CN105866791A - Method for improving precision of vehicle-mounted LiDAR point cloud data through target control network - Google Patents
Method for improving precision of vehicle-mounted LiDAR point cloud data through target control network Download PDFInfo
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- CN105866791A CN105866791A CN201610336068.6A CN201610336068A CN105866791A CN 105866791 A CN105866791 A CN 105866791A CN 201610336068 A CN201610336068 A CN 201610336068A CN 105866791 A CN105866791 A CN 105866791A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
- G01S7/481—Constructional features, e.g. arrangements of optical elements
- G01S7/4817—Constructional features, e.g. arrangements of optical elements relating to scanning
Abstract
The invention relates to a method for improving precision of vehicle-mounted LiDAR point cloud data through a target control network. The LiDAR point cloud data cannot meet the precision requirement of line measurement due to errors generated in a process of measuring a railway line by a vehicle-mounted LiDAR system, and target control points are required to be used for constraining measurement errors of vehicle-mounted LiDAR data to eliminate the errors and improve the precision. The method comprises steps as follows: a distribution position and an installation method of the target control points are determined firstly, and measurement and adjustment calculation of the target control network are completed on the basis of a basic control network; the size of a target box is selected according to influence factors of scanning spot density, and accuracy of target control point coordinates extracted from the point cloud data is guaranteed; the point cloud data are subjected to segmented constraint adjustment on the basis of two sets of coordinates of the target control points, and the precision of the point cloud data is improved. According to the method for improving the precision of the vehicle-mounted LiDAR point cloud data through the target control network, the measurement errors are eliminated by the target control points in the target control network, and the problem of precision loss of the vehicle-mounted LiDAR point cloud data due to the measurement errors is solved.
Description
Technical field
The invention belongs to railway construction technical field of mapping, be specifically related to a kind of target establishment of control net method for existing railway circuit repetition measurement.
Background technology
Along with China's general speed railway operation speed and being greatly improved of frequency, the requirement to railway operation safety is more and more higher, therefore safeguards that the demand of the railway in operation maintenance being measured as foundation is also gradually increased with railway operation.Vehicle-mounted three-dimensional laser radar (LiDAR) scanning system receives much concern as the data acquisition means of a kind of advanced person, its data can more intuitively, quickly represent and analyze rail track situation, play critical effect to improving measurement efficiency, be that railway operation safeguards a kind of advanced effectively means measured.
But vehicle-mounted LiDAR scanning system is a complicated integrated system, the data precision of scanning is by the joint effect of all parts in system, the error produced during source of error predominantly measurement and hardware integration error, including: position error, laser ranging error, angle error, angle of setting error, IMU drift error and system integration error, data process error etc..These errors largely have impact on the precision of scan data, in order to obtain the cloud data of higher precision, needs the target control point constraint measurement error used in Controling network, improves the precision of vehicle-mounted LiDAR point cloud data.
The cloud data obtained due to LiDAR measuring method can not set up accurate corresponding relation with known point, therefore uses target control point to set up the corresponding relation of some cloud and known point.Target control point is defined as the intersection point of three target planes, accurate target control point is extracted from a cloud, corresponding relation is set up at the target control point measured with reality, set up error equation by the two set coordinates at target control point to carry out retraining an adjustment raising point cloud precision, obtain and meet the cloud data that operation maintenance certainty of measurement requires.
Summary of the invention
It is an object of the invention to provide a kind of method using target Controling network to improve vehicle-mounted LiDAR point cloud data precision, the operating line repetition measurement of the railway that can be used for growing up, provide high-precision data basis for repetition survey of existing rail way.
The technical solution adopted in the present invention is:
1, the method that target Controling network improves vehicle-mounted LiDAR point cloud data precision is used, it is characterised in that:
Realized by following steps:
Step one: determine the installation position at target control point and the installation method of target apparatus:
Target control point is uniformly laid in both sides, downline road, and sets up target control point at the characteristic point of the region that POS calculation accuracy difference or gps signal are easily lost and rail track；Target box body is measured built-in fitting by connecting bolt with target and is connected and is fixed, and box body is provided with target anchor point, and when disposing box body, target anchor point is all the time towards the direction of train driving；
Step 2: according to the size dimension of field working conditions selection target apparatus:
Target apparatus includes three scanning planes, i.e. target end face, driving face, face and face of vertically driving a vehicle, and three intersection points are target anchor point.According to the influence factor of scanning element density, calculate the spacing of scanning element on target face；For ensureing the extraction accuracy of target anchor point, select different size of target according to working environment, it is ensured that on three scanning target faces, obtain at least three scan-line datas；
Step 3: complete measurement and the compensating computation at target control point:
The testing and the adjustment that complete target Controling network resolve, it is provided that meet the Controling network achievement that subsequent data analysis uses；
Step 4: cloud data is carried out segmentation constraint adjustment, the overall precision of raising point cloud:
Utilize the accurate target control point coordinate that field operation is surveyed, original point cloud data is carried out segmentation constraint adjustment, is obtained the cloud data of higher precision by compensating computation.
In step one, target apparatus is target box body, is the rigidity polyhedron with four faces, vertical driving face, face and face of vertically driving a vehicle below the target end face being sloped downwardly including vertical back of the body driving face, back of the body driving face one and target end face；Target end face, driving face, face and vertical driving face intersect at a point, and this point is target anchor point；
When carrying out vehicle-mounted LiDAR line scanning, target box body is measured built-in fitting by connecting bolt with target and is connected and is fixed, and target anchor point is all the time towards the direction of train driving；Vertical driving face is perpendicular to that This train is bound for XXX, and driving face, face is with This train is bound for XXX in 30 ° of angles；When This train is bound for XXX on the contrary, target apparatus rotates to rightabout, dough-making powder driving face, vertical driving face swaps, former driving face rotates to vertically This train is bound for XXX the vertical driving face becoming new, former vertical driving face rotate to the direction that This train is bound for XXX in 30 ° of angles, as new driving face, face.
In step 2, the influence factor of scanning element density includes scanner measuring rate, rate of scanning, train running speed, the target distance away from scanner, target from the distance of rail less than 5 meters, train running speed is less than 30-70km/h.
In step 4, the segmentation constraint adjustment of some cloud is realized by following steps:
Acquisition scanning survey data and field operation measured data two overlap the three-dimensional coordinate information of target anchor point, and this target anchor point is designed as obtaining the summit that three target faces of scanning element are intersected；Each target scanning plane is considered as two dimensional surface smooth in three dimensions, according to three dimensions plane equation, being fitted the cloud data on three scanning planes respectively, obtain the intersection point of three fit Plane, the three-dimensional coordinate of this point is the coordinate of target anchor point；
Use the actual measurement target anchor point coordinate measured in step 3 and obtain after compensating computation and the target anchor point coordinate extracted from cloud data as the characteristic point between two set coordinates, by scan data precision, cloud data is carried out segmentation, overlapping more than one pair of target data are guaranteed between two adjacent sectional, target dominating pair of vertices cloud data is used to carry out segmentation constraint adjustment, obtain and meet the cloud data that rail track operation maintenance certainty of measurement requires, it is achieved the raising of cloud data precision.
The invention have the advantages that
The present invention is directed to the rail track of long and narrow banding, it is proposed that the method setting up target Controling network according to the distribution situation in Along Railway landform, tunnel and high cutting, it is ensured that the carrying out of some cloud precision controlling；
The method proposing the use target vehicle-mounted Point Cloud Data from Three Dimension Laser Scanning precision of raising of novelty of the present invention, solve the key issue using vehicle-mounted LiDAR technology to ensure certainty of measurement during carrying out rail track operation maintenance measurement, largely overcome vehicle-mounted LiDAR equipment and cause a defect for cloud loss of significance, achieve the purpose improving some cloud precision, make this measuring method may be used for railway operation and safeguard in measurement.
Accompanying drawing explanation
Fig. 1 is the laying schematic diagram of target Controling network；
Fig. 2 is the installation method of measurement target drone during line scanning.
Fig. 3 is target apparatus front view.
Fig. 4 is target apparatus side view.
Fig. 5 is target apparatus top view.
In figure, 1-target box body, 2-carries on the back driving face, 3-target end face, driving face, 4-face, and 5-vertically drives a vehicle face, 6-target anchor point.
Detailed description of the invention
Below in conjunction with detailed description of the invention, the present invention will be described in detail.
The method using target Controling network to improve vehicle-mounted LiDAR point cloud data precision that the present invention relates to, relate to special target apparatus structure, target apparatus is target box body, it is the rigidity polyhedron with four faces, vertical driving face, face and face of vertically driving a vehicle below the target end face being sloped downwardly including vertical back of the body driving face, back of the body driving face one and target end face；Target anchor point is designed as target end face, driving face, face and the intersection point in vertical face of driving a vehicle.According to field working conditions during use, determine the size of target, installation position and dispose direction.
Said method is realized by following steps:
Step one: determine the installation position at target control point and the installation method of target apparatus:
Owing to railway is existing line, inertial navigation system cannot can only be restrained by left and right alternately turning by acceleration and deceleration on railway, and after therefore starting to measure, the drift of inertial navigation measuring unit IMU cannot eliminate, and along with the growth of the time of measurement, inertial navigation drift is gradually increased.Therefore for eliminating inertial navigation system error and GPS measurement error, determine that the installation position at target control point answers downline road to lay.
Additionally, along with the modernization of railway operation and automatization, the electronics that railway is buried underground, electromagnetic equipment are more and more, these all can disturb the normal work of IMU, and once IMU operating is not normal, and POS calculation accuracy can be caused to be substantially reduced；Simultaneously, for obtaining highdensity cloud data on railway, vehicle-mounted scanning device is generally 2.5-4.0m in the setting height(from bottom) of last vehicle of train, in the highest cutting, tunnel, the region such as railway limit massif is steep, easily blocked by surrounding higher building body, occur that vehicle-mounted LiDAR system does not receives the situation of gps signal.Therefore the region easily lost in POS calculation accuracy difference or gps signal, should lay target control point.
Seeing Fig. 1, according to Along Railway tunnel, bridge, embankment, cutting and high mountain cliff along the line, the vegetation circumstance of occlusion to satellite-signal on the basis of ground base station Controling network, target Controling network is laid in downline both sides.
Target control point should be arranged near roadbed along the line, bridge, tunnel, high cutting and steep high mountain according to Along Railway features of terrain.Typically in broad view, railway smooth-going, location that gps signal is strong, it is diluter that target can be laid, and general spacing is about 1km, is laid at curvilinear characteristic point or at knick point as far as possible.Easily cause the area of gps signal losing lock at alpine region tunnel group etc., the time of gps signal losing lock will be calculated according to length of tunnel, train speed.When time of losing lock was less than 2 minutes, POS data loss of significance is little, can set up target in tunnel entrance, outlet.When GPS time of losing lock was more than 4 minutes, need to suitably increase the layout density of target, particularly curve, hyperbola location, the laying of target wants the trend of controlling curve, should be laid in such as: point of tangent to spiral, point of spiral to curve, curve intermediate point, point of curve to spiral, point of spiral to tangent, the gradient rise at the characteristic points such as close point.
Such as Fig. 2, during line scanning, target box body is connected by connecting bolt with target measurement built-in fitting and is fixed, and target anchor point is installed towards changing along with the difference that This train is bound for XXX.Scanning process hits demarcation site should be towards the direction of train driving, and the vertical driving face of target need to be perpendicular to that This train is bound for XXX simultaneously, and driving face, face is with This train is bound for XXX in 30 ° of angles.When This train is bound for XXX on the contrary, target box body should rotate to rightabout, dough-making powder driving face, vertical driving face swaps, former driving face rotates to vertically This train is bound for XXX the vertical driving face becoming new, former vertical driving face rotate to the direction that This train is bound for XXX in 30 ° of angles, as new driving face, face.
Step 2: according to the size dimension of field working conditions selection target:
The target control point laid is used to carry out a cloud accuracy constraint, therefore it is required that target control point has the highest extraction accuracy.Scanning element positioning precision the most at most on three scanning planes of target is the highest, and the extraction accuracy of target is the highest.
Scanning element density on target is affected by multiple factors: scanner measuring rate is the highest, rate of scanning is the highest, and scanning density is the biggest；Train running speed is the lowest, target is the nearest away from scanner distance, and scanning density is the biggest, then the some cloud quantity obtained on target face is the most.
Owing to determine a target face in a cloud, at least need three scan line cloud datas on this face, therefore measure and need before starting, according to above-mentioned rule, to calculate the scanning element spacing on target, thus select various sizes of target box body, to ensure the some quantity that target face obtains.
Step 3: complete measurement and the compensating computation at target control point:
The testing and the adjustment that complete target Controling network resolve, it is provided that meet the Controling network achievement that subsequent data analysis uses；
Step 4: cloud data is carried out segmentation constraint adjustment:
The purpose that a cloud carries out accuracy constraint is exactly the accurate target control point coordinate utilizing field operation to survey, and original point cloud carries out segmentation constraint adjustment, obtains the cloud data of higher precision.Therefore, carrying out a cloud constraint needs acquisition scanning survey data and field operation measured data two to overlap the three-dimensional coordinate information of target anchor point, and this target anchor point is designed as obtaining the summit that three target faces of scanning element are intersected.Each target face is considered as two dimensional surface smooth in three dimensions, according to three dimensions plane equation, is fitted the cloud data on three faces respectively, and obtains the intersection point of three fit Plane, and the three-dimensional coordinate of this point is the coordinate of target anchor point.
Use the actual measurement target anchor point coordinate measured in step 3 and obtain after compensating computation and the target anchor point coordinate extracted from cloud data, by scan data precision, original point cloud data is carried out segmentation, overlapping more than one pair of target point is ensured between two adjacent sectional, target point-to-point cloud data are used to carry out segmentation constraint adjustment, thus realize the raising of cloud data precision, reach to meet the purpose that rail track operation maintenance certainty of measurement requires.
Present disclosure is not limited to cited by embodiment, and the conversion of any equivalence that technical solution of the present invention is taked by those of ordinary skill in the art by reading description of the invention, the claim being the present invention is contained.
Claims (4)
1. use the method that target Controling network improves vehicle-mounted LiDAR point cloud data precision, it is characterised in that:
Realized by following steps:
Step one: determine the installation position at target control point and the installation method of target apparatus:
Target control point is uniformly laid in both sides, downline road, and sets up target control point at the characteristic point of the region that POS calculation accuracy difference or gps signal are easily lost and rail track；Target box body is measured built-in fitting by connecting bolt with target and is connected and is fixed, and box body is provided with target anchor point, and when disposing box body, target anchor point is all the time towards the direction of train driving；
Step 2: according to the size dimension of field working conditions selection target apparatus:
Target apparatus includes three scanning planes, i.e. target end face, driving face, face and face of vertically driving a vehicle, and three intersection points are target anchor point；
According to the influence factor of scanning element density, calculate the spacing of scanning element on target face；For ensureing the extraction accuracy of target anchor point, select different size of target according to working environment, it is ensured that on three scanning target faces, obtain at least three scan-line datas；
Step 3: complete measurement and the compensating computation at target control point:
The testing and the adjustment that complete target Controling network resolve, it is provided that meet the Controling network achievement that subsequent data analysis uses；
Step 4: cloud data is carried out segmentation constraint adjustment, the overall precision of raising point cloud:
Utilize the accurate target control point coordinate that field operation is surveyed, original point cloud data is carried out segmentation constraint adjustment, is obtained the cloud data of higher precision by compensating computation.
Employing target Controling network the most according to claim 1 improves the method for vehicle-mounted LiDAR point cloud data precision, it is characterised in that:
In step one, target apparatus is target box body, is the rigidity polyhedron with four faces, vertical driving face, face and face of vertically driving a vehicle below the target end face being sloped downwardly including vertical back of the body driving face, back of the body driving face one and target end face；Target end face, driving face, face and vertical driving face intersect at a point, and this point is target anchor point；
When carrying out vehicle-mounted LiDAR line scanning, target box body is measured built-in fitting by connecting bolt with target and is connected and is fixed, and target anchor point is all the time towards the direction of train driving；Vertical driving face is perpendicular to that This train is bound for XXX, and driving face, face is with This train is bound for XXX in 30 ° of angles；When This train is bound for XXX on the contrary, target apparatus rotates to rightabout, dough-making powder driving face, vertical driving face swaps, former driving face rotates to vertically This train is bound for XXX the vertical driving face becoming new, former vertical driving face rotate to the direction that This train is bound for XXX in 30 ° of angles, as new driving face, face.
Employing target Controling network the most according to claim 1 improves the method for vehicle-mounted LiDAR point cloud data precision, it is characterised in that:
In step 2, the influence factor of scanning element density includes scanner measuring rate, rate of scanning, train running speed, the target distance away from scanner, target from the distance of rail less than 5 meters, train running speed is less than 30-70km/h.
Employing target Controling network the most according to claim 1 improves the method for vehicle-mounted LiDAR point cloud data precision, it is characterised in that:
In step 4, the segmentation constraint adjustment of some cloud is realized by following steps:
Acquisition scanning survey data and field operation measured data two overlap the three-dimensional coordinate information of target anchor point, and this target anchor point is designed as obtaining the summit that three target faces of scanning element are intersected；Each target scanning plane is considered as two dimensional surface smooth in three dimensions, according to three dimensions plane equation, being fitted the cloud data on three scanning planes respectively, obtain the intersection point of three fit Plane, the three-dimensional coordinate of this point is the coordinate of target anchor point；
Use the actual measurement target anchor point coordinate measured in step 3 and obtain after compensating computation and the target anchor point coordinate extracted from cloud data as the characteristic point between two set coordinates, by scan data precision, cloud data is carried out segmentation, overlapping more than one pair of target data are guaranteed between two adjacent sectional, target dominating pair of vertices cloud data is used to carry out segmentation constraint adjustment, obtain and meet the cloud data that rail track operation maintenance certainty of measurement requires, it is achieved the raising of cloud data precision.
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Cited By (6)
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| CN107792115A (en) * | 2017-09-07 | 2018-03-13 | 中铁二院工程集团有限责任公司 | One kind automatically extracts both wired rail crest level methods using three-dimensional laser point cloud |
| CN109613555A (en) * | 2018-11-09 | 2019-04-12 | 广西壮族自治区遥感信息测绘院 | Verify the sea and land integration calibration field distribution method of double frequency LiDAR survey meter |
| CN111007530A (en) * | 2019-12-16 | 2020-04-14 | 武汉汉宁轨道交通技术有限公司 | Laser point cloud data processing method, device and system |
| CN111896938A (en) * | 2019-05-06 | 2020-11-06 | 山东鲁邦地理信息工程有限公司 | Vehicle-mounted laser radar scanning target laying and measuring method |
| TWI805007B (en) * | 2021-04-07 | 2023-06-11 | 湛積股份有限公司 | Trajectory reducing method and device |
| CN116859410A (en) * | 2023-06-08 | 2023-10-10 | 中铁第四勘察设计院集团有限公司 | Method for improving laser radar measurement accuracy of unmanned aerial vehicle on existing railway line |
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| CN107792115A (en) * | 2017-09-07 | 2018-03-13 | 中铁二院工程集团有限责任公司 | One kind automatically extracts both wired rail crest level methods using three-dimensional laser point cloud |
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| CN116859410A (en) * | 2023-06-08 | 2023-10-10 | 中铁第四勘察设计院集团有限公司 | Method for improving laser radar measurement accuracy of unmanned aerial vehicle on existing railway line |
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