CN106887020A - A kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud - Google Patents
A kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud Download PDFInfo
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- CN106887020A CN106887020A CN201510915095.4A CN201510915095A CN106887020A CN 106887020 A CN106887020 A CN 106887020A CN 201510915095 A CN201510915095 A CN 201510915095A CN 106887020 A CN106887020 A CN 106887020A
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Abstract
The invention discloses a kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud, it is related to field of measuring technique, comprises the following steps：Road data is obtained：By road ground cloud data after the step generation filtering such as field operation control, data acquisition, data prediction, Coordinate Conversion, point cloud filtering；Data tissue piecemeal：According to it is suitable apart from grid to cloud data block management；Data build TIN nets：According to existing road axis, TIN nets are built according to specific range buffering；Calculate section achievement：According to mileage file generated vertical and horizontal section file.Advantage：The thinking that method in the embodiment of the present invention passes through project file tissue cloud data, by Engineering Document Control cloud data, calls the ground cloud data in the respective range of road axis automatically, by being used to build TIN nets after form conversion.By the method for seamless piecemeal, cleverly solve the problems, such as that cloud data amount cannot be unified to read greatly.
Description
Technical field
It is more particularly to a kind of that road millet cake cloud, in units of kilometer, structure are dispatched by piecemeal the present invention relates to survey field
Build the triangulation network（TIN nets）, a kind of road based on LiDAR point cloud of high accuracy road vertical and horizontal section is finally obtained according to special algorithm
Road vertical and horizontal section acquisition methods.
Background technology
LiDAR（Light Detaction And Ranging）With many echoing characteristics, can with quick obtaining high density,
High-precision earth's surface altitude data, the advantage of airborne so far, vehicle-mounted LiDAR technologies has obtained highway at home and abroad design industry
Accreditation, the sector application increasingly extensively, be mainly used in obtaining generation DEM products, generation road model and vertical and horizontal section.
Because road section measurement is mainly obtained by the method for traditional measurement, the highway layout of country's main flow is soft at present
Part（Such as latitude ground software）The model data for being used is most of for text formatting or other main software forms（Such as the * of CAD
.dwg）, do not support the cloud data (* .las forms) of big data quantity.Laser point cloud data not only high precision, and point cloud is close
Collection, uniform precision, unification.Laser point cloud is applied to the extraction of road vertical and horizontal section and can increase substantially section precision, it is possible to keep away
Exempt from because vertical and horizontal section is remeasured caused by design modified line.After tested, if highway layout software is wanted to use cloud data, can only
The mode for vacuating a little extracts elevational point generation model, can so have impact on the precision of road model.
Prior art can not simultaneously meet following demand：
1st, the tissue treatment of mass cloud data：One of the characteristics of data volume is LiDAR point cloud greatly, flight airborne laser thunder
Up to or day car carry laser radar collection cloud data amount up to tens GB even GB up to a hundred, it is more difficult to management and dispatching.2nd, it is many
The data for planting form are supported；3rd, the demand such as section achievement accurate expression.
The content of the invention
The embodiment of the present invention provides a kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud, and the present invention passes through work
The thinking of journey file organization cloud data, by Engineering Document Control cloud data, calls road axis respective range automatically
Interior ground cloud data, by being used to build TIN nets after form conversion.By the method for seamless piecemeal, cleverly solve
Cloud data amount cannot unify greatly the problem for reading.The vector format of design road and the reading of latitude ground level form are supported, is protected
The reading of multi-format is demonstrate,proved.With reference to latitude ground Software on Drawing horizontal plan, figure below is the cross section generated by latitude ground software modeling
With the contrast in the cross section generated by this method point of use cloud, it is evident that the cross section generated by a cloud more can accurately reflect landform
Rise and fall, more reliable finer design basis data can be provided for highway layout.This method employs cloud data and directly participates in
Highway layout, realizes application of the airborne laser radar new technology in highway layout.Make high density, high accuracy, big data quantity
Point cloud be accurately applied to Road Traffic Design industry, it is to avoid low, precision disunity of efficiency of traditional section survey etc. lacks
Point.
The embodiment of the present invention provides a kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud, wherein, the method bag
Include following steps：
Road data is obtained：Generated by steps such as field operation control, data acquisition, data prediction, Coordinate Conversion, point cloud filtering
Road ground cloud data after filtering；
Data tissue piecemeal：According to it is suitable apart from grid to cloud data block management；
Data build TIN nets：According to existing road axis, TIN nets are built according to specific range buffering；
Calculate section achievement：According to mileage file generated vertical and horizontal section file.
A kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud, wherein, the road data is obtained：By outer
Road ground cloud data after the step generation filtering such as industry control, data acquisition, data prediction, Coordinate Conversion, point cloud filtering；
Wherein, what road data was obtained concretely comprises the following steps：
Field operation is controlled：Multiple control points are gathered by traditional measurement mode, by control point obtain cloud data carry out correct with
Check；
Data acquisition：Work system is started in operation area by airborne radar or trailer-mounted radar, starts global positioning system, inertia
Navigation system, opens each sensor, equipment recording laser echo raw data, and the original of road and both sides is obtained by scanning strip
Airborne laser data；
Data prediction：After the completion of data acquisition, fusion treatment is carried out to sensing data, generate track and attitude, then
The return laser beam range data and attitude data of laser scanner measurement are merged, is obtained under a coordinate system of cloud WGS- 84
Absolute coordinate, finally carry out matching correction to initial data using field operation control measurement data, generation meets required precision
Initial data；
Coordinate Conversion：Using seven-parameter transformation model, the parameter that the coordinate systems of WGS- 84 are changed to local coordinate system is calculated, to point
Cloud data are changed, and obtain the cloud data under local coordinate system；
Point cloud filtering：A cloud is classified, ground point and non-ground points are separated, rough sort is carried out by the parameter for setting, so
Manually the point cloud on the non-ground such as the trees in a cloud, electric wire, traffic sign, house, vehicle, electric pole is filtered out afterwards, it is final to obtain
Road and both sides ground point.
A kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud, wherein, the data tissue piecemeal：According to suitable
Close apart from grid to cloud data block management, wherein data tissue piecemeal is concretely comprised the following steps：By the side for being adapted to distance
Lattice are indexed to a cloud piecemeal by the coordinate pair point cloud of grid corner, for the position coordinates for obtaining, first determine whether the position
Put and which grid block belonged to, the region cloud data is then dispatched according to grid block title.
A kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud, wherein, according to suitable in the data tissue piecemeal
The distance range of conjunction is between 100 meters to 1000 meters.
A kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud, wherein, the data build TIN nets：According to
Some road axis, TIN nets are built according to specific range buffering, and wherein data build concretely comprising the following steps for TIN nets：
Loading road axis：By vector format or the loading road axis of format；
It is segmented by road axis and builds TIN nets：By the road axis for importing, TIN is built with the range segment separating being adapted to
Pessimistic concurrency control, and overlap is respectively required between segmentation, it is ensured that data do not have leak；
Generation network forming vector data is netted by building TIN：Formed according to triangular net by discrete elevational point and corresponded to therewith
Three-dimensional surface, obtain constitute each triangle side vector data.
A kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud, wherein, the range segment separating to be adapted to builds
Distance range in TIN pessimistic concurrency controls between 1 kilometer to 2 kilometers, overlapping range between each segmentation 20 meters to 100 meters it
Between.
A kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud, wherein, the calculating section achievement：According to inner
Journey file generated vertical and horizontal section file, wherein described calculate concretely comprising the following steps for section achievement：
Loading kilometer stone data：Because design road has the kilometer stone of standard, therefore kilometer stone file is imported；
Corresponding TIN pessimistic concurrency controls and vector file are dispatched according to kilometer stone coordinate：Extract the cross sections point in kilometer stone file
Coordinate, according to the corresponding TIN pessimistic concurrency controls of coordinated indexing and network forming vector edges file；
This point or so section line file is obtained according to kilometer stone coordinate and road axis：According to stake point coordinates obtain perpendicular to
The section line of road axis tangent line, direct of travel road axis left side is left section line, and right side is right section line；
Obtain section point height value：The selected sampled point in cross section is the key point that section line intersects with the triangulation network, is passed through
Error propagation final conclusion calculates key point precision on triangulation network sideline, and its intersection point is recorded according to the intersection point of left and right section line and the triangulation network
The height value of interpolation, the loss-free height value obtained on section line of milli；
Generation section file：According to such as latitude road design software form generate road vertical and horizontal section file.
A kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud, wherein, the acquisition section point height value：It is horizontal
The selected sampled point of section is the key point that section line intersects with the triangulation network, is come to a conclusion by error propagation and calculates triangle selvage
Key point precision on line, the height value of its intersection point interpolation is recorded according to the intersection point of left and right section line and the triangulation network, and milli is loss-free
Obtain the height value on section line；It is wherein described to obtain concretely comprising the following steps for section point height value：
Determine key point：The selected sampled point in cross section is the key point that section line intersects with the triangulation network；
Calculate point cloud level journey：Precision crucial on sideline is now calculated by law of propagation of errors；Assuming that constituting the three of the triangulation network
Individual point（The height value of A points）、（The height value of B points）、（The height value of C points）Precision for point a cloud height accuracy；
Calculate section point height precision：The elevation of L points on AB lines（Formula
InIt is the height value at l points,It is the height value of point cloud at a cloud A,It is the height value of point cloud at a cloud B,、、X-coordinate value respectively at each point）, according to law of propagation of errors,Obtain accuracy value；Due to triangulation network spacing very
It is small, it is that the precision of this key point is not almost lost；
Obtain the height value on section line：Intersection point according to left and right section line and the triangulation network records the height value of its intersection point interpolation,
The height value obtained on section line loss-free in the least.
As can be seen here：The thinking that method in the embodiment of the present invention passes through project file tissue cloud data, by engineering
File management cloud data, calls the ground cloud data in the respective range of road axis automatically, is used by after form conversion
In structure TIN nets.By the method for seamless piecemeal, cleverly solve the problems, such as that cloud data amount cannot be unified to read greatly.Branch
Hold the vector format of design road and the reading of latitude ground level form, it is ensured that the reading of multi-format.With reference to latitude ground Software on Drawing
Horizontal plan, figure below is right with the cross section generated by this method point of use cloud by the cross section of latitude ground software modeling generation
Than, it is evident that the cross section generated by a cloud more can accurately reflect hypsography, can provide more reliable finer for highway layout
Design basis data.This method employs cloud data and directly participates in highway layout, realizes airborne laser radar new technology
Application in highway layout.The point cloud of high density, high accuracy, big data quantity is set accurately to be applied to Road Traffic Design row
Industry, it is to avoid the efficiency of traditional section survey is low, precision disunity the shortcomings of.
Brief description of the drawings
Fig. 1 shows for the flow of the road vertical and horizontal section acquisition methods based on LiDAR point cloud that embodiments of the invention are provided
It is intended to；
The road data obtaining step schematic flow sheet that Fig. 2 is provided for embodiments of the invention；
Fig. 3 builds TIN net steps flow chart schematic diagrams for the data that embodiments of the invention are provided；
The calculating section achievement steps flow chart schematic diagram that Fig. 4 is provided for embodiments of the invention；
The acquisition section point height value steps flow chart schematic diagram that Fig. 5 is provided for embodiments of the invention；
Specific embodiment
In order that those skilled in the art more fully understand the present invention program, below in conjunction with accompanying drawing and specific implementation
Example describes the present invention in detail, and illustrative examples of the invention and explanation are used for explaining the present invention herein, but are not intended as
Limitation of the invention.
Embodiment 1：
A kind of schematic flow sheet of road vertical and horizontal section acquisition methods based on LiDAR point cloud that Fig. 1 is provided for the present embodiment, such as
Shown in Fig. 1, the method comprises the following steps：
Road data is obtained：Generated by steps such as field operation control, data acquisition, data prediction, Coordinate Conversion, point cloud filtering
Road ground cloud data after filtering；
Data tissue piecemeal：According to it is suitable apart from grid to cloud data block management；
Data build TIN nets：According to existing road axis, TIN nets are built according to specific range buffering；
Calculate section achievement：According to mileage file generated vertical and horizontal section file.
As shown in Fig. 2 a kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud, the road data acquisition：Through
Cross road ground point cloud after the step generation filtering such as field operation control, data acquisition, data prediction, Coordinate Conversion, point cloud filtering
Data；Wherein, what road data was obtained concretely comprises the following steps：
Field operation is controlled：Multiple control points are gathered by traditional measurement mode, by control point obtain cloud data carry out correct with
Check；
Data acquisition：Work system is started in operation area by airborne radar or trailer-mounted radar, starts global positioning system, inertia
Navigation system, opens each sensor, equipment recording laser echo raw data, and the original of road and both sides is obtained by scanning strip
Airborne laser data；
Data prediction：After the completion of data acquisition, fusion treatment is carried out to sensing data, generate track and attitude, then
The return laser beam range data and attitude data of laser scanner measurement are merged, is obtained under a coordinate system of cloud WGS- 84
Absolute coordinate, finally carry out matching correction to initial data using field operation control measurement data, generation meets required precision
Initial data；
Coordinate Conversion：Using seven-parameter transformation model, the parameter that the coordinate systems of WGS- 84 are changed to local coordinate system is calculated, to point
Cloud data are changed, and obtain the cloud data under local coordinate system；
Point cloud filtering：A cloud is classified, ground point and non-ground points are separated, rough sort is carried out by the parameter for setting, so
Manually the point cloud on the non-ground such as the trees in a cloud, electric wire, traffic sign, house, vehicle, electric pole is filtered out afterwards, it is final to obtain
Road and both sides ground point.
In specific embodiment, the data tissue piecemeal：According to it is suitable apart from grid to cloud data block management, its
Middle data tissue piecemeal is concretely comprised the following steps：By being adapted to the grid of distance to a cloud piecemeal, by the coordinate pair of grid corner
Point cloud is indexed, and for the position coordinates for obtaining, first determines whether which grid block the position belongs to, then according to grid block name
Claim to dispatch the region cloud data.
In specific embodiment, in the data tissue piecemeal according to suitable distance be 500 meters.
As shown in figure 3, a kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud, the data structure TIN nets：
According to existing road axis, TIN nets are built according to specific range buffering, wherein data build concretely comprising the following steps for TIN nets：
Loading road axis：By vector format or the loading road axis of format；
It is segmented by road axis and builds TIN nets：By the road axis for importing, TIN is built with the range segment separating being adapted to
Pessimistic concurrency control, and overlap is respectively required between segmentation, it is ensured that data do not have leak；
Generation network forming vector data is netted by building TIN：Formed according to triangular net by discrete elevational point and corresponded to therewith
Three-dimensional surface, obtain constitute each triangle side vector data.
In specific embodiment, the distance with the range segment separating structure TIN pessimistic concurrency controls being adapted to is 1 kilometer.
In specific embodiment, the overlap between each segmentation is 50 meters.
As shown in figure 4, a kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud, the calculating section achievement：Root
According to mileage file generated vertical and horizontal section file, wherein described calculate concretely comprising the following steps for section achievement：
Loading kilometer stone data：Because design road has the kilometer stone of standard, therefore kilometer stone file is imported；
Corresponding TIN pessimistic concurrency controls and vector file are dispatched according to kilometer stone coordinate：Extract the cross sections point in kilometer stone file
Coordinate, according to the corresponding TIN pessimistic concurrency controls of coordinated indexing and network forming vector edges file；
This point or so section line file is obtained according to kilometer stone coordinate and road axis：According to stake point coordinates obtain perpendicular to
The section line of road axis tangent line, direct of travel road axis left side is left section line, and right side is right section line；
Obtain section point height value：The selected sampled point in cross section is the key point that section line intersects with the triangulation network, is passed through
Error propagation final conclusion calculates key point precision on triangulation network sideline, and its intersection point is recorded according to the intersection point of left and right section line and the triangulation network
The height value of interpolation, the loss-free height value obtained on section line of milli；
Generation section file：According to such as latitude road design software form generate road vertical and horizontal section file.
As shown in figure 5, a kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud, the acquisition section point height
Value：The selected sampled point in cross section is the key point that section line intersects with the triangulation network, is come to a conclusion by error propagation and calculates three
Key point precision on the selvage line of angle, the height value of its intersection point interpolation is recorded according to the intersection point of left and right section line and the triangulation network, is had no
The height value obtained on section line of loss；It is wherein described to obtain concretely comprising the following steps for section point height value：
Determine key point：The selected sampled point in cross section is the key point that section line intersects with the triangulation network；
Calculate point cloud level journey：Precision crucial on sideline is now calculated by law of propagation of errors；Assuming that constituting the three of the triangulation network
Individual point（The height value of A points）、（The height value of B points）、（The height value of C points）Precision for point a cloud height accuracy；
Calculate section point height precision：The elevation of L points on AB lines（Formula
InIt is the height value at l points,It is the height value of point cloud at a cloud A,It is the height value of point cloud at a cloud B,、、X-coordinate value respectively at each point）, according to law of propagation of errors,Obtain accuracy value；Due to triangulation network spacing very
It is small, it is that the precision of this key point is not almost lost.
Obtain the height value on section line：Intersection point according to left and right section line and the triangulation network records the elevation of its intersection point interpolation
Value, the loss-free height value obtained on section line of milli.
The above is illustrated with the example in terms of more specifically one details below.
As shown in figure 1, the method comprises the following steps：
Road data is obtained：Generated by steps such as field operation control, data acquisition, data prediction, Coordinate Conversion, point cloud filtering
Road ground cloud data after filtering；
Data tissue piecemeal：According to it is suitable apart from grid to cloud data block management；The point cloud of each square can meet computer
It is disposable to read, convenient operation.
Data build TIN nets：According to existing road axis, TIN nets are built according to specific range buffering；To in road
Axis segmentation buffering generation road buffering face, according to corresponding cloud data in buffering suface extraction scope, builds generation TIN nets.
Calculate section achievement：According to mileage file generated vertical and horizontal section file.The coordinate of kilometer stone file is extracted, according to road
Lu Zhizheng line, generates section line, generates vertical and horizontal section file.
As shown in Fig. 2 a kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud, the road data acquisition：By outer
Road ground cloud data after the step generation filtering such as industry control, data acquisition, data prediction, Coordinate Conversion, point cloud filtering；
Wherein, what road data was obtained concretely comprises the following steps：
Field operation is controlled：Multiple control points are gathered by traditional measurement mode, by control point obtain cloud data carry out correct with
Check；Point cloud is corrected mainly to be corrected including elevation and corrected with plane.
Data acquisition：Work system is started in operation area by airborne radar or trailer-mounted radar, start global positioning system,
Inertial navigation system, opens each sensor, equipment recording laser echo raw data, and road and both sides are obtained by scanning strip
Original airborne laser data；Require that equipment is working properly in data acquisition, parameters meet project required precision, original laser
Data completely can not be leaky etc..
Data prediction：After the completion of data acquisition, fusion treatment is carried out to sensing data, generates track and attitude,
Then the return laser beam range data and attitude data of laser scanner measurement are merged, is obtained a coordinate of cloud WGS- 84
Absolute coordinate under system, finally carries out matching correction to initial data using field operation control measurement data, and generation meets precision will
The initial data asked；The region of the requirement of data and data leak can not meet to(for) precision should resurvey the area data.
Coordinate Conversion：Using seven-parameter transformation model, the parameter that the coordinate systems of WGS- 84 are changed to local coordinate system is calculated,
Cloud data is changed, the cloud data under local coordinate system is obtained；Cloud data converts rear, it is necessary to pass through precision
Test point carries out detection evaluation, it is ensured that the correctness of the cloud data under local coordinate system.
Point cloud filtering：A cloud is classified, ground point and non-ground points are separated, rough segmentation is carried out by the parameter for setting
Class, then manually filters out the point cloud on the non-ground such as the trees in a cloud, electric wire, traffic sign, house, vehicle, electric pole, finally
Obtain road and both sides ground point.Artificial point cloud filtering is by a cloud cut section, manually separating ground point and non-ground points.
In specific embodiment, the data tissue piecemeal：According to it is suitable apart from grid to cloud data block management, its
Middle data tissue piecemeal is concretely comprised the following steps：By being adapted to the grid of distance to a cloud piecemeal, by the coordinate pair of grid corner
Point cloud is indexed, and for the position coordinates for obtaining, first determines whether which grid block the position belongs to, then according to grid block name
Claim to dispatch the region cloud data.By the whole cloud data of coordinate position management and dispatching.
In specific embodiment, in the data tissue piecemeal according to suitable distance be 500 meters.
As shown in figure 3, a kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud, the data structure TIN nets：
According to existing road axis, TIN nets are built according to specific range buffering, wherein data build concretely comprising the following steps for TIN nets：
Loading road axis：Road axis is loaded by vector format or format；Road axis is highway
The road axis of designing unit's design generation, is the center line of road.
It is segmented by road axis and builds TIN nets：By the road axis for importing, built with the range segment separating being adapted to
TIN pessimistic concurrency controls, and overlap is respectively required between segmentation, it is ensured that data do not have leak；Overlap between segmentation will at least ensure two
The distance in individual cross section.
Generation network forming vector data is netted by building TIN：Formed therewith according to triangular net by discrete elevational point
Corresponding three-dimensional surface, obtains the vector data on the side for constituting each triangle.The vector data of triangle edges is mainly and section
Line seeks joining.
In specific embodiment, the distance with the range segment separating structure TIN pessimistic concurrency controls being adapted to is 1 kilometer.
In specific embodiment, the overlap between each segmentation is 50 meters.The overlap distance is used to ensure that the section at edge fit is complete
It is whole.
As shown in figure 4, a kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud, the calculating section achievement：Root
According to mileage file generated vertical and horizontal section file, wherein described calculate concretely comprising the following steps for section achievement：
Loading kilometer stone data：Because design road has the kilometer stone of standard, therefore kilometer stone file is imported；Kilometer stone text
Part is the coordinate file for accurately representing whole kilometer stake.
Corresponding TIN pessimistic concurrency controls and vector file are dispatched according to kilometer stone coordinate：Each in extraction kilometer stone file breaks
Millet cake coordinate, according to the corresponding TIN pessimistic concurrency controls of coordinated indexing and network forming vector edges file；
This point or so section line file is obtained according to kilometer stone coordinate and road axis：According to stake point coordinates obtain perpendicular to
The section line of road axis tangent line, direct of travel road axis left side is left section line, and right side is right section line；Section line
Length be generally 50 meters.
Obtain section point height value：The selected sampled point in cross section is the key point that section line intersects with the triangulation network,
Come to a conclusion by error propagation and calculate key point precision on triangulation network sideline, it is recorded according to the intersection point of left and right section line and the triangulation network
The height value of intersection point interpolation, the loss-free height value obtained on section line of milli；
Generation section file：According to such as latitude road design software form generate road vertical and horizontal section file.Vertical and horizontal section has
Multiple format, vertical and horizontal section file is obtained according to call format.
As shown in figure 5, a kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud, the acquisition section point height
Value：The selected sampled point in cross section is the key point that section line intersects with the triangulation network, is come to a conclusion by error propagation and calculates three
Key point precision on the selvage line of angle, the height value of its intersection point interpolation is recorded according to the intersection point of left and right section line and the triangulation network, is had no
The height value obtained on section line of loss；It is wherein described to obtain concretely comprising the following steps for section point height value：
Determine key point：The selected sampled point in cross section is the key point that section line intersects with the triangulation network；The key point is
Point on cross section, the density of the point depends on the dot density of cloud data.Dot density is bigger, and the key point on section line is got over
Many, expression of the section line to true ground is finer.
Calculate point cloud level journey：Precision crucial on sideline is now calculated by law of propagation of errors；Assuming that constituting the triangulation network
Three points（The height value of A points）、（The height value of B points）、（The height value of C points）Precision be point cloud level journey essence
Degree；
Calculate section point height precision：The elevation of L points on AB lines（Formula
InIt is the height value at l points,It is the height value of point cloud at a cloud A,It is the height value of point cloud at a cloud B,、、X-coordinate value respectively at each point）, according to law of propagation of errors,Obtain accuracy value；Due to triangulation network spacing very
It is small, it is that the precision of this key point is not almost lost.The formula shows the height accuracy of this method interpolated point.
Obtain the height value on section line：Intersection point according to left and right section line and the triangulation network records the elevation of its intersection point interpolation
Value, the loss-free height value obtained on section line of milli.The height value of each joining is obtained, the form according to section requirement is defeated
Go out.
As can be seen here：
The thinking that method in the embodiment of the present invention passes through project file tissue cloud data, by Engineering Document Control point cloud number
According to, the ground cloud data in the respective range of road axis is called automatically, it is used to build TIN nets after form is changed.It is logical
The method for crossing seamless piecemeal, cleverly solves the problems, such as that cloud data amount cannot be unified to read greatly.Support the arrow of design road
The reading of amount form and latitude ground level form, it is ensured that the reading of multi-format.With reference to latitude ground Software on Drawing horizontal plan, figure below
It is the contrast in the cross section and cross section generated by this method point of use cloud generated by latitude ground software modeling, it is evident that by a cloud
The cross section of generation more can accurately reflect hypsography, can provide more reliable finer design basis data for highway layout.
This method employs cloud data and directly participates in highway layout, realizes airborne laser radar new technology answering in highway layout
With.The point cloud of high density, high accuracy, big data quantity is set accurately to be applied to Road Traffic Design industry, it is to avoid traditional section
The efficiency of measurement is low, precision disunity the shortcomings of.
Although depicting the embodiment of the present invention by embodiment, it will be appreciated by the skilled addressee that the present invention has many
Deformation and change are without deviating from spirit of the invention, it is desirable to which appended claim includes these deformations and changes without deviating from this
The spirit of invention.
Claims (8)
1. a kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud, it is characterised in that the method comprises the following steps：
Road data is obtained：Generated by steps such as field operation control, data acquisition, data prediction, Coordinate Conversion, point cloud filtering
Road ground cloud data after filtering；
Data tissue piecemeal：According to it is suitable apart from grid to cloud data block management；
Data build TIN nets：According to existing road axis, TIN nets are built according to specific range buffering；
Calculate section achievement：According to mileage file generated vertical and horizontal section file.
2. a kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud according to claim 1, it is characterised in that
The road data is obtained：Generated by steps such as field operation control, data acquisition, data prediction, Coordinate Conversion, point cloud filtering
Road ground cloud data after filtering；Wherein, what road data was obtained concretely comprises the following steps：
Field operation is controlled：Multiple control points are gathered by traditional measurement mode, by control point obtain cloud data carry out correct with
Check；
Data acquisition：Work system is started in operation area by airborne radar or trailer-mounted radar, starts global positioning system, inertia
Navigation system, opens each sensor, equipment recording laser echo raw data, and the original of road and both sides is obtained by scanning strip
Airborne laser data；
Data prediction：After the completion of data acquisition, fusion treatment is carried out to sensing data, generate track and attitude, then
The return laser beam range data and attitude data of laser scanner measurement are merged, is obtained under a coordinate system of cloud WGS- 84
Absolute coordinate, finally carry out matching correction to initial data using field operation control measurement data, generation meets required precision
Initial data；
Coordinate Conversion：Using seven-parameter transformation model, the parameter that the coordinate systems of WGS- 84 are changed to local coordinate system is calculated, to point
Cloud data are changed, and obtain the cloud data under local coordinate system；
Point cloud filtering：A cloud is classified, ground point and non-ground points are separated, rough sort is carried out by the parameter for setting, so
Manually the point cloud on the non-ground such as the trees in a cloud, electric wire, traffic sign, house, vehicle, electric pole is filtered out afterwards, it is final to obtain
Road and both sides ground point.
3. a kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud according to claim 1, it is characterised in that
The data tissue piecemeal：According to it is suitable apart from grid to cloud data block management, wherein data tissue piecemeal is specific
Step is：By being adapted to the grid of distance to a cloud piecemeal, it is indexed by the coordinate pair point cloud of grid corner, for obtaining
Position coordinates, first determine whether which grid block the position belongs to, the region cloud data is then dispatched according to grid block title.
4. a kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud according to claim 3, it is characterised in that
In the data tissue piecemeal according to suitable distance range between 100 meters to 1000 meters.
5. a kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud according to claim 1, it is characterised in that
The data build TIN nets：According to existing road axis, TIN nets are built according to specific range buffering, wherein data build
What TIN was netted concretely comprises the following steps：
Loading road axis：Road axis is loaded by vector format or specific data form；
It is segmented by road axis and builds TIN nets：By the road axis for importing, TIN is built with the range segment separating being adapted to
Pessimistic concurrency control, and overlap is respectively required between segmentation, it is ensured that data do not have leak；
Generation network forming vector data is netted by building TIN：Formed according to triangular net by discrete elevational point and corresponded to therewith
Three-dimensional surface, obtain constitute each triangle side vector data.
6. a kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud according to claim 5, it is characterised in that
Between the distance range built with the range segment separating being adapted in TIN pessimistic concurrency controls is segmented between 1 kilometer to 2 kilometers, respectively
Overlapping range is between 20 meters to 100 meters.
7. a kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud according to claim 1, it is characterised in that
The calculating section achievement：According to mileage file generated vertical and horizontal section file, wherein the specific steps for calculating section achievement
For：
Loading kilometer stone data：Because design road has the kilometer stone of standard, therefore kilometer stone file is imported；
Corresponding TIN pessimistic concurrency controls and vector file are dispatched according to kilometer stone coordinate：Extract the cross sections point in kilometer stone file
Coordinate, according to the corresponding TIN pessimistic concurrency controls of coordinated indexing and network forming vector edges file；
This point or so section line file is obtained according to kilometer stone coordinate and road axis：According to stake point coordinates obtain perpendicular to
The section line of road axis tangent line, direct of travel road axis left side is left section line, and right side is right section line；
Obtain section point height value：The selected sampled point in cross section is the key point that section line intersects with the triangulation network, is passed through
Error propagation final conclusion calculates key point precision on triangulation network sideline, and its intersection point is recorded according to the intersection point of left and right section line and the triangulation network
The height value of interpolation, the loss-free height value obtained on section line of milli；
Generation section file：According to such as latitude road design software form generate road vertical and horizontal section file.
8. a kind of road vertical and horizontal section acquisition methods based on LiDAR point cloud according to claim 7, it is characterised in that
The acquisition section point height value：The selected sampled point in cross section is the key point that section line intersects with the triangulation network, is passed through
Error propagation final conclusion calculates key point precision on triangulation network sideline, and its intersection point is recorded according to the intersection point of left and right section line and the triangulation network
The height value of interpolation, the loss-free height value obtained on section line of milli；The wherein described specific step for obtaining section point height value
Suddenly it is：
Determine key point：The selected sampled point in cross section is the key point that section line intersects with the triangulation network；
Calculate point cloud level journey：Precision crucial on sideline is now calculated by law of propagation of errors；Assuming that constituting the three of the triangulation network
Individual point（The height value of A points）、（The height value of B points）、（The height value of C points）Precision for point a cloud height accuracy；
Calculate section point height precision：The elevation of L points on AB lines（Formula
InIt is the height value at l points,It is the height value of point cloud at a cloud A,It is the height value of point cloud at a cloud B,、、X-coordinate value respectively at each point）, according to law of propagation of errors,Obtain accuracy value；Due to triangulation network spacing
Very little, is that the precision of this key point is not almost lost；
Obtain the height value on section line：Intersection point according to left and right section line and the triangulation network records the height value of its intersection point interpolation,
The height value obtained on section line loss-free in the least.
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