CN113514072B - Road matching method oriented to navigation data and large-scale drawing data - Google Patents
Road matching method oriented to navigation data and large-scale drawing data Download PDFInfo
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- CN113514072B CN113514072B CN202111071483.0A CN202111071483A CN113514072B CN 113514072 B CN113514072 B CN 113514072B CN 202111071483 A CN202111071483 A CN 202111071483A CN 113514072 B CN113514072 B CN 113514072B
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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
Abstract
The invention discloses a road matching method facing navigation data and large-scale drawing data, and aims to solve the problem of matching of the navigation data and the drawing road data. The method starts from the parallel characteristics and similarity judgment indexes of single and double lines, considers one-to-many matching relationship, and determines the road matching relationship by adopting sample threshold value constraint according to the processes of constructing a candidate set, obtaining optimal matching and optimizing a matching result. The method has the advantages that the navigation data and the large-scale drawing road data are matched, the problem of difficulty in matching between the navigation data and the large-scale drawing road data due to scale difference and expression difference is well solved, and the method has low time complexity and high accuracy; the distance constraint parameter which is provided by the method and takes the improved DTW distance threshold value as the matching has stability, can be expanded to the matching of other data sources, and is particularly suitable for one-to-many matching scenes. The method ensures the data availability and provides high-quality map service for government decision, investigation and emergency guarantee.
Description
Technical Field
The invention relates to the field of navigation electronic map data, in particular to a road matching method facing navigation data and large-scale drawing data.
Background
The matching method based on the double-line road polygon has a good effect on matching of single-line and double-line roads of normative data, but the matching accuracy is not high in the matching scene of navigation data and drawing data, and missing matching and mismatching are more. In addition, the technical method needs to carry out topology preprocessing on the road data to generate node-arc segment type data, and the workload is increased. The matching method based on the double-line road polygon excessively depends on the geometric normalization of the matching data, is more suitable for matching between the normative urban road data of the same type and different periods, and the proposed double-line road polygon type can not well describe the double-line road characteristics of the navigation data: the minimum unit in the navigation data road network, namely the road sections are divided by the intersections and are independent, so that the left road section and the right road section which form the double lines keep parallel overall trend but are not aligned end to end, the left road section and the right road section have one-to-many and many-to-many composition relations, and more irregular polygons exist, thereby greatly influencing the matching result. The navigation electronic map data keeps high current situation and certain normalization through marketization operation. It is possible to update a map database by using navigation electronic map road data (hereinafter referred to as navigation road data), but how to realize accurate matching of the two in the case of large difference between the expression form and the spatial logic description is critical.
Disclosure of Invention
The present invention is directed to a road matching method for navigation data and large-scale drawing data to solve the above problems. The problem of matching of navigation data and drawing road data is solved. The method starts from the parallel characteristics and similarity judgment indexes of single and double lines, considers one-to-many matching relationship, and determines the road matching relationship by adopting sample threshold value constraint according to the processes of constructing a candidate set, obtaining optimal matching and optimizing a matching result. The method is divided into two parts of contents of double-line matching and single-line matching.
The invention realizes the purpose through the following technical scheme:
the invention comprises double-line matching and single-line matching, wherein the double-line matching comprises the following steps:
s11: acquiring a candidate parallel pair set: carrying out buffer area search on the target single line to obtain a candidate road section set
，nIs a positive integer greater than 1, judgingP can Whether each road section in the set is parallel to other road sections in the set or not is recordedn-1 linen-1 column matrix
Wherein
The value is 0 or 1, 0 represents unparallel, and 1 represents parallel;
s12: obtaining an optimal matching pair: acquiring an optimal matching pair from the candidate parallel pair set by using the vector similarity as an index;
s13: and (3) matching result sorting: sorting the matching results, and solving the problem of uneven head and tail of the road section by adopting a many-to-many description mode;
the single line matching is based on an improved DTW (Dynamic Time Warping) distance to perform single line matching, and comprises the following steps:
s21: acquiring a candidate matching set: to the target single linePCarrying out buffer area search to obtain a candidate road section setpath；
S22: obtaining an optimal matching road section: respectively calculatepathEach road section in the road andPtaking the road section corresponding to the minimum DTW distance as the optimal matching road section;
s23: obtaining an optimal matching set: respectively inquiring road sections connected with the starting point and the end point of the optimal matching road section from the candidate matching set by taking the optimal matching road section as a seed road section, calculating the combined improved DTW distance, stopping if the distance exceeds a threshold value, adding the road section into the optimal matching set if the improved DTW distance is within the threshold value, and continuously judging by taking the combined road section as a new seed road section until iteration of no connected road section in the candidate matching set is finished; and if a plurality of connected road sections are inquired, merging the road sections with the minimum improved DTW distance.
The invention has the beneficial effects that:
the invention relates to a road matching method oriented to navigation data and large-scale drawing data, which has the following technical effects compared with the prior art:
(1) the functions are as follows: the patent provides a road matching method facing navigation data and large-scale drawing data, which starts from the difference of the navigation data and the drawing data and adopts the strategy of respectively matching a double line and a single line: matching double lines, mainly acquiring matching pairs by using parallel characteristics, and then expanding and arranging a many-to-many relationship from head to tail; aiming at the common one-to-many condition, a single line matching method is constructed by taking the improved DTW distance as a main constraint condition. The road matching of the navigation data and the drawing data is realized, and the foundation for updating the drawing data by the navigation data is laid.
(2) The effect is as follows: the method has the advantages that the navigation data and the large-scale drawing road data are matched, the problem of difficulty in matching between the navigation data and the large-scale drawing road data due to scale difference and expression difference is well solved, and the method has low time complexity and high accuracy; the distance constraint parameter which is provided by the method and takes the improved DTW distance threshold value as the matching has stability, can be expanded to the matching of other data sources, and is particularly suitable for one-to-many matching scenes.
(3) Social benefits are as follows: the matching method provided by the invention can ensure the data availability and provide high-quality map service for government decision, investigation and emergency guarantee.
Drawings
FIG. 1 is a schematic diagram illustrating a two-line matching result arrangement according to the present invention; a is before finishing, and b is after finishing.
FIG. 2 is a head-to-tail point position difference diagram of the present invention; a is the true position (origin); b is true (end); c is dislocation (origin exceeded); d is dislocation (starting point indentation); e is an inclusion.
FIG. 3 is a diagram of the new sequence correspondence of the present invention; a is the true position (origin); b is true (end); c is dislocation (origin exceeded); d is dislocation (starting point indentation); e is an inclusion.
Detailed Description
The invention will be further described with reference to the accompanying drawings in which: considering that the double lines have higher grade and strong importance compared with the single line, the pairing of the double lines is preferentially ensured, so the navigation data is divided into two types of the double lines and the single line according to the attributes, the double lines are matched firstly, and then the single line is matched with the drawing data which is not matched.
1) Two-wire matching
(1) Acquiring a candidate parallel pair set: carrying out buffer area search on the target single line to obtain a candidate road section set
，nIs a positive integer greater than 1 and is,
it is indicated that the n-th road segment,
is thatnRoad stripA set of segments; judgment ofP can Whether each road section in the set is parallel to other road sections in the set or not is recordedn-1 linen-1 column matrix
Is recording
A matrix of each road segment in the set in parallel relationship with other road segments in the set, wherein
Is shown asiRoad section andjwhether the road segments are parallel or not,
the value is 0 or 1, 0 indicates nonparallel, and 1 indicates parallel. Recording according to parallel relationshipM para Set of discrete candidate road segmentsP can The parallel pairings are arranged according to the following method
combining all the road sections to form a line element which is necessarily composed of a plurality of parts; performing multi-component to single-component processing on the generated line elements to generate a plurality of continuous line elements; and according to the parallel and non-collinear principle, combining the generated line elements pairwise and distinguishing left and right relations to form a candidate parallel pair set.
(2) Obtaining an optimal matching pair: and acquiring the optimal matching pair from the candidate parallel pair set by using the vector similarity as an index. The calculation method comprises the following steps: eyes of a userBill linePThe nodes are sequentially from the starting point to the end pointpt 1 ,pt 2 ,…,pt n Length between nodes ofpl 1 ,pl 2 , …,pl n-1 Will be parallel topyLeft road section (the right road section has the same calculation method) is combined into continuous road sectionlinepyAnd adjusting the starting direction to be equal toPThe directions are consistent, and the nodes are sequentially from the starting point to the end pointpyt 1 ,pyt 2 , …,pyt m FromlinepyStarting point starting length ispl 1 The distance of (c) is taken from this point and recorded aspt 1 Topt 2 Vector isXMemory for recordingpyt 1 To c vector isYThen the vector similarity is:
in the formula:
the vector similarity of the representation X Y is,Xis shown bypt 1 Topt 2 The vector of the vector is then calculated,y represents pyt 1 Vector to point c;
) Express getX YThe smaller value of the length in the vector,
express getX YThe value of the vector having the larger length,
to representX YDot product of the vectors;
the left road section total vector similarity is as follows:
. General assemblyThe vector similarity is the weighted sum of the left and right road sections, and the left and right road sections are equally important in the environment of the text, so the weights are both 0.5, and the total vector similarity is as follows:
. In the formula
Represents the sum of the vector similarities of the left and right road segments,
、
respectively representing the vector similarity of the left road section and the right road section;
pt n representing a single line of an objectPThe naming rule of the nodes is that the nodes are counted from the starting point to the end point in sequence;
Pl n-1 representing a single linePUpper nodept n-1 Andpt n the length of (d);
Linepyis a parallel pairpyObtaining a continuous road section after merging;
pyt n representing a single line of an objectLinepyThe node above, named as counting in order from the starting point to the end point.
(3) And (3) matching result sorting: and sorting the matching results, and solving the problem of uneven head and tail of the road section by adopting a many-to-many description mode. The specific method comprises the following steps:
a sets the target single linep old The starting point is the starting pointpt start Determining the left road segment end pointpy start Andpt start whether the distance is within a threshold (the value is set according to the data condition and is recorded as a two-line expansion threshold), and if the distance exceeds the threshold, the distance is determinedpt start Search for neighboring single lines for the center, if any andwith only one adjacent single wirep merge The dual-line matching pairs (ensuring that the merged road section is dual-line and not an intersection) are connectedp merge Form a new single wirep new Inherit its two-line matching pair and thenp new Of (2) ispt sart And taking the endpoint as a starting point to continue judging propagation until a threshold condition is met or no facultative object exists. When the processing of the starting point left road section is finished, judgingp new Corresponding to the right road section (isp old Andp merge right road section combined) withp new Whether the starting point is within the threshold value, if so, stopping, otherwise, continuing the processing in the same way. As shown in (a) of figure 1, p old andp merge satisfies the condition that they are combined intop new The matching pair is alsol new And r new The processing results are shown in FIG. 1 (b).
B treats the endpoint in the same manner. When the merge operation is finished, the one-to-one description is converted into a many-to-many description mode.
2) Single line matching
The cartographic road data is smoothed and simplified, so that the geometric positions of the cartographic road data and the cartographic road data are different even if the matching degree is the best, and the same road section has the general characteristics of point density difference and point position deviation and shows a one-to-many relationship of points in matching. The method performs single line matching based on improved DTW (dynamic time warping) distance.
Improved DTW distance
And (5) carrying out head-to-tail point position difference analysis. As shown in fig. 2, the two segment directions are appointed from left to right, and are named and described by the starting point (hollow point in fig. 2) and the ending point (solid point in fig. 2) of the upper shorter segment, and the head-to-tail point difference can be summarized as: the righting (starting point) of FIG. 2 (a) means that the starting points of two paths of segments are aligned; FIG. 2 (b) is a normal position (end point), which means that the end points of two segments are aligned; fig. 2 (c) is staggered (starting point exceeds), which means that the position of the starting point of the short-circuit section exceeds the long-circuit section in the extending trend direction of the circuit section; fourthly, the position of the starting point of the long road section exceeds the position of the short road section in the extending trend direction of the road section in the dislocation (the starting point is retracted) of the figure 2 (d); FIG. 2 (e) includes the special case of the long section starting point position exceeding the short section in the section extending trend direction and the long section ending point position exceeding the short section in the section extending trend direction, which is also the case of the short line matching the long line, and is listed especially.
When the short line is matched with the long line, in order to obtain more accurate DTW distance, the exceeding or insufficient part can be cut off, so that the starting points and the end points of the two paths of sections are all in the correct position, and the DTW distance of the part is calculated. The DTW distance is calculated by adopting a 'head and tail removing' twice calculation method, and the method mainly comprises the following steps (two-dimensional space sequences are arranged)
And
,Ais a collection of sequences of points of length n,
to representAAt the point of the nth point in the drawing,Bis a set of sequences of points of length m,
to representBM th point):
step 2: judgment ofA、BWhether the Euclidean distance of the sequence starting point is larger than a threshold value (the value is equivalent to the maximum deviation degree of data) or not, and eliminating the normal position deviation condition; cut outP match With one-to-many parts of the starting point, i.e.P match The following are not allowed to exist:
Afirst point of sequence andBmultiple points of the sequence correspond to each other:
Bfirst point of sequence andAmultiple points of the sequence correspond to each other:
and step 3: judgment ofA、BWhether the Euclidean distance of the sequence end point is greater than a threshold value or not is judged, and the normal position deviation condition is eliminated; cut outP match Middle terminal point one-to-many portion, i.e.P match The following are not allowed to exist:
Alast point of the sequence andBmultiple points of the sequence correspond to each other:
Blast point of the sequence andAmultiple points of the sequence correspond to each other:
and 4, step 4: according to the newP match New structureA、BAnd (5) calculating the DTW distance. The "pruning" operation and the new sequence point location correspondence for five generalized head-to-tail point differences are shown in fig. 3: firstly, righting (starting point) in the figure 3 (a), and cutting off the terminal point excess part of the lower road section; aligning (end point) in fig. 3 (b), cutting off the exceeding part of the starting point of the lower road section; thirdly, the part (c) in the figure 3 is staggered (the starting point exceeds), and the starting point exceeding part of the upper road section and the terminal point exceeding part of the lower road section are cut off; fourthly, the position of the figure 3 (d) is staggered (the starting point is retracted), and the terminal point exceeding part of the upper road section and the starting point exceeding part of the lower road section are cut off; FIG. 3 (e) includes cutting the excess part of the starting point and the ending point of the lower road section.
Improve DTW distance threshold determination. Partial matching sample t group is selected from data, and improved DTW distance is calculated
While taking into account the length of the road section
And (3) taking the threshold value as the sum of the average value of the DTW distance-length ratio and 3 times of standard deviation according to the positive-Taiji distribution 3 principle:
(1) single line matching procedure
1) And acquiring a candidate matching set. To the target single linePCarrying out buffer area search to obtain a candidate road section setpath。
2) And obtaining the optimal matching road section. Respectively calculatepathEach road section in the road andPand taking the road section corresponding to the minimum DTW distance as the optimal matching road section.
3) And obtaining an optimal matching set. And respectively inquiring road sections connected with the starting point and the end point of the candidate matching set from the candidate matching set by taking the optimal matching road section as a seed road section, calculating the combined improved DTW distance, stopping if the distance exceeds a threshold value, adding the road section into the optimal matching set if the improved DTW distance is within the threshold value, and continuously judging by taking the combined road section as a new seed road section until iteration of no connected road section in the candidate matching set is finished. And if a plurality of connected road sections are inquired, merging the road sections with the minimum improved DTW distance.
The foregoing shows and describes the general principles and features of the present invention, together with the advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (3)
1. A road matching method facing navigation data and large-scale drawing data is characterized in that: the method comprises double-line matching and single-line matching, wherein the double-line matching comprises the following steps:
s11: acquiring a candidate parallel pair set: carrying out buffer area search on the target single line to obtain a candidate road section set,
judging whether each road section is parallel to other road sections in the candidate road section set or not and recording the result into a parallel relation matrix;
s12: obtaining an optimal matching pair: acquiring an optimal matching pair from the candidate parallel pair set by using the vector similarity as an index;
s13: and (3) matching result sorting: sorting the matching results, and solving the problem of uneven head and tail of the road section by adopting a many-to-many description mode;
the specific method of step S13 is as follows:
s131: taking the starting point of the target single line as a starting point, judging whether the distance between the end point of the left road section and the starting point is within a threshold value, if the distance exceeds the threshold value, taking the starting point as a center to search for an adjacent single line, if one or only one adjacent single line has a double-line matching pair, connecting to form a new single line and inheriting the double-line matching pair, and then taking the end point of the new single line as the starting point to continuously judge spreading until the threshold value condition is met or no doubling object exists; when the processing of the left road section of the starting point is finished, judging whether the end point of the right road section corresponding to the new single line and the starting point are within a threshold value, if so, stopping, otherwise, continuing the processing in the same way;
s132: processing the end point in the manner of step S131, and when the merge operation is finished, converting the multiple one-to-one descriptions into a multiple-to-multiple description manner;
the single line matching is based on the improved DTW distance and comprises the following steps:
s21: acquiring a candidate matching set: carrying out buffer area search on the target single line to obtain a candidate road section set;
s22: obtaining an optimal matching road section: respectively calculating the DTW distance between each road section in the candidate road section set and the target single line, and marking the road section corresponding to the minimum DTW distance as an optimal matching road section;
s23: obtaining an optimal matching set: respectively inquiring road sections connected with the starting point and the end point of the optimal matching road section from the candidate matching set by taking the optimal matching road section as a seed road section, calculating the combined improved DTW distance, stopping if the distance exceeds a threshold value, adding the road section into the optimal matching set if the improved DTW distance is within the threshold value, and continuously judging by taking the combined road section as a new seed road section until iteration of no connected road section in the candidate matching set is finished; if a plurality of connected road sections are inquired, combining the road sections with the minimum improved DTW distance;
the improved DTW distance specifically comprises the following steps: is provided with two-dimensional space sequences
And
，Ais a collection of sequences of points of length n,
to representAAt the point of the nth point in the drawing,Bis a set of sequences of points of length m,
to representBM-th point:
step 1: obtaining an optimal regular path by using a conventional DTW calculation method, and obtaining A, B sequence point position corresponding relation:
,
to representASequence NoiPoint and pointBSequence NojPoints are correspondences;
step 2: judgment ofA、BWhether the Euclidean distance of the sequence starting point is greater than a threshold value or not is judged, and the normal position deviation condition is eliminated; cut outP match With one-to-many parts of the starting point, i.e.P match The following are not allowed to exist:
Afirst point of sequence andBmultiple points of the sequence correspond to each other:
Bfirst point of sequence andAmultiple points of the sequence correspond to each other:
and step 3: judgment ofA、BWhether the Euclidean distance of the sequence end point is greater than a threshold value or not is judged, and the normal position deviation condition is eliminated; cut outP match Middle terminal point one-to-many portion, i.e.P match The following are not allowed to exist:
Alast point of the sequence andBmultiple points of the sequence correspond to each other:
Blast point of the sequence andAmultiple points of the sequence correspond to each other:
and 4, step 4: according to the newP match New structureA、BAnd (5) calculating the DTW distance.
2. The road matching method for navigation data and large-scale drawing data according to claim 1, wherein: the step S11 arranges the discrete candidate road segment sets into parallel pair sets according to the parallel relation recording matrix by the following method:
s111: combining all the road sections to form a line element which is necessarily composed of a plurality of parts;
s112: performing multi-component to single-component processing on the line elements generated in the step S111 to generate a plurality of continuous line elements;
s113: and according to the parallel and non-collinear principle, pairwise combination is carried out on the line elements generated in the step S112, and left-right relations are distinguished, so that a candidate parallel pair set is formed.
3. The road matching method for navigation data and large-scale drawing data according to claim 1, wherein: the calculation method of the step S12 is as follows: and measuring the matching degree of the parallel pair concentrated road section and the target single line by using vector similarity, wherein the total similarity is the sum of the left road section and the right road section, so as to obtain the optimal matching pair.
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