CN114092658A - High-precision map construction method - Google Patents
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Abstract
本发明公开了一种高精度的地图构建方法，包括以下步骤：S1、通过车载激光测量系统采集车辆行驶过程中的姿态位置数据、三维道路点云数据和全景影像数据；S2、根据姿态位置数据，对三维道路点云数据进行分割，得到多段三维道路点云数据；S3、对每段三维道路点云数据提取道路地面点；S4、对道路地面点进行抽稀，构建道路路面模型；S5、根据全景影像数据，构建道路标线模型；S6、根据道路标线模型和道路路面模型，得到地图建模；本发明解决了现有地图精度不高的问题。
The invention discloses a high-precision map construction method, comprising the following steps: S1, collecting attitude and position data, three-dimensional road point cloud data and panoramic image data during vehicle driving through a vehicle-mounted laser measurement system; S2, according to the attitude and position data , segment the 3D road point cloud data to obtain multi-segment 3D road point cloud data; S3, extract road ground points from each segment of 3D road point cloud data; S4, thin out road ground points to construct a road pavement model; S5, According to the panoramic image data, a road marking model is constructed; S6, a map model is obtained according to the road marking model and the road surface model; the present invention solves the problem of low accuracy of the existing map.
Description
Technical Field
The invention relates to the field of map construction, in particular to a high-precision map construction method.
Background
The high-precision map (HighDefinitionMap) is an indispensable part of the safety of automatic driving, can effectively strengthen the perception capability and decision-making capability of automatic driving, and improves the level of automatic driving. At present, a plurality of scholars, research institutions and enterprises and public institutions (including Google, Baidu, four-dimensional map, Goodpasture and the like) at home and abroad carry out deep research on the automatic driving high-precision map.
The high-precision map is mainly used for finely expressing road traffic layer objects (such as roads, lane ground marks, traffic lights, traffic signs, guard rails, poles and the like), and comprises the geometric positions and attributes of the road traffic layer objects. The high-precision map is divided into a point cloud map, a vector map and a model map.
Although the high-precision vector map can show more information compared with the common electronic navigation map, people need to clearly know the current surrounding environment in driving, such as surrounding buildings, intersections, traffic signs, guideboards and the like, and the establishment of a three-dimensional model map is necessary.
Extracting a road base map of the high-precision map according to the remote sensing image and the aerial photograph; extracting elevation data information at important road nodes according to a local digital elevation model; importing the road base map into modeling software, and manufacturing a basic frame of a road three-dimensional model according to elevation data information of important nodes of the road; and finally modeling the road sign and adding other attribute information.
The existing highest resolution of the remote sensing image is 0.3M, and the precision of the remote sensing image cannot meet the requirement of the existing high-precision map;
the aviation images can obtain centimeter-level precision, but are subject to the problems of urban airspace management and control, complex airspace application process, long period and the like, so that the requirement of high-precision map production cannot be met;
the remote sensing image and the aviation flying image data are influenced by the coverage of urban trees, and the acquisition of urban road base map data is limited.
Disclosure of Invention
Aiming at the defects in the prior art, the high-precision map construction method provided by the invention solves the problem that the existing map is low in precision.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that: a high-precision map construction method comprises the following steps:
s1, acquiring attitude position data, three-dimensional road point cloud data and panoramic image data in the driving process of the vehicle through a vehicle-mounted laser measuring system;
s2, segmenting the three-dimensional road point cloud data according to the attitude position data to obtain a plurality of sections of three-dimensional road point cloud data;
s3, extracting road ground points from each section of three-dimensional road point cloud data;
s4, performing rarefaction on road ground points to construct a road surface model;
s5, constructing a road marking model according to the panoramic image data;
and S6, obtaining a map modeling according to the road marking model and the road pavement model.
Further, the on-vehicle laser measurement system in step S1 includes: the system comprises a vehicle, a three-dimensional laser scanner, a GPS navigator, an inertial guidance instrument and a panoramic camera.
Further, step S2 includes the following substeps:
s21, generating a driving track of the three-dimensional laser scanner in the scanning process according to the posture position data of the vehicle in the driving process;
and S22, dividing the three-dimensional road point cloud data at fixed intervals along the direction of the driving track of the scanning process of the three-dimensional laser scanner to obtain a plurality of sections of three-dimensional road point cloud data.
The beneficial effects of the above further scheme are: the huge three-dimensional road point cloud data are processed in a segmented mode along the direction of the driving track, and therefore each segment of three-dimensional road point cloud data can be processed separately or in parallel.
Further, step S3 includes the following substeps:
s31, setting a point cloud time interval threshold value, and segmenting each section of three-dimensional road point cloud data again based on the point cloud time interval threshold value to obtain a plurality of scanning lines;
s32, calculating the distance difference and the elevation difference between adjacent data points on each scanning line;
and S33, when the distance difference and the height difference between the adjacent data points are both smaller than a set threshold value, extracting the data points as road ground points.
The beneficial effects of the above further scheme are: the road ground points are extracted through the scanning lines, the noise points with large distance and elevation difference are filtered, and all the obtained road ground points form the whole road surface.
Further, step S4 includes the following substeps:
s41, taking the starting point of each scanning line as a road edge;
s42, performing rarefaction on the road sideline according to a fixed distance to obtain the rarefaction road sideline;
s43, establishing road surface grids for the road surface points, and aggregating and averaging data points in each grid range to obtain rarefied ground data points;
and S44, constructing a road triangulation network by adopting a point cloud triangle growing algorithm according to the road sideline after rarefaction and the ground data point after rarefaction to obtain a road surface model.
The beneficial effects of the above further scheme are: and (4) thinning the road side line and the road ground point, so that a point cloud triangle growing algorithm can construct a road surface model conveniently.
Further, step S5 includes the following substeps:
s51, constructing a road marking vectorization graph;
s52, according to the panoramic image data, correcting and recording parameters of the road marking in the road marking vectorization image to obtain a standard road marking vectorization image;
and S53, constructing a road marking model according to the standard road marking vectorization graph.
The beneficial effects of the above further scheme are: the road marking in the road marking vectorization graph is perfected through the panoramic image data, so that the road marking model can comprehensively reflect the actual road condition of the road.
Further, step S51 includes the following substeps:
s511, extracting road marking point cloud data in the three-dimensional road point cloud data according to the reflectivity value of each data point in the three-dimensional road point cloud data;
s512, removing outliers from the road marking point cloud data;
and S513, vectorizing the point cloud data of the road marking from which the outliers are removed to obtain a road marking vectorized graph.
Further, step S512 includes the following substeps:
s5121, carrying out meshing processing on the road marking point cloud data;
and S5122, removing the data points which are less than or equal to 2 in the grid to obtain the road marking point cloud data after the outliers are removed.
Further, step S513 includes the following sub-steps:
s5131, extracting boundary points of the road marking point cloud data after the outliers are removed to obtain a boundary profile of the road marking;
and S5132, calculating the central point and the head-tail middle line of the road marking according to the boundary outline of the road marking to obtain a road marking vectorization image.
In conclusion, the beneficial effects of the invention are as follows: the three-dimensional laser scanner can obtain millimeter-level-precision three-dimensional space road point cloud data, and the panoramic image data can comprehensively record element information of the current point location. The invention provides a method for quickly generating a three-dimensional model map by acquiring high-precision road information data and related parameters of a road object based on vehicle-mounted laser point cloud and a panoramic image.
Drawings
FIG. 1 is a flow chart of a high precision map construction method;
FIG. 2 is a schematic view of a road pavement model;
fig. 3 is a schematic diagram of removing outliers from road marking point cloud data.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, a high-precision map construction method includes the following steps:
s1, acquiring attitude position data, three-dimensional road point cloud data and panoramic image data in the driving process of the vehicle through a vehicle-mounted laser measuring system;
the on-vehicle laser measurement system includes: the system comprises a vehicle, a three-dimensional laser scanner, a GPS navigator, an inertial guidance instrument and a panoramic camera, wherein the data acquisition process is carried out during the running of the vehicle, the three-dimensional laser scanner is used for acquiring road point cloud data, the panoramic camera is used for acquiring panoramic image data of a road, and a POS system formed by the GPS navigator and the inertial guidance instrument is used for acquiring attitude position information.
S2, segmenting the three-dimensional road point cloud data according to the attitude position data to obtain a plurality of sections of three-dimensional road point cloud data;
step S2 includes the following substeps:
s21, generating a driving track of the scanning process of the three-dimensional laser scanner according to the attitude position data of the vehicle in the driving process;
and S22, dividing the three-dimensional road point cloud data at fixed intervals along the direction of the driving track of the scanning process of the three-dimensional laser scanner to obtain a plurality of sections of three-dimensional road point cloud data.
The amount of original data collected by the three-dimensional laser scanner is generally very large, and it is very difficult for a computer to process all road point cloud data at one time. Meanwhile, road scenes in the global scanning range are complex and changeable, and a uniform road model is difficult to construct. Therefore, the road point cloud data needs to be segmented, and a plurality of methods are used for segmenting the road point cloud data.
S3, extracting road ground points from each section of three-dimensional road point cloud data;
step S3 includes the following substeps:
s31, setting a point cloud time interval threshold value, and segmenting each section of three-dimensional road point cloud data again based on the point cloud time interval threshold value to obtain a plurality of scanning lines;
the three-dimensional laser scanner adopts the section scanner, and section scanner laser head rotates a week and can obtains a scanning line, because scanning point can not appear in the sky, so must have a great jump in time between last point on a scanning line and the first scanning point of next scanning line. And setting a point cloud time interval threshold delta T to segment each scanning line.
S32, calculating the distance difference and the elevation difference between adjacent data points on each scanning line;
and S33, when the distance difference and the height difference between the adjacent data points are both smaller than a set threshold value, extracting the data points as road ground points.
In the present invention, step S33 refers to: when the distance difference and the height difference between a certain data point and the left and right data points of the certain data point are smaller than a set threshold value, the certain data point is the road ground point.
Let the coordinate of a certain data point be Pi(Xi，Yi，Hi) With coordinates of adjacent data points Pi+1(Xi+1，Yi+1，Hi+1)；
Height difference | Hi-Hi+1|，
Wherein, | | is an absolute value operation.
S4, performing rarefaction on road ground points to construct a road surface model;
the road point cloud data is discrete, and the extracted road side line is composed of a series of three-dimensional coordinate points which are linearly arranged and unevenly distributed. In order to reduce redundancy of the road data amount, fewer line segments are used for representing the road characteristic line, and characteristic points need to be thinned. And respectively thinning the data points of the road side line and the road surface.
Step S4 includes the following substeps:
s41, taking the starting point of each scanning line as a road edge;
s42, performing rarefaction on the road sideline according to a fixed distance to obtain the rarefaction road sideline;
in this embodiment, first, the starting point of each scan line on the road surface is extracted as a road edge, and the road edge is thinned according to a distance of 1 meter.
S43, establishing road surface grids for the road surface points, and carrying out aggregation and averaging on data points in each grid range to obtain rarefied ground data points;
the road surface thinning is completed through aggregation, road surface grids are established firstly, the size of the grids is set according to the slope condition of the regional road (the threshold value is between 0.5 and 5 meters), and points in the grid range are aggregated respectively to obtain the average value of the points in the grids
Wherein, X1To XnFor the abscissa, Y, of the data point in each road surface grid1To YnFor the ordinate, H, of the data point in each road surface grid1To HnFor each road surface netThe vertical coordinates of the data points in the grid, n, is the number of data points in each road pavement grid.
S44, constructing a road triangulation network by using a point cloud triangle growing algorithm according to the road sideline after rarefaction and the ground data point after rarefaction to obtain a road surface model, as shown in figure 2.
S5, constructing a road marking model according to the panoramic image data;
step S5 includes the following substeps:
s51, constructing a road marking vectorization graph;
step S51 includes the following substeps:
s511, extracting road marking point cloud data in the three-dimensional road point cloud data according to the reflectivity value of each data point in the three-dimensional road point cloud data;
the road marking is generally yellow or white, the road marking has high reflectivity, and the road marking point cloud data can be effectively extracted according to the reflectivity value of each data point in the road point cloud data.
S512, removing outliers from the road marking point cloud data;
the road marking point cloud extracted by using the reflectivity threshold has a plurality of noise points, one part of the noise points are high-reflection parts in a road surface material, the distribution is relatively random, the other part of the noise points are caused by that the road surface is rubbed and smoothed for a long time, and the distribution is relatively uniform. However, the noise points are relatively dispersed and less in number relative to the point cloud data of the road marking lines, and can be removed by adopting a method for removing outliers.
Step S512 comprises the following substeps:
s5121, carrying out meshing processing on the road marking point cloud data;
and S5122, removing the data points less than or equal to 2 in the grid to obtain the road marking point cloud data after the outliers are removed.
In this embodiment, 4 times of the dot pitch is used as the size of the grid unit, and the threshold of the number of data dots is set to be 2, that is, the number of data dots in the grid is less than or equal to 2, so that outliers can be effectively removed. The point cloud after outlier removal is shown in fig. 3.
And S513, vectorizing the point cloud data of the road marking from which the outliers are removed to obtain a road marking vectorized graph.
The point cloud data of the road markings obtained in step S512 is difficult to use as it is, and the data needs to be converted into usable vector data. The vectorization of the road marking is mainly characterized in that marking boundary points are extracted, the boundary outline of the road marking can be obtained through the extraction of the boundary points, then the marking center point, the head and tail center lines and the like are further calculated, and the generation and vectorization output of the boundary lines are realized.
The vectorization scheme for the road guide arrow or the character mark is to directly obtain an external contour to match with a standard template and correct the external contour; the virtual road dividing lines are all straight lines, so the vectorization scheme is a head-tail central point connecting line; the vectorization scheme of the real lane line is to take the center points of the marked lines at regular intervals for connection, namely the center line of the real lane line.
Step S513 includes the following sub-steps:
s5131, extracting boundary points of the road marking point cloud data after the outliers are removed to obtain a boundary profile of the road marking;
and S5132, calculating the central point and the head-tail middle line of the road marking according to the boundary outline of the road marking to obtain a road marking vectorization image.
S52, according to the panoramic image data, correcting and recording parameters of the road marking in the road marking vectorization image to obtain a standard road marking vectorization image;
and checking the road marking in the road marking vectorization image by referring to the panoramic image data and the road point cloud data, mainly finishing the checking in a man-machine interaction mode, and modifying the marked lines which have problems and are clear due to abrasion and are shielded and missed to be lifted in a man-machine interaction mode.
And the parameter input refers to panoramic image data and related standards of GB5768-2009 road traffic signs and marking lines to complete the input of related parameters of the road marking lines, including marking line types, marking line colors and other parameters.
And S53, constructing a road marking model according to the standard road marking vectorization graph.
In order to ensure that the constructed road marking model is completely attached to the road model, elevation fitting needs to be carried out on the road marking model before modeling of the road marking.
Firstly, a triangular net of a road pavement model is converted into an elevation model, and nodes of a standard road marking vectorization graph are used for fitting the road elevation model to obtain the elevation values of corresponding point positions.
And reading the data of the road marking vectorization graph, and automatically constructing a road marking model by different types of road markings through the outer contour or the middle line of the vectorization graph and the parameters of the markings.
And selecting corresponding materials according to the type or color of the marked line to carry out mapping, thereby completing the construction of the road marked line model. And S6, obtaining a map modeling according to the road marking model and the road pavement model.
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