating interference point clouds in the three-dimensional point clouds collected by the multi-line laser radar to obtain effective point clouds;
a gradient calculation module: the system is used for selecting the point cloud with the minimum height in the effective point cloud to form ground point cloud, performing plane fitting on the ground point cloud, and calculating a normal vector of a fitting plane to solve a pitch angle;
a compensation module: the system is used for compensating a pitch angle for the residual effective point cloud after the ground point cloud is removed;
an extraction module: the system is used for extracting point clouds with upward emission angles from the effective point clouds after pitch angle compensation to form a characteristic point cloud of a tunnel type target, and projecting the characteristic point cloud on a projection plane which is parallel to an axle and vertical to the advancing direction to generate a section point cloud;
a classification module: the system is used for identifying boundary point clouds in the section point clouds and distinguishing the section point clouds into top boundary point clouds, left boundary point clouds and right boundary point clouds by taking the boundary point clouds as boundary points;
constructing a module: the mathematical description model is used for constructing a tunnel type target section according to the distinguished section point cloud;
a calculation module: the method is used for calculating the height and the width of the tunnel-type target according to the mathematical description model.
In a third aspect, the present invention provides an electronic device comprising:
a processor, a memory, an interface to communicate with a gateway;
the memory is used for storing programs and data, and the processor calls the programs stored in the memory to execute the method for extracting and measuring the target characteristics of the tunnel class in real time provided by any one of the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium, which includes a program, when executed by a processor, for executing the method for extracting and measuring the target feature of the tunnel class provided in any one of the first aspect in real time.
From the above description, the embodiment of the invention provides a method and a device for extracting and measuring tunnel type target features in real time, the tunnel type target features can be extracted in real time in the vehicle driving process, the mathematical description of the tunnel type target features and the measurement of the height and width of the tunnel type target are realized at the same time, the requirement of intelligent driving advance judgment is met, the vehicle driving is assisted, and the safety and the reliability of the vehicle driving are improved.
Drawings
Fig. 1 is a schematic flow chart illustrating a method for extracting and measuring a tunnel type target feature in real time according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating the identification of boundary point clouds in cross-sectional point clouds in accordance with one embodiment of the present invention;
fig. 3 is a schematic structural diagram of a device for extracting and measuring a tunnel type target feature in real time according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer and more obvious, the present invention is further described below with reference to the accompanying drawings and the detailed description. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The method for extracting and measuring the tunnel type target characteristics in real time in the embodiment of the invention is shown in figure 1. In fig. 1, the method comprises:
s101, eliminating interference point clouds in three-dimensional point clouds collected by a multi-line laser radar to obtain effective point clouds;
specifically, the multi-line laser radar is installed on the roof, three-dimensional point cloud information in front of the vehicle is collected through the multi-line laser radar, interference point clouds such as redundant point clouds, invalid point clouds or isolated point clouds are doped in the three-dimensional point cloud collected by the multi-line laser radar, the interference point clouds are removed by preprocessing the three-dimensional point cloud collected by the multi-line laser radar in the step, the valid point clouds are obtained, the subsequent calculated amount is reduced, and the accuracy of point cloud collection is improved.
S102, selecting the point cloud with the minimum height range in the effective point clouds to form ground point clouds, carrying out plane fitting on the ground point clouds, and calculating a normal vector of a fitting plane to solve a pitch angle;
specifically, the point cloud after preprocessing comprises a ground point cloud and a point cloud of tunnel type target features above the ground, and a vehicle actually runs on a road and generally has a slope, so that a certain included angle exists between a multi-line laser radar measuring plane and the road plane, and further an error occurs in a tunnel type target measuring result, so that the slope (namely a pitch angle) of the road needs to be calculated, the ground point cloud is subjected to plane fitting, a fitting plane for the ground plane is obtained, and the slope of the road can be known by calculating a normal vector of the fitting plane.
S103, compensating a pitch angle for the residual effective point cloud after removing the ground point cloud;
specifically, the pitch angle compensation is carried out on the residual effective point clouds after the ground point clouds are removed according to the road gradient obtained in the previous step, the pitch angle compensation is carried out on the effective point clouds obtained by the multi-line laser radar, the included angle between the measuring plane of the multi-line laser radar and the road plane is reduced, and then the measuring error is reduced.
S104, extracting point clouds with upward emission angles in the effective point clouds after pitch angle compensation to form a characteristic point cloud of a tunnel type target, and projecting the characteristic point cloud on a projection plane which is parallel to the axle and vertical to the advancing direction to generate a cross-section point cloud;
specifically, the multi-line laser radar is installed on the roof and has a certain distance from the ground, so that the radar plane of the multi-line laser radar is higher than the ground, and the point cloud below the radar plane detects ground information instead of tunnel-type target characteristic information; the point cloud information above the radar plane is the effective feature point cloud for describing the features of the tunnel type targets, the feature point cloud comprises three-dimensional information of the tunnel type targets in front of the vehicle, and the feature point cloud is projected on a plane which is parallel to the axle and vertical to the advancing direction, so that the section point cloud comprising the section information of the tunnel type targets is integrated on the plane.
S105, identifying boundary point clouds in the section point clouds, and distinguishing the section point clouds into top boundary point clouds, left boundary point clouds and right boundary point clouds by taking the boundary point clouds as boundary points;
specifically, it can be understood that a cross section of the tunnel-type target is composed of two boundaries and a top edge connecting the two boundaries, how to extract or identify feature information of the tunnel-type target represented by each part in the cross-section point cloud needs to distinguish the side boundary from the top boundary, and the side boundary and the top boundary have an obvious boundary, so that which cross-section point clouds belong to the top boundary point cloud and which belong to the left boundary point cloud and the right boundary point cloud can be determined through the boundary point cloud.
S106, constructing a mathematical description model of the tunnel type target section according to the distinguished section point cloud;
specifically, a mathematical description model of geometric description of the tunnel-type target can be constructed by adopting ceres curve fitting according to the top boundary point cloud, the left boundary point cloud and the right boundary point cloud obtained in the previous step.
And S107, calculating the height and the width of the tunnel type target according to the mathematical description model.
Specifically, according to the mathematical description model of the geometric description of the tunnel-type target, the width of the tunnel-type target can be measured by calculating the distance between the left boundary point cloud and the right boundary point cloud, and the height of the tunnel-type target can be measured by calculating the height of the highest point of the top boundary point cloud.
In this embodiment, the tunnel type target features can be extracted in real time in the vehicle driving process, the mathematical description of the tunnel type target features and the measurement of the height and the width of the tunnel type target are realized, the requirement of intelligent driving advanced prediction is met, the vehicle driving is assisted, and the safety and the reliability of the vehicle driving are improved.
Based on the above embodiment, as a preferred embodiment, the step 101 specifically includes the following steps:
determining an area not in front of the vehicle as an redundant point cloud;
specifically, in order to meet the advance judgment of vehicle running, the point cloud which is not in the range of 30m × 20m in front of the vehicle head is determined as the redundant point cloud.
Determining the point cloud with the data value not being NAF as invalid point cloud;
specifically, NAF is a non-adjacent expression type, and the point cloud of which the point cloud data value type does not belong to NAF is determined as an invalid point cloud.
Determining a point cloud without adjacent point clouds around as an isolated point cloud;
specifically, some point clouds in the point clouds acquired by the multi-line laser radar have large distance from adjacent points, so that the connection with the adjacent point clouds cannot be established, the clustering algorithm result is influenced, and the point clouds with the distance greater than 0.3m from the adjacent point clouds in the point clouds can be regarded as isolated point clouds.
And removing the redundant point clouds, the invalid point clouds and the isolated point clouds to obtain the valid point clouds.
In the embodiment, the point cloud information acquired by the multi-line laser radar is preprocessed, and interference point clouds such as redundant point clouds, invalid point clouds and isolated point clouds in the acquired point clouds are eliminated, and the interference point clouds do not participate in subsequent tunnel type target feature description and width and height measurement, so that the precision and accuracy of the feature description, the width and the height of the tunnel type target can be improved.
Based on the above embodiment, as a preferred embodiment, the step 102 specifically includes the following steps:
rasterizing the effective point cloud data space, wherein each grid is a cuboid with the same bottom surface and the height changed according to the point cloud space distribution;
the point clouds in each grid are sorted according to the elevation, the point cloud with the lowest elevation in each grid is compared with the point cloud with the lowest elevation in the adjacent grid, the point cloud with the lowest elevation is obtained, and the point cloud with the lowest elevation and the distance smaller than the threshold range is used as the ground point cloud;
and performing plane fitting on the ground point cloud, solving a normal vector of a fitting plane, and solving the pitch angle according to the normal vector of the fitting plane.
In this embodiment, because of the slope of the actual road, there is an included angle between the radar plane of the multi-line laser radar and the road plane, which causes an error between the measured height of the tunnel-type target and the actual height of the tunnel. Therefore, the pitch angle of the point cloud needs to be compensated, and the generation of the error is reduced. When point clouds are compensated, the road gradient needs to be measured, and the road gradient can be rapidly acquired through the steps.
Based on the above embodiment, as a preferred embodiment, the step 104 specifically includes the following steps:
determining an XOY plane of a multiline laser radar coordinate system as a radar plane;
specifically, the multiline radar coordinate system is as follows: the radar body is the original point, and the radar body dead ahead is the Y axle, and the radar body right side is the X axle, and the Z axle is up, is decided into the radar plane with multi-thread laser radar coordinate system XOY plane.
Calculating the included angle theta between the single-beam laser point cloud and the radar plane in the effective point cloud
In the formula: p is a radical ofix、piy、pizRespectively representing coordinate values of the ith point cloud.
Screening single-beam laser point clouds with an included angle theta of 10-15 degrees as characteristic point clouds according to the radar installation angle and the radar vertical field angle parameter;
and projecting the characteristic point cloud to a plane vertical to the axle to generate the cross-section point cloud of the tunnel type target.
In the embodiment, whether the emission angle of the single laser beam is above the radar plane or not is judged by calculating the included angle between the single laser beam and the radar plane, the single laser point cloud with the emission angle above the radar plane is extracted from the compensated residual effective point cloud, the ground point cloud collected by the multi-line laser radar is eliminated, the calculated amount is reduced, and meanwhile, the influence of the ground point cloud on the measurement result is eliminated. And after the ground point cloud is removed, obtaining the characteristic point cloud of the tunnel type target above the radar plane, projecting the characteristic point cloud onto a plane vertical to the axle to obtain the section point cloud of the tunnel type target, and further obtaining the information of the section of the tunnel type target.
Based on the above embodiment, as a preferred embodiment, the step 105 specifically includes the following steps:
selecting upper, middle and lower adjacent point clouds from the cross-section point clouds to form a scanning triangle, and dividing the cross-section point clouds into a left part and a right part from the right front of the vehicle according to clockwise and anticlockwise directions for scanning; calculating the angle of the vertex of the scanning triangle by taking the intermediate point cloud as the vertex;
specifically, the actual cross-section point cloud is consistent with the cross section of the tunnel-type target, and the cross-section point cloud is divided into two parts to be scanned, so that the identification speed can be increased. The shape of the cross-section point cloud is as shown in fig. 2, three adjacent point clouds A, B, C in the cross-section point cloud are selected, the point cloud B is selected as a vertex, and the angle value of the vertex B can be calculated according to the triangle cosine law.
Taking the point with the abrupt change of the angle value of the scanning triangle vertex as the boundary point cloud in the cross-section point cloud;
in particular, it can be understood that the cross section of the tunnel-like object actually consists of left and right side edges and a top edge, and the junction between the left and right side edges and the top edge is actually a boundary point. The angle value of the vertex B of the scanning triangle is changed along with the change of the angle value of the scanning triangle in the scanning process, when the boundary between the top edge and the side edge is scanned, the sudden change of the angle value of the vertex is inevitable, and the point cloud with the sudden change of the angle value can be identified as the boundary point cloud.
Taking the point cloud before the boundary point as the top boundary point cloud of the tunnel type target, taking the point cloud after the boundary point cloud of the left half part as the left boundary point cloud, and taking the point cloud after the boundary point cloud of the right half part as the right boundary point cloud;
and respectively storing the top boundary point cloud, the Left boundary point cloud and the Right boundary point cloud in a top boundary container U _ lanes, a Left boundary container Left _ lanes and a Right boundary container Right _ lanes.
Specifically, the top boundary container U _ lanes, the Left boundary container Left _ lanes and the Right boundary container Right _ lanes are actually point cloud arrays and used for storing the sorted point clouds to form a point cloud set with corresponding attributes, so that clustering and fitting of the top boundary point cloud, the Left boundary point cloud and the Right boundary point cloud are facilitated.
In the embodiment, the obtained section point cloud is scanned to identify the boundary point cloud, the section point cloud is classified through the boundary point cloud, and the classified section point cloud is stored in the boundary container corresponding to each category.
Based on the above embodiment, as a preferred embodiment, the step 106 specifically includes the following steps:
performing quadratic curve fitting on the top boundary point cloud to obtain a top boundary curve parameter U (a)1,b1,c)，U(a1,b1And c) representing the coefficient of the top edge secondary curve, and constructing a top edge secondary curve model y as a1x2+b1x+c；
It can be understood that the top boundary of the tunnel-like target is mostly in a parabolic form, so the top boundary point cloud is subjected to quadratic curve fitting by using a ceres curve fitting method, and a quadratic curve model is constructed for the top boundary point cloud.
Performing straight line fitting on the left boundary point cloud and the right boundary point cloud to obtain a left boundary curve parameter L and a right boundary curve parameter L (a)2,b2) And R (a)3,b3) Wherein L (a)2,b2) Represents the left straight line coefficient; r (a)3,b3) And constructing a left straight line model y as a by using the right straight line coefficient2x+b2And the right straight line model y ═ a3x+b3；
It can be understood that the left and right borders of the tunnel-like target are mostly in the form of straight lines, so that the side borders are subjected to straight line fitting, and a straight line model is constructed for the side borders.
In the embodiment, the accurate mathematical description of the tunnel-type target is realized through a mathematical fitting method, and an accurate external operation environment description data source is provided for intelligent driving of the vehicle.
Based on the above embodiment, as a preferred embodiment, the step 107 specifically includes the following steps:
according to the left and right boundary straight line model, according to the formula width ═ fabs (L _ b)2-R_b3) Calculating the target width;
in the formula: l _ b2Representing the left boundary linear constant term coefficient; r _ b3Respectively, the right boundary linear constant term coefficients.
According to the top boundary curve model and the formula:
computing a targetA height;
in the formula: h is0The height of the multiline laser radar from the ground is adopted.
It will be appreciated that the elevation of the multiline lidar from the ground is a known datum, which is determined primarily by vehicle elevation.
In the embodiment, the width and the height of the tunnel targets are measured by calculating the parameters in the mathematical description formed by the tunnel targets, so that the perception of intelligent driving on the tunnel targets is improved, and the driving safety of vehicles is guaranteed.
In conclusion, the invention realizes the real-time measurement of the height and the width of the tunnel target, and the data updating frequency reaches 10 HZ; the device has advanced measurement capability, and the measurement precision can reach 0.5 m; the use condition is not changed by the ambient light, and the LED lamp can work normally in the environment of backlight, night and the like.
Based on the same inventive concept, the embodiment of the present application further provides a device for extracting and measuring the target feature of the tunnel type in real time, which can be used for implementing the method for extracting and measuring the target feature of the tunnel type in real time described in the above embodiment, as described in the following embodiment. The principle of solving the problems of the device for extracting and measuring the tunnel type target features in real time is similar to that of a method for extracting and measuring the tunnel type target features in real time, so that the method for implementing the device for extracting and measuring the tunnel type target features in real time can be referred to, and repeated parts are not repeated. As used hereinafter, the term "module" may include a combination of software and/or hardware that implements a predetermined function. Although the following embodiments describe a tunnel type target feature real-time extraction and measurement apparatus preferably implemented in software, hardware, or a combination of software and hardware implementations are also possible and contemplated.
As shown in fig. 2, an embodiment of the present invention provides a device for extracting and measuring target features of tunnel type in real time, including:
the point cloud preprocessing module 201: eliminating interference point clouds in the three-dimensional point clouds collected by the multi-line laser radar to obtain effective point clouds;
the gradient calculation module 202: the system is used for selecting the point cloud with the minimum height in the effective point cloud to form ground point cloud, performing plane fitting on the ground point cloud, and calculating a normal vector of a fitting plane to solve a pitch angle;
the compensation module 203: the system is used for compensating a pitch angle for the residual effective point cloud after the ground point cloud is removed;
the extraction module 204: the system is used for extracting point clouds with upward emission angles from the effective point clouds after pitch angle compensation to form a characteristic point cloud of a tunnel type target, and projecting the characteristic point cloud on a projection plane which is parallel to an axle and vertical to the advancing direction to generate a section point cloud;
the classification module 205: the system is used for identifying boundary point clouds in the section point clouds and distinguishing the section point clouds into top boundary point clouds, left boundary point clouds and right boundary point clouds by taking the boundary point clouds as boundary points;
the building module 206: the mathematical description model is used for constructing a tunnel type target section according to the distinguished section point cloud;
the calculation module 207: the method is used for calculating the height and the width of the tunnel-type target according to the mathematical description model.
An embodiment of the present application further provides a specific implementation manner of an electronic device, which is capable of implementing all steps in the method for extracting and measuring a tunnel type target feature in real time according to the foregoing embodiment, and referring to fig. 3, the electronic device 300 specifically includes the following contents:
a processor 310, a memory 320, a communication unit 330, and a bus 340;
the processor 310, the memory 320 and the communication unit 330 complete communication with each other through the bus 340; the communication unit 330 is used for implementing information transmission between server-side devices and terminal devices and other related devices.
The processor 310 is used to call the computer program in the memory 320, and when the processor executes the computer program, the processor implements all the steps in the real-time extraction and measurement method of the tunnel class target feature in the above embodiment.
Those of ordinary skill in the art will understand that: the Memory may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory is used for storing programs, and the processor executes the programs after receiving the execution instructions.
Further, the software programs and modules within the aforementioned memories may also include an operating system, which may include various software components and/or drivers for managing system tasks (e.g., memory management, storage device control, power management, etc.), and may communicate with various hardware or software components to provide an operating environment for other software components.
The processor may be an integrated circuit chip having signal processing capabilities. The processor may be a general-purpose processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The present application further provides a computer-readable storage medium, which includes a program, when executed by a processor, for executing a method for extracting and measuring a target feature of a tunnel class in real time provided by any of the foregoing method embodiments.
Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media capable of storing program codes, such as ROM, RAM, magnetic or optical disk, etc., and the specific type of media is not limited in this application.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A real-time extraction and measurement method for tunnel type target features is characterized by comprising the following steps:
eliminating interference point clouds in the three-dimensional point clouds collected by the multi-line laser radar to obtain effective point clouds;
selecting the point cloud with the minimum height range in the effective point cloud to form ground point cloud, performing plane fitting on the ground point cloud, and calculating a normal vector of a fitting plane to solve a pitch angle;
after ground point clouds are removed, the pitch angle of the residual effective point clouds is compensated;
extracting point clouds with upward emission angles in the effective point clouds after pitch angle compensation to form a characteristic point cloud of a tunnel type target, and projecting the characteristic point cloud on a projection plane which is parallel to the axle and vertical to the advancing direction to generate a section point cloud;
identifying boundary point clouds in the section point clouds, and distinguishing the section point clouds into top boundary point clouds, left boundary point clouds and right boundary point clouds by taking the boundary point clouds as boundary points;
constructing a mathematical description model of the tunnel type target section according to the distinguished section point cloud;
and calculating the height and width of the tunnel-type target according to the mathematical description model.
2. The method for extracting and measuring the target feature of the tunnel type according to claim 1, wherein the step of eliminating the interference point cloud in the three-dimensional point cloud collected by the multi-line laser radar to obtain the effective point cloud comprises the following steps:
determining an area not in front of the vehicle as an redundant point cloud;
determining the point cloud with the data value not being NAF as invalid point cloud;
determining a point cloud without adjacent point clouds around as an isolated point cloud;
and removing the redundant point clouds, the invalid point clouds and the isolated point clouds to obtain the valid point clouds.
3. The method for extracting and measuring the target features of the tunnel type according to claim 1, wherein the selecting the point cloud with the minimum height in the effective point clouds to form a ground point cloud, and performing plane fitting on the ground point cloud and calculating a normal vector solution pitch angle of a fitting plane comprises:
rasterizing the effective point cloud data space, wherein each grid is a cuboid with the same bottom surface and the height changed according to the point cloud space distribution;
the point clouds in each grid are sorted according to the elevation, the point cloud with the lowest elevation in each grid is compared with the point cloud with the lowest elevation in the adjacent grid, the point cloud with the lowest elevation is obtained, and the point cloud with the lowest elevation and the distance smaller than the threshold range is used as the ground point cloud;
and performing plane fitting on the ground point cloud, solving a normal vector of a fitting plane, and solving the pitch angle according to the normal vector of the fitting plane.
4. The method for extracting and measuring the characteristics of the tunnel-like target in real time according to claim 1, wherein the extracting of the point clouds with upward emission angles from the effective point clouds after the pitch angle compensation to form the characteristic point clouds of the tunnel-like target, and the projecting of the characteristic point clouds on the projection plane parallel to the axle and perpendicular to the advancing direction to generate the cross-section point clouds comprises:
determining an XOY plane of a multiline laser radar coordinate system as a radar plane;
calculating the included angle theta between the single-beam laser point cloud and the radar plane in the effective point cloud
Screening single-beam laser point clouds with an included angle theta of 10-15 degrees as characteristic point clouds according to the radar installation angle and the radar vertical field angle parameter;
and projecting the characteristic point cloud to a plane vertical to the axle to generate the cross-section point cloud of the tunnel type target.
5. The method for extracting and measuring characteristics of a tunnel-like target in real time according to claim 1, wherein the step of identifying a boundary point cloud in the cross-section point cloud, and the step of dividing the cross-section point cloud into a top boundary point cloud, a left boundary point cloud and a right boundary point cloud by using the boundary point cloud as a boundary point comprises the steps of:
selecting upper, middle and lower adjacent point clouds from the cross-section point clouds to form a scanning triangle, and dividing the cross-section point clouds into a left part and a right part from the right front of the vehicle according to clockwise and anticlockwise directions for scanning; calculating the angle of the vertex of the scanning triangle by taking the intermediate point cloud as the vertex;
taking the point with the abrupt change of the angle value of the scanning triangle vertex as the boundary point cloud in the cross-section point cloud;
taking the point cloud before the boundary point as the top boundary point cloud of the tunnel type target, taking the point cloud after the boundary point cloud of the left half part as the left boundary point cloud, and taking the point cloud after the boundary point cloud of the right half part as the right boundary point cloud;
and respectively storing the top boundary point cloud, the left boundary point cloud and the right boundary point cloud in a top boundary container, a left boundary container and a right boundary container.
6. The method for extracting and measuring the characteristics of the tunnel-type target in real time according to claim 1, wherein the step of constructing a mathematical description model of the cross section of the tunnel-type target according to the differentiated cross-section point cloud comprises the following steps:
performing quadratic curve fitting on the top boundary point cloud to obtain a top boundary curve parameter U (a)1,b1,c)，U(a1,b1And c) representing the coefficient of the top edge secondary curve, and constructing a top edge secondary curve model y as a1x2+b1x+c；
Performing straight line fitting on the left boundary point cloud and the right boundary point cloud to obtain a left boundary curve parameter L and a right boundary curve parameter L (a)2,b2) And R (a)3,b3) Wherein L (a)2,b2) Represents the left straight line coefficient;R(a3,b3) And constructing a left straight line model y as a by using the right straight line coefficient2x+b2And the right straight line model y ═ a3x+b3。
7. The method for extracting and measuring the characteristics of the tunnel-like target in real time according to claim 6, wherein the step of calculating the height and the width of the tunnel-like target according to the mathematical description model comprises the following steps:
calculating a target width according to a formula width, namely fabs (L _ b-R _ b) according to the left and right boundary straight line model;
in the formula: l _ b represents a left boundary linear constant term coefficient; r _ b respectively represents a right boundary linear constant term coefficient;
according to the top boundary curve model and the formula:
calculating the target height;
in the formula: h is0The height of the multiline laser radar from the ground is adopted.
8. The utility model provides a real-time extraction of tunnel class target feature and measuring device which characterized in that, the device includes:
a point cloud preprocessing module: eliminating interference point clouds in the three-dimensional point clouds collected by the multi-line laser radar to obtain effective point clouds;
a gradient calculation module: the system is used for selecting the point cloud with the minimum height in the effective point cloud to form ground point cloud, performing plane fitting on the ground point cloud, and calculating a normal vector of a fitting plane to solve a pitch angle;
a compensation module: the system is used for compensating a pitch angle for the residual effective point cloud after the ground point cloud is removed;
an extraction module: the system is used for extracting point clouds with upward emission angles from the effective point clouds after pitch angle compensation to form a characteristic point cloud of a tunnel type target, and projecting the characteristic point cloud on a projection plane which is parallel to an axle and vertical to the advancing direction to generate a section point cloud;
a classification module: the system is used for identifying boundary point clouds in the section point clouds and distinguishing the section point clouds into top boundary point clouds, left boundary point clouds and right boundary point clouds by taking the boundary point clouds as boundary points;
constructing a module: the mathematical description model is used for constructing a tunnel type target section according to the distinguished section point cloud;
a calculation module: the method is used for calculating the height and the width of the tunnel-type target according to the mathematical description model.
9. An electronic device, comprising:
a processor, a memory, an interface to communicate with a gateway;
the memory is used for storing programs and data, and the processor calls the programs stored in the memory to execute the tunnel class target feature real-time extraction and measurement method of any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a program which, when executed by a processor, is configured to perform the method for real-time extraction and measurement of target features of tunnel classes of any of claims 1 to 7.
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