CN110018490A - A kind of shade tree posture automatic identifying method - Google Patents
A kind of shade tree posture automatic identifying method Download PDFInfo
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- CN110018490A CN110018490A CN201910217971.4A CN201910217971A CN110018490A CN 110018490 A CN110018490 A CN 110018490A CN 201910217971 A CN201910217971 A CN 201910217971A CN 110018490 A CN110018490 A CN 110018490A
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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
- G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
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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
- G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
-
- 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
- G01S7/483—Details of pulse systems
- G01S7/486—Receivers
- G01S7/487—Extracting wanted echo signals, e.g. pulse detection
- G01S7/4876—Extracting wanted echo signals, e.g. pulse detection by removing unwanted signals
-
- 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
- G01S7/491—Details of non-pulse systems
- G01S7/493—Extracting wanted echo signals
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- Computer Networks & Wireless Communication (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Electromagnetism (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The invention discloses a kind of shade tree posture automatic identifying methods, belong to Surveying and mapping technical field.Include the following steps: that (1) acquires shade tree data using vehicle-mounted LiDAR；(2) satellite data of the base station combined ground GNSS acquisition resolves the initial data of mobile platform acquisition；(3) shade tree point cloud is extracted from the shade tree point cloud data that vehicle-mounted LiDAR is acquired；(4) mesh point stratified density calculating method is utilized, identification single plant shade tree point converges conjunction；(5) shade tree posture feature is calculated automatically；(6) rule of thumb reference value, compares with calculated result and judges that reasonability is verified.Automatic identification is carried out to shade tree posture based on in-vehicle LiDAR data, calculates shade tree height, hat width, the diameter of a cross-section of a tree trunk 1.3 meters above the ground improves the efficiency of shade tree gesture recognition, provides foundation for shade tree health evaluating；Dot density method is projected using layering, provides a kind of high-efficient, good method of precision for shade tree point cloud segmentation.
Description
[technical field]
The invention belongs to Surveying and mapping technical field, especially a kind of shade tree posture automatic identifying method.
[background technique]
The roadside greenings vegetation such as shade tree have the effects that oxygen release carbon sequestration, rise heat absorption, eliminate the noise lay the dust and sterilize it is antifouling, in addition to
For watching, moreover it is possible to reduce evaporation from topsoil, inhibit saline and alkaline and move up, prevent the soil salinization, to environment, soil and weather
The factor has good adjustment effect.The acquisition of the roads vegetation information such as shade tree and monitoring technology are increasingly by domestic and international political affairs
The attention of mansion department and relevant industries studies the growth distributions situation such as the shade tree diameter of a cross-section of a tree trunk 1.3 meters above the ground and spacing in the rows, facilitates municipal administration department
Information accurately and in time provides foundation for vegetation growth monitoring and management.
Acquisition for shade tree information, conventional method are mainly measured using total station or RTK, the data of acquisition
Also imperfect, it is not enough to carry out the analysis of deep application.Shade tree application study based on in-vehicle LiDAR data, is mainly asked
Topic concentrates on how quickly extracting the point cloud of shade tree from LiDAR data, and is believed by the parameter that data reduction goes out shade tree
Breath；Then it proposes analysis strategy, the state of shade tree is analyzed.Currently, being ground both at home and abroad for the data reduction of shade tree
Study carefully more but not mature enough.
[summary of the invention]
It is an object of the invention to overcome the above-mentioned prior art, a kind of shade tree posture automatic identification side is provided
Method.Data are acquired using vehicle-mounted LiDAR, height, the diameter of a cross-section of a tree trunk 1.3 meters above the ground and the hat width of shade tree is real-time dynamicly obtained, improves existing trade
Information collection mode is set, provides data foundation for shade tree health evaluating and dynamic monitoring.
In order to achieve the above objectives, the present invention is achieved by the following scheme:
A kind of shade tree posture automatic identifying method, includes the following steps:
Step 1: acquiring shade tree data using vehicle-mounted LiDAR；
Step 2: the satellite data of the base station combined ground GNSS acquisition solves the initial data of mobile platform acquisition
It calculates；
Step 3: shade tree point cloud is extracted from the shade tree point cloud data that vehicle-mounted LiDAR is acquired；
Step 4: utilizing mesh point stratified density calculating method, identification single plant shade tree point converges conjunction；
Step 5: shade tree posture feature is calculated automatically；
Step 6: rule of thumb reference value compares with calculated result and judges that reasonability is verified.
A further improvement of the present invention lies in that:
After resolving in step 2, to the processing of data advanced row framing, then coordinate conversion is carried out.
Step 3 specifically:
Step 3.1: according to vehicle driving trace and road axis, expanding suitable distance to two sides, it is outer to delete the distance
Point cloud to reduce calculation amount；
Step 3.2: establishing initial ground model, traversal model completes the extraction to shade tree point cloud by iteration.
Establish initial ground model specifically: separation ground point sets square net according to intended size, from each net
The minimum point of elevation is extracted in lattice as ground seed point, is added in irregular triangle network, is established initial ground model.
Traverse model and iteration specifically: traversal irregular triangle network is calculated and is included in each three in irregular triangle network
Point and its plane distance d in angular provide that the point is ground point, are added to ground point set if d is less than the threshold value of setting
It closes, repeats this step, the ground point until meeting condition is all added in irregular triangle network, completes iteration, i.e. completion pair
The extraction of shade tree point cloud.
Step 4 specifically:
Step 4.1: establishing regular grid, establish one according to the range that shade tree point cloud projects to horizontal plane and be suitable for respectively
The network standard of layer, the corresponding grid of each floor are set as identical ranks number；
Step 4.2: the maximum value and minimum value of elevation in all point cloud datas are obtained, according to the very poor of height value, by point
Cloud is divided into N layers, assigns every layer of laser point to layer label respectively, is denoted as Layeri, the laser point in different layers has not
Same Layeri attribute；
Step 4.3: since the second layer, the grid of given threshold is selected more than, and marks its ranks number, it is adjacent to grid eight
The grid that domain is both greater than threshold value is grouped label, scans for adjacent layer, searches corresponding with the grid that upper one layer marks
Grid, rejected to lower than threshold value, same group of addition for meeting threshold value be iterated, until all grids complete mark
Note completes identification.
When establishing regular grid in step 4.1, the initial position for choosing automobile is origin, using North and South direction as Y-axis, thing
Direction is X-axis.
Step 5 specifically: shade tree height, hat width and the diameter of a cross-section of a tree trunk 1.3 meters above the ground are calculated automatically.
Step 5 specifically:
Step 5.1: calculating shade tree height H=Zmax-Zmin, wherein ZmaxFor height value in single plant shade tree laser point
Maximum value, ZminFor the minimum value of height value in single plant shade tree laser point；
Step 5.2: the measurement shade tree diameter of a cross-section of a tree trunk 1.3 meters above the ground extracts the shade tree diameter of a cross-section of a tree trunk 1.3 meters above the ground interval point at the shade tree diameter of a cross-section of a tree trunk 1.3 meters above the ground between the interception of point cloud
Cloud, carries out equatorial projection, and the single plant shade tree diameter of a cross-section of a tree trunk 1.3 meters above the ground is calculated using the round range as the diameter of a cross-section of a tree trunk 1.3 meters above the ground of least square method fitting；
Step 5.3: under plane coordinates, by all laser points X-direction very poor value and very poor value meter in the Y direction
Calculation obtains shade tree hat width.
Compared with prior art, the invention has the following advantages:
The present invention is based on in-vehicle LiDAR data to shade tree posture carry out automatic identification, calculate shade tree height, hat width,
The diameter of a cross-section of a tree trunk 1.3 meters above the ground improves the efficiency of shade tree gesture recognition, provides foundation for shade tree health evaluating；Tilly of the present invention layering projection
Dot density method provides a kind of high-efficient, good method of precision for shade tree point cloud segmentation；Utilize the single plant shade tree of the segmentation
The method that point cloud carries out gesture recognition, extract real-time shade tree information.
[Detailed description of the invention]
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 is shade tree point cloud schematic diagram of the invention；
Fig. 3 is the performance figure that isolated tree layering projects in grid in the present invention；
Fig. 4, which is that shade tree tree of the present invention is high, extracts schematic diagram；
Fig. 5 is that the shade tree diameter of a cross-section of a tree trunk 1.3 meters above the ground of the present invention extracts schematic diagram.
[specific embodiment]
In order to enable those skilled in the art to better understand the solution of the present invention, below in conjunction in the embodiment of the present invention
Attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is only
The embodiment of a part of the invention, the embodiment being not all of, and it is not intended to limit range disclosed by the invention.In addition, with
In lower explanation, descriptions of well-known structures and technologies are omitted, obscures concept disclosed by the invention to avoid unnecessary.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment should fall within the scope of the present invention.
The invention will be described in further detail with reference to the accompanying drawing:
Referring to Fig. 1, a kind of shade tree posture automatic identifying method of the invention, specifically includes the following steps:
(1) data acquire, and acquire shade tree point cloud data using vehicle-mounted LiDAR, data acquisition should be lesser good in wind-force
Fair weather carries out, and reducing leads to rocking for tree with wind by vehicle, blocking for pedestrian's generation, should ensure that the base station GNSS sample frequency just
Often, it should be guaranteed that running speed is steady when operation, strive for utmostly coverage goal object.
(2) data preprocessing, the satellite data of the base station combined ground GNSS acquisition, to the original of mobile platform acquisition
Data are resolved；To realize engineering management, framing processing can be carried out to the point cloud data after resolving, be calculated and outputted to original
Treated the point cloud data of point cloud data or framing afterwards carries out coordinate conversion.
(3) point cloud data classification is extracted, and to realize shade tree gesture recognition, is needed to vehicle-mounted LiDAR complexity collected
Shade tree point cloud is extracted in culture point cloud.
Specifically, expand appropriately distance according to vehicle driving trace and road axis to two sides, it is outer to delete the distance
Point cloud to reduce calculation amount.Thereafter ground point is separated, square net is set according to intended size, is extracted from each grid
The minimum point of elevation is added on irregular triangle network (TIN) as ground seed point, establishes initial ground model.Usual net
Lattice should be greater than maximum object of reference (shade tree) in point cloud data.TIN is traversed, calculating includes in the triangulation network in each triangle
Point and its plane distance d provide that the point is ground point, are added to ground point set if d is less than the threshold value of setting.It repeats
This step, the ground point until meeting condition are all added in the triangulation network, complete iteration.
(4) referring to fig. 2, single plant shade tree point cloud identifies, using mesh point stratified density calculating method, identifies single plant shade tree
Point converges conjunction.
Step 4.1, regular grid is established, establishes one according to the range that cloud projects to horizontal plane (in X-Y coordinate)
Suitable for the mesh standard of each layer, the corresponding grid ranks number having the same of each floor.When grid is established, the initial of automobile is chosen
Position is origin, using North and South direction as Y-axis, using east-west direction as X-axis.Sizing grid can establish survey region point cloud after determining
Projection rule grid.
Step 4.2, the point cloud layering based on elevation obtains the maximum value of elevation and minimum in all point cloud datas first
Value, then according to the very poor of height value, is divided into N layers for a cloud.It assigns every layer of laser point to layer label respectively, is denoted as
Layeri, the laser point in different layers have different Layeri attributes.
Step 4.3, point cloud is successively extracted, based on projection dot density concept, layering projection dot density is calculated, utilizes single plant row
There is relevance in road tree, extract point cloud between each layer grid.Detailed process is as follows, since the second layer, selects more than
The grid of certain threshold value, and mark its ranks number；Label, each networking are grouped to the grid that grid eight neighborhood is both greater than threshold value
Laser point difference flag attribute " tree k " in lattice, is expressed as the fraction of laser light point for the shade tree that composition number is k, to adjacent
Layer is scanned for, and threshold will be met to rejecting lower than threshold value by searching grid corresponding with the grid that upper one layer marks
Same group of the addition of value.
Top-down search is carried out, is the laser point label pair in the grid in first layer with same mesh ranks number
The attribute answered；
Search from bottom to top is carried out, into upper one layer, the grid with upper one layer record is searched first in this layer of grid
The identical grid of ranks number, the grid for being 0 for grid dot density are rejected；For remaining grid, if its eight neighborhood
Grid dot density is greater than given threshold limit value, then the group is added in its neighborhood grid.
It is iterated, until all grids complete label.
It scans for meeting condition to 8 directions if Fig. 3 specifically illustrates this process when progress grid neighborhood searches
Grid, grey grid hypothesis is a grid identical with the mesh row row number that a upper layer choosing takes in the first step, is assumed simultaneously
This grid mark is in k group, using this grid as starting mesh；Into second step, dot density in 8 grids of grid periphery is searched
It is not 0 grid, by the grid for the condition that meets while is labeled as k group；Third step carries out neighborhood trellis search, if met a little
The grid that density is greater than threshold value is then labeled as k group.And so on, it is obtained after the completion with the grid labeled as k group, these grids
Interior point labelled tree k attribute.
After the completion of entering group, the lookup of other groups is continued to complete, marking the laser point of corresponding grid respectively is corresponding label,
It is currently located after the completion of the operation of layer, records the mesh row row number of the corresponding group of this layer, and enter next layer and carry out the above behaviour, until
All grids, which are searched, to be completed.
(5) as shown in Figure 4 and Figure 5, shade tree height, hat width, the postures such as diameter of a cross-section of a tree trunk 1.3 meters above the ground calculate automatically.
Step 5.1, the maximum value Z of available height value in the laser point of single plant shade tree is constitutedmaxAnd minimum value
Zmin, the height H of shade tree can be by H=Zmax-ZminIt is calculated.
Step 5.2, in order to measure the diameter of a cross-section of a tree trunk 1.3 meters above the ground, need to extract the shade tree diameter of a cross-section of a tree trunk 1.3 meters above the ground section at the diameter of a cross-section of a tree trunk 1.3 meters above the ground of shade tree between the interception of point cloud
Point cloud, carries out equatorial projection, and the single plant shade tree diameter of a cross-section of a tree trunk 1.3 meters above the ground is calculated using the round range as the diameter of a cross-section of a tree trunk 1.3 meters above the ground of least square method fitting.
Step 5.3, under plane coordinates, hat width can by all laser points X-direction very poor value and in the Y direction
Very poor value be calculated.
(6) shade tree gesture recognition verifying correctness, rule of thumb and reference value to being calculated using vehicle-mounted LiDAR automatically
Data compare, and judge its reasonability.
The above content is merely illustrative of the invention's technical idea, and this does not limit the scope of protection of the present invention, all to press
According to technical idea proposed by the present invention, any changes made on the basis of the technical scheme each falls within claims of the present invention
Protection scope within.
Claims (9)
1. a kind of shade tree posture automatic identifying method, which comprises the steps of:
Step 1: acquiring shade tree data using vehicle-mounted LiDAR；
Step 2: the satellite data of the base station combined ground GNSS acquisition resolves the initial data of mobile platform acquisition；
Step 3: shade tree point cloud is extracted from the shade tree point cloud data that vehicle-mounted LiDAR is acquired；
Step 4: utilizing mesh point stratified density calculating method, identification single plant shade tree point converges conjunction；
Step 5: shade tree posture feature is calculated automatically；
Step 6: rule of thumb reference value compares with calculated result and judges that reasonability is verified.
2. a kind of shade tree posture automatic identifying method as described in claim 1, which is characterized in that after being resolved in step 2, logarithm
According to first progress framing processing, then carry out coordinate conversion.
3. a kind of shade tree posture automatic identifying method as described in claim 1, which is characterized in that step 3 specifically:
Step 3.1: according to vehicle driving trace and road axis, expanding suitable distance to two sides, delete the point outside the distance
Cloud is to reduce calculation amount；
Step 3.2: establishing initial ground model, traversal model completes the extraction to shade tree point cloud by iteration.
4. a kind of shade tree posture automatic identifying method as claimed in claim 3, which is characterized in that establish initial ground model tool
Body are as follows: separation ground point sets square net according to intended size, and the minimum point of elevation is extracted from each grid as ground
Face seed point, is added in irregular triangle network, establishes initial ground model.
5. a kind of shade tree posture automatic identifying method as described in claim 3 or 4, which is characterized in that traversal model and iteration
Specifically: traversal irregular triangle network, calculate include point in irregular triangle network in each triangle with its plane away from
From d, if d is less than the threshold value of setting, provides that the point is ground point, be added to ground point set, repeat this step, until meeting
The ground point of condition is all added in irregular triangle network, is completed iteration, that is, is completed the extraction to shade tree point cloud.
6. a kind of shade tree posture automatic identifying method as described in claim 1, which is characterized in that step 4 specifically:
Step 4.1: establishing regular grid, establish one according to the range that shade tree point cloud projects to horizontal plane and be suitable for each layer
Network standard, the corresponding grid of each floor are set as identical ranks number；
Step 4.2: the maximum value and minimum value of elevation in all point cloud datas are obtained, according to the very poor of height value, by point Yun Ping
N layers are divided into, every layer of laser point is assigned to layer label respectively, is denoted as Layeri, the laser point in different layers has different
Layeri attribute；
Step 4.3: since the second layer, selecting more than the grid of given threshold, and mark its ranks number, all to grid eight neighborhood
Grid greater than threshold value is grouped label, scans for adjacent layer, searches net corresponding with the grid that upper one layer marks
Lattice, to rejecting lower than threshold value, same group of addition for meeting threshold value is iterated, until all grids complete label, i.e.,
Complete identification.
7. a kind of shade tree posture automatic identifying method as claimed in claim 6, which is characterized in that establish rule in step 4.1
When grid, the initial position for choosing automobile is origin, and using North and South direction as Y-axis, east-west direction is X-axis.
8. a kind of shade tree posture automatic identifying method as claimed in claim 6, which is characterized in that step 5 specifically: to trade
Tree height, hat width and the diameter of a cross-section of a tree trunk 1.3 meters above the ground are calculated automatically.
9. a kind of shade tree posture automatic identifying method as claimed in claim 8, which is characterized in that step 5 specifically:
Step 5.1: calculating shade tree height H=Zmax-Zmin, wherein ZmaxFor the maximum of height value in single plant shade tree laser point
Value, ZminFor the minimum value of height value in single plant shade tree laser point；
Step 5.2: the measurement shade tree diameter of a cross-section of a tree trunk 1.3 meters above the ground extracts the shade tree diameter of a cross-section of a tree trunk 1.3 meters above the ground interval point cloud at the shade tree diameter of a cross-section of a tree trunk 1.3 meters above the ground between the interception of point cloud, into
Row equatorial projection, the single plant shade tree diameter of a cross-section of a tree trunk 1.3 meters above the ground are calculated using the round range as the diameter of a cross-section of a tree trunk 1.3 meters above the ground of least square method fitting；
Step 5.3: under plane coordinates, being calculated by all laser points in the very poor value of X-direction and very poor value in the Y direction
To shade tree hat width.
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Application publication date: 20190716 |