CN111061820A - Method and apparatus for storing high-precision map - Google Patents
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Abstract
The invention provides a method and a device for storing a high-precision map. The method may comprise and the apparatus may be for: obtaining a road model of a high-precision map; for each road section in the road model, determining an object feature for each object in the road section, the object feature comprising a point cloud of the object; and storing the road model and the object features in a map description file. By adopting the method and the device, the obtained high-precision map has a relatively small size and can be downloaded easily and quickly. Further, the point clouds of the respective objects in the high-precision map may be selectively used as needed.
Description
Technical Field
The present invention relates to a high-precision map, and more particularly, to a method and apparatus for storing a high-precision map.
Background
A High-precision Map (High Definition Map) refers to a High-precision and fine-defined Map, and the precision of the Map can be distinguished only when the Map reaches a decimeter level. With the development of positioning technology, high-precision positioning has become possible. Unlike conventional electronic maps, the main service object of high-precision maps is an unmanned vehicle, or a machine driver. Unlike human drivers, machine drivers lack inherent visual recognition, logic analysis capabilities. For example, a person can easily and accurately identify obstacles, people, traffic lights, etc. by using images, positioning himself by GPS, but this is a very difficult task for current robots. Therefore, high-precision maps are an essential component of current unmanned vehicle technology. High-precision maps contain a large amount of driving assistance information, the most important of which is an accurate three-dimensional representation of the road network (centimeter-level precision). Such as the geometry of the road surface, the location of road sign lines, point cloud models of the surrounding road environment, etc. With these high-precision three-dimensional representations, the in-vehicle robot can accurately determine its current position by comparing the in-vehicle GPS, laser radar (LiDAR), or camera data.
In general, high precision maps are divided into two layers: road Model (Road Model) and feature map (FeatureMap). Fig. 1 shows a prior art hierarchy 100 of a high-precision map. As illustrated in fig. 1, the first layer 110 is a road model that includes lane information (such as the location, type, width, etc. of the lane lines). The second layer 120 is a feature map that includes point cloud information for various objects in the lane and its surroundings, which can be used to compare with real-time measurement data of the unmanned vehicle to accurately determine the current location of the unmanned vehicle.
Currently, the road model and the feature map are stored separately in two map description files. In order to associate an object in a road model in one map description file with a feature (e.g., a point cloud) of the object in another map description file, an index table needs to be additionally established to establish a correspondence relationship between the two. Such index tables typically occupy a large storage space. Furthermore, the feature map contains a point cloud of all objects in the map. Therefore, when a user wants only a point cloud of a specific object, the user has to first load the entire map description file containing the feature map and then extract the point cloud of the specific object therefrom. This would take up more resources and is also inconvenient.
It is therefore desirable to provide a new method of storing high precision maps in order to overcome the above mentioned disadvantages.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
According to an embodiment of the present invention, there is provided a method for storing a high-precision map, which may include: obtaining a road model of a high-precision map; for each road section in the road model, determining an object feature for each object in the road section, the object feature comprising a point cloud of the object; and storing the road model and the object features in a map description file.
According to an embodiment of the present invention, there is provided an apparatus for storing a high-precision map, including: an obtaining unit configured to obtain a road model of a high-precision map; a determination unit configured to determine, for each road section in the road model, an object feature of each object in the road section, the object feature comprising a point cloud of the object; and a storage unit configured to store the road model and the object feature in one map description file.
According to an embodiment of the present invention, there is provided an apparatus for storing a high-precision map, including: a memory storing computer-executable instructions; and a processor coupled to the memory and configured to: obtaining a road model of a high-precision map; for each road section in the road model, determining an object feature for each object in the road section, the object feature comprising a point cloud of the object; and storing the road model and the object features in a map description file.
According to an embodiment of the invention, a non-transitory computer-readable medium is provided storing a computer program which, when executed by a processor, performs the method of the invention.
By adopting the method and the device disclosed by the invention, the obtained high-precision map has a relatively small size and can be downloaded easily and quickly. Further, the point clouds of the respective objects in the high-precision map may be selectively used as needed.
These and other features and advantages will become apparent upon reading the following detailed description and upon reference to the accompanying drawings. It is to be understood that both the foregoing general description and the following detailed description are explanatory only and are not restrictive of aspects as claimed.
Drawings
So that the manner in which the above recited features of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only some typical aspects of this invention and are therefore not to be considered limiting of its scope, for the description may admit to other equally effective aspects.
Fig. 1 shows a hierarchical structure of a high-precision map in the related art.
Fig. 2 illustrates a structure of a map description file according to an embodiment of the present invention.
FIG. 3 shows a schematic diagram of compressing a point cloud using octree according to one embodiment of the invention.
FIG. 4 illustrates a bounding box according to one embodiment of the present invention.
Fig. 5 shows a flow diagram of a method for storing a high accuracy map according to an embodiment of the invention.
Fig. 6 shows a block diagram of an apparatus for storing a high-precision map according to an embodiment of the present invention.
FIG. 7 shows a block diagram of an exemplary computing device, according to an embodiment of the invention.
Detailed Description
The present invention will be described in detail below with reference to the attached drawings, and the features of the present invention will be further apparent from the following detailed description.
High-precision maps for unmanned driving include accurate three-dimensional representations of road networks (centimeter-level precision). Such as the geometry of the road surface, the location of road sign lines, point cloud models of the surrounding road environment, etc. With these high-precision three-dimensional representations, the current location of the vehicle can be accurately determined by comparing the real-time data of the onboard GPS, LiDAR (laser radar), or camera to the feature maps in the high-precision map. However, the road model and the feature map in the current high-precision map are stored in two different map description files, respectively. An index table needs to be created between the two map description files in order to establish an association between an object in the road model and a point cloud of the object in the feature map. Furthermore, the point cloud of a specific object cannot be efficiently acquired. To overcome these drawbacks, the present invention seeks to provide a novel method for storing high-precision maps. In one embodiment of the invention, by storing the road model and object features (e.g., point clouds) in one map description file, the need for an index table is eliminated and the point clouds of a particular object can be conveniently retrieved. In another embodiment of the present invention, octree can be used to compress the point clouds of the respective objects, thereby further saving the storage space of the high-precision map. In yet another embodiment of the invention, bounding boxes containing point clouds of individual objects may also be stored in a high precision map in order to improve localization speed.
Fig. 2 illustrates the structure of a map description file 200 according to one embodiment of the present invention. As shown in fig. 2, the map description file 200 includes a header and a road model 210, the road model 210 including a plurality of road segments 220 and junctions between the road segments. For each road section 220, individual objects 230 along the road section are defined in the map description file 200, which may for example comprise road signs, lane lines, obstacles, bridges, utility poles, overhead structures, or traffic signs, etc. For each of these objects, the object characteristics of the object may be stored in the map description file 200. Table 1 below shows an example of object features that may be stored in a map description file.
TABLE 1
As shown in Table 1, the object characteristics for each object may include a name, an identifier, a type, a subtype, a characteristic value, and/or a point cloud, where the subtype and characteristic value apply only to certain types of objects. For example, when the type of object is a traffic sign, the subtype may indicate whether the traffic sign is a circular sign or a rectangular sign, and the characteristic value may indicate a diameter of the circular sign or a length and a width of the rectangular sign, as examples.
According to one embodiment of the invention, the point cloud of each object may be surrounded by a bounding box containing the point cloud. As known to those skilled in the art, bounding boxes are an algorithm for solving an optimal bounding space of a discrete set of points, the basic idea being to approximately replace complex geometric objects with slightly larger and characteristically simple geometries (called bounding boxes). Commonly used bounding box forming algorithms may include an Axis Aligned Bounding Box (AABB) algorithm, a bounding sphere algorithm, an Oriented Bounding Box (OBB) algorithm, or a fixed orientation convex hull (FDH) algorithm. In one embodiment according to the invention, the bounding box defines a minimum cuboid that completely contains the point cloud of one object. In the unmanned field, point clouds are mainly used for self-localization of vehicles and often have complex contours, and by representing the point clouds using a minimum cuboid, the localization process can be significantly simplified and the localization speed increased. In one embodiment according to the invention, a three-dimensional rectangular coordinate system may be established around the center point of the bounding box and the coordinates of the center point are stored in the map description file 200, while the coordinates of the individual points of the point cloud contained in the bounding box may be stored with respect to the coordinates of the center point.
It will be understood by those skilled in the art that the map description file 200 shown in fig. 2 also includes other information, such as lane models, road properties, etc. These information are already included in the map description file in the prior art, and therefore are not described herein again.
In yet another embodiment of the present invention, an Octree (Octree) may be used to compress the point cloud for each object. An octree is a tree-like data structure used to describe a three-dimensional space. Each node of the octree represents a cuboid volume element, each node has eight child nodes, and the volume elements represented by the eight child nodes are added together to be equal to the volume of the parent node.
FIG. 3 shows a schematic diagram 300 of compressing a point cloud using octree, according to one embodiment of the invention. As shown in fig. 3, it is assumed that a rectangular parallelepiped 301 (e.g., bounding box) contains all the point clouds of an object. Here, a rectangular parallelepiped 301 is represented as a node 310. The cuboid 301 may be divided into 8 cuboids 302 of equal size. Each cuboid 302 may be represented as a first level child node 320. If a cuboid 302 contains a partial point cloud of the object, the cuboid and its corresponding child nodes may be labeled with a label (e.g., binary '1'). If a cuboid 302 does not contain any point cloud of the object, the cuboid and its corresponding child nodes may be labeled with another marker (e.g., a binary '0'). Next, each cuboid 302 may be further divided into 8 equal sized cuboids 303. Cuboid 303 may be represented as a second level child node 330. If a cuboid 303 contains part of the point cloud of the object, the cuboid and its corresponding child nodes may be labeled with a label (e.g., binary '1'). If a cuboid 303 does not contain any point cloud of the object, the cuboid and its corresponding child nodes may be labeled with another marker (e.g., a binary '0'). The above process may continue iteratively such that the octree may be divided into a plurality of levels, the cuboids in each level may be represented by binary '0' or '1', thereby enabling compression of the point cloud with different precision. For example, the higher the level, the more the number of divisions, the smaller the volume of the rectangular solid, and the higher the accuracy of the point cloud.
In operation (e.g., during road testing), a user can request a point cloud for each object with a certain accuracy. Therefore, the user can obtain the point cloud with specific precision according to the requirement without downloading the whole point cloud with huge volume.
Fig. 4 shows a bounding box 400 according to an embodiment of the invention. As shown in fig. 4, the bounding box 400 may be generated with an Axis Aligned Bounding Box (AABB) algorithm such that the bounding box 400 completely contains a point cloud of one object. In the bounding box 400, an XYZ rectangular coordinate system is established around a center point of the bounding box 400, wherein coordinates of points (i.e., laser points) in the point cloud may be stored relative to coordinates of the center point.
FIG. 5 shows a flow diagram of a method 500 for storing high accuracy maps, according to one embodiment of the present invention. For example, method 500 may be implemented within at least one processor (e.g., processor 704 of fig. 7), which may be located in an on-board computer system, a remote server, or a combination thereof. Of course, in various aspects of the invention, the method 500 may be implemented by any suitable apparatus capable of performing the relevant operations.
The method 500 begins at step 510. At step 510, the method 500 may include obtaining a road model of the high-precision map. The road model may include information about individual objects (e.g., road signs, lane lines, obstacles, bridges, utility poles, elevated structures, or traffic signs, etc.) in the lane and its surroundings. In one embodiment, the road model may be obtained directly from high precision maps provided by map vendors (e.g., Google, HERE, etc.). In another embodiment, the road model may be established by information gathered by a map-gathering vehicle.
At step 520, the method 500 may include, for each road section in the road model, determining an object characteristic for each object in the road section. The object features for each object may include a name, an identifier, a type, a subtype, a feature value, and/or a point cloud, where the subtype and feature value apply only to certain types of objects. In one embodiment, these object features may be obtained directly from a high-precision map provided by a map vendor. Alternatively, in another embodiment, these object features may be determined by information gathered by a map-gathering vehicle. For example, LiDAR (laser radar) installed on a map-acquisition vehicle may obtain a point cloud for each object, and through analysis of the point cloud, the type, subtype, and/or eigenvalue of the object may be determined.
At step 530, the method 500 may include storing the road model and the determined object features in a map description file. Thereby, the need for an index table is obviated and the point cloud of a specific object can be conveniently retrieved.
In one embodiment, the method 500 may also optionally include compressing the point cloud of each object using octree. The octree may be divided into a plurality of levels, each level representing a different precision of the point cloud information.
In one embodiment, the method 500 may also optionally include enclosing the point cloud of each object with an enclosure. For example, the bounding box may be generated using an Axis Aligned Bounding Box (AABB) algorithm, a bounding sphere algorithm, an Oriented Bounding Box (OBB) algorithm, or a fixed orientation convex hull (FDH) algorithm, and the coordinates of the center point of the bounding box and/or the coordinates of the boundary lines of the bounding box may be stored in a map description file.
Fig. 6 shows a block diagram of an apparatus 600 for storing high precision maps according to one embodiment of the present invention. All of the functional blocks of the apparatus 600 (including the respective units in the apparatus 600) may be implemented by hardware, software, or a combination of hardware and software. Those skilled in the art will appreciate that the functional blocks depicted in fig. 6 may be combined into a single functional block or divided into multiple sub-functional blocks.
The apparatus 600 may comprise an obtaining unit 610, the obtaining unit 610 being configured to obtain a road model of the high precision map. The apparatus 600 may further comprise a determination unit 620, the determination unit 620 being configured to determine, for each road section in the road model, an object feature of each object in the road section. The apparatus 600 may further comprise a storage unit 630, the storage unit 630 being configured to store the road model and the determined object features in one map description file.
FIG. 7 shows a block diagram of an exemplary computing device 700, which is one example of a hardware device that may be applied to aspects of the present invention, according to one embodiment of the present invention.
With reference to FIG. 7, a computing device 700, which is one example of a hardware device that may be employed in connection with aspects of the present invention, will now be described. Computing device 700 may be any machine that may be configured to implement processing and/or computing, and may be, but is not limited to, a workstation, a server, a desktop computer, a laptop computer, a tablet computer, personal digital processing, a smart phone, an in-vehicle computer, or any combination thereof. The various methods/apparatus/servers/client devices described above may be implemented in whole or at least in part by a computing device 700 or similar device or system.
When the computing device 700 is used as an in-vehicle device, it may also be connected with external devices, such as a GPS receiver, sensors for sensing different environmental data, such as acceleration sensors, wheel speed sensors, gyroscopes, etc. In this manner, the computing device 700 may receive, for example, positioning data and sensor data indicative of a vehicle-form condition. When computing device 700 is used as an in-vehicle device, it may also be connected with other devices for controlling the travel and operation of the vehicle (e.g., engine systems, wipers, anti-lock brake systems, etc.).
Further, the non-transitory storage device 710 may have map information and software components so that the processor 704 may implement route guidance processing. Further, the output device 706 may include a display for displaying a map, displaying a location marker of the vehicle, and displaying an image indicating the running condition of the vehicle. The output device 706 may also include a speaker or headphone interface for audio guidance.
The bus 702 may include, but is not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an enhanced ISA (eisa) bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus. In particular, for an in-vehicle device, bus 602 may also include a Controller Area Network (CAN) bus or other structure designed for applications in an automobile.
Software components may be located in the working memory 714, including, but not limited to, an operating system 716, one or more application programs 718, drivers, and/or other data and code. Instructions for implementing the above-described methods and steps may be included in the one or more application programs 718, and the aforementioned modules/units/components of the various apparatus/server/client devices may be implemented by the processor 704 reading and executing the instructions of the one or more application programs 718.
It should also be appreciated that variations may be made according to particular needs. For example, customized hardware might also be used, and/or particular components might be implemented in hardware, software, firmware, middleware, microcode, hardware description speech, or any combination thereof. In addition, connections to other computing devices, such as network input/output devices and the like, may be employed. For example, some or all of the disclosed methods and apparatus can be implemented with logic and algorithms in accordance with the present invention through programming hardware (e.g., programmable logic circuitry including Field Programmable Gate Arrays (FPGAs) and/or Programmable Logic Arrays (PLAs)) having assembly language or hardware programming languages (e.g., VERILOG, VHDL, C + +).
Although the various aspects of the present invention have been described thus far with reference to the accompanying drawings, the above-described methods, systems, and apparatuses are merely examples, and the scope of the present invention is not limited to these aspects but only by the appended claims and equivalents thereof. Various components may be omitted or may be replaced with equivalent components. In addition, the steps may also be performed in a different order than described in the present invention. Further, the various components may be combined in various ways. It is also important that as technology develops that many of the described components can be replaced by equivalent components appearing later.
Claims (10)
1. A method for storing a high-precision map, comprising:
obtaining a road model of a high-precision map;
for each road section in the road model, determining an object feature for each object in the road section, the object feature comprising a point cloud of the object; and
storing the road model and the object features in a map description file.
2. The method of claim 1, wherein the object characteristics further include a name, identifier, type, subtype, and/or characteristic value of the object.
3. The method of claim 1, wherein the point cloud is surrounded by a bounding box containing the point cloud.
4. The method of claim 3, wherein the bounding box is generated using an Axis Aligned Bounding Box (AABB) algorithm, a bounding sphere algorithm, an Orientation Bounding Box (OBB) algorithm, or a fixed orientation convex hull (FDH) algorithm.
5. The method of claim 4, wherein coordinates of a center point of the bounding box are stored in the map description file.
6. The method of claim 1, further comprising:
the point cloud is compressed using octree.
7. The method of claim 6, wherein the octree is divided into a plurality of levels, each level representing a different precision of point cloud information.
8. An apparatus for storing a high accuracy map, comprising:
an obtaining unit configured to obtain a road model of a high-precision map;
a determination unit configured to determine, for each road section in the road model, an object feature of each object in the road section, the object feature comprising a point cloud of the object; and
a storage unit configured to store the road model and the object feature in one map description file.
9. An apparatus for storing a high accuracy map, comprising:
a memory storing computer-executable instructions; and
a processor coupled to the memory and configured to:
obtaining a road model of a high-precision map;
for each road section in the road model, determining an object feature for each object in the road section, the object feature comprising a point cloud of the object; and
storing the road model and the object features in a map description file.
10. A non-transitory computer readable medium storing a computer program which, when executed by a processor, performs the method of any of claims 1-7.
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