CN110838178B - Road scene model determining method and device - Google Patents
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Abstract
The application provides a method and a device for determining a road scene model, relates to the technical field of traffic, and can accurately determine the road scene model and reduce labor cost. The method comprises the following steps: acquiring point cloud data of a target road; the target link includes one or more markers; according to the point cloud data, vector data corresponding to the markers and the data types of the vector data are determined; the vector data comprises position information and attribute information of the markers; determining a geometric model corresponding to the marker according to the vector data and the data type of the vector data; the geometric model is used for representing the shape of the marker; determining a three-dimensional model of the marker according to the attribute information and the geometric model; and determining a road scene model of the target road according to the three-dimensional model of one or more markers and the position information of the markers.
Description
Technical Field
The present disclosure relates to the field of data processing, and in particular, to a method and an apparatus for determining a road scene model.
Background
At present, a method for determining a road scene model mainly adopts artificial modeling. The method for artificial modeling comprises the following steps: the staff collects the geographical position information of the road and the descriptive information such as photos, videos and the like. And the staff determines the specific position of the target road according to the geographical position information and determines the three-dimensional model of the target road according to the description information. And the staff builds a road scene model in the three-dimensional modeling software according to the specific position of the target road and the three-dimensional model.
However, the above-described manual modeling method is only applicable to a case where the scene of the road is relatively simple. In the case where the scene of the target road is complex, the workload of the worker is very large if it is modeled manually. Therefore, the efficiency of road modeling is greatly reduced by the way of manual modeling, and meanwhile, the error rate of the road modeling is higher by the way of manual modeling.
Disclosure of Invention
The application provides a method and a device for determining a road scene model, which are used for accurately determining the road scene model and reducing labor cost.
In order to achieve the above purpose, the present application adopts the following technical scheme:
in a first aspect, the present application provides a method for determining a road scene model, which may include:
The server acquires point cloud data of a target road, wherein the target road comprises one or more markers. And the server determines vector data corresponding to the target marker and the data type of the vector data according to the point cloud data. The vector data includes position information and attribute information of a marker, and the target marker is any one of one or more markers. And then, the server determines the geometric model corresponding to the target marker according to the vector data and the data type of the vector data. The geometric model is used to characterize the shape of the target marker. Further, the server determines a three-dimensional model of the target marker from the attribute information and the geometric model. And finally, the server determines a road scene model of the target road according to the three-dimensional model of one or more markers and the corresponding position information.
In a second aspect, the present application provides a device for determining a road scene model, the device comprising: and the communication unit is used for acquiring the point cloud data of the target road, and the target road comprises one or more markers. And the processing unit is used for determining vector data corresponding to the target marker and the data type of the vector data according to the point cloud data, wherein the target marker is any one of one or more markers. The vector data includes position information and attribute information of the target marker. The processing unit is also used for determining the geometric model corresponding to the target marker according to the vector data and the data type of the vector data. The geometric model is used to characterize the shape of the target marker. And the processing unit is also used for determining a three-dimensional model of the target marker according to the attribute information and the geometric model. And the processing unit is also used for determining a road scene model of the target road according to the three-dimensional model of one or more markers and the corresponding position information.
In a third aspect, the present application provides a device for determining a road scene model, the device comprising: a processor, a transceiver, and a memory. Wherein the memory is used to store one or more programs. The one or more programs include computer-executable instructions that, when executed by the apparatus, cause the apparatus to perform the method of determining a road scene model according to the first aspect and any of its various alternative implementations.
In a fourth aspect, the present application provides a computer-readable storage medium, in which instructions are stored, which when executed by a computer, perform a method for determining a road scene model according to any of the above first aspect and various alternative implementations thereof.
In a fifth aspect, the present application provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method of determining a road scene model according to any of the above-mentioned first aspect and its various alternative implementations.
In a sixth aspect, there is provided a chip comprising at least one processor and a communication interface, the communication interface being coupled to the at least one processor, the at least one processor being for executing computer programs or instructions to carry out the method of the first aspect.
The method and the device for determining the road scene model are characterized in that firstly, a server acquires point cloud data of a target road. Because the point cloud data can be acquired through automated equipment, for among the prior art, the manual work shoots the picture or the video of road, the time and the cost that this application can reduce manual acquisition. Further, the server determines vector data and types of vector data of one or more markers of the target road according to the point cloud data of the target road, and determines a geometric model of the markers according to the vector data and the types of the vector data. The marker has corresponding attribute information, and the geometric model is used for representing the shape of the marker. Since the vector data includes the spatial geographical information of the markers and the types of the markers. Therefore, the server can determine the geometric model corresponding to the marker through the vector data and the type of the vector data. Thus, the staff does not need to manually delineate the geometric model of the markers by three-dimensional modeling software. The problem of work efficiency low among the prior art has been solved. Further, the server may determine a three-dimensional model of the marker according to attribute information of the marker and the geometric model. Thus, problems that in some cases, due to artifacts (e.g., artificial visual deviations), the three-dimensional model of the marker does not conform to the marker may be avoided. And finally, the server can obtain a road scene model of the target road according to the geographic position information of one or more markers and the three-dimensional model. Therefore, the method for determining the road scene model can efficiently and accurately determine the road scene model. Meanwhile, high cost and error risks caused by a large amount of manpower are avoided.
Drawings
Fig. 1 is a schematic diagram of point cloud data of a road according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a communication system according to an embodiment of the present application;
fig. 3 is a flow chart of a method for determining a road scene model according to an embodiment of the present application;
fig. 4 is a schematic diagram of point cloud data of another road according to an embodiment of the present disclosure;
fig. 5 is a schematic diagram of a road scene model provided in an embodiment of the present application;
FIG. 6 is a flowchart illustrating another method for determining a road scene model according to an embodiment of the present disclosure;
fig. 7 is a flowchart of another method for determining a road scene model according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of a determining device for a road scene model according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of another determining device for a road scene model according to an embodiment of the present application;
fig. 10 is a schematic structural diagram of a determining device for a road scene model according to another embodiment of the present application;
fig. 11 is a schematic structural diagram of a chip according to an embodiment of the present application.
Detailed Description
The method and the device for determining the road scene model provided by the embodiment of the application are described in detail below with reference to the accompanying drawings.
The term "and/or" is herein merely an association relationship describing an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone.
Furthermore, references to the terms "comprising" and "having" and any variations thereof in the description of the present application are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed but may optionally include other steps or elements not listed or inherent to such process, method, article, or apparatus.
It should be noted that, in the embodiments of the present application, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "for example" should not be construed as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.
In the description of the present application, unless otherwise indicated, the meaning of "a plurality" means two or more.
In order to facilitate understanding of the technical solutions of the present application, some technical terms are described below.
1. Point cloud data
Point cloud data refers to a set of vectors in a three-dimensional coordinate system. These vectors are typically represented in three-dimensional coordinates and are generally primarily intended to represent the shape of the exterior surface of an object. The point cloud data may also represent Red Green Blue (RGB) colors, gray values, depths, segmentation results, etc. of one point.
Exemplary, as shown in fig. 1, the point cloud data of a road is provided in an embodiment of the present application.
2. Laser point cloud data
The laser point cloud data includes a plurality of laser point data. Each laser spot data contains coordinates, acquisition time and light intensity of the laser spot. Wherein the coordinates include longitude and latitude and altitude.
3. Vector data
The vector data is data representing the position of a map graphic or a geographical entity by coordinates in a coordinate system. Vector data generally represent the spatial location of a geographic entity as accurately as possible by recording coordinates.
The vector data has a corresponding data type. The data types of the vector data may include point vector data, line vector data, and plane vector data. Vector data of these three data types will be described below.
(1) And (5) point vector data. The point vector data refers to vector data corresponding to a marker of a stereoscopic structure. For example, traffic lights in a three-dimensional configuration are taken as examples. In the point cloud data, the traffic light is composed of a plurality of points, each having a three-dimensional coordinate. The server can determine the longitude and latitude and the altitude of the traffic light according to the three-dimensional coordinates of the plurality of points. In the three-dimensional coordinate system, the server can take the corresponding point of longitude and latitude and altitude of the traffic light as the traffic light. For example, the traffic light has a longitude of 116 °, a latitude of 39 °, and a height of 3 meters, and the three-dimensional coordinates of the point a are (116 °,39 °, 3) in the three-dimensional coordinate system. The server may then use the vector data at point a as vector data for traffic lights. That is, the server may use vector data of one point as vector data corresponding to a marker of the stereoscopic structure.
(2) Line vector data. The line vector data refers to vector data corresponding to the marker of the bar-type structure. The line vector data includes a plurality of coordinates and a width of a line. For example, taking a line as a road marking, the vector data corresponding to the road marking includes a plurality of continuous coordinates and a width of the road marking.
(3) Face vector data. The plane vector data refers to vector data corresponding to a marker of a planar structure. The face vector data includes coordinates of a plurality of vertices of the plane. Taking a planar structure as an example, the zebra stripes are quadrilateral planes, and the planes comprise a plurality of continuous lines. The vector data of the zebra stripes includes four vertex coordinates, coordinates corresponding to a plurality of consecutive lines. Taking a curved road surface as an example, the vector data of the road surface may include vertex coordinates of the road surface, and a plurality of coordinates corresponding to two boundary lines of the road surface, respectively.
4. Road marker
The road markers comprise street lamps, road marks, zebra crossings, road signs, road boundary lines, road guardrails, road isolation barriers and the like.
5. Geometric model
Geometric models refer to describing physical or mathematical object shapes using geometric concepts. Geometric models include planar models, such as lines and planes. Also included are three-dimensional models, such as stereoscopic models. The geometric model does not have colors, gray scale, etc.
As shown in fig. 2, a system structure is provided in an embodiment of the present application. The system structure includes a server 100 and a vehicle radar 200.
The vehicle radar 200 is used for collecting laser point cloud data and image data of a road, and transmitting the laser point cloud data and the image data to the server 100. The server 100 is configured to receive laser spot data and image data from a vehicle radar. The server 100 is further configured to determine a road scene model of the road from the laser point cloud data and/or the image data.
The following describes a method for determining a road scene model according to an embodiment of the present application with reference to the accompanying drawings.
The method for determining the road scene model provided by the embodiment of the application, as shown in fig. 3, includes:
and step 101, the server acquires point cloud data of the target road.
Wherein the server may be the server 100 of fig. 2. The target link includes one or more markers. The point cloud data includes position information and attribute information of a plurality of points. For example, color data of dots, light intensity, etc.
In the embodiment of the present application, the server may replace the point cloud data with laser point cloud data.
Step 102, the server determines vector data of the target marker and data types of the vector data according to the point cloud data.
Wherein each vector data has a unique identification (e.g., identification number (identity document, ID)). The target marker is any one of one or more markers of the target road. The vector data includes position information and attribute information of the markers. The position information of the marker can be longitude and latitude and height of the marker. The attribute information of the markers includes the type, texture, and the like of the markers. The types of the markers include linear markers (such as road marks and boundary lines of roads), plane markers (such as road surfaces) and three-dimensional markers (such as traffic lights and street lamps). The texture of the marker is used to represent the color, gray scale of the marker. For example, the texture of the road markings may be yellow or the like. The textures of the guideboard may include blue (color of guideboard) and white (color of text) and the like.
In one possible implementation manner, the server may determine vector data corresponding to a plurality of markers of the target road and a data type of the vector data according to three-dimensional coordinates of a plurality of points of the point cloud data and/or attribute information (such as color and light intensity) of each of the plurality of points.
Illustratively, the target markers are road surfaces and road markings. In the three-dimensional coordinate system of the point cloud data shown in fig. 4, the road surface and the road marking each include a plurality of points, and the color of each point of the road surface is the same (for example, both are black) and the color of each point of the road marking is the same (for example, both are white or yellow). The three-dimensional coordinates of the plurality of points of the road surface and the three-dimensional coordinates of the plurality of points of the road marking are each 0 in the Z-axis direction in the three-dimensional coordinate system. Because the colors of the plurality of points on the road surface and the colors of the plurality of points on the road mark are different, the server can determine the vector data corresponding to the road surface and the data type (namely, the face vector data) of the vector data according to the point with the data value of 0 in the Z-axis direction in the point cloud data and the black color data. The server may determine vector data corresponding to the road identifier and a data type of the vector data (i.e., line vector data) according to a plurality of points in which a data value in the Z-axis direction in the point cloud data is 0 and the color data is yellow or white.
Still another example is for a target marker to be a stereoscopic type of marker, such as a traffic light of a road. In the three-dimensional system of point cloud data as shown in fig. 4, the traffic light includes a plurality of points, and data values of each point in the X-axis and Y-axis directions in the three-dimensional coordinate system are similar or identical, or data values in the X-axis and Y-axis directions are concentrated in a preset range. But the data values in the Z-axis direction are not identical and the data values are consecutive. Therefore, the server can determine the vector data corresponding to the traffic light and the data type (i.e. the point data type) of the vector data according to the points in the X-axis direction and the Y-axis direction in the point cloud data, wherein the data values in the z-axis direction are the same and the data values in the z-axis direction are a plurality of continuous points.
Step 103, the server determines the geometric model corresponding to the target marker according to the vector data and the data type of the vector data.
Wherein the geometric model is used to characterize the shape of the marker. For example, taking a target marker as an example of a road marking, the geometric model of the road marking is a line. Taking the marker as a zebra crossing as an example, the geometric model of the zebra crossing is a plane. Taking a marker as an example of a road signboard, the geometric model of the road signboard is a three-dimensional model.
And 104, the server determines a three-dimensional model of the target marker according to the attribute information and the geometric model.
The three-dimensional model of the marker may include vertex coordinates, normal vectors, and texture vectors. The vertex coordinates are used to determine the structure of the marker. The normal vector is used to determine the shade of the marker. The texture vector is used to determine the pattern (e.g., text of a guideboard, arrow of a road direction sign, etc.) and color of the marker.
In one possible implementation manner, the server uses the attribute information of the markers as the attribute information of the geometric model corresponding to the markers. And then the server determines a three-dimensional model of the marker according to the attribute information of the geometric model and the geometric model.
For example, after the server determines that the geometric model corresponding to the road marking is a line, the color of the geometric model is determined to be yellow according to the attribute information of the road marking. The server may determine that the three-dimensional model of the road marking is a yellow line.
Step 105, the server determines a road scene model of the target road according to the three-dimensional model of one or more markers and the corresponding position information.
In one possible implementation, the server is preset with three-dimensional modeling software. The server establishes a three-dimensional blank template through the three-dimensional modeling software. The server then adds the markers to the blank template based on the location information of the one or more markers of the target link. In this way, the server can determine the road scene model of the target road.
Illustratively, the road surface of the target road is taken as an example. After determining the three-dimensional model of the road surface of the target road, the server converts the position information corresponding to the road surface into coordinate data in a three-dimensional blank template. For example, the origin of the three-dimensional coordinate system in the three-dimensional blank template is (0, 0), and the unit length of the coordinate axis is 1. The vertex coordinates of the road surface are A (116 DEG 23',39 DEG 54', 0), B (116 DEG 23',39 DEG 60', 0), C (116 DEG 50',39 DEG 54', 0) and D (116 DEG 50',39 DEG 60', 0), respectively. The server may convert a as the origin, i.e., a (116 deg. 23',39 deg. 54', 0) to (0, 0), then B corresponds to coordinates (0,0.6,0), C corresponds to coordinates (0.27,0,0), and D corresponds to coordinates (0.27,0.6,0). And the server sets the three-dimensional model of the road surface at a corresponding position in the three-dimensional blank template according to the coordinate data corresponding to the road surface. Further, the server may determine a road scene model including a three-dimensional model corresponding to the road surface. And the server determines the positions of the three-dimensional models corresponding to the markers of the target road in the blank scene template according to the same method as the road surface. Then, after determining the positions of one or more markers of the target road in the blank scene template, the server may obtain a road scene model of the target road.
Exemplary, as shown in fig. 5, a road scene model is provided in an embodiment of the present application.
It should be noted that the road scene model in the embodiment of the present application has spatial geographic information. The road scene model can be used on a geographic information system (geographic information system, GIS) platform, and can also be browsed and secondarily developed in three-dimensional modeling software.
According to the method for determining the road scene model, firstly, the server acquires point cloud data of the target road. Because the point cloud data can be acquired through automated equipment, for among the prior art, the manual work shoots the picture or the video of road, the time and the cost that this application can reduce manual acquisition. Further, the server determines vector data and types of vector data of one or more markers of the target road according to the point cloud data of the target road, and determines a geometric model of the markers according to the vector data and the types of the vector data. The marker has corresponding attribute information, and the geometric model is used for representing the shape of the marker. Since the vector data includes the spatial geographical information of the markers and the types of the markers. Therefore, the server can determine the geometric model corresponding to the marker through the vector data and the type of the vector data. Thus, the staff does not need to manually delineate the geometric model of the markers by three-dimensional modeling software. The problem of work efficiency low among the prior art has been solved. Further, the server may determine a three-dimensional model of the marker according to attribute information of the marker and the geometric model. Thus, problems that in some cases, due to artifacts (e.g., artificial visual deviations), the three-dimensional model of the marker does not conform to the marker may be avoided. And finally, the server can obtain a road scene model of the target road according to the geographic position information of one or more markers and the three-dimensional model. Therefore, the method for determining the road scene model can efficiently and accurately determine the road scene model. Meanwhile, high cost and error risks caused by a large amount of manpower are avoided.
Alternatively, as shown in fig. 6, in the embodiment of the present application, step 103 may be implemented in the following manner.
1. In the case that the data type of the vector data is a line vector data type and/or a plane vector data type, the server may determine the geometric model corresponding to the target marker by:
step 1031, the server determines a plurality of coordinate data corresponding to the target marker according to the vector data.
Wherein the coordinate data includes a plurality of latitudes and longitudes of the target marker.
For example, the coordinate data of the road marking includes a plurality of continuous coordinate data from the start point to the end point of the road marking and a width. The coordinate data of the zebra stripes include a plurality of vertex coordinate data of the zebra stripes, widths of a plurality of lines constituting the zebra stripes, distances between the plurality of lines, and the like.
Step 1032, the server determines the geometric model corresponding to the target marker according to the plurality of coordinate data.
For example, for line vector data, the server may determine start point coordinates, end point coordinates, and width data of the line from the line vector data. And then the server determines the length of the line according to the starting point coordinates and the ending point coordinates in the line vector data. Finally, the server determines the width of the line from the line width data. The server can then determine the geometric model corresponding to the line based on the length and width of the line.
For face vector data, the server may determine a plurality of vertex coordinates of the face from the face vector data. The server determines the shape of the surface from the plurality of vertex coordinates. If the surface includes a plurality of lines (such as zebra stripes), the server may also determine the latitude and longitude of the plurality of lines of the surface and the width of the plurality of lines. And then the server obtains a geometric model corresponding to the surface according to the longitude and latitude, the width and the shape of the surface.
2. In the case that the data type of the vector data is a point vector data type, the server may determine a geometric model corresponding to the target marker by:
step 1033, the server determines the type of the target marker according to the vector data.
In one possible implementation, the server may determine the type of the marker based on attribute information of the vector data.
Step 1034, the server determines a geometric model corresponding to the target marker from a plurality of preset geometric models according to the type of the target marker.
In one possible implementation, the server may have a plurality of preset geometric models. The server can be matched with a plurality of preset geometric models according to the types of the markers. The server takes the geometric model with the highest similarity with the marker in the plurality of preset geometric models as the geometric model corresponding to the marker.
It should be noted that, in some cases (for example, the plurality of preset geometric models in the server do not have geometric models corresponding to the markers), the server may determine vertex coordinates, normal vectors, and texture coordinates of the markers according to the vector data. And then the server constructs a geometric model corresponding to the marker in the three-dimensional modeling software according to the vertex coordinates of the marker. And finally, the server determines attribute information of the geometric model according to the normal vector and the texture coordinates of the markers.
For example, the server first determines a geometric model of the marker from its vertex coordinates. The server then determines the pattern or text of the geometric model based on the texture coordinates of the markers. And finally, the server determines the gray scale and the brightness of the geometric model according to the normal vector of the marker.
Optionally, as shown in fig. 7, the method for determining a road scene model provided in the embodiment of the present application may further include:
step 201, the server acquires the picture data of the target road.
It should be noted that, when the server acquires the point cloud data of the target road, the server may also acquire the image data of the target road.
Optionally, the server may acquire the image data of the target road after determining the road scene model of the target road.
Step 202, under the condition that the point cloud data is inconsistent with the picture data or the point cloud data is incomplete, the server determines a geometric model corresponding to the target marker according to the picture data.
The point cloud data and the picture data are inconsistent, which means that the road scene model determined by the server is inconsistent with the actual scene of the target road. For example, the road may have different types of street lamps, e.g., road a includes two types of street lamps, a and b. But the road scene model of the road a determined in the server is a for the street lamp type.
Incomplete point cloud data refers to the missing of point cloud data corresponding to part of markers in the point cloud data acquired by a server. For example, since the photographing range of the vehicle radar is limited, a part of the markers of the target road are not acquired, resulting in a lack of partial data of the point cloud data of the target road. In another example, in the process of acquiring the point cloud data of the target road, the vehicle radar cannot acquire the point cloud data of the marker of the target road because the marker is blocked by other vehicles.
In a possible implementation manner, in the case that the point cloud data is inconsistent with the picture data, the server can correct the road scene model through the picture data to obtain the road scene model consistent with the picture data. For example, after determining the three-dimensional model and the position information of the b-type street lamp according to the picture data, the server replaces the a-type street lamp of the position information in the road scene model with the b-type street lamp. Thus, the server can determine a road scene model consistent with the picture data.
In another possible implementation manner, in the case that the point cloud data is incomplete, the server analyzes the picture data and determines the complete point cloud data of the target road by combining the point cloud data of the target road. Then, the server determines a road scene model of the target road according to the complete point cloud data. For another example, after determining the incomplete road scene model, the server determines a three-dimensional model corresponding to a marker lacking in the incomplete road scene model according to the picture data. And then, the server modifies the incomplete road scene model according to the three-dimensional model of the marker, so as to obtain the complete road scene model.
Optionally, the embodiment of the application may further perfect the incomplete road scene model by other methods, for example, manually perfect the road scene model.
The embodiment of the application may divide the functional modules or functional units of the determining device of the road scene model according to the above method example, for example, each functional module or functional unit may be divided corresponding to each function, or two or more functions may be integrated in one processing module. The integrated modules may be implemented in hardware, or in software functional modules or functional units. The division of the modules or units in the embodiments of the present application is merely a logic function division, and other division manners may be implemented in practice.
Fig. 8 shows a possible structural diagram of the road scene model determination device involved in the above embodiment. The determination device includes a communication unit 801, a processing unit 802.
The communication unit 801 is configured to obtain point cloud data of a target road.
The target link includes one or more markers.
The processing unit 802 is configured to determine vector data corresponding to the target marker and a data type of the vector data according to the point cloud data.
The vector data includes position information and attribute information of the markers. The target marker is any one of one or more markers.
The processing unit 802 is further configured to determine a geometric model corresponding to the target marker according to the vector data and the data type of the vector data.
The geometric model is used to characterize the shape of the target marker.
The processing unit 802 is further configured to determine a three-dimensional model of the target marker according to the attribute information and the geometric model.
The processing unit 802 is further configured to determine a road scene model of the target road according to the three-dimensional model of the one or more markers and the corresponding position information.
Optionally, the data types of the vector data include a line vector data type, a plane vector data type, and a point vector data type.
Optionally, in the case that the data type of the vector data is a line vector data type and/or a plane vector data type, the processing unit 802 is specifically configured to: determining a plurality of coordinate data of the target marker according to the vector data; and determining a geometric model corresponding to the target marker according to the plurality of coordinate data.
Optionally, in the case that the data type of the vector data is a point vector data type, the processing unit 802 is specifically configured to: determining the type of the target marker according to the vector data; and determining a geometric model corresponding to the target marker from a plurality of preset geometric models according to the type of the target marker.
Optionally, the communication unit 801 is further configured to obtain picture data of the target road.
In case the road scene model is inconsistent with the picture data or the point cloud data is incomplete, the processing unit 802 is configured to determine the road scene model according to the picture data.
The determining means may further comprise a storage unit. The storage unit is used for storing computer program codes, and the computer program codes comprise instructions. If the device is determined to be a chip applied to the server, the storage unit may be a storage unit (e.g., a register, a cache, etc.) in the chip, or may be a storage unit (e.g., a read-only memory, a random access memory, etc.) of the server that is located outside the chip.
In case of an integrated unit, fig. 9 shows a schematic diagram of one possible logical structure of the determination device involved in the above-described embodiment. The determining device includes: a processing module 902 and a communication module 901. The processing module 902 is configured to control and manage an action of the determination device, for example, the processing module 902 is configured to perform a step of performing information/data processing in the determination device. The communication module 901 is used to support the step of information/data transmission or reception in the determination device.
In a possible embodiment, the determining means may further comprise a storage module 903 for storing program code and data of the determining means.
Wherein the processing module 902 may perform the steps performed by the processing unit 802 described above. The communication module 901 may perform the steps performed by the communication unit 801 described above.
Figure 10 shows a further possible constructional schematic of the determining device involved in the above-described embodiments. The device comprises: one or more processors 101 and a communication interface 102. The processor 101 is configured to control and manage the actions of the apparatus, e.g., perform the steps performed by the processing unit 802 described above, and/or to perform other processes of the techniques described herein.
In a particular implementation, as one embodiment, processor 101 may include one or more CPUs, such as CPU0 and CPU1 in FIG. 10.
In a particular implementation, as one embodiment, the communication device may include a plurality of processors, such as processor 101 in FIG. 10. Each of these processors may be a single-core (single-CPU) processor or may be a multi-core (multi-CPU) processor. A processor herein may refer to one or more devices, circuits, and/or processing cores for processing data (e.g., computer program instructions).
Optionally, the apparatus may further comprise a memory 103 and a communication line 104, the memory 103 being used for storing program codes and data of the apparatus.
Fig. 11 is a schematic structural diagram of a chip 110 according to an embodiment of the present application. Chip 110 includes one or more (including two) processors 1110 and a communication interface 1130.
Optionally, the chip 110 also includes a memory 1140, which memory 1140 may include read only memory and random access memory, and provides operating instructions and data to the processor 1110. A portion of memory 1140 may also include non-volatile random access memory (non-volatile random access memory, NVRAM).
In some implementations, the memory 1140 stores elements, execution modules or data structures, or a subset thereof, or an extended set thereof.
In the embodiment of the present application, the corresponding operation is performed by calling the operation instruction stored in the memory 1140 (the operation instruction may be stored in the operating system).
Wherein the processor 1110 may implement or execute the various exemplary logic blocks, units and circuits described in connection with the present disclosure. The processor may be a central processing unit, a general purpose processor, a digital signal processor, an application specific integrated circuit, a field programmable gate array or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof. Which may implement or perform the various exemplary logic blocks, units and circuits described in connection with this disclosure. The processor may also be a combination that performs the function of a computation, e.g., a combination comprising one or more microprocessors, a combination of a DSP and a microprocessor, etc.
Memory 1140 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk or solid state disk; the memory may also comprise a combination of the above types of memories.
Bus 1120 may be an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus or the like. Bus 1120 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one line is shown in FIG. 11, but not only one bus or one type of bus.
From the foregoing description of the embodiments, it will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of functional units is illustrated, and in practical application, the above-described functional allocation may be performed by different functional units, that is, the internal structure of the apparatus is divided into different functional units, so as to perform all or part of the functions described above. The specific working processes of the above-described systems, devices and units may refer to the corresponding processes in the foregoing method embodiments, which are not described herein.
The embodiment of the application further provides a computer readable storage medium, in which instructions are stored, and when the computer executes the instructions, the computer executes each step in the method flow shown in the method embodiment.
The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: electrical connections having one or more wires, portable computer diskette, hard disk. Random access Memory (Random Access Memory, RAM), read-Only Memory (ROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), registers, hard disk, optical fiber, portable compact disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any other form of computer-readable storage medium suitable for use by a person or persons of skill in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an application specific integrated circuit (Application Specific Integrated Circuit, ASIC). In the context of the present application, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The embodiments of the present application provide a computer program product storing instructions that, when run on a computer, cause the computer to perform a method of determining a road scene model as described in fig. 3, 6 or 7.
Since the determining apparatus, the computer readable storage medium, and the computer program product of the road scene model in the embodiments of the present application may be applied to the above-mentioned method, the technical effects that can be obtained by the determining apparatus, the computer readable storage medium, and the computer program product may also refer to the above-mentioned method embodiments, and the embodiments of the present application are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interface, indirect coupling or communication connection of devices or units, electrical, mechanical, or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (12)
1. A method for determining a road scene model, comprising:
acquiring point cloud data of a target road, wherein the target road comprises one or more markers; the point cloud data is a set of vectors in a three-dimensional coordinate system;
According to the point cloud data, vector data corresponding to a target marker and the data type of the vector data are determined, wherein the vector data comprise position information and attribute information of the marker; the target marker is any one of the one or more markers, and the position information of the target marker is longitude, latitude and height of the target marker;
determining a geometric model corresponding to the target marker according to the vector data and the data type of the vector data, wherein the geometric model is used for representing the shape of the target marker; under the condition that a geometric model corresponding to the target marker does not exist in a plurality of preset geometric models, determining vertex coordinates of the target marker according to vector data of the target marker, and constructing the geometric model corresponding to the target marker in three-dimensional modeling software according to the vertex coordinates of the target marker;
determining a three-dimensional model of the target marker according to the attribute information and the geometric model;
and determining a road scene model of the target road according to the three-dimensional model of the one or more markers and the corresponding position information.
2. The determination method according to claim 1, wherein the data types of the vector data include a line vector data type, a plane vector data type, and a point vector data type.
3. The determining method according to claim 2, wherein, in the case where the data type of the vector data is a line vector data type and/or a plane vector data type, the determining the geometric model corresponding to the target marker according to the vector data and the data type of the vector data includes:
determining a plurality of coordinate data of the target marker according to the vector data;
and determining a geometric model corresponding to the target marker according to the plurality of coordinate data.
4. The determining method according to claim 2, wherein, in the case where the data type of the vector data is the point vector data type and a geometric model corresponding to the target marker exists in the plurality of preset geometric models, the determining the geometric model corresponding to the target marker according to the vector data and the data type of the vector data includes:
determining the type of the target marker according to the vector data;
And determining a geometric model corresponding to the marker from a plurality of preset geometric models according to the type of the target marker.
5. The method according to any one of claims 1-4, further comprising:
acquiring picture data of the target road;
and determining the road scene model according to the picture data under the condition that the road scene model is inconsistent with the picture data or the point cloud data is incomplete.
6. A road scene model determining apparatus, characterized by comprising:
the communication unit is used for acquiring point cloud data of a target road, wherein the target road comprises one or more markers; the point cloud data is a set of vectors in a three-dimensional coordinate system;
the processing unit is used for determining vector data corresponding to the target marker and the data type of the vector data according to the point cloud data, wherein the vector data comprises the position information and the attribute information of the target marker; the target marker is any one of the one or more markers, and the position information of the target marker is longitude, latitude and height of the target marker;
The processing unit is further used for determining a geometric model corresponding to the target marker according to the vector data and the data type of the vector data, and the geometric model is used for representing the shape of the target marker; under the condition that a geometric model corresponding to the target marker does not exist in a plurality of preset geometric models, determining vertex coordinates of the target marker according to vector data of the target marker, and constructing the geometric model corresponding to the target marker in three-dimensional modeling software according to the vertex coordinates of the target marker;
the processing unit is further used for determining a three-dimensional model of the target marker according to the attribute information and the geometric model;
the processing unit is further configured to determine a road scene model of the target road according to the three-dimensional model of the one or more markers and the corresponding position information.
7. The determination apparatus according to claim 6, wherein the data types of the vector data include a line vector data type, a plane vector data type, and a point vector data type.
8. The determination device according to claim 7, wherein, in case the data type of the vector data is a line vector data type and/or a plane vector data type, the processing unit is specifically configured to:
Determining a plurality of coordinate data of the target marker according to the vector data;
and determining a geometric model corresponding to the target marker according to the plurality of coordinate data.
9. The determining device according to claim 7, wherein, in the case where the data type of the vector data is the point vector data type and a geometric model corresponding to the target marker exists in the plurality of preset geometric models, the processing unit is specifically configured to:
determining the type of the target marker according to the vector data;
and determining a geometric model corresponding to the target marker from a plurality of preset geometric models according to the type of the target marker.
10. Determination device according to any of the claims 6-9, characterized in that,
the communication unit is also used for acquiring the picture data of the target road;
the processing unit is further configured to determine, according to the picture data, a three-dimensional model corresponding to the target marker when the point cloud data is inconsistent with the picture data or the point cloud data is incomplete.
11. A determination apparatus for a road scene model, characterized in that the determination apparatus comprises: a processor, transceiver, and memory; wherein the memory is configured to store one or more programs, the one or more programs comprising computer-executable instructions that, when executed by the determining device, cause the determining device to perform the method of determining a road scene model of any of claims 1 to 5.
12. A computer-readable storage medium, characterized in that instructions are stored in the computer-readable storage medium, which, when executed by a computer, perform the method of determining a road scene model according to any one of the preceding claims 1 to 5.
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| CN111949173B (en) * | 2020-07-31 | 2022-02-15 | 广州启量信息科技有限公司 | Panoramic VR (virtual reality) picture switching method and device, terminal equipment and storage medium |
| CN112633722B (en) * | 2020-12-29 | 2024-01-12 | 交通运输部公路科学研究所 | Vehicle-mounted road safety risk assessment system and method |
| CN112927370A (en) * | 2021-02-25 | 2021-06-08 | 苍穹数码技术股份有限公司 | Three-dimensional building model construction method and device, electronic equipment and storage medium |
| CN114427876B (en) * | 2021-12-15 | 2023-11-28 | 武汉中海庭数据技术有限公司 | Automatic checking method and system for association relation of traffic sign |
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