CN110287276B - High-precision map updating method and device and storage medium - Google Patents
High-precision map updating method and device and storage medium Download PDFInfo
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- CN110287276B CN110287276B CN201910445725.4A CN201910445725A CN110287276B CN 110287276 B CN110287276 B CN 110287276B CN 201910445725 A CN201910445725 A CN 201910445725A CN 110287276 B CN110287276 B CN 110287276B
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Abstract
The application provides a high-precision map updating method, a high-precision map updating device and a storage medium, wherein the method comprises the following steps: the method comprises the steps of obtaining crowdsourcing images uploaded by crowdsourcing vehicle terminals, enabling each image to carry position information corresponding to the vehicle terminal when the image is collected, determining effective change elements of a current high-precision map and the position information of the effective change elements based on the crowdsourcing images and a high-precision map database, obtaining video data uploaded by a first vehicle terminal set according to the position information carried by each image and the position information of the effective change elements, and updating the high-precision map database according to the video data uploaded by the first vehicle terminal set. In the technical scheme, the high-precision map database is updated based on the images and video information uploaded by crowdsourcing vehicle terminals, the collection of an expensive collection vehicle is not needed, the updating speed is high, the timeliness of map updating can be guaranteed, and the driving safety problem of automatic driving vehicles is avoided.
Description
Technical Field
The present application relates to the field of automatic driving technologies, and in particular, to a high-precision map updating method, apparatus, and storage medium.
Background
High-precision maps are designed specifically for unmanned vehicles, including road definitions, intersections, traffic signals, lane rules, and other elements for vehicle navigation. Since the high-precision map can provide detailed information about the driving environment to ensure the safety of the autonomous vehicle, how to ensure timely update of the high-precision map is critical to ensure the safety of autonomous driving.
In the high-precision map updating scheme in the prior art, road track data are generally acquired by high-precision sensors such as a laser point cloud sensor and irrigation equipment arranged on a special acquisition vehicle and are uploaded to a cloud server, and the map is updated by the cloud server based on the received road track data.
However, due to the high cost and limited number of the dedicated collection vehicles, the updating of the high-precision map can be performed only regionally, and the updating timeliness cannot be guaranteed, which may cause a driving safety problem for scenes with high timeliness requirements, such as an automatic driving vehicle.
Disclosure of Invention
The application provides a high-precision map updating method, a high-precision map updating device and a storage medium, which are used for solving the problems that the updating timeliness in the existing map updating method cannot be guaranteed and the driving safety can be possibly caused.
The application provides a high-precision map updating method in a first aspect, which includes:
acquiring crowdsourcing images uploaded by crowdsourcing vehicle terminals, wherein each image carries position information of a corresponding vehicle terminal when the image is acquired;
determining effective change elements of a current high-precision map and position information of the effective change elements based on the crowdsourcing images and the high-precision map database;
acquiring video data uploaded by a first vehicle terminal set according to position information carried by each image and position information of the effective change elements, wherein the position of each vehicle terminal in the first vehicle terminal set and the position of the effective change elements meet preset distance constraint conditions;
and updating the high-precision map database according to the video data uploaded by the first vehicle terminal set.
In the embodiment, the high-precision map database is updated based on the image and video information uploaded by crowdsourcing social vehicles, the collection of expensive collection vehicles is not needed, the updating speed is high, the timeliness of map updating can be ensured, and the driving safety problem of automatic driving vehicles is avoided
In one possible design of the first aspect, the determining, based on the crowd-sourced image and the high-precision map database, an effective change element of a current high-precision map and position information of the effective change element includes:
analyzing each image in the crowdsourcing images to determine map identification elements in each image and position information of each map identification element;
judging whether each map identification element is a map change element or not based on the position information of each map identification element and the high-precision map database;
if the preset number of images have the same map change element at the same geographic position, determining that the map change element is an effective change element, and the same geographic position is position information of the effective change element.
In the embodiment, based on the way that the preset number of images have the same map change elements at the same geographic position, the effective change elements in the current high-precision map can be accurately determined, and the problems that a vehicle terminal sends a fake picture or a hacker invades to make a fake or the vehicle terminal acquires information inaccurately and the like are effectively prevented.
In another possible design of the first aspect, the obtaining, according to the position information carried in each image and the position information of the effective change element, video data uploaded by the first set of vehicle terminals includes:
determining the first vehicle terminal set according to the position information carried by each image and the position information of the effective change elements;
sending a video acquisition task to each vehicle terminal in the first set of vehicle terminals, wherein the video acquisition task comprises an acquisition area range, and the effective variation elements are located in the acquisition area range;
receiving the video data acquired by each vehicle terminal in the first vehicle set within the acquisition area.
In this embodiment, the cloud server determines an effective first vehicle terminal set, acquires video data for high-precision map reconstruction, and lays a foundation for timely updating of the high-precision map.
In the foregoing possible design of the first aspect, the determining the first vehicle terminal set according to the position information carried in each image and the position information of the effective variation element includes:
according to the position information carried by each image, determining the terminal basic information of the vehicle terminal corresponding to each image, wherein the terminal basic information comprises at least one of the following information: current position information, historical driving track information and current navigation route information;
and determining the first vehicle terminal set according to the terminal basic information of the vehicle terminal corresponding to each image.
In yet another possible design of the first aspect, the updating the high-precision map database according to video data uploaded by the first set of vehicle terminals includes:
creating a picture point cloud according to the video data uploaded by the first vehicle terminal set;
determining basic information of the effective change elements based on the picture point cloud and the laser point cloud in the high-precision map database;
and updating the high-precision map database according to the basic information of the effective change elements.
In this embodiment, the cloud server can update effective change elements in the high-precision map to the high-precision map database, and has the characteristics of timely update, easy implementation, low cost and high accuracy.
In yet another possible design of the first aspect, the effective variation element includes at least one of: lane lines, guardrails, curbs, signs and ground markings.
A second aspect of the present application provides a high-precision map updating apparatus, including: the device comprises an acquisition module, a processing module and an updating module;
the acquisition module is used for acquiring crowdsourcing images uploaded by crowdsourcing vehicle terminals, and each image carries position information of a corresponding vehicle terminal when the image is acquired;
the processing module is used for determining effective change elements of the current high-precision map and position information of the effective change elements based on the crowdsourcing images and the high-precision map database;
the obtaining module is further configured to obtain video data uploaded by a first vehicle terminal set according to the position information carried by each image and the position information of the effective change element, where a position of each vehicle terminal in the first vehicle terminal set and a position of the effective change element meet a preset distance constraint condition;
the updating module is used for updating the high-precision map database according to the video data uploaded by the first vehicle terminal set.
In a possible design of the second aspect, the processing module is specifically configured to analyze each image in the crowd-sourced images, determine a map identification element and position information of each map identification element in each image, determine whether each map identification element is a map change element based on the position information of each map identification element and the high-precision map database, and determine that the map change element is an effective change element when a preset number of images have the same map change element at the same geographic position, where the same geographic position is the position information of the effective change element.
In another possible design of the second aspect, the obtaining module includes: a determining unit and a transmitting and receiving unit;
the determining unit is configured to determine the first vehicle terminal set according to the position information carried by each image and the position information of the effective change element;
the transceiver unit is configured to send a video capture task to each vehicle terminal in the first vehicle terminal set, where the video capture task includes a capture area range, and the effective variation element is located in the capture area range, and receive the video data captured by each vehicle terminal in the first vehicle set in the capture area range.
In the foregoing possible design of the second aspect, the determining unit is specifically configured to determine, according to the position information carried by each image, terminal basic information of a vehicle terminal corresponding to each image, where the terminal basic information includes at least one of: and determining the first vehicle terminal set according to the current position information, the historical driving track information, the current navigation route information and the terminal basic information of the vehicle terminal corresponding to each image.
In yet another possible design of the second aspect, the updating module is specifically configured to create a picture point cloud according to the video data uploaded by the first vehicle terminal set, determine basic information of the effective change element based on the picture point cloud and the laser point cloud in the high-precision map database, and update the high-precision map database according to the basic information of the effective change element.
A third aspect of the present application provides a high-precision map updating apparatus, including a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to the first aspect and each possible design of the first aspect.
A fourth aspect of the present application provides a storage medium having stored therein instructions that, when executed on a computer, cause the computer to perform a method as set forth in the first aspect and each possible design of the first aspect.
According to the high-precision map updating method, device and storage medium, crowdsourcing images uploaded by crowdsourcing vehicle terminals are obtained, each image carries position information when the corresponding vehicle terminal collects the image, effective change elements of the current high-precision map and the position information of the effective change elements are determined based on the crowdsourcing images and the high-precision map database, then video data uploaded by a first vehicle terminal set are obtained according to the position information carried by each image and the position information of the effective change elements, and the high-precision map database is updated according to the video data uploaded by the first vehicle terminal set. In the technical scheme, the high-precision map database is updated based on the images and video information uploaded by crowdsourcing vehicle terminals, the collection of an expensive collection vehicle is not needed, the updating speed is high, the timeliness of map updating can be guaranteed, and the driving safety problem of automatic driving vehicles is avoided.
Drawings
Fig. 1 is a schematic view of an application scenario of a high-precision map updating method according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a high-precision map updating method according to a first embodiment of the present application;
fig. 3 is a schematic flowchart of a second high-precision map updating method provided in the embodiment of the present application;
fig. 4 is a schematic flowchart of a third embodiment of a high-precision map updating method provided in the embodiment of the present application;
fig. 5 is a schematic flowchart of a fourth embodiment of a high-precision map updating method provided in the embodiment of the present application;
fig. 6 is a schematic structural diagram of a first high-precision map updating apparatus according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of a second high-precision map updating apparatus according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of a third high-precision map updating apparatus according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The high-precision map updating method provided by the embodiment of the application mainly realizes high-precision map updating in a crowdsourcing mode, and specifically means that a cloud end collects tracks and pictures of social ordinary vehicles with low cost in a large scale or identification result data collected by an intelligent vehicle end, map visual data processing is carried out on the cloud end, and then updating of a high-precision map database is completed. The problem that the high-precision map is updated quickly and timely can be solved by using the crowdsourcing mode to update the high-precision map, the safety of an automatic driving vehicle is improved, and the problem of driving safety possibly existing in the prior art due to the fact that timeliness of updating the high-precision map cannot be guaranteed is solved.
An application scenario diagram of the embodiment of the present application is described below with reference to fig. 1. Fig. 1 is a schematic view of an application scenario of a high-precision map updating method according to an embodiment of the present application. As shown in fig. 1, in an embodiment of the present application, the application scenario may include: crowdsourced vehicle terminals 11, a pipe 12, a cloud server 13, and a storage device 14.
The crowdsourced vehicle terminal 11 may be a large-scale common social vehicle, an intelligent vehicle with data processing capability and identification capability, or a common social vehicle and an intelligent vehicle.
In practical applications, the pipeline 12 may be a network, that is, the crowdsourced vehicle terminal 11 uploads the acquired image to the cloud server 13 through the network, and the cloud server 13 performs the identification and processing of the map identification element. Optionally, in the cloud server 13, the crowdsourcing platform may determine effective change elements of the current high-precision map based on an image reported by a crowdsourcing vehicle terminal, and perform update work of the high-precision map database.
Optionally, the storage device 14 may be configured to store images or GPS track information uploaded by crowdsourced vehicle terminals, and may also be configured to store map element information and map feature information of a high-precision map.
Illustratively, the storage device 14 may include: a crowd-sourced database, a high-precision map database and a high-precision map feature library. The crowd-sourcing database is used for storing information such as images or GPS track information uploaded by the crowd-sourcing vehicle terminals, the high-precision map database is used for storing element information of a high-precision map, and the high-precision map feature library is used for storing feature information of the high-precision map. It should be noted that the embodiment of the present application does not limit the specific composition of the storage device, and may be determined according to actual situations.
It should be noted that fig. 1 is only a schematic diagram of an application scenario provided in this embodiment of the present application, and a positional relationship between the devices shown in fig. 1 does not limit any limitation, for example, in fig. 1, the storage device may be an external memory with respect to the cloud server, and in other cases, the storage device may also be placed in the cloud server.
The technical solution of the present application will be described in detail below with reference to specific examples. It should be noted that the following specific embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.
Fig. 2 is a schematic flowchart of a first high-precision map updating method provided in the embodiment of the present application. The high-precision map updating method can be executed by a cloud server, and can also be executed by a crowdsourcing platform on the cloud server. In this embodiment, the cloud server executes the method. As shown in fig. 2, the high-precision map updating method may include the steps of:
step 21: and acquiring crowdsourcing images uploaded by crowdsourcing vehicle terminals, wherein each image carries position information when the corresponding vehicle terminal acquires the image.
Optionally, in this embodiment, the cloud server may execute the high-precision map updating method under the trigger of an external application. Specifically, the cloud server first obtains a crowdsourcing image uploaded by a crowdsourcing vehicle terminal. Specifically, the cloud server can directly acquire the crowdsourcing image uploaded by the crowdsourcing vehicle terminal from the crowdsourcing database of the storage device, and also can receive the crowdsourcing image directly reported by the crowdsourcing vehicle terminal.
For example, in this embodiment, a common social vehicle, that is, a crowdsourced vehicle terminal, may acquire an image at intervals (for example, 20 meters) by using an acquisition device installed on the vehicle, record GPS track position information of the vehicle terminal on the image, and upload the GPS track position information to the cloud server.
That is to say, every vehicle terminal in crowdsourcing vehicle terminal can upload the image of once gathering every distance of traveling and also can report in real time, and this application embodiment does not restrict the mode that vehicle terminal reported the image, and it can be confirmed according to actual conditions.
It should be noted that the crowdsourcing images include at least one image reported by each vehicle terminal, and the number of images reported by each vehicle terminal is not limited in the embodiment of the present application.
Step 22: and determining effective change elements of the current high-precision map and position information of the effective change elements based on the crowdsourcing images and the high-precision map database.
Optionally, in an embodiment of the application, the cloud server may first analyze the received crowdsourcing image, determine a map identification element from each image in the crowdsourcing image, determine, by combining with a map element in the high-precision map database, whether the map identification element in each image is a map change element and determine the map change element in each image, and finally determine, based on the category and the position of the map change element in each image, an effective change element of the current high-precision map and position information of the effective map element from the map change elements in all images.
For example, in the embodiments of the present application, the map identification elements include, but are not limited to, roads, lane lines, signs, ground marks, etc., wherein the roads may include guardrails, curbs, etc., and the signs include: road sign, indicative tablet, limit for height tablet etc. various types, ground sign includes: a shunting mark, an entrance and exit mark, a speed limit mark, a time limit mark and the like.
Accordingly, in this embodiment, when the map identification element includes the above-mentioned categories of roads (guardrails and curbs), lane lines, signs, ground marks, and the like, the effective change element may also include at least one of the following elements: lane lines, guardrails, curbs, signs and ground markings.
It should be noted that, the embodiment of the present application does not limit the specific categories and expressions of the map identification element, the map change element, and the effective change element, and may be determined according to the specific situation of the actual application.
Step 23: and acquiring video data uploaded by the first vehicle terminal set according to the position information carried by each image and the position information of the effective change element.
And the position of each vehicle terminal in the first vehicle terminal set and the position of the map change element meet a preset distance constraint condition.
For example, in this embodiment, the cloud server may determine, according to the position information carried by each image, a position where each vehicle terminal that uploads the image is located, and then determine, in combination with the position information of the effective variation element, a first vehicle terminal set that meets a preset distance constraint condition from the crowd-sourced vehicle terminals. That is, the position of each vehicle terminal in the first set of vehicle terminals and the position of the effective variation element satisfy the preset distance constraint condition.
For example, the preset distance constraint condition may mean that the distance between the position of the vehicle terminal and the effective change element is less than a preset threshold. For example, all vehicle terminals in a distance range (e.g., 100-200 meters, etc.) before and after the position of the effective variation element satisfy the preset distance constraint condition.
Step 24: and updating the high-precision map database according to the video data uploaded by the first vehicle terminal set.
In the embodiment of the application, after the cloud server acquires the video data uploaded by each vehicle terminal in the first vehicle terminal set, the three-dimensional reconstruction of the high-precision map can be executed based on a preset reconstruction algorithm and the video data to obtain the picture point cloud, and then the basic information of effective changing elements is determined by combining the laser point cloud of the high-precision map database in the creating process, so that the basic information is favorably used for updating the high-precision map database.
According to the high-precision map updating method, crowdsourcing images uploaded by crowdsourcing vehicle terminals are obtained, each image carries position information corresponding to the vehicle terminal to collect the image, effective change elements of the current high-precision map and the position information of the effective change elements are determined based on the crowdsourcing images and the high-precision map database, then video data uploaded by a first vehicle terminal set are obtained according to the position information carried by each image and the position information of the effective change elements, and the high-precision map database is updated according to the video data uploaded by the first vehicle terminal set. According to the technical scheme, the high-precision map database is updated based on the image and video information uploaded by crowdsourcing social vehicles, the collection of expensive collection vehicles is not needed, the updating speed is high, the timeliness of map updating can be guaranteed, and the driving safety problem of automatic driving vehicles is avoided.
For example, on the basis of the foregoing embodiments, fig. 3 is a schematic flowchart of a second embodiment of a high-precision map updating method provided in the embodiment of the present application. As shown in fig. 3, in the present embodiment, the step 22 can be implemented by:
step 31: and analyzing each image in the crowdsourcing image to determine the map identification elements in each image and the position information of each map identification element.
For example, in this embodiment, after the cloud server acquires the crowdsourced images sent by the crowdsourced vehicle terminals, each image in the crowdsourced images is analyzed and subjected to image recognition processing, and a map identification element in each image is recognized. For example, element information such as lane lines, roads (guardrails, curbs), signs, and ground marks.
Optionally, after the cloud server determines the map identification element in each image, the cloud server may also determine the location information of the map identification element based on the location information carried by the image.
Step 32: and judging whether each map identification element is a map change element or not based on the position information of each map identification element and the high-precision map database.
Illustratively, the cloud server queries a high-precision map database according to the position information of each map identification element, judges whether the map element at the corresponding position in the map database is the map identification element, and determines that the map identification element is not a map change element and does not perform any processing if the map element at the corresponding position in the map database is the map identification element; and if the map identification element and the map change element are not consistent, determining that the map identification element is a map change element.
It should be noted that after the position information of each map identification element is determined, the cloud server may further perform a difference operation on each map identification element and the high-precision map database, determine whether the map element at the position of each map identification element changes (including addition, modification, deletion, and the like), and determine the map identification element at the position of the map element that changes as a map change element.
Step 33: if the preset number of images have the same map change element at the same geographic position, determining that the map change element is an effective change element, and the same geographic position is position information of the effective change element.
In this embodiment, for determining each map change element, it may be determined, in combination with map change elements in other images, whether a preset number of images in the crowd-sourced image have the same type of map change element at the same geographic position, and if so, the map change element is considered as an effective change element, and correspondingly, the same geographic position is position information of the effective change element.
It should be noted that, in this embodiment, the preset number of images may be sent by a preset number of different vehicles, that is, if the preset number of vehicle terminals all consider that the map identification element of the same geographic location and the map element in the high-precision map database have the same change, the map identification element of the same geographic location is determined to be an effective change element.
Optionally, the preset number is a threshold set in the cloud server, for example, 3, 5, etc. The specific value of the preset number is not limited in the embodiment of the application, and can be determined according to actual conditions.
The high-precision map updating method provided by the embodiment of the application determines map identification elements and position information of each map identification element in each image by analyzing each image in the crowd-sourced images, judges whether each map identification element is a map change element or not based on the position information of each map identification element and the high-precision map database, and determines that the map change element is an effective change element and the same geographical position is the position information of the effective change element when a preset number of images have the same map change element at the same geographical position. The technical scheme can accurately determine the effective change elements in the current high-precision map, and effectively prevents the problems that a vehicle terminal sends fake pictures or hackers invade the fake or the vehicle terminal acquires inaccurate information and the like.
Exemplarily, in an embodiment of the present application, fig. 4 is a schematic flowchart of a third embodiment of a high-precision map updating method provided in the embodiment of the present application. As shown in fig. 4, in this embodiment, the step 23 can be implemented by:
step 41: and determining a first vehicle terminal set according to the position information carried by each image and the position information of the effective change elements.
For example, in this embodiment, the position of the vehicle terminal reporting each image may be determined based on the position information carried by each image, and the area range including the effective change element is determined according to the position information of the effective change element. Therefore, the first vehicle terminal set is determined by determining whether the position of each vehicle terminal is located within the area range of the effective variation element, that is, the position of each vehicle terminal in the first vehicle terminal set is located within the area range of the effective variation element.
Optionally, the area range of the effective variation element may be a range formed by a distance before and after the position of the effective variation element.
Illustratively, in a possible design of this embodiment, the step 41 may also be implemented by:
step A1: according to the position information carried by each image, determining the terminal basic information of the vehicle terminal corresponding to each image, wherein the terminal basic information comprises at least one of the following information: current position information, historical driving track information and current navigation route information;
step A2: and determining the first vehicle terminal set according to the terminal basic information of the vehicle terminal corresponding to each image.
In this embodiment, for a common social vehicle, when a user drives a certain vehicle to go from a certain departure location to a certain destination, the GPS function of the vehicle is generally enabled to view road condition information, route information, and the like of a current road. Thus, the cloud server may obtain current location information of the vehicle based on the GPS function enabled by the vehicle.
Further, when the user uses the current high-precision map to query the route information or the current navigation route information from the departure place to the destination, the cloud server can determine the current position information and the current navigation route information of the vehicle terminal.
In addition, the cloud server can also inquire historical driving track information of the user when the user drives the vehicle terminal based on the login information of the user.
Therefore, in this embodiment, the cloud server may determine, based on one or more of the determined current position information, the historical travel track information, the current navigation route information, and other terminal basic information of the vehicle terminal corresponding to each image, a first vehicle terminal set located in the area where the effective change element is located, where each vehicle terminal in the first vehicle terminal set is used to perform a video capture task.
Step 42: and sending a video acquisition task to each vehicle terminal in the first vehicle terminal set.
The video acquisition task comprises an acquisition area range, and the effective variation elements are located in the acquisition area range.
Optionally, in this embodiment, in order to perform three-dimensional reconstruction on the area where the effective variable element is located, the cloud server needs to send a video acquisition task including an acquisition area range to the determined first vehicle terminal set, and the effective variable element is located in the acquisition area range, so that each vehicle terminal in the first vehicle terminal set can acquire video data based on the received video acquisition task.
Step 43: and receiving video data acquired by each vehicle terminal in the first vehicle set within the acquisition area.
For example, each vehicle terminal in the first vehicle set may determine, based on the received video capture task, a capture area range corresponding to the video capture task, and capture video data including valid change elements within the capture area range.
According to the high-precision map updating method provided by the embodiment of the application, a first vehicle terminal set is determined according to the position information carried by each image and the position information of the effective change elements, a video acquisition task is sent to each vehicle terminal in the first vehicle terminal set, and video data acquired by each vehicle terminal in the first vehicle set in the acquisition area range is received. In the technical scheme, the cloud server determines an effective first vehicle terminal set and acquires video data for high-precision map reconstruction, and a realization basis is laid for timely updating of the high-precision map.
Exemplarily, in an embodiment of the present application, fig. 5 is a schematic flowchart of a fourth embodiment of a high-precision map updating method provided in the embodiment of the present application. As shown in fig. 5, in the present embodiment, the step 24 may be implemented by:
step 51: and creating a picture point cloud according to the video data uploaded by the first vehicle terminal set.
Optionally, in this embodiment, after the cloud server obtains the video data uploaded by the first vehicle terminal set, a preset reconstruction algorithm may be adopted to create a picture point cloud according to the received video data.
The embodiment of the present application does not limit the specific implementation of the preset reconstruction algorithm, and for example, it may also be any one or more suitable deep learning algorithms or models, such as a deep learning network or a convolutional neural network. The specific implementation can be determined according to actual conditions.
For example, the cloud server may use a structure-from-motion (SFM) algorithm or other types of visual three-dimensional reconstruction algorithms to complete three-dimensional reconstruction of a high-precision map, so as to obtain a picture point cloud.
The SFM algorithm is an off-line algorithm for three-dimensional reconstruction based on various collected disordered pictures. In the practical application of the embodiment, the cloud server first selects an image suitable for three-dimensional reconstruction from the received video information, and then creates a picture point cloud based on the selected image.
It should be noted that, because the data volume required for three-dimensional reconstruction is large, but full map element modeling is impossible, the cloud server only models the area or road related to the effective change element, so that the creation of the picture point cloud in this embodiment is possible.
Step 52: and determining basic information of effective change elements based on the picture point cloud and the laser point cloud in the high-precision map database.
For example, in this embodiment, the high-precision map database may be a laser point cloud generated by using a centralized mapping mode, so the cloud server may match the created picture point cloud with the laser point cloud in the high-precision map database, match some significant semantic features, such as lamp posts, guardrails, signs, etc., generated by using the centralized mapping mode, regarding the high-precision map, and further extract basic information of effective changing elements that need to be updated from the picture point cloud.
Optionally, the basic information of the effective change element may include: and vector information such as geometric position and attribute information of the effective change element. The geometric location may specifically be a geographic location (longitude and latitude) and a coordinate, and the attribute information includes: color information and/or numerical values and/or directions, etc.
It should be noted that the embodiment of the present application does not limit the concrete representation form of the basic information of the effective variation element, and it can be determined according to the actual situation.
Step 53: and updating the high-precision map database according to the basic information of the effective change elements.
Optionally, in an embodiment of the application, the cloud server may update the extracted basic information of the effective change elements to the high-precision map database with a small amount of manual assistance. Illustratively, the human assistance may include, but is not limited to, information confirmation, information check, information adjustment, etc. included in the human-computer interaction interface, which may be determined according to actual situations.
According to the high-precision map updating method provided by the embodiment of the application, the picture point cloud is created according to the video data uploaded by the first vehicle terminal set, the basic information of the effective change elements is determined based on the picture point cloud and the laser point cloud in the high-precision map database, and the high-precision map database is updated according to the basic information of the effective change elements. In the technical scheme, the cloud server can update effective change elements in the high-precision map to the high-precision map database, and has the characteristics of timely update, easiness in implementation, low cost and high accuracy.
It is worth noting that the high-precision map updating of the embodiment of the application can be summarized as finding effective change elements of the high-precision map based on crowdsourcing images uploaded by crowdsourcing terminals, collecting video data including the effective change elements according to a first vehicle terminal set meeting a preset distance constraint condition, performing three-dimensional reconstruction on the obtained video data through a preset reconstruction algorithm on a cloud server, and extracting basic information of the effective change elements, so that updating of the high-precision map database is achieved. The scheme has the advantages of high updating speed, easiness in realization, reduction in updating difficulty and avoidance of possible driving safety problems to a certain extent.
In another possible design of the application, the crowdsourcing terminal can also be an intelligent vehicle with edge computing capabilities such as data processing and change recognition, at the moment, the intelligent vehicle can process the acquired image or video to determine map change elements, basic information of effective change elements is determined through three-dimensionally created picture point clouds and laser point clouds in the high-precision map database, and then the basic information is sent to the cloud server, and the high-precision map database is updated through the cloud server or the crowdsourcing platform.
The following are embodiments of the apparatus of the present application that may be used to perform embodiments of the method of the present application. For details which are not disclosed in the embodiments of the apparatus of the present application, reference is made to the embodiments of the method of the present application.
Fig. 6 is a schematic structural diagram of a first high-precision map updating apparatus according to an embodiment of the present application. The device can be integrated in a cloud server and can also be realized through a crowdsourcing platform on the cloud server. As shown in fig. 6, the high-precision map updating apparatus may include: an acquisition module 61, a processing module 62 and an update module 63.
The acquiring module 61 is configured to acquire crowdsourcing images uploaded by crowdsourcing vehicle terminals, where each image carries position information of a corresponding vehicle terminal when the image is acquired;
the processing module 62 is configured to determine, based on the crowd-sourced image and the high-precision map database, effective change elements of a current high-precision map and position information of the effective change elements;
the obtaining module 61 is further configured to obtain video data uploaded by a first vehicle terminal set according to the position information carried by each image and the position information of the effective change element, where a position of each vehicle terminal in the first vehicle terminal set and a position of the effective change element meet a preset distance constraint condition;
the updating module 63 is configured to update the high-precision map database according to the video data uploaded by the first vehicle terminal set.
For example, in a possible design of the present application, the processing module 62 is specifically configured to analyze each image in the crowd-sourced images, determine a map identification element and position information of each map identification element in each image, determine whether each map identification element is a map change element based on the position information of each map identification element and the high-precision map database, and determine that the map change element is an effective change element when a preset number of images have the same map change element at the same geographic location, where the same geographic location is the position information of the effective change element.
For example, in another possible design of the present application, fig. 7 is a schematic structural diagram of a second embodiment of a high-precision map updating apparatus provided in the present application. As shown in fig. 7, in the present embodiment, the obtaining module 61 includes: a determination unit 71 and a transceiving unit 72.
The determining unit 71 is configured to determine the first vehicle terminal set according to the position information carried by each image and the position information of the effective change element;
the transceiver unit 72 is configured to send a video capture task to each vehicle terminal in the first vehicle terminal set, where the video capture task includes a capture area range, and the effective variation element is located in the capture area range, and receive the video data captured by each vehicle terminal in the first vehicle set in the capture area range.
For example, in this embodiment, the determining unit 71 is specifically configured to determine, according to the position information carried by each image, terminal basic information of a vehicle terminal corresponding to each image, where the terminal basic information includes at least one of the following: and determining the first vehicle terminal set according to the current position information, the historical driving track information, the current navigation route information and the terminal basic information of the vehicle terminal corresponding to each image.
For example, in yet another possible design of the present application, the updating module 63 is specifically configured to create a picture point cloud according to video data uploaded by the first vehicle terminal set, determine basic information of the effective change element based on the picture point cloud and the laser point cloud in the high-precision map database, and update the high-precision map database according to the basic information of the effective change element.
The apparatus provided in the embodiment of the present application may be used to execute the method in the embodiments shown in fig. 2 to fig. 5, and the implementation principle and the technical effect are similar, which are not described herein again.
It should be noted that the division of the modules of the above apparatus is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity, or may be physically separated. And these modules can be realized in the form of software called by processing element; or may be implemented entirely in hardware; and part of the modules can be realized in the form of calling software by the processing element, and part of the modules can be realized in the form of hardware. For example, the determining module may be a processing element separately set up, or may be implemented by being integrated in a chip of the apparatus, or may be stored in a memory of the apparatus in the form of program code, and the function of the determining module is called and executed by a processing element of the apparatus. Other modules are implemented similarly. In addition, all or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, each step of the above method or each module above may be implemented by an integrated logic circuit of hardware in a processor element or an instruction in the form of software.
For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more Application Specific Integrated Circuits (ASICs), or one or more microprocessors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs), among others. For another example, when some of the above modules are implemented in the form of a processing element scheduler code, the processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor that can call program code. As another example, these modules may be integrated together, implemented in the form of a system-on-a-chip (SOC).
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
Fig. 8 is a schematic structural diagram of a third high-precision map updating apparatus according to an embodiment of the present application. As shown in fig. 8, the apparatus may include: the system comprises a processor 81, a memory 82, a communication interface 83 and a system bus 84, wherein the memory 82 and the communication interface 83 are connected with the processor 81 through the system bus 84 and complete mutual communication, the memory 82 is used for storing computer execution instructions, the communication interface 83 is used for communicating with other devices, and the processor 81 implements the scheme of the embodiment shown in fig. 2 to 5 when executing the computer execution instructions.
The system bus mentioned in fig. 8 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The system bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus. The communication interface is used for realizing communication between the database access device and other equipment (such as a client, a read-write library and a read-only library). The memory may comprise Random Access Memory (RAM) and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The processor may be a general-purpose processor, including a central processing unit CPU, a Network Processor (NP), and the like; but also a digital signal processor DSP, an application specific integrated circuit ASIC, a field programmable gate array FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components.
Optionally, an embodiment of the present application further provides a storage medium, where instructions are stored in the storage medium, and when the storage medium is run on a computer, the storage medium causes the computer to execute the method according to the embodiment shown in fig. 2 to 5.
Optionally, an embodiment of the present application further provides a chip for executing the instruction, where the chip is configured to execute the method in the embodiment shown in fig. 2 to 5.
In the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone, wherein A and B can be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship; in the formula, the character "/" indicates that the preceding and following related objects are in a relationship of "division". "at least one of the following" or similar expressions refer to any combination of these items, including any combination of the singular or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple.
It is to be understood that the various numerical references referred to in the embodiments of the present application are merely for descriptive convenience and are not intended to limit the scope of the embodiments of the present application.
It should be understood that, in the embodiment of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiment of the present application.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (13)
1. A high-precision map updating method is characterized by comprising the following steps:
acquiring crowdsourcing images uploaded by crowdsourcing vehicle terminals, wherein each image carries position information of a corresponding vehicle terminal when the image is acquired;
determining effective change elements of a current high-precision map and position information of the effective change elements based on the crowdsourcing images and the high-precision map database;
acquiring video data uploaded by a first vehicle terminal set according to position information carried by each image and position information of the effective change elements, wherein the position of each vehicle terminal in the first vehicle terminal set and the position of the effective change elements meet preset distance constraint conditions;
and updating the high-precision map database according to the video data uploaded by the first vehicle terminal set.
2. The method of claim 1, wherein determining the effective change element of the current high-precision map and the position information of the effective change element based on the crowd-sourced image and the high-precision map database comprises:
analyzing each image in the crowdsourcing images to determine map identification elements in each image and position information of each map identification element;
judging whether each map identification element is a map change element or not based on the position information of each map identification element and the high-precision map database;
if the preset number of images have the same map change element at the same geographic position, determining that the map change element is an effective change element, and the same geographic position is position information of the effective change element.
3. The method according to claim 1, wherein the obtaining video data uploaded by the first set of vehicle terminals according to the position information carried by each image and the position information of the effective change element comprises:
determining the first vehicle terminal set according to the position information carried by each image and the position information of the effective change elements;
sending a video acquisition task to each vehicle terminal in the first set of vehicle terminals, wherein the video acquisition task comprises an acquisition area range, and the effective variation elements are located in the acquisition area range;
receiving the video data acquired by each vehicle terminal in the first vehicle set within the acquisition area.
4. The method according to claim 3, wherein the determining the first set of vehicle terminals according to the position information carried by each image and the position information of the effective variation element comprises:
according to the position information carried by each image, determining the terminal basic information of the vehicle terminal corresponding to each image, wherein the terminal basic information comprises at least one of the following information: current position information, historical driving track information and current navigation route information;
and determining the first vehicle terminal set according to the terminal basic information of the vehicle terminal corresponding to each image.
5. The method according to any of claims 1-4, wherein said updating said high-precision map database based on video data uploaded by said first set of vehicle terminals comprises:
creating a picture point cloud according to the video data uploaded by the first vehicle terminal set;
determining basic information of the effective change elements based on the picture point cloud and the laser point cloud in the high-precision map database;
and updating the high-precision map database according to the basic information of the effective change elements.
6. The method according to any one of claims 1-4, wherein the effective change element comprises at least one of the following elements: lane lines, guardrails, curbs, signs and ground markings.
7. A high-precision map updating apparatus, comprising: the device comprises an acquisition module, a processing module and an updating module;
the acquisition module is used for acquiring crowdsourcing images uploaded by crowdsourcing vehicle terminals, and each image carries position information of a corresponding vehicle terminal when the image is acquired;
the processing module is used for determining effective change elements of the current high-precision map and position information of the effective change elements based on the crowdsourcing images and the high-precision map database;
the obtaining module is further configured to obtain video data uploaded by a first vehicle terminal set according to the position information carried by each image and the position information of the effective change element, where a position of each vehicle terminal in the first vehicle terminal set and a position of the effective change element meet a preset distance constraint condition;
the updating module is used for updating the high-precision map database according to the video data uploaded by the first vehicle terminal set.
8. The apparatus of claim 7, wherein the processing module is specifically configured to analyze each of the crowd-sourced images, determine a map identification element and position information of each map identification element in each of the images, determine whether each map identification element is a map change element based on the position information of each map identification element and the high-precision map database, and determine that the map change element is a valid change element when a preset number of images have the same map change element at the same geographic location, where the same geographic location is the position information of the valid change element.
9. The apparatus of claim 7, wherein the obtaining module comprises: a determining unit and a transmitting and receiving unit;
the determining unit is configured to determine the first vehicle terminal set according to the position information carried by each image and the position information of the effective change element;
the transceiver unit is configured to send a video capture task to each vehicle terminal in the first vehicle terminal set, where the video capture task includes a capture area range, and the effective variation element is located in the capture area range, and receive the video data captured by each vehicle terminal in the first vehicle set in the capture area range.
10. The apparatus according to claim 9, wherein the determining unit is specifically configured to determine, according to the position information carried by each image, terminal basic information of a vehicle terminal corresponding to each image, where the terminal basic information includes at least one of: and determining the first vehicle terminal set according to the current position information, the historical driving track information, the current navigation route information and the terminal basic information of the vehicle terminal corresponding to each image.
11. The apparatus according to any of claims 7-10, wherein the updating module is specifically configured to create a picture point cloud from the video data uploaded by the first set of vehicle terminals, determine the basic information of the effective change elements based on the picture point cloud and the laser point cloud in the high-precision map database, and update the high-precision map database according to the basic information of the effective change elements.
12. A high precision map updating apparatus comprising a processor, a memory and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1 to 6 when executing the program.
13. A storage medium having stored therein instructions which, when run on a computer, cause the computer to perform the method of any one of claims 1-6.
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| EP4194807A1 (en) * | 2021-12-10 | 2023-06-14 | Beijing Baidu Netcom Science Technology Co., Ltd. | High-precision map construction method and apparatus, electronic device, and storage medium |
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