CN109579859A - A kind of high-precision navigation map altitude data processing method and processing device - Google Patents
A kind of high-precision navigation map altitude data processing method and processing device Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3407—Route searching; Route guidance specially adapted for specific applications
- G01C21/343—Calculating itineraries, i.e. routes leading from a starting point to a series of categorical destinations using a global route restraint, round trips, touristic trips
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/36—Input/output arrangements for on-board computers
- G01C21/3667—Display of a road map
- G01C21/367—Details, e.g. road map scale, orientation, zooming, illumination, level of detail, scrolling of road map or positioning of current position marker
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Abstract
The present invention relates to geographical fields of measurement, disclose a kind of high-precision navigation map altitude data processing method and processing device, by splicing the section LINK between road junction, to generate splicing section；Calculate relative elevation data of the sum of the height difference of every section of LINK as splicing section in each splicing section；According to least independent close loop searching algorithm, the least independent close loop set of the road network of splicing section composition is obtained；Calculate the mis-tie misclosure of each least independent close loop in least independent close loop set；According to the mis-tie misclosure of least independent close loop, to obtain the elevation correction value in splicing section；According to preset weights and the elevation correction value in splicing section, the elevation correction value of each shape point between start node and peripheral node on splicing section is calculated；The node of known practical elevation is chosen as Fixed Initial Point, to realize the reconstruction of altitude data.Effective amendment to height anomaly data in road survey data is realized, the accuracy of high-precision navigation map is improved.
Description
Technical field
The present invention relates to geographical fields of measurement more particularly to a kind of high-precision navigation map altitude data processing method and dresses
It sets.
Background technique
With the arrival in vehicle intellectualized epoch, automobile industry undergoes drastic change, and automatic Pilot tide is attacked.And have one at present
The kind advanced DAS (Driver Assistant System) of technology ADAS, belongs to the simplification version of automatic Pilot.High-precision navigation map is as the important of ADAS
Prior information, the important information such as curvature, gradient for providing road for vehicle, elevation are wanted as one crucial in ADAS data
Element has a very important role to the human-computer interaction in ADAS decision and driving.
Current high-precision navigation map data acquisition relies on substantially mobile mapping system, based on mobile road drive test
Integrated various sensors, GNSS, INS system in amount system, acquire the coordinate and elevation with the point at certain distance interval
Information.Based on these point information, by the Information expansion of point be line geometry information, it is a certain number of point constitute line geometries just at
For the basic unit LINK of LANE in ADAS data.It should be continuous for theoretically meeting the elevation between actual LINK.But
In the prior art, due to a series of unpredictable factors such as the environment of data acquisition, the parameter, personnel, the time that acquire equipment
In the presence of and data acquisition after the completion of inevitable data screening and place in the last handling process that is carried out to data of surveyor
Reason process etc., these factors all cause the node (NODE point) in ADAS data between actual LINK to have on different LINK
There is the Road in entire ADAS data for entire ADAS data in different height values, the appearance of such case
Elevation jumping phenomenon.
It is existing because elevation is as important decision and human-computer interaction reference information in ADAS high-precision navigation map field
The method for thering is the height anomaly problem being badly in need of in a kind of pair of data in technology to carry out post-processing correction.
Summary of the invention
The present invention provides a kind of high-precision navigation map altitude data processing method and processing device, solves high-precision in the prior art
There is the technical issues of elevation Sudden Anomalies in the altitude data measured during degree ground mapping.
The purpose of the present invention is what is be achieved through the following technical solutions:
A kind of high-precision navigation map altitude data processing method, comprising:
Section LINK between road junction is spliced, to generate splicing section, wherein the road junction
There are three the above node NODE of the same name, every section LINK to have unique identification for tool, and including 1 start node NODE, 1
A peripheral node NODE, the shape point that N number of density is 5 meters, N is positive integer；
Calculate relative elevation data of the sum of the height difference of every section of LINK as splicing section in each splicing section；
According to least independent close loop searching algorithm, the least independent close loop of the road network of splicing section composition is obtained
Set；
Calculate the mis-tie misclosure of each least independent close loop in least independent close loop set；
According to the mis-tie misclosure of least independent close loop, adjustment of condition equation resolving is carried out, to obtain the elevation amendment in splicing section
Value；
According to preset weights and the elevation correction value in splicing section, start node and peripheral node on splicing section are calculated
Between each shape point elevation correction value；
The node of known practical elevation is chosen as Fixed Initial Point, according to the practical height value of the Fixed Initial Point, splicing section
Relative elevation data, splice section each shape point elevation correction value calculate it is each splicing section each shape point reality
Height value, to realize the reconstruction of altitude data.
A kind of high-precision navigation map altitude data processing unit, comprising:
Section splicing module, for the section LINK between road junction to be spliced, to generate splicing section,
In, road junction tool has a unique identification there are three the above node NODE of the same name, every section LINK, and including
1 start node NODE, 1 peripheral node NODE, the shape point that N number of density is 5 meters, N is positive integer；
Grid DEM module, for calculating phase of the sum of the height difference of every section of LINK as splicing section in each splicing section
To altitude data；
Closed polyline module, for obtaining the road of splicing section composition according to least independent close loop searching algorithm
The least independent close loop set of road network；
Mis-tie misclosure computing module, for calculating the closure of each least independent close loop in least independent close loop set
Difference；
Correction value computing module carries out adjustment of condition equation resolving for the mis-tie misclosure according to least independent close loop, to obtain
Splice the elevation correction value in section；
Elevation correction module calculates on splicing section for the elevation correction value according to preset weights and splicing section
The elevation correction value of each shape point between start node and peripheral node；
Data reconstruction module, for choosing the node of known practical elevation as Fixed Initial Point, according to the reality of the Fixed Initial Point
Border height value, the relative elevation data for splicing section, the elevation correction value for each shape point for splicing section calculate each splicing road
The practical height value of each shape point of section, to realize the reconstruction of altitude data.
The present invention provides a kind of high-precision navigation map altitude data processing method and processing device, according to.
Detailed description of the invention
It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will be to institute in embodiment
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the invention
Example, for those of ordinary skill in the art, without any creative labor, can also obtain according to these attached drawings
Obtain other attached drawings.
Fig. 1 is a kind of flow chart of high-precision navigation map altitude data processing method of the embodiment of the present invention；
Fig. 2 is a kind of structural schematic diagram of high-precision navigation map altitude data processing unit of the embodiment of the present invention.
Specific embodiment
In order to make the foregoing objectives, features and advantages of the present invention clearer and more comprehensible, with reference to the accompanying drawing and specific real
Applying mode, the present invention is described in further detail.
The embodiment of the invention provides a kind of high-precision navigation map altitude data processing methods, as shown in Figure 1, comprising:
Step 101 splices the section LINK between road junction, to generate splicing section；
Wherein, line geometry is formed by connecting by each independent segment LINK in ADAS data.LINK indicates real road
The shape on road, each LINK have a unique number, and have 1 starting NODE, 1 terminal NODE, and N number of density is 5
The shape point of rice.When several LINK intersect, the same NODE can be shared.Each NODE has a unique number.This step
In for subsequent convenience of calculation, calculate the complete topology relationship of NODE point and LINK first, can be shared together according to intersection LINK
The characteristic of name NODE first extracts all road junctions (the NODE point of the same name there are three having or more), then by road
LINK splicing between the crosspoint of road is defined as EDGE (i.e. splicing section), while being recorded according to the direction that EDGE is established
The beginning and end NODE of EDGE, and will be added the height difference of every section of LINK according to the direction of EDGE to obtain the elevation of EDGE.
Step 102 calculates relative elevation number of the sum of the height difference of every section of LINK as splicing section in each splicing section
According to；
Step 103, according to least independent close loop searching algorithm, obtain the minimum of the road network of splicing section composition solely
Vertical close ring set；
Step 104, the mis-tie misclosure for calculating each least independent close loop in least independent close loop set；
Step 105, the mis-tie misclosure according to least independent close loop carry out adjustment of condition equation resolving, to obtain splicing section
Elevation correction value；
Step 106, according to preset weights and splice section elevation correction value, calculate splicing section on start node and
The elevation correction value of each shape point between peripheral node；
Step 107 chooses the node of known practical elevation as Fixed Initial Point, according to the practical height value of the Fixed Initial Point,
The elevation correction value of the relative elevation data, each shape point for splicing section of splicing section calculates each shape in each splicing section
The practical height value of point, to realize the reconstruction of altitude data.
Wherein, step 106 carries out road network reconstruction, road in conjunction with given known date for after the correction of all shape points
The known known date that net is rebuild is the known NODE node with practical height value constrained in adjustment, such as can choose one
Fixed Initial Point extends around, can be by the EDGE data assignment with accurate relative elevation relationship with absolute elevation.
Step 103 can specifically include:
Step a: according to the road network of splicing section composition, dendritic structure P and remaining branch structure Q are established；
Step b: count value N is set to zero；
Step c: present tree and cotree structure are determined, while determining current cotree quantity M；
Step d: the more than N+1 articles branch of current Yu Zhizhong is added in dendritic structure, and is searched for by the adjacent node of two sides
Close ring；
Step e: judging whether sufficiently to traverse, and when not sufficiently traversal, the d that gos to step is continued to execute；When abundant traversal
When, the f that gos to step is continued to execute；
Step f: the minimal closure ring of the current remaining branch of record, count value N add one；
Step g: judging whether current count value N is equal to current cotree quantity M, when current count value N is not equal to current
When cotree quantity M, jump procedure c is continued to execute；When current count value N is equal to current cotree quantity M, jump procedure h after
It is continuous to execute；
Step h: choose the least close ring of number of edges it is corresponding with its more than branch, and multiple close rings are judged whether there is, when depositing
In multiple close rings for meeting condition, the shortest close ring of close ring perimeter and its corresponding remaining branch are chosen, and store selection
Close ring and its corresponding remaining branch information；When only existing a close ring for meeting condition, directly storage close ring and its
Corresponding remaining branch information；
Step i: current cotree quantity M subtracts one；
Step j: judge whether current cotree quantity M is zero；When current cotree quantity M be greater than zero when, go to step b after
It is continuous to execute；When current cotree quantity M is equal to zero, the close ring of storage and its corresponding remaining branch information output, process are terminated,
To obtain the least independent close loop set for the road network that splicing section forms.
Wherein, step d can specifically include:
Step d1: using another endpoint j of the endpoint i of remaining branch and remaining branch as its adjacent node of start point search, i is searched for starting point
The adjacent node that rope arrives is removed j, the information searched is stored in array C, the adjacent node arrived using j as start point search；It removes
The information searched is stored in array D by i, and in the array for the information that storage searches, first row stores the starting point of remaining branch,
Subsequent each column are the neighbors of previous column；
Step d2: compare new search go out neighbors between whether have same place, if there is same place, stop, if without of the same name
Point, the then d3 that gos to step are continued to execute；
Step d3: its neighbors is searched for as starting point using last column node of array C, and is stored in the next column of array C
In, compared with the column second from the bottom and column last of last current column of array C and array D, if there is same place, is stopped
Only；Starting point is divided into the most rank rear of array D if without same place, searches for its neighbors, and is stored in the next column of array D,
Compared with the non-zero column second from the bottom and non-zero column last of last current column of array D and array C, if there is same place,
A close ring is then found, until finding all close rings.
Traditional remaining branch searching algorithm can all search for all close rings of remaining branch when carrying out the search of independent close ring
Out, but in step d, do not need include remaining branch all close rings, it is only necessary to the smallest free ring of number of edges, therefore, step
Rapid d uses the algorithm searched for outward simultaneously from remaining branch both sides.The calculation amount of program, and resulting closure are not only reduced in this way
Ring can guarantee number of edges minimum.
Step a can specifically include:
The degree for calculating each node in net structure, finds the maximum node M of its moderate；
Using M as starting point, the neighbors M adjacent with M is accessed1, M2..., and record adjacent adjacent side；
Respectively with M1, M2... it sets out, accesses the neighbors of their not visited mistakes, record adjacent adjacent side；
When also not visited node, then continue to access next stage neighbors, until all nodes are all accessed
When, for the tree that the adjacent side of record is formed as optimal tree construction P, the set of the side composition of not visited mistake is remaining branch structure
Q。
In the prior art, when a net type is complex, it may appear that many different spanning trees, step a is in order to reduce
Calculation amount, quickly obtains least independent close loop, and tree generated should make the number of branches of composition elementary cycle as far as possible
Ground lacks, and generates optimal tree by step a.
Step 104 calculates the mis-tie misclosure of each least independent close loop in least independent close loop set, in order to spelling
The elevation for connecing section is modified, and is controlled whole net form adjusted simultaneously and be consistent with practical road network, is used in step 105
Constraint adjustment with restrictive condition, step 105 can specifically include:
The elevation correction value in splicing section is obtained by conditional equation AV+W=0, wherein V is that the elevation in splicing section is repaired
The numerical value of positive value, V passes through equation V=P-1ATK, K are connection number, and K solves K=N by normal equation NK+W=0-1W, normal equation
Coefficient N=ATP-1A, A are factor arrays, and W is the mis-tie misclosure of least independent close loop, and P is Quan Zhen, and K is connection number, and N is normal equation
Coefficient, M are errors in weight unit, and r is redundant observation number.
The minimum unit of data acquired in the data acquisition modes of ADAS is shape point.Due to the LINK in ADAS data
Length is different, to avoid the elevation change amplitude of the shape point when distributing correction, on a certain LINK excessive, influences actually to adopt
The grade information of collection.When distributing the elevation correction value of each shape point, using the method for salary distribution weighed surely by length, section will be spliced
Elevation correction value be reasonably allocated on each shape point, therefore, step 106 can specifically include:
Length according to each shape point apart from start node multiplied by unit length elevation correction value M0=± (VTPV)/r is obtained
Obtain the elevation correction value of each shape point.
A kind of high-precision navigation map altitude data processing method is provided in the embodiment of the present invention, by by intersection
Section LINK between point is spliced, to generate splicing section；Calculate the sum of the height difference of every section of LINK in each splicing section
Relative elevation data as splicing section；According to least independent close loop searching algorithm, the road of splicing section composition is obtained
The least independent close loop set of road network；Calculate the mis-tie misclosure of each least independent close loop in least independent close loop set；
According to the mis-tie misclosure of least independent close loop, adjustment of condition equation resolving is carried out, to obtain the elevation correction value in splicing section；According to pre-
If weight and splicing the elevation correction value in section, each shape point between start node and peripheral node on splicing section is calculated
Elevation correction value；The node of known practical elevation is chosen as Fixed Initial Point, according to the practical height value of the Fixed Initial Point, splicing road
The relative elevation data of section, the elevation correction value for each shape point for splicing section calculate the reality of each shape point in each splicing section
Border height value, to realize the reconstruction of altitude data.Effective amendment to height anomaly data in road survey data is realized, is mentioned
The high accuracy of high-precision navigation map.
The embodiment of the invention also provides a kind of high-precision navigation map altitude data processing units, as shown in Fig. 2, packet
It includes:
Section splicing module 210, for splicing the section LINK between road junction, to generate splicing road
Section, wherein there are three the above node NODE of the same name, every section LINK to have unique identification for the road junction tool,
And including 1 start node NODE, 1 peripheral node NODE, the shape point that N number of density is 5 meters, N is positive integer；
Grid DEM module 220, the sum of height difference for calculating every section of LINK in each splicing section are used as splicing section
Relative elevation data；
Closed polyline module 230, for obtaining the road of splicing section composition according to least independent close loop searching algorithm
The least independent close loop set of road road network；
Mis-tie misclosure computing module 240, for calculating closing for each least independent close loop in least independent close loop set
It is poor to close；
Correction value computing module 250 carries out adjustment of condition equation resolving for the mis-tie misclosure according to least independent close loop, with
Obtain the elevation correction value in splicing section；
Elevation correction module 260 calculates splicing section for the elevation correction value according to preset weights and splicing section
The elevation correction value of each shape point between upper start node and peripheral node；
Data reconstruction module 270, for choosing the node of known practical elevation as Fixed Initial Point, according to the Fixed Initial Point
Practical height value, the relative elevation data for splicing section, the elevation correction value for each shape point for splicing section calculate each splicing
The practical height value of each shape point in section, to realize the reconstruction of altitude data.
Wherein, the closed polyline module 230 includes:
Optimal tree establishes unit 231, for calculating the degree of each node in net structure, finds the maximum node of its moderate
M；Using M as starting point, the neighbors M adjacent with M is accessed1, M2..., and record adjacent adjacent side；Respectively with M1, M2... go out
Hair, accesses the neighbors of their not visited mistakes, records adjacent adjacent side；When also not visited node, then continue
Next stage neighbors is accessed, when all nodes are all accessed, the tree that the adjacent side of record is formed is as optimal tree knot
Structure P, the set of the side composition of not visited mistake are remaining branch structure Q；
Minimal closure loops detection unit 232, for according to least independent close loop searching algorithm, dendritic structure P and remaining branch
Structure Q obtains the least independent close loop set of the road network of splicing section composition.
Through the above description of the embodiments, those skilled in the art can be understood that the present invention can be by
Software adds the mode of required hardware platform to realize, naturally it is also possible to all implemented by hardware, but in many cases before
Person is more preferably embodiment.Based on this understanding, technical solution of the present invention contributes to background technique whole or
Person part can be embodied in the form of software products, which can store in storage medium, such as
ROM/RAM, magnetic disk, CD etc., including some instructions are used so that a computer equipment (can be personal computer, service
Device or the network equipment etc.) execute method described in certain parts of each embodiment of the present invention or embodiment.
Claims (8)
1. a kind of high-precision navigation map altitude data processing method characterized by comprising
Section LINK between road junction is spliced, to generate splicing section, wherein the road junction has
Three or more node NODE of the same name, every section LINK have unique identification, and including 1 start node NODE, 1 end
Point node NODE, the shape point that N number of density is 5 meters, N is positive integer；
Calculate relative elevation data of the sum of the height difference of every section of LINK as splicing section in each splicing section；
According to least independent close loop searching algorithm, the least independent close loop collection of the road network of splicing section composition is obtained
It closes；
Calculate the mis-tie misclosure of each least independent close loop in least independent close loop set；
According to the mis-tie misclosure of least independent close loop, adjustment of condition equation resolving is carried out, to obtain the elevation correction value in splicing section；
According to preset weights and the elevation correction value in splicing section, calculate on splicing section between start node and peripheral node
The elevation correction value of each shape point；
The node of known practical elevation is chosen as Fixed Initial Point, according to the practical height value of the Fixed Initial Point, the phase in splicing section
The practical elevation of each shape point in each splicing section is calculated to altitude data, the elevation correction value for each shape point for splicing section
Value, to realize the reconstruction of altitude data.
2. high-precision navigation map altitude data processing method according to claim 1, which is characterized in that the basis is most
The step of small independent closed polyline algorithm, the least independent close loop set of the road network of acquisition splicing section composition, packet
It includes:
Step a: according to the road network of splicing section composition, dendritic structure P and remaining branch structure Q are established；
Step b: count value N is set to zero；
Step c: present tree and cotree structure are determined, while determining current cotree quantity M；
Step d: the more than N+1 articles branch of current Yu Zhizhong is added in dendritic structure, and is searched for and be closed by the adjacent node of two sides
Ring；
Step e: judging whether sufficiently to traverse, and when not sufficiently traversal, the d that gos to step is continued to execute；When abundant traversal, jump
Step f is gone to continue to execute；
Step f: the minimal closure ring of the current remaining branch of record, count value N add one；
Step g: judging whether current count value N is equal to current cotree quantity M, when current count value N is not equal to current cotree
When quantity M, jump procedure c is continued to execute；When current count value N is equal to current cotree quantity M, jump procedure h continues to hold
Row；
Step h: choosing the least close ring of number of edges remaining branch corresponding with its, and judge whether there is multiple close rings, more when existing
When a close ring for meeting condition, the shortest close ring of close ring perimeter and its corresponding remaining branch are chosen, and store closing for selection
Cyclization and its corresponding remaining branch information；When only existing a close ring for meeting condition, close ring and its correspondence are directly stored
Remaining branch information；
Step i: current cotree quantity M subtracts one；
Step j: judge whether current cotree quantity M is zero；When current cotree quantity M is greater than zero, the b that gos to step continues to hold
Row；When current cotree quantity M is equal to zero, by the close ring of storage and its corresponding remaining branch information output, process terminates, to obtain
The least independent close loop set of the road network of section composition must be spliced.
3. high-precision navigation map altitude data processing method according to claim 2, which is characterized in that the step d
It can specifically include:
Step d1: using another endpoint j of the endpoint i of remaining branch and remaining branch as its adjacent node of start point search, i is arrived for start point search
Adjacent node, remove j, the information searched is stored in array C, the adjacent node arrived using j as start point search；I is removed, it will
The information searched is stored in array D, and in the array for the information that storage searches, first row stores the starting point of remaining branch, subsequent
Each column be previous column neighbors；
Step d2: compare new search go out neighbors between whether have same place, if there is same place, stop, if without same place,
The d3 that gos to step is continued to execute；
Step d3: searching for its neighbors as starting point using last column node of array C, and be stored in the next column of array C, with
Last current column of array C and the column second from the bottom of array D and column last compare, if there is same place, stop；If not yet
There is same place to be then divided into starting point with the most rank rear of array D, search for its neighbors, and be stored in the next column of array D, with array D
It is current last column and array C non-zero column second from the bottom and non-zero column last compare, if there is same place, find one
A close ring, until finding all close rings.
4. high-precision navigation map altitude data processing method according to claim 2, which is characterized in that described according to spelling
The step of connecing the road network that section forms, establishing dendritic structure P and remaining branch structure Q, comprising:
The degree for calculating each node in net structure, finds the maximum node M of its moderate；
Using M as starting point, the neighbors M adjacent with M is accessed1, M2..., and record adjacent adjacent side；
Respectively with M1, M2... it sets out, accesses the neighbors of their not visited mistakes, record adjacent adjacent side；
When also not visited node, then continue to access next stage neighbors, it, will when all nodes are all accessed
For the tree of the adjacent side composition of record as optimal tree construction P, the set of the side composition of not visited mistake is remaining branch structure Q.
5. high-precision navigation map altitude data processing method according to claim 1, which is characterized in that the basis is most
The step of mis-tie misclosure of small independent close ring carries out adjustment of condition equation resolving, splices the elevation correction value in section with acquisition, comprising:
The elevation correction value in splicing section is obtained by conditional equation AV+W=0, wherein V is the elevation correction value for splicing section,
The numerical value of V passes through equation V=P-1ATK, K are connection number, and K solves K=N by normal equation NK+W=0-1W, normal equation coefficient N
=ATP-1A, A are factor arrays, and W is the mis-tie misclosure of least independent close loop, and P is Quan Zhen, and K is connection number, and N is normal equation coefficient, M
It is error in weight unit, r is redundant observation number.
6. high-precision navigation map altitude data processing method according to claim 1, which is characterized in that the basis is pre-
If weight and splicing the elevation correction value in section, each shape point between start node and peripheral node on splicing section is calculated
The step of elevation correction value, comprising:
Length according to each shape point apart from start node multiplied by unit length elevation correction value M0=± (VTPV)/r obtains each
The elevation correction value of shape point.
7. a kind of high-precision navigation map altitude data processing unit characterized by comprising
Section splicing module, for splicing the section LINK between road junction, to generate splicing section, wherein
There are three the above node NODE of the same name, every section LINK to have unique identification for the road junction tool, and including 1
Start node NODE, 1 peripheral node NODE, the shape point that N number of density is 5 meters, N is positive integer；
Grid DEM module, for calculating the sum of height difference of every section of LINK in each splicing section as splicing the relatively high of section
Number of passes evidence；
Closed polyline module, for obtaining the road network of splicing section composition according to least independent close loop searching algorithm
Least independent close loop set；
Mis-tie misclosure computing module, for calculating the mis-tie misclosure of each least independent close loop in least independent close loop set；
Correction value computing module carries out adjustment of condition equation resolving for the mis-tie misclosure according to least independent close loop, to obtain splicing
The elevation correction value in section；
Elevation correction module is calculated and is originated on splicing section for the elevation correction value according to preset weights and splicing section
The elevation correction value of each shape point between node and peripheral node；
Data reconstruction module, for choosing the node of known practical elevation as Fixed Initial Point, according to the practical height of the Fixed Initial Point
Journey value, the relative elevation data for splicing section, the elevation correction value for each shape point for splicing section calculate each splicing section
The practical height value of each shape point, to realize the reconstruction of altitude data.
8. high-precision navigation map altitude data processing unit according to claim 7, which is characterized in that the close ring
Search module includes:
Optimal tree establishes unit, for calculating the degree of each node in net structure, finds the maximum node M of its moderate；It is with M
Starting point accesses the neighbors M adjacent with M1, M2..., and record adjacent adjacent side；Respectively with M1, M2... it sets out, accesses
The neighbors of their not visited mistakes, records adjacent adjacent side；When also not visited node, then continue to access next
Grade neighbors, when all nodes are all accessed, the tree that the adjacent side of record is formed as optimal tree construction P, not by
The set of the side composition accessed is remaining branch structure Q；
Minimal closure loops detection unit, for obtaining according to least independent close loop searching algorithm, dendritic structure P and remaining branch structure Q
The least independent close loop set of the road network of section composition must be spliced.
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