CN114646321A

CN114646321A - Automatic generation method and system of high-precision map data and map data cloud platform

Info

Publication number: CN114646321A
Application number: CN202011498040.5A
Authority: CN
Inventors: 李峰; 李萌
Original assignee: Navinfo Co Ltd
Current assignee: Navinfo Co Ltd
Priority date: 2020-12-17
Filing date: 2020-12-17
Publication date: 2022-06-21

Abstract

The invention discloses an automatic generation method and system of high-precision map data and a map data cloud platform, belonging to the field of high-precision map manufacturing, and the method comprises the following steps: determining a rough coordinate according to the data acquisition planning route, and determining at least one virtual reference station required for acquisition according to the rough coordinate; in a first time period, acquiring GNSS data of a first time period from at least one virtual reference station according to preset information, and preprocessing the GNSS data of the first time period; in a second time period, according to the data acquisition planning route, acquiring GNSS data and inertial navigation data in the second time period; the first time period covers the second time period; performing difference on the preprocessed first-time-period GNSS data and the preprocessed second-time-period GNSS data to obtain difference GNSS data; and fusing and resolving the inertial navigation data and the differential GNSS data, and correcting the GNSS data to obtain high-precision track coordinate data. By implementing the method and the device, high-precision map field collection and base station removal can be realized, and integration of online drawing and output can be realized.

Description

Automatic generation method and system of high-precision map data and map data cloud platform

Technical Field

The disclosure relates to high-precision map making and service, in particular to an automatic generation method and system of high-precision map data and a map data cloud platform.

Background

At present, in the production process of Advanced Driver Assistance System (ADAS) maps and high-precision maps, high-precision field collection is often the only data source. The rationality of base station erection and the quality of base station data play key factors for the quality of high-precision map data collected by the industry, and currently, the following problems exist in the mode that the observation data of a GNSS (global navigation satellite system) is received basically by manually selecting addresses and erecting physical base stations:

1) the erection condition of the base station is severer

The method generally requires that an erection area is open and free of shading, other signal sources (radio, high-voltage line and the like), rivers and reflective buildings, and the power failure condition cannot occur in the observation process, otherwise, high-precision field collection reworking operation is caused.

2) High cost of labor

At least one GNSS receiving device and a nurse person are required to be equipped for each high-precision map data acquisition vehicle, and the market has higher expectation on the coverage rate and precision of the high-precision maps at present when the high-precision maps are rapidly developed.

Therefore, on the premise of ensuring the precision, how to reduce the labor and equipment costs becomes a technical problem that graphics manufacturers need to solve urgently.

Disclosure of Invention

In view of this, the present disclosure discloses an automatic generation method and system for high-precision map data, and a map data cloud platform, which can realize high-precision map field collection, base station removal, online mapping, and output integration.

In order to achieve the above object, the present disclosure adopts a technical solution that an automatic generation method of high-precision map data is provided, the method including:

determining a rough coordinate according to a data acquisition planning route, and determining at least one virtual reference station required for acquisition according to the rough coordinate; in a first time period, acquiring first time period Global Navigation Satellite System (GNSS) data from the at least one virtual reference station according to preset information, and preprocessing the first time period GNSS data; in a second time period, acquiring GNSS data and inertial navigation data in the second time period according to the data acquisition planning route; wherein the first period encompasses the second period; performing difference on the preprocessed GNSS data in the first time period and the preprocessed GNSS data in the second time period to obtain difference GNSS data; and performing fusion calculation on the inertial navigation data and the differential GNSS data, and correcting the GNSS data to obtain high-precision track coordinate data.

Accordingly, to implement the above method, the present disclosure also discloses an automatic generation system of high-precision map data, the system comprising:

the base station replacing device is used for acquiring first-period GNSS data from the at least one virtual reference station according to preset information and preprocessing the first-period GNSS data;

the mobile acquisition equipment is provided with a GNSS receiver and inertial navigation equipment and is used for acquiring GNSS data and inertial navigation data in a second time period according to the data acquisition planning route in the second time period; wherein the first time period covers the second time period, and the virtual reference station and the mobile acquisition device are time-synchronized;

the difference fusion module is used for carrying out difference on the preprocessed GNSS data in the first time period and the preprocessed GNSS data in the second time period to obtain difference GNSS data; and the system is used for performing fusion calculation on the inertial navigation data and the differential GNSS data, and correcting the GNSS data to obtain high-precision track coordinate data.

In addition, this disclosure also discloses a map data cloud platform, which includes: any one of the automatic generation system and the data customization and output system of the high-precision map data; the data customizing and outputting system is provided with a data product customizing module, a visual outputting module, an API interface module and an API gateway module, and is used for providing an access interface for an authorized user, providing a visual product customizing operation interface, outputting according to a high-precision map data product customized by the authorized user, and monitoring a network and an access state; the API gateway module further comprises an operation and maintenance monitoring unit, a log management unit and an identity authentication unit.

In particular, the present disclosure also discloses a lightweight map data cloud platform comprising:

the difference fusion module is used for carrying out difference on the preprocessed GNSS data in the first time period and the preprocessed GNSS data in the second time period to obtain difference GNSS data; the inertial navigation data and the differential GNSS data are fused and resolved, and the GNSS data are corrected to obtain high-precision track coordinate data;

the point cloud data resolving module is used for automatically resolving the point cloud data and extracting the road information; the road information at least comprises a road line, a sign plate and other road related data; the mobile acquisition equipment is configured with sensing equipment, wherein the sensing equipment acquires point cloud data;

the data making platform is used for generating high-precision map data reflecting the real ground objects in a geographic coordinate system based on the high-precision track coordinate data and by combining the road information; the high-precision map data are compiled according to requirements and output in a preset format;

the map data warehouse is used for storing and managing customizable productized high-precision map data, and comprises map full-quantity data and/or incremental data of a current version;

the data customizing and outputting system is provided with a data product customizing module, a visual outputting module, an API interface module and an API gateway module, and is used for providing an access interface for an authorized user, providing a visual product customizing operation interface, outputting according to a high-precision map data product customized by the authorized user, and monitoring a network and an access state; the API gateway module further comprises an operation and maintenance monitoring unit, a log management unit and an identity authentication unit.

Compared with the prior art, the technical scheme disclosed by the disclosure has the following technical effects:

the automatic generation method and the system of the high-precision map data disclosed by the disclosure adopt a base Station replacement device which can replace an entity base Station in the high-precision map data acquisition, and acquire high-precision and high-quality Virtual base Station data through setting a Virtual Reference Station (VRS) and analytical quality inspection operations such as network transmission, format conversion and the like, thereby realizing the non-base Station acquisition of the high-precision map field acquisition, avoiding the large-scale investment of large-scale manpower and material resources, releasing the labor productivity and reducing the equipment cost consumption as much as possible on the premise of guaranteeing the precision of the high-precision map data, rapidly finishing the acquisition of the high-precision map data, and carrying out carrier phase difference correction on a mobile acquisition device and the Virtual Reference Station on the premise of not needing the investment of manpower and surveying and mapping equipment, thereby realizing real-time kinematic (RTK). Through the technical scheme disclosed by the disclosure, the map acquisition and manufacturing efficiency can be effectively improved.

Drawings

Fig. 1 is a schematic flow chart of an automated generation method of high-precision map data according to an embodiment of the present disclosure;

FIG. 2 is a schematic diagram of an automated generation system for high-precision map data according to an embodiment of the present disclosure;

FIG. 3 is a schematic diagram of data interaction during high-precision map data acquisition, production, and output in an embodiment of the present disclosure;

FIG. 4 is a schematic diagram illustrating a map data cloud platform according to an embodiment of the present disclosure;

fig. 5 is a schematic composition diagram of a lightweight map data cloud platform disclosed in an embodiment of the present disclosure.

Detailed Description

The preferred embodiments of the present disclosure are described in detail below with reference to the accompanying drawings so that the advantages and features of the present disclosure can be more readily understood by those skilled in the art, and the scope of the present disclosure can be more clearly defined.

It should be noted that, herein, relationships such as first and second, etc., are intended to distinguish one entity or operation from another entity or operation without necessarily requiring or implying any actual such relationship or order between such actual operations. Furthermore, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include elements inherent in the list. The term "comprising", without further limitation, means that the element so defined is not excluded from the group of processes, methods, articles, or devices that include the element.

The method comprises the following steps:

the embodiment discloses an automatic generation method of high-precision map data, which comprises the following steps:

s100: a route is planned according to the data acquisition, and the approximate coordinates are determined, and at least one virtual reference station required for acquisition is determined according to the approximate coordinates.

S200: and acquiring the GNSS data of the global navigation satellite system from at least one virtual reference station in a first period according to preset information.

S300: and preprocessing the GNSS data in the first time period.

S400: and in a second time period, acquiring GNSS data and inertial navigation data in the second time period according to the data acquisition planning route. Wherein the first period covers the second period.

S500: and differentiating the preprocessed first-time GNSS data and the preprocessed second-time GNSS data to obtain differential GNSS data.

S600: and performing fusion resolving on the inertial navigation data and the differential GNSS data, and correcting the GNSS data to obtain high-precision track coordinate data.

In this embodiment, a base Station replacement apparatus capable of replacing an entity base Station in high-precision map data acquisition is adopted, and on the premise that no manpower or surveying and mapping equipment is required to be invested, base Station data is acquired from a Virtual Reference Station (VRS), and high-precision and high-quality Virtual base Station data is acquired through analytic quality inspection operations such as network transmission and format conversion.

As an alternative implementation manner, based on the above embodiment, the automatic generation method of high-precision map data may further include the following steps:

s700: and in a second time period, collecting point cloud data, automatically analyzing the point cloud data, and extracting road information. The road information includes at least a lane line, a sign board, and other road-related data.

S800: and generating high-precision map data reflecting the real ground objects in a geographic coordinate system based on the high-precision track coordinate data and by combining road information.

Optionally, in the embodiment S300, the preprocessing the first period GNSS data further includes: and analyzing, detecting the quality of the GNSS data in the first period, and screening the data meeting preset conditions.

The analyzing of the GNSS data in the first period of time may further include: and converting the transmission data format of the GNSS data in the first period into a GNSS standard data format. In the acquisition process, according to the determined position coordinates of the virtual reference station, a virtual observation data, i.e., a first-period GNSS data, near the position of the virtual reference station may be applied to a VRS server (CORS system) by NMEA (National Marine Electronics Association, standard format established by Marine electronic devices), where the first-period GNSS data is transmitted by a network protocol and in an RTCM (Radio Technical communication for landmark services, GNSS standard protocol) format, for example: the output type is NIRIP Client network protocol, the receiving type is File form, and the transmission standard format is RTCM 3. The RTCM Format is a differential service binary universal Format, and before differential fusion, the RTCM Format needs to be converted into a decimal RINEX Format (GNSS standard data Format).

Optionally, the above embodiments may employ two or more virtual reference stations to acquire base station GNSS data at different distances from the mobile acquisition device. In the subsequent resolving process, base station data of a plurality of virtual reference stations can be loaded simultaneously, the data of the mobile acquisition equipment in the same time period are resolved, the data of each base station are automatically weighted and distributed according to the distance between the base station and the mobile acquisition equipment, and the adjustment stability and the resolving accuracy of the mobile acquisition equipment are improved by utilizing the multiple base stations.

As an optional implementation manner, based on the foregoing embodiment, the performing quality detection on the GNSS data in the first period further includes: transmission network monitoring and/or GNSS data quality detection. Wherein:

1) the transport network monitoring may further comprise: and performing real-time transmission monitoring on the network transmission signals at the side of the virtual reference station, feeding the monitored data packet loss information back to the virtual reference station, and retransmitting the corresponding GNSS data by the virtual reference station according to the received feedback information.

2) The transport network monitoring may further comprise: monitoring and alarming by adopting a network abnormal log, monitoring a network transmission signal at the side of the virtual reference station, alarming when the abnormal log is monitored, feeding back the packet loss information of the monitored data to the virtual reference station, and retransmitting the corresponding GNSS data by the virtual reference station according to the received feedback information.

As an optional implementation manner, based on the foregoing embodiment, the GNSS data quality detection may further include: and judging whether the GNSS data in the first time period is qualified or not according to the satellite number of each epoch, the satellite spatial distribution factor, the signal-to-noise ratio of each frequency band and the multi-path noise of each frequency band, and calculating the qualification rate. And when the qualified rate of the first period GNSS data is lower than a preset threshold value, discarding the first period GNSS data of the corresponding virtual reference station. The first time GNSS data with the qualification rate reaching or higher than the preset threshold value is qualified data of quality detection. For example, the yield preset threshold may be between 99% and 99.9%.

As an optional implementation manner, based on the above embodiment, the inertial navigation data and the differential GNSS data are fused and resolved, and the GNSS data is corrected, further including:

and calculating the data of the GNSS data missing caused by the satellite signal quality by using the inertial navigation data and dead reckoning through the track. For example, when the satellite signal is poor, after the initial coordinate is accurately determined, the coordinate of the missing GNSS data is obtained through inertial navigation accelerometer and dead reckoning

As an optional implementation manner, based on the above embodiment, in the fusion resolving process, GNSS data of two or more virtual reference stations are loaded, each virtual reference station is configured with a weight set based on the quality of the GNSS data, and according to the weight, the data resolving accuracy of the virtual reference stations is fused.

Before collection, the position of the base station can be determined according to a route planned by field collection, for example, the base station is roughly determined at the middle position of the route according to a collection plan of the field on the same day, an outline coordinate is obtained, a virtual base station (namely a virtual reference station) is determined according to the outline coordinate, and the collection route is positioned in the radiation range of the virtual base station. For example, within a radius of 20 km around the base station, if the radius exceeds 20 km, the number of virtual base stations can be increased. In the course of the acquisition process,

the virtual reference station and the mobile acquisition equipment are time-synchronized, and the data of the base station can be shared for difference only when the virtual reference station and the mobile acquisition equipment correspond to the same satellite. The data of a plurality of satellites can be acquired in the same time period, the base station and the mobile acquisition equipment form a group of differences by the same satellite at the same time, and the satellites in the same time period can form a plurality of groups of differences.

In addition, the present disclosure also discloses an example of an automatic generation method of high-precision map data, which includes a process S101, a process S102, a process S103, and a process S104, as shown in fig. 1.

The process S101 shown in fig. 1 is a process of determining a position of at least one virtual reference station required for field acquisition according to a field acquisition plan of a mobile acquisition device, and replacing a base station for acquiring data in the prior art with a virtual reference station, so that a preparation for data acquisition can be conveniently and quickly completed on the premise of avoiding large-scale investment of large-scale manpower and material resources, so as to further acquire global navigation satellite system observation data corresponding to the determined virtual reference station.

In an optional embodiment of the disclosure, the determining the position of the at least one virtual reference station required for field collection according to the field collection planning of the mobile collection device includes planning a collection route before the field collection, and determining the position of the virtual reference station so that the planned field collection route is within a radiation range of the virtual reference station, so as to further apply for obtaining the global navigation satellite system observation data corresponding to the determined position of the at least one virtual reference station.

In an optional embodiment of the disclosure, the step of planning the acquisition route before the field acquisition, wherein the step of determining the position of the virtual reference station comprises determining the virtual reference station approximately at a middle position of the acquisition route so that the planned field acquisition route is within a radiation range of the virtual reference station, so as to further request for obtaining global navigation satellite system observation data corresponding to the determined position of the at least one virtual reference station. In an optional embodiment of the disclosure, the step of planning the acquisition route before the field acquisition, and the step of determining the position of the virtual reference station comprises determining the virtual reference station approximately at the middle position of the acquisition route so that the planned field acquisition route is within a radiation range of 20 kilometers of the virtual reference station, wherein the radiation range is centered on the base station, so as to further apply for acquiring the global navigation satellite system observation data corresponding to the determined position of the at least one virtual reference station.

The process S102 shown in fig. 1 represents a process of applying for global navigation satellite system observation data corresponding to at least one virtual reference station from a virtual reference station server according to the position of the at least one virtual reference station, so as to further obtain global navigation satellite system observation data corresponding to the position of the at least one virtual reference station.

In an optional embodiment of the disclosure, the applying for the global navigation satellite system observation data corresponding to the at least one Virtual Reference Station from the Virtual Reference Station server includes applying for the global navigation satellite system observation data corresponding to the at least one Virtual Reference Station from an operator with a Virtual Reference Station (a type of CORS application) service capability, so as to further obtain the global navigation satellite system observation data corresponding to the position of the at least one Virtual Reference Station.

In an optional embodiment of the disclosure, the process of applying for the gnss observation data corresponding to the at least one virtual reference station from the virtual reference station server according to the position of the at least one virtual reference station includes that the virtual reference station server uses an existing Continuously Operating Reference Station (CORS) at the position as a Virtual Reference Station (VRS) according to the position of the at least one virtual reference station to collect and provide gnss observation data required for field collection, so as to further obtain gnss observation data in a collection area of a field collection plan.

In an optional embodiment of the disclosure, the process of applying for the gnss observation data corresponding to the at least one virtual reference station from the virtual reference station server according to the position of the at least one virtual reference station includes that the virtual reference station server additionally sets at least one continuously operating reference station serving as a virtual reference station according to the position of the at least one virtual reference station, so as to further acquire gnss observation data in an acquisition area of a field acquisition plan.

In an optional embodiment of the disclosure, the adding, by the virtual reference station server, at least one continuously operating reference station serving as a virtual reference station according to the position of the at least one virtual reference station includes, when there is no continuously operating reference station at the position, using an existing continuously operating reference station at the position of the at least one virtual reference station by the virtual reference station server, and virtually adding at least one continuously operating reference station serving as a virtual reference station so that the field planned acquisition area is within the radiation range of the virtual reference station, so as to further acquire global navigation satellite system observation data within the field planned acquisition area.

In an optional embodiment of the disclosure, the virtual reference station server uses an existing continuously operating reference station at the location of the at least one virtual reference station, and the process of virtually adding and setting the at least one continuously operating reference station used as a virtual reference station includes that the virtual reference station server adds and sets the at least one continuously operating reference station used as a virtual reference station in an interpolation manner, so that the field-planned acquisition area is within the radiation range of the virtual reference station, so as to further acquire the global navigation satellite system observation data within the field-planned acquisition area.

In an optional embodiment of the disclosure, the applying for the gnss observation data corresponding to the at least one virtual reference station from the virtual reference station server according to the position of the at least one virtual reference station includes applying for obtaining the gnss observation data corresponding to the virtual reference station from the virtual reference station server through a standard format (NMEA) established by marine electronic equipment, so as to smoothly obtain the required gnss observation data corresponding to the virtual reference station.

In an optional embodiment of the disclosure, the applying for the gnss observation data corresponding to the at least one virtual reference station from the virtual reference station server is the gnss observation data near the approximate coordinates of the position of the at least one virtual reference station determined in step S101, so as to further obtain the gnss observation data corresponding to the position of the at least one virtual reference station.

In an optional embodiment of the disclosure, the gnss observation data in the vicinity of the approximate coordinates of the position of the at least one virtual reference station is gnss observation data within a range of the virtual reference station, i.e. within 10-20 km, so as to further acquire gnss observation data within a range of the position of the virtual reference station.

The process S103 shown in fig. 1 is a process of acquiring gnss observation data from the virtual reference station server and performing inspection processing on the gnss observation data, so as to conveniently acquire gnss observation data and enable the data to be inspected, thereby further ensuring that the received gnss observation data is complete and reliable, and facilitating post-resolution of the gnss observation data.

In an optional embodiment of the present disclosure, in acquiring the global navigation satellite system observation data from the virtual reference station server, the background server is used to receive and acquire the global navigation satellite system observation data, so as to perform data inspection processing, and further ensure that the received global navigation satellite system observation data is complete and reliable.

In an optional embodiment of the disclosure, the process of acquiring gnss observation data from the virtual reference station server includes acquiring data in a standard protocol differential message (RTCM) format through network protocol transmission, so as to further perform inspection processing on the observation data.

In an optional embodiment of the disclosure, the process of checking the gnss observation data includes converting the format of the gnss observation data, so that the format-converted data can be checked next.

In an optional embodiment of the disclosure, the format conversion process of the gnss observation data includes converting the gnss observation data obtained in step S102 from a binary standard protocol differential text format (RTCM) to a decimal standard data format (RINEX) so as to further check the converted data.

In an optional embodiment of the disclosure, the process of checking and processing the gnss observation data includes checking whether there is a signal loss in the gnss observation data and/or checking the quality of the gnss observation data in real time, which is convenient for further realizing problem reporting, so as to ensure that the received gnss observation data is complete and reliable.

In an optional embodiment of the disclosure, the process of checking and processing the gnss observation data includes checking whether the gnss observation data has packet loss in a network transmission process, so as to avoid signal loss caused by a network signal or a problem of a server, and further implement immediate reporting of a signal loss problem, so as to ensure that the received gnss observation data is complete.

In an optional embodiment of the disclosure, the checking whether there is a packet loss in the network transmission process of the gnss observation data includes monitoring the network transmission condition of the gnss observation data, avoiding signal loss caused by a network signal or a problem of the server, and facilitating further implementation of reporting the network signal and the problem of the server as soon as possible, so as to ensure that the received gnss observation data is complete.

Optionally, in the above process of monitoring the network transmission condition of the gnss observation data, when a network abnormality occurs, an alarm is issued to realize that a network signal or a problem of the server is reported immediately, so as to ensure that the received gnss observation data is complete.

In an optional embodiment of the disclosure, the process of checking the gnss observation data includes checking the quality of the gnss observation data, so as to further report the quality problem of the gnss observation data on demand, so as to ensure that the received gnss observation data is reliable.

In an optional embodiment of the disclosure, the checking the quality of the gnss observation data includes checking the number of satellites in each epoch of the gnss observation data, so as to further report the problem of the number of satellites in each epoch of the gnss observation data promptly, so as to ensure that the received gnss observation data is reliable.

Optionally, the process of checking the quality of the gnss observation data includes checking whether each gnss epoch observation satellite in the gnss corresponding to the gnss observation data meets the requirement of at least 4, so as to report the problem that the number of each epoch satellite in the gnss observation data is insufficient, thereby ensuring that the received gnss observation data is reliable.

In an optional embodiment of the disclosure, the checking the quality of the gnss observation data includes checking a satellite space distribution factor of the gnss observation data, so as to further report the satellite space distribution factor problem of the gnss observation data, so as to ensure that the received gnss observation data is reliable.

Optionally, the process of checking the quality of the gnss observation data includes checking whether the satellite spatial distribution factor of the gnss observation data conforms to an ideal condition of less than 6, so as to further report the problem that the satellite spatial distribution factor of the gnss observation data is too large, thereby ensuring that the received gnss observation data is reliable.

In an optional embodiment of the disclosure, the checking the quality of the gnss observation data includes checking the snr of each frequency band of the gnss observation data, so as to further report the snr of the gnss observation data in real time, so as to ensure that the received gnss observation data is reliable.

Optionally, the process of checking the quality of the gnss observation data includes checking whether the L1 carrier snr meets an ideal condition of 40dbhz and/or the L2 carrier snr meets an ideal condition of greater than 20dbhz in snrs of various frequency bands of the gnss observation data, so as to further solve the problem that the snr of the gnss observation data does not meet the ideal condition, that is, to ensure that the received gnss observation data is reliable.

In an optional embodiment of the disclosure, the checking the quality of the gnss observation data includes checking whether the L1 carrier multipath meets an ideal condition of less than 0.6 m and/or the L2 carrier multipath meets an ideal condition of less than 0.6 m in the multipath noise of each frequency band of the gnss observation data, so as to further report the problem that the multipath noise of each frequency band of the gnss observation data does not meet the ideal condition, thereby ensuring that the received gnss observation data is reliable.

In an optional embodiment of the disclosure, the above checking whether the L1 carrier multipath meets the ideal condition of less than 0.6 m and/or the L2 carrier multipath meets the ideal condition of less than 0.6 m in the multipath noise of each frequency band of the gnss observation data includes checking a data path reflected by a river and/or a glass in the environment around the receiving mobile station, so as to further solve the problem that the multipath noise of each frequency band affected by the environment around the mobile station in the gnss observation data does not meet the ideal condition, that is, reporting the problem, to ensure that the received gnss observation data is reliable.

In an optional embodiment of the present disclosure, after the global navigation satellite system observation data is checked and processed, the data is downloaded and stored in a local path in a file form, so as to further perform difference and solution on the global navigation satellite system observation data.

The process S104 shown in fig. 1 is a process of performing fusion calculation to obtain high-precision track coordinate points by using the gnss observation data and the mobile acquisition apparatus data acquired by the mobile acquisition apparatus after the inspection process, so as to finally obtain high-precision track coordinate points required by the high-precision map.

In an optional embodiment of the present disclosure, the number of the mobile collection devices collected by the mobile collection device includes that the mobile collection device utilizes inertial integrated navigation data collected by an inertial integrated navigation system (GNSS/INS integrated navigation system) to be fused and resolved with the global navigation satellite system observation data after the inspection processing, so as to finally obtain a high-precision track coordinate point required by a high-precision map.

In an optional embodiment of the disclosure, the process of acquiring the high-precision trajectory coordinate point by fusion calculation using the global navigation satellite system observation data after the inspection and the mobile acquisition device data acquired by the mobile acquisition device includes receiving and acquiring the mobile acquisition device data by using a background server, so as to further perform fusion calculation with the global navigation satellite system observation data. In an optional embodiment of the disclosure, the process of performing fusion calculation to obtain the high-precision trajectory coordinate point by using the global navigation satellite system observation data after the inspection processing and the mobile collection device data collected by the mobile collection device includes differentiating the global navigation satellite system observation data after the inspection processing and the mobile collection device data at the same time, and performing fusion calculation to obtain the high-precision trajectory coordinate point.

In an optional embodiment of the disclosure, the step of differentiating the gnss observation data after the inspection processing from the mobile acquisition device data at the same time includes forming a group of differences between each satellite data of the gnss observation data after the inspection processing and the mobile acquisition device data at the same time, and then performing fusion calculation to obtain a high-precision trajectory coordinate point.

In an optional embodiment of the present disclosure, the process of performing fusion calculation to obtain the high-precision trajectory coordinate point by using the checked and processed gnss observation data and the mobile acquisition device data acquired by the mobile acquisition device includes performing fusion calculation to obtain the high-precision trajectory coordinate point by using a plurality of sets of gnss observation data corresponding to a plurality of virtual reference stations acquired from the virtual reference station server and the mobile acquisition device data acquired by the mobile acquisition device; the acquired global navigation satellite system observation data corresponding to a plurality of groups of different virtual reference stations and the data of the mobile acquisition equipment are used for fusion calculation, so that under the condition that packet loss or quality problems occur in one group of data, the data of other groups are used for supplement replacement, and the calculation precision can be improved.

In an optional embodiment of the disclosure, the process of performing fusion calculation to obtain the high-precision trajectory coordinate point by using the global navigation satellite system observation data after the inspection processing and the mobile acquisition device data acquired by the mobile acquisition device includes performing fusion calculation to obtain the high-precision trajectory coordinate point by using a plurality of sets of mobile acquisition device data acquired by a plurality of mobile acquisition devices, performing fusion calculation to obtain the high-precision trajectory coordinate point by using mobile acquisition device data of the same target acquired by different mobile acquisition devices, and improving adjustment stability and calculation precision of the mobile acquisition devices.

In an optional implementation of the disclosure, the process of performing fusion calculation to obtain the high-precision track coordinate point by using the global navigation satellite system observation data after the inspection processing and the mobile acquisition device data acquired by the mobile acquisition device includes performing fusion calculation to obtain the high-precision track coordinate point by using a plurality of groups of global navigation satellite system observation data corresponding to a plurality of virtual reference stations acquired from a virtual reference station server after the inspection processing and a group of mobile acquisition device data acquired by one mobile acquisition device, and performing the complementary replacement by using data of other groups under the condition that packet loss or quality problems occur in one group of global navigation satellite system observation data, so that adjustment stability and calculation precision of the mobile acquisition device can be improved.

In an optional embodiment of the present disclosure, after the check processing in step S103, a mobile acquisition device may obtain multiple sets of gnss observation data, which may be used to solve gnss observation data of multiple mobile acquisition devices in the same area in the same time period, and reuse data obtained from one acquisition point, so that resources may be saved and field acquisition efficiency may be improved.

For example, the global navigation satellite system observation data corresponding to three to four virtual reference stations acquired from the virtual reference station server can cover the calculation of the global navigation satellite system observation data of the mobile acquisition equipment in the same time period in the whole administrative region, so that the resources can be saved, and the field acquisition efficiency can be improved.

In an optional embodiment of the disclosure, the process of acquiring the high-precision track coordinate point by fusion and solution using the global navigation satellite system observation data after the inspection processing and the mobile collection device data collected by the mobile collection device includes that the global navigation satellite system observation data corresponding to a plurality of groups of virtual reference base stations acquired from the virtual reference station server after the inspection processing can be simultaneously loaded by using commercial solution software, and the global navigation satellite system observation data and the mobile collection device data in the same time period are subjected to fusion and solution, so that the solution precision can be improved.

In an optional embodiment of the disclosure, the process of acquiring the high-precision trajectory coordinate point by fusing and resolving the global navigation satellite system observation data after the inspection and the mobile acquisition device data acquired by the mobile acquisition device includes that the mobile acquisition device data acquired by different mobile acquisition devices can be loaded simultaneously by using commercial resolving software to perform fusion and resolving, and adjustment stability and resolving precision of the mobile acquisition device can be improved.

In an optional embodiment of the disclosure, the process of acquiring the high-precision trajectory coordinate point by fusion and solution using the checked and processed gnss observation data and the mobile acquisition device data acquired by the mobile acquisition device includes loading multiple sets of gnss observation data corresponding to multiple virtual reference stations simultaneously using commercial solution software, and acquiring a set of mobile acquisition device data acquired by one mobile acquisition device, performing fusion and solution, and performing supplementary replacement using data of other sets when a packet loss or a quality problem occurs in one set of gnss observation data, so as to improve adjustment stability and solution precision of the mobile acquisition device.

In an optional embodiment of the disclosure, the process of performing fusion calculation with the mobile acquisition device data in the same time period by using the plurality of groups of gnss observation data after the inspection processing includes performing weighted distribution on each group of data in the plurality of groups of gnss observation data after the inspection processing according to a distance between a virtual reference station corresponding to the plurality of groups of gnss observation data and a mobile acquisition device, and performing fusion calculation by using the gnss observation data after the inspection processing and the mobile acquisition device data in the same time period, so that adjustment stability and calculation accuracy of the mobile acquisition device can be improved.

The process of fusion calculation with the data of the mobile acquisition equipment in the same time period by using the multiple groups of global navigation satellite system observation data after the inspection processing comprises the steps of performing weighted distribution on each group of mobile acquisition equipment data in the multiple groups of mobile acquisition equipment data according to the distance between the mobile acquisition equipment corresponding to the multiple groups of mobile acquisition equipment data and a virtual reference station, and performing fusion calculation by using the global navigation satellite system observation data and the mobile acquisition equipment data after the inspection processing in the same time period, so that the adjustment stability and the calculation precision of the mobile acquisition equipment can be improved.

In an optional embodiment of the disclosure, the process of performing fusion calculation with the mobile acquisition device data in the same time period by using the plurality of sets of global navigation satellite system observation data after the inspection processing includes performing weighted distribution on each set of data in the plurality of sets of global navigation satellite system observation data after the inspection processing and the plurality of sets of mobile acquisition device data according to a distance between a virtual reference station corresponding to the plurality of sets of global navigation satellite system observation data and the mobile acquisition device corresponding to the plurality of sets of mobile acquisition device data, and performing fusion calculation by using the global navigation satellite system observation data and the mobile acquisition device data after the inspection processing in the same time period, so that adjustment stability and calculation accuracy of the mobile acquisition device can be improved.

Product example:

the above is a description of an embodiment of the method for automatically generating high-precision map data according to the present disclosure, and the following description is made with reference to fig. 2 to 5 for an automatic generation system of high-precision map data according to the present disclosure:

to implement the above method, as shown in fig. 3, the automatic generation system of high-precision map data disclosed in the present disclosure includes:

the base station replacing device is used for acquiring first-period GNSS data from at least one virtual reference station according to preset information and preprocessing the first-period GNSS data;

the mobile acquisition equipment is provided with a GNSS receiver and inertial navigation equipment and is used for acquiring GNSS data and inertial navigation data in a second time period according to the data acquisition planning route in the second time period; the first time period covers the second time period, and the virtual reference station and the mobile acquisition equipment are time-synchronized;

the difference fusion module is used for carrying out difference on the preprocessed first-time-period GNSS data and the preprocessed second-time-period GNSS data to obtain difference GNSS data; and the system is used for performing fusion calculation on the inertial navigation data and the differential GNSS data, and correcting the GNSS data to obtain high-precision track coordinate data.

The automatic generation of the high-precision map data disclosed in the embodiment is the same, a base station replacing device capable of replacing an entity base station is designed, GNSS data are obtained from a virtual reference station, the non-base station mode of high-precision map field acquisition is realized, large-scale investment of large-scale manpower and material resources can be avoided, on the premise of guaranteeing the precision of the high-precision map data, the labor productivity is released as much as possible, the equipment cost consumption is reduced, the high-precision map data acquisition is completed quickly, on the premise of not needing manpower and surveying and mapping equipment investment, the carrier phase difference component correction is carried out on a mobile acquisition device and the virtual reference station, and the real-time RTK is realized. Through the technical scheme disclosed by the disclosure, the map acquisition and manufacturing efficiency can be effectively improved.

In an optional embodiment, the base station replacement apparatus further comprises:

the acquisition presetting module is used for setting preset information for acquisition, including a data acquisition planning route, rough coordinates determined according to the implementation of the acquisition planning route and at least one virtual reference station required for acquisition;

the data configuration module is used for setting preset information for acquiring GNSS data in a first period according to an acquisition planning route, wherein the preset information comprises a network output and receiving type and an output and receiving data format;

the data transceiver module is used for acquiring GNSS data of a first time period from at least one virtual reference station and receiving the GNSS data and inertial navigation data of a second time period;

the data analysis module is used for analyzing and format converting the GNSS data in the first time period;

and the quality detection module is used for carrying out quality monitoring on the GNSS data in the first period, and comprises transmission network monitoring and/or GNSS data quality detection.

As an optional implementation manner, the automatic generation system of high-precision map data may further include: a point cloud data resolving module and a data making platform. Wherein:

the point cloud data resolving module is used for automatically resolving point cloud data and extracting road information; the road information at least comprises road lines, sign boards and other road related data; the mobile acquisition equipment is provided with sensing equipment and is used for acquiring point cloud data;

the data production platform is used for generating high-precision map data reflecting real ground objects in a geographic coordinate system based on the high-precision track coordinate data and in combination with road information; and the high-precision map data are compiled according to the requirements and output in a preset format.

In an optional embodiment, the automatic generation system for high-precision map data may further include a map data warehouse for storing and managing customizable production high-precision map data, including the full-scale data and/or the incremental data of the map of the current version.

Referring to fig. 3 and 4, an embodiment of the present disclosure further discloses a map data cloud platform, including: the automatic generation system of high-precision map data and the data customization and output system disclosed in any of the foregoing embodiments;

In this embodiment, the base station replacement device may be disposed on a map data cloud platform, and data interaction between the map data cloud platform and the virtual reference station and between the map data cloud platform and the mobile acquisition device may be specifically as shown in fig. 3, which shows key data interaction in the processes of obtaining, manufacturing, and outputting high-precision map data.

Referring to fig. 5, an embodiment of the present disclosure further discloses a lightweight map data cloud platform, which includes the following components:

the difference fusion module is used for carrying out difference on the preprocessed first-time-period GNSS data and the preprocessed second-time-period GNSS data to obtain difference GNSS data; the system comprises a differential GNSS data acquisition unit, a high-precision track coordinate data acquisition unit and a data processing unit, wherein the differential GNSS data acquisition unit is used for acquiring the differential GNSS data;

the point cloud data resolving module is used for automatically resolving the point cloud data and extracting road information; the road information at least comprises road lines, sign boards and other road related data; the system comprises a mobile acquisition device, a sensing device and a data processing device, wherein the mobile acquisition device is configured with point cloud data acquired by the sensing device;

the data making platform is used for generating high-precision map data reflecting the real ground objects in a geographic coordinate system based on the high-precision track coordinate data and in combination with road information; the high-precision map data are compiled according to requirements and output in a preset format;

the data customizing and outputting system is configured with a data product customizing module, a visual outputting module, an API interface module and an API gateway module, and is used for providing an access interface for an authorized user, providing a visual product customizing operation interface, outputting according to a high-precision map data product customized by the authorized user, and monitoring a network and an access state; the API gateway module further comprises an operation and maintenance monitoring unit, a log management unit and an identity authentication unit.

In an optional embodiment of the disclosure, the module for obtaining the high-precision trajectory coordinate point by fusion and calculation using the gnss observation data after the inspection process and the mobile acquisition device data acquired by the mobile acquisition device may obtain the high-precision trajectory coordinate point by fusion and calculation using a plurality of sets of the gnss observation data corresponding to the plurality of virtual reference stations acquired from the virtual reference station server after the inspection process and the mobile acquisition device data acquired by the mobile acquisition device, so that when one set of data has a packet loss or a quality problem, the data of other sets can be used for supplementary replacement, and the adjustment stability and the calculation precision of the mobile acquisition device can be improved.

In an optional embodiment of the disclosure, the module for fusing and resolving the high-precision trajectory coordinate point is used for fusing and resolving the global navigation satellite system observation data and the mobile acquisition device data acquired by the mobile acquisition device after the inspection processing, fusing and resolving the high-precision trajectory coordinate point by using a plurality of groups of mobile acquisition device data acquired by the mobile acquisition device, fusing and resolving the mobile acquisition device data of the same target acquired by different mobile acquisition devices, and improving the adjustment stability and resolving precision of the mobile acquisition device.

In an alternative embodiment of the present disclosure, the modules of the automated generation system of high-precision map data of the present disclosure may be directly in hardware, in a software module executed by a processor, or in a combination of both.

A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known 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.

The Processor may be a Central Processing Unit (CPU), other general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), other Programmable logic devices, discrete Gate or transistor logic, discrete hardware components, or any combination thereof. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.

In an alternative embodiment mode of the present disclosure, a computer-readable storage medium stores computer instructions that are operated to execute the automatic generation method of high-precision map data described in any one of the embodiment.

In an optional embodiment, the map cloud platform may further comprise the following components:

the data acquisition device is used for acquiring high-precision track coordinate point cloud data based on the virtual reference station, uploading the high-precision track coordinate point cloud data to the data updating module, and replacing a base station for acquiring data in the prior art with the virtual reference station, so that the high-precision track coordinate point cloud data corresponding to the virtual reference station can be conveniently and quickly acquired on the premise of avoiding large-scale investment of large-scale manpower and material resources.

And the data updating module is used for automatically extracting the high-precision track coordinate point cloud data and the data of the area with larger difference from the result library according to the data uploaded by the data acquisition device, transmitting the data to the updating operation module for updating and perfecting, and storing the high-precision track coordinate point cloud data before and after updating and perfecting.

In an optional embodiment of the disclosure, the data updating module stores the high-precision track coordinate point cloud data acquired and uploaded by the module and/or the high-precision track coordinate point cloud data after the updating operation module is completely updated through a resolving program.

And the updating operation module is used for accurately positioning the suspected area according to the high-precision track coordinate point cloud data sent by the data updating module and the data of the area with larger difference in the achievement library, and transmitting the updated high-precision track coordinate point cloud data and the data of the area with larger difference in the achievement library back to the data updating module after the suspected area is updated completely.

In the embodiments provided in the present disclosure, it should be understood that the disclosed method and system may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, for example, the division of the units is only one division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some interfaces, and may be in a typical, mechanical or other form.

The units described as separate but not illustrated may or may not be physically separate, and components displayed 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 can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

The above description is only an example of the present disclosure, and not intended to limit the scope of the present disclosure, and all equivalent structural changes made by using the contents of the specification and drawings, or any other related technical fields, are all included in the scope of the present disclosure.

Claims

1. An automatic generation method of high-precision map data is characterized by comprising the following steps:

determining a rough coordinate according to a data acquisition planning route, and determining at least one virtual reference station required for acquisition according to the rough coordinate;

in a first time period, acquiring first time period Global Navigation Satellite System (GNSS) data from the at least one virtual reference station according to preset information, and preprocessing the first time period GNSS data;

in a second time period, acquiring GNSS data and inertial navigation data in the second time period according to the data acquisition planning route; wherein the first period encompasses the second period;

differentiating the preprocessed first-period GNSS data and the preprocessed second-period GNSS data to obtain differential GNSS data;

and performing fusion calculation on the inertial navigation data and the differential GNSS data, and correcting the GNSS data to obtain high-precision track coordinate data.

2. The automated generation method of high accuracy map data according to claim 1, characterized in that the method further comprises:

collecting point cloud data in the second time period, automatically analyzing the point cloud data, and extracting road information; the road information at least comprises a road line, a sign plate and other road related data;

and generating high-precision map data reflecting the real ground objects in a geographic coordinate system based on the high-precision track coordinate data and by combining the road information.

3. The automatic generation method of high-precision map data according to claim 1 or 2, characterized in that:

two or more virtual reference stations are adopted; and/or the presence of a gas in the gas,

preprocessing the first period of GNSS data further comprises: analyzing, detecting the quality of the GNSS data in the first period, and screening the data meeting preset conditions;

the parsing of the first time period GNSS data may include converting a format of transmission data of the first time period GNSS data into a GNSS standard data format.

4. The method as claimed in claim 3, wherein the quality detection of the first-period GNSS data further comprises: transmission network monitoring and/or GNSS data quality detection;

wherein:

the transport network monitoring further comprises: real-time transmission monitoring is carried out on network transmission signals at the side of the virtual reference station, monitored data packet loss information is fed back to the virtual reference station, and the virtual reference station issues corresponding GNSS data again according to the received feedback information; and/or, the transport network monitoring further comprises: monitoring and alarming network transmission signals at the side of the virtual reference station by adopting network abnormal log monitoring and alarming, alarming when abnormal logs are monitored, feeding back monitored data packet loss information to the virtual reference station, and retransmitting corresponding GNSS data by the virtual reference station according to the received feedback information;

and/or the presence of a gas in the gas,

the GNSS data quality detection further comprises: judging whether the GNSS data in the first time period is qualified or not according to the number of satellites in each epoch, the spatial distribution factor of the satellites, the signal-to-noise ratio of each frequency band and the multi-path noise of each frequency band, and calculating the qualification rate; when the qualified rate of the first period GNSS data is lower than a preset threshold value, discarding the first period GNSS data of the corresponding virtual reference station; the first period GNSS data with the qualification rate reaching or higher than the preset threshold value is qualified data of quality detection.

5. The method for automatically generating high-precision map data according to any one of claims 1 to 4, wherein the inertial navigation data and the differential GNSS data are fused and solved, and GNSS data are corrected, further comprising:

calculating the data of GNSS data loss caused by satellite signal quality by using the inertial navigation data and dead reckoning to obtain the GNSS data loss; and/or the presence of a gas in the gas,

in the fusion resolving process, GNSS data of two or more virtual reference stations are loaded, each virtual reference station is configured with a weight set based on the quality of the GNSS data, and the data resolving accuracy of the virtual reference stations is fused according to the weight.

6. An automated generation system of high-precision map data, characterized by comprising:

7. The system for automatically generating high-precision map data according to claim 6, wherein the base station replacement means further comprises:

the data configuration module is used for setting preset information for acquiring the GNSS data in the first period according to the acquisition planning route, wherein the preset information comprises a network output and receiving type and an output and receiving data format;

the data transceiver module is used for acquiring GNSS data of a first time period from the at least one virtual reference station and receiving the GNSS data of a second time period and the inertial navigation data;

the data analysis module is used for analyzing and format converting the GNSS data in the first period;

and the quality detection module is used for carrying out quality monitoring on the GNSS data in the first time period, and comprises transmission network monitoring and/or GNSS data quality detection.

8. The automatic generation system of high-precision map data according to claim 6 or 7, characterized by further comprising:

the point cloud data resolving module is used for automatically resolving the point cloud data and extracting the road information; the road information at least comprises a road line, a sign plate and other road related data; the mobile acquisition equipment is provided with sensing equipment and is used for acquiring the point cloud data;

the data making platform is used for generating high-precision map data reflecting the real ground objects in a geographic coordinate system based on the high-precision track coordinate data and in combination with the road information; the high-precision map data are compiled according to requirements and output in a preset format; and/or

And the map data warehouse is used for storing and managing customizable productized high-precision map data, including the full-quantity data and/or the incremental data of the map of the current version.

9. A map data cloud platform, comprising: an automated generation system and data customization and output system of high precision map data according to any one of claims 6 to 8;

10. A lightweight map data cloud platform, comprising:

the difference fusion module is used for carrying out difference on the preprocessed GNSS data in the first time period and the preprocessed GNSS data in the second time period to obtain difference GNSS data; the system comprises a differential GNSS data acquisition unit, a high-precision track coordinate data acquisition unit and a data processing unit, wherein the differential GNSS data acquisition unit is used for acquiring the inertial navigation data and the differential GNSS data;