CN104007436A - Road survey and design method based on airborne LiDAR data - Google Patents
Road survey and design method based on airborne LiDAR data Download PDFInfo
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- CN104007436A CN104007436A CN201410230292.8A CN201410230292A CN104007436A CN 104007436 A CN104007436 A CN 104007436A CN 201410230292 A CN201410230292 A CN 201410230292A CN 104007436 A CN104007436 A CN 104007436A
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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
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Abstract
The invention belongs to a road survey and design method based on airborne LiDAR data. The method includes the following steps that (1) blocking pre-processing is carried out on the LiDAR data, (2) indexes of the LiDAR data are built, (3) terrain factors of the LiDAR data are extracted, (4) the vertical section of a road is calculated, and (5) the cross section of the road is calculated. According to the method, the LiDAR data are directly used as original data, and the needed vertical section and the needed cross section in road design are calculated, so that the vertical section and the cross section of the road are directly generated by means of the LiDAR data, the steps of generating a DOM and a DEM are omitted, precision loss caused by data processing is avoided, efficiency of road survey and design is improved, time of road survey and design is shortened, meanwhile, influences of landform on the vertical section and the cross section are taken into consideration, influences of natural terrain, geology, hydrology, climate and other factors along the road on road survey and design are comprehensively considered, the method can automatically adapt to changes of the terrain, practicability of the method is high, and the method is easy to achieve and meets requirements of the road survey and design industry.
Description
Technical field
The invention belongs to mapping and Highway Survey technical field, relate in particular to a kind of Highway Investigation Design method based on airborne LiDAR data.
Background technology
LiDAR-Light Detection And Ranging, i.e. photodetection and measurement.To utilize GPS(Global Position System) and IMU(Inertial Measurement Unit, inertial measuring unit) airborne laser scanning.Its measured data are that the discrete point of digital surface model (Digital Surface Model, DSM) represents, contain space three-dimensional information and laser intensity information in data.LiDAR is roughly divided into the airborne and large class in ground two, and wherein airborne laser radar is that a kind of installation airborne laser is aboard surveyed and range measurement system, can measure the three-dimensional coordinate of ground object.Airborne LiDAR is a kind of active earth observation systems, is first grown up by western countries the early 1990s and drop into an emerging technology of commercial applications.Its integrated laser ranging technology, computer technology, Inertial Measurement Unit (IMU)/DGPS differential position are in one, this technology is producing important breakthrough aspect the Real-time Obtaining of three-dimensional spatial information, provides a kind of brand-new technological means for obtaining high-spatial and temporal resolution geospatial information.It has automaticity high, be subject to that weather effect is little, data are with short production cycle, precision high.The laser pulse energy of airborne LiDAR sensor emission partly penetrates the woods and blocks, and directly obtains high-precision three-dimensional earth's surface terrain data.Airborne LiDAR data, after related software data processing, can generate high-precision digital terrain model DTM, contour map, have the superiority that traditional photography is measured and ground routine measuring technique cannot replace, and have therefore caused the great interest on mapping circle.The commercial applications of airborne laser radar technology, makes aerial photogrammetry as more convenient in the automatic extraction of generation DEM, level line and atural object key element, and its ground data is easy to merge in various digitized maps by software processing.
Along with taking GPS, RS, GIS technology as basis, the development of the earth observation technology taking modern high-resolution satellite earth observation, building global gravitational field model and techniques of spatial data analysis as core, GPS, aerial survey remote sensing, high-resolution satellite are widely used in planning, prospecting, design and the information system management of highway.Due to the high request to elevation and efficiency in actual Highway Survey process, the task that GPS-RTK (GPS-Real Time Kinematic), high-definition remote sensing technology, conventional aerial survey and low latitude Aerial survey precision are all difficult to meet the requirement of highway survey and set up highway data bank.Nowadays abroad, airborne LiDAR system is for survey and design and the maintenance of highway.Current, also LiDAR equipment and technology have been introduced although domestic, in Highway Survey route selection, line design, obtain application, but majority also needs to adopt ripe LiDAR data processing software first to obtain the products such as DEM, DOM and DLG, and then these data processing products are imported to design softwares and carry out the operation such as exploration and vertical and horizontal section design of highway, like this, owing to having increased data handling procedure, will inevitably have influence on the measurement result of precision, increase the operation of Highway Survey simultaneously, lowered the design efficiency of Highway Survey.In addition, existing highway vertical and horizontal section design and generation often do not consider factor and the requirement of each side, easily cause Highway Survey job costs high, long in time limit, and precision and quality requirements can not be guaranteed.
Summary of the invention
The object of the invention is to overcome the deficiencies in the prior art and a kind of Highway Investigation Design method based on airborne LiDAR data is provided, in taking topography and geomorphology impact into account, save the step that generates DOM and DEM, avoid factor data to process the loss of significance producing, improve the efficiency of Highway Investigation Design, met the demand of Highway Investigation Design industry.
The object of the present invention is achieved like this:
A Highway Investigation Design method based on airborne LiDAR data, is characterized in that: comprise the following steps:
Step 1), LiDAR data are carried out to partitioning pretreatment: first, by the traversal to LiDAR data or query metadata, obtain the coordinate range of LiDAR data, then, the sizing grid of LiDAR deblocking is set, last, according to the size of LiDAR data block, calculate the scope of each piece, calculate accordingly the filename of every storage and the some cloud within the scope of this is stored as to file;
Step 2), LiDAR data are set up to index: first, the LiDAR data coordinates scope and the sizing grid that obtain according to step 1 are set up ground floor index, then, LiDAR cloud data in every is set up to second layer spatial index, second layer index adopts KD tree index, realizes storage and the inquiry of data, last, second layer index and ground floor index are carried out to space correlation, the correspondence position by the information association of second layer index to ground floor index;
Step 3), extract the terrain factor of LiDAR data: the terrain factor that extracts successively each LiDAR data block according to LiDAR data and index thereof;
The vertical section of step 4), calculating highway: extract vertical section according to LiDAR data and index, according to Road Design specification, each terrain factor is carried out to modeling, thereby vertical section is carried out to local auto-adaptive adjustment;
Step 5), the transversal section of calculating highway: extract transversal section according to LiDAR data and index, according to Road Design specification, each terrain factor is carried out to modeling, thereby local auto-adaptive adjustment is carried out in transversal section.
The terrain factor of described LiDAR data block comprises the gradient, slope aspect, length of grade, planar curvature, profile curvature, terrain roughness, topographic relief degree, the elevation coefficient of variation, earth's surface depth of cut.
Described grid adopts incremental method to divide,
Newlat=Lat+AddLat
Newlon=Lon+AddLon
In formula, NewLat and NewLon are that Lat and Lon are previous grid lower right corner coordinates when previous grid lower right corner coordinate, and AddLat and AddLon are that grid is divided increment.
Calculating highway vertical section in described step 4 comprises following sub-step:
①Cong highway main line starting point starts, and obtains the some V on highway main line every L rice (L sets as the case may be)
i(X, Y),
2. based on LIDAR cloud data, utilize index, inquiry is with V
ipoint is the center of circle, and the point in the circle taking R as radius, gets the distance weighted mean value of its elevation as V
ithe elevation of point, puts V for i that obtains longitudinal data
i(X, Y, Z);
3. repeat 1. 2. step, until reach the terminal of highway main line;
N the some V getting on ④Jiang highway main line
1, V
2..., V
i..., V
nconnect successively, can obtain the initial longitudinal data of this highway;
according to Road Design specification, each terrain factor is carried out to modeling, by terrain modeling result, vertical section is carried out to local auto-adaptive adjustment.
Calculating highway cross-section in described step 5 comprises following sub-step:
along highway main line, from highway starting point, the point on L meter Qu highway, finally obtains a W
1, W
2..., W
i..., W
n;
cross some W
imake the vertical line section V perpendicular to this place highway straight-line segment, setting vertical line segment length is that S(S sets as the case may be) rice, and some W
ifor the mid point of vertical line section.From line segment V one end, set as the case may be every s(s) rice get a little, obtain a P
1, P
2..., P
n, based on the index of LiDAR cloud data and foundation, obtain with P
ipoint is the center of circle, and all points in the circle that R is radius, ask the weighted mean value of its elevation as P
ielevation.Repeat this step, finally obtain and on this vertical line section V, have a P
1, P
2..., P
i..., P
nheight value H
i, H
2..., H
i..., H
n, and the coordinate (V of transversal section data
1, H
i), (V
2, H
2) ..., (V
1, H
i) ..., (V
n, H
n), wherein v
ip
ito the distance of vertical line section starting point, H
ifor the height value of this point, will put P
1, P
2..., P
i..., P
nconnect successively, can obtain a W
ithe initial horizontal profile data of place's highway;
according to Road Design specification, each terrain factor is carried out to modeling, by terrain modeling result, local auto-adaptive adjustment is carried out in transversal section;
4. repeat
step, until generate a have W
1, W
2..., W
i..., W
ntransversal section data, and the transversal section data of each point are from bottom to top arranged successively, obtain the transversal section data of Liao Gai highway.
The present invention has following good effect:
1, the present invention directly utilizes LiDAR data as raw data, build the two-layer Indexing Mechanism for quick-searching by dividing data piece, calculate required vertical and horizontal section in Road Design, realize the direct generating road vertical and horizontal section of LiDAR data, improve the efficiency of Highway Investigation Design, shortened the time of Highway Investigation Design;
2, the present invention is in strict conformity with the relevant regulations of " highway technical standard ", fully take the impact of topography and geomorphology on vertical and horizontal section into account, consider the impact that the factors such as physiographic relief along the line, geology, the hydrology, weather cause Highway Investigation Design, deformation self-adaptation over the ground, method practicality is high, is easy to realize;
3, the present invention is from economic angle, consider to fill out to dig equilibrium as far as possible, reduce construction costs with this, and the needs of highway construction means of transport with due regard to, the vertical and horizontal section in LIDAR point cloud direct construction Highway Investigation Design when therefore taking terrain feature into account, utilized, save the step that generates DOM and DEM, avoid factor data to process the loss of significance producing, improved the efficiency of Highway Investigation Design, and then to meet the demand of Highway Investigation Design industry.
Brief description of the drawings
Fig. 1 is process flow diagram of the present invention.
Embodiment
Embodiment 1, as shown in Figure 1, a kind of Highway Investigation Design method based on airborne LiDAR data, comprises the following steps:
Step 1), LiDAR data are carried out to partitioning pretreatment, this step further comprises following sub-step:
1. by traversal or query metadata to LiDAR data, obtain the coordinate range of LiDAR data;
2. the sizing grid of suitable LiDAR deblocking is set, as 1KM*1KM;
3. according to the size of LiDAR data block, calculate the scope of each piece, calculate accordingly the filename of every storage and the some cloud within the scope of this is stored as to file.This method adopts the method for increment type to carry out grid division.
Newlat=Lat+AddLat
Newlon=Lon+AddLon
In formula, NewLat and NewLon are that Lat and Lon are previous grid lower right corner coordinates when previous grid lower right corner coordinate, and AddLat and AddLon are that grid is divided increment.
Step 2), LiDAR data are set up to index, this step further comprises following sub-step:
1. the LiDAR data coordinates scope and the graticule mesh block size that obtain according to step 1 are set up ground floor index;
2. the LiDAR cloud data in every is set up to second layer spatial index, second layer index adopts KD tree index technology, realizes storage, the inquiry of data;
3. second layer index and ground floor index are carried out to space correlation, by the information association of second layer index to ground floor index correspondence position.
The terrain factor of step 3), extraction LiDAR data, the terrain factor of extraction LiDAR data: according to LiDAR
Data and index thereof extract the terrain factor of each LiDAR data block successively, comprise the gradient, slope aspect, length of grade, planar curvature, profile curvature, terrain roughness, topographic relief degree, the elevation coefficient of variation, earth's surface depth of cut etc.
The vertical section of step 4), calculating highway, this step further comprises following sub-step:
①Cong highway main line starting point starts, and obtains the some V on highway main line every L rice (L sets as the case may be)
i(X, Y),
2. based on LIDAR cloud data, utilize index, inquiry is with V
ipoint is the center of circle, and the point in the circle taking R as radius, gets the distance weighted mean value of its elevation as V
ithe elevation of point, puts V for i that obtains longitudinal data
i(X, Y, Z);
3. repeat 1. 2. step, until reach the terminal of highway main line;
N the some V getting on ④Jiang highway main line
1, V
2..., V
i..., V
nconnect successively, can obtain the initial longitudinal data of this highway;
according to Road Design specification, each terrain factor is carried out to modeling, by terrain modeling result, vertical section is carried out to local auto-adaptive adjustment.
Step 5), the transversal section of calculating highway, this step further comprises following sub-step:
along highway main line, from highway starting point, the point on L meter Qu highway, finally obtains a W
1, W
2..., W
i..., W
n;
cross some W
imake the vertical line section V perpendicular to this place highway straight-line segment, setting vertical line segment length is that S(S sets as the case may be) rice, and some W
ifor the mid point of vertical line section.From line segment V one end, set as the case may be every s(s) rice get a little, obtain a P
1, P
2..., P
n, based on the index of LiDAR cloud data and foundation, obtain with P
ipoint is the center of circle, and all points in the circle that R is radius, ask the weighted mean value of its elevation as P
ielevation.Repeat this step, finally obtain and on this vertical line section V, have a P
1, P
2..., P
i..., P
nheight value H
i, H
2..., H
i..., H
n, and the coordinate (V of transversal section data
1, H
i), (V
2, H
2) ..., (V
1, H
i) ..., (V
n, H
n), wherein v
ip
ito the distance of vertical line section starting point, H
ifor the height value of this point, will put P
1, P
2..., P
i..., P
nconnect successively, can obtain a W
ithe initial horizontal profile data of place's highway;
according to Road Design specification, each terrain factor is carried out to modeling, by terrain modeling result, local auto-adaptive adjustment is carried out in transversal section;
4. repeat
step, until generate a have W
1, W
2..., W
i..., W
ntransversal section data, and the transversal section data of each point are from bottom to top arranged successively, obtain the transversal section data of Liao Gai highway.
The foregoing is only the preferred embodiments of the present invention; not in order to limit the present invention; for a person skilled in the art; the present invention can have various modifications and variations; within the spirit and principles in the present invention all; any amendment of doing, be equal to replacement, improvement etc., within protection scope of the present invention all should be included in.
Claims (5)
1. the Highway Investigation Design method based on airborne LiDAR data, is characterized in that: comprise the following steps:
Step 1), LiDAR data are carried out to partitioning pretreatment: first, by the traversal to LiDAR data or query metadata, obtain the coordinate range of LiDAR data, then, the sizing grid of LiDAR deblocking is set, last, according to the size of LiDAR data block, calculate the scope of each piece, calculate accordingly the filename of every storage and the some cloud within the scope of this is stored as to file;
Step 2), LiDAR data are set up to index: first, the LiDAR data coordinates scope and the sizing grid that obtain according to step 1 are set up ground floor index, then, LiDAR cloud data in every is set up to second layer spatial index, second layer index adopts KD tree index, realizes storage and the inquiry of data, last, second layer index and ground floor index are carried out to space correlation, the correspondence position by the information association of second layer index to ground floor index;
Step 3), extract the terrain factor of LiDAR data: the terrain factor that extracts successively each LiDAR data block according to LiDAR data and index thereof;
The vertical section of step 4), calculating highway: extract vertical section according to LiDAR data and index, according to Road Design specification, each terrain factor is carried out to modeling, thereby vertical section is carried out to local auto-adaptive adjustment;
Step 5), the transversal section of calculating highway: extract transversal section according to LiDAR data and index, according to Road Design specification, each terrain factor is carried out to modeling, thereby local auto-adaptive adjustment is carried out in transversal section.
2. the Highway Investigation Design method based on airborne LiDAR data according to claim 1, is characterized in that: the terrain factor of described LiDAR data block comprises the gradient, slope aspect, length of grade, planar curvature, profile curvature, terrain roughness, topographic relief degree, the elevation coefficient of variation, earth's surface depth of cut.
3. the Highway Investigation Design method based on airborne LiDAR data according to claim 1, is characterized in that: described grid adopts incremental method to divide,
Newlat=Lat+AddLat
Newlon=Lon+AddLon
In formula, NewLat and NewLon are that Lat and Lon are previous grid lower right corner coordinates when previous grid lower right corner coordinate, and AddLat and AddLon are that grid is divided increment.
4. the Highway Investigation Design method based on airborne LiDAR data according to claim 1, is characterized in that: the calculating highway vertical section in described step 4 comprises following sub-step:
①Cong highway main line starting point starts, and obtains the some V on highway main line every L rice (L sets as the case may be)
i(X, Y),
2. based on LIDAR cloud data, utilize index, inquiry is with V
ipoint is the center of circle, and the point in the circle taking R as radius, gets the distance weighted mean value of its elevation as V
ithe elevation of point, puts V for i that obtains longitudinal data
i(X, Y, Z);
3. repeat 1. 2. step, until reach the terminal of highway main line;
N the some V getting on ④Jiang highway main line
1, V
2..., V
i..., V
nconnect successively, can obtain the initial longitudinal data of this highway;
according to Road Design specification, each terrain factor is carried out to modeling, by terrain modeling result, vertical section is carried out to local auto-adaptive adjustment.
5. the Highway Investigation Design method based on airborne LiDAR data according to claim 1, is characterized in that: the calculating highway cross-section in described step 5 comprises following sub-step:
along highway main line, from highway starting point, the point on L meter Qu highway, finally obtains a W
1, W
2..., W
i..., W
n;
cross some W
imake the vertical line section V perpendicular to this place highway straight-line segment, setting vertical line segment length is that S(S sets as the case may be) rice, and some W
ifor the mid point of vertical line section, from line segment V one end, set as the case may be every s(s) rice get a little, obtain a P
1, P
2..., P
n, based on the index of LiDAR cloud data and foundation, obtain with P
ipoint is the center of circle, and all points in the circle that R is radius, ask the weighted mean value of its elevation as P
ielevation, repeat this step, finally obtain and on this vertical line section V, have a P
1, P
2..., P
i..., P
nheight value H
i, H
2..., H
i..., H
n, and the coordinate (V of transversal section data
1, H
i), (V
2, H
2) ..., (V
1, H
i) ..., (V
n, H
n), wherein v
ip
ito the distance of vertical line section starting point, H
ifor the height value of this point, will put P
1, P
2..., P
i..., P
nconnect successively, can obtain a W
ithe initial horizontal profile data of place's highway;
according to Road Design specification, each terrain factor is carried out to modeling, by terrain modeling result, local auto-adaptive adjustment is carried out in transversal section;
4. repeat
step, until generate a have W
1, W
2..., W
i..., W
ntransversal section data, and the transversal section data of each point are from bottom to top arranged successively, obtain the transversal section data of Liao Gai highway.
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|---|---|---|---|
| CN201410230292.8A CN104007436B (en) | 2014-05-28 | 2014-05-28 | Highway Investigation Design method based on on-board LiDAR data |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110516653A (en) * | 2019-09-03 | 2019-11-29 | 武汉天擎空间信息技术有限公司 | A kind of method for extracting roads based on multispectral airborne laser radar point cloud data |
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2014
- 2014-05-28 CN CN201410230292.8A patent/CN104007436B/en active Active
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| CN101159066A (en) * | 2007-11-20 | 2008-04-09 | 中交第二公路勘察设计研究院有限公司 | Highway measuring and setting method based on three-dimensional airborne LIDAR |
| US20130016896A1 (en) * | 2011-07-12 | 2013-01-17 | Raytheon Company | 3D Visualization of Light Detection and Ranging Data |
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| CN110516653A (en) * | 2019-09-03 | 2019-11-29 | 武汉天擎空间信息技术有限公司 | A kind of method for extracting roads based on multispectral airborne laser radar point cloud data |
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