CN114281832A

CN114281832A - High-precision map data updating method and device based on positioning result and electronic equipment

Info

Publication number: CN114281832A
Application number: CN202111575729.8A
Authority: CN
Inventors: 杨坤; 黄杰; 蔡仁澜; 彭亮
Original assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Current assignee: Beijing Baidu Netcom Science and Technology Co Ltd
Priority date: 2021-12-21
Filing date: 2021-12-21
Publication date: 2022-04-05

Abstract

The disclosure provides a map data updating method, a map data updating device, electronic equipment, a storage medium and a program product, and relates to the technical field of automatic driving, in particular to the technical field of high-precision maps. The specific implementation scheme is as follows: determining a target road section from the preset road section based on the respective positioning results of a plurality of points to be tested of the preset road section, wherein the respective positioning results of the plurality of points to be tested are obtained by matching the respective current point cloud data of the plurality of points to be tested with a plurality of historical point cloud data in a map; and determining the area to be updated of the map according to the geographical position information of the target road section.

Description

High-precision map data updating method and device based on positioning result and electronic equipment

Technical Field

The present disclosure relates to the field of automatic driving technologies, and in particular, to the field of high-precision maps, and in particular, to a map data updating method, apparatus, electronic device, storage medium, and program product.

Background

The automatic driving vehicle can sense the surrounding environment without manual control and can make driving decision and control according to the sensing. In the process of driving of the automatic driving vehicle, the map plays an important role in driving decision and control of the automatic driving vehicle according to perception. The high-precision map is also called as a high-precision map and is used for an automatic driving automobile. The high-precision map has accurate vehicle position information and abundant road element data information, can help an automobile to predict road surface complex information such as gradient, curvature, course and the like, and can better avoid potential risks.

Disclosure of Invention

The present disclosure provides a map data updating method, apparatus, electronic device, storage medium, and program product.

According to an aspect of the present disclosure, there is provided a map data updating method, including: determining a target road section from a preset road section based on the positioning result of each of a plurality of to-be-tested points of the preset road section, wherein the positioning result of each of the plurality of to-be-tested points is obtained by matching the current point cloud data of each of the plurality of to-be-tested points with a plurality of historical point cloud data in the map; and determining the area to be updated of the map according to the geographical position information of the target road section.

According to another aspect of the present disclosure, there is provided a map data updating apparatus including: the map locating device comprises a first determining module, a second determining module and a third determining module, wherein the first determining module is used for determining a target road section from a preset road section based on the locating result of each of a plurality of to-be-tested points of the preset road section, and the locating result of each of the plurality of to-be-tested points is obtained by matching the current point cloud data of each of the plurality of to-be-tested points with a plurality of historical point cloud data in the map; and the second determination module is used for determining the area to be updated of the map according to the geographical position information of the target road section.

According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described above.

According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method as described above.

According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method as described above.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The drawings are included to provide a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

fig. 1 schematically illustrates an exemplary system architecture to which the map data update method and apparatus may be applied, according to an embodiment of the present disclosure;

fig. 2 schematically shows a flow chart of a map data update method according to an embodiment of the present disclosure;

FIG. 3 schematically shows a flow chart for determining a target road segment according to another embodiment of the present disclosure;

fig. 4 schematically shows a flowchart of a map data update method according to another embodiment of the present disclosure;

fig. 5 schematically shows a block diagram of a map data update apparatus according to an embodiment of the present disclosure; and

fig. 6 schematically shows a block diagram of an electronic device adapted to implement a map data updating method according to an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

According to an embodiment of the present disclosure, there is provided a map data updating method including: determining a target road section from the preset road section based on the respective positioning results of a plurality of points to be tested of the preset road section, wherein the respective positioning results of the plurality of points to be tested are obtained by matching the current point cloud data of the plurality of points to be tested with a plurality of historical point cloud data in a map; and determining the area to be updated of the map according to the geographical position information of the target road section.

In the technical scheme of the disclosure, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the personal information of the related user are all in accordance with the regulations of related laws and regulations and do not violate the good customs of the public order.

Fig. 1 schematically illustrates an exemplary system architecture to which the map data update method and apparatus may be applied, according to an embodiment of the present disclosure.

It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.

As shown in fig. 1, a system architecture 100 according to this embodiment may include an autonomous vehicle 101, a network 102, and a server 103. Network 102 is the medium used to provide a communication link between autonomous vehicle 101 and server 103. Network 102 may include various connection types, such as wireless communication links.

The autonomous vehicle 101 may be equipped with a camera device, for example, the camera device may be used to capture image data on a road. The autonomous vehicle 101 may further include a laser sensor, and for example, the laser sensor may collect point cloud data on a road. A positioning system may also be installed on autonomous vehicle 101, and for example, the geographic location information where autonomous vehicle 101 is currently located may be determined in real time by the positioning system.

A user may use autonomous vehicle 101 to interact with server 103 through network 102 to receive or transmit operational data, such as point cloud data, image data, geographic location information, and the like.

Server 103 may be a server that provides various services, such as a background management server (for example only) that provides support for a user utilizing a navigation application used by autonomous vehicle 101.

The Server 103 may also be a cloud Server, which is also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service extensibility in a conventional physical host and VPS service ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server incorporating a blockchain.

It should be noted that the map data updating method provided by the embodiment of the present disclosure may be generally executed by the server 103. Accordingly, the map data updating apparatus provided by the embodiment of the present disclosure may also be disposed in the server 103.

For example, the autonomous vehicle 101 may acquire current point cloud data of each of a plurality of points to be tested for a predetermined road segment, and match the current point cloud data of each point to be tested with a plurality of historical point cloud data in a map loaded on the autonomous vehicle 101, to obtain a plurality of positioning results corresponding to the plurality of points to be tested one to one. The plurality of positioning results are transmitted to the server 103. The server determines a target road section from the preset road section according to the respective positioning result of the multiple points to be tested of the preset road section, and determines the area to be updated of the map according to the geographical position information of the target road section.

It should be understood that the number of autonomous vehicles, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

Fig. 2 schematically shows a flowchart of a map data update method according to an embodiment of the present disclosure.

As shown in fig. 2, the method includes operations S210 to S220.

In operation S210, a target road segment is determined from a predetermined road segment based on a positioning result of each of a plurality of points to be tested of the predetermined road segment, wherein the positioning result of each of the plurality of points to be tested is obtained by matching current point cloud data of each of the plurality of points to be tested with a plurality of historical point cloud data in a map.

In operation S220, an area of the map to be updated is determined according to the geographical location information of the target link.

According to an embodiment of the present disclosure, the predetermined section may be a continuous section or a discontinuous section; the predetermined road section may be a highway section, a downtown section, or a section within a community, etc.

According to the embodiment of the disclosure, the autonomous vehicle runs on the predetermined road section, the predetermined road section may be divided at equal intervals according to the distance, each interval point is a point to be tested, but the autonomous vehicle is not limited to this, and the autonomous vehicle may also be based on the acquisition frequency of the lidar sensor. For example, the collection frequency may be 10 frames of point cloud data per second, a position point corresponding to one frame of point cloud data collected every 10 seconds may be used as the point to be tested, or a position point corresponding to one frame of point cloud data collected every 1 second may be used as the point to be tested. The test point can be a plurality of test points to be tested, which can achieve the full coverage detection effect on the preset road section.

According to an embodiment of the present disclosure, the current point cloud data may be point cloud data for the surroundings of the point to be tested acquired with a lidar sensor mounted on the autonomous vehicle. For example, a target object is detected by using a non-contact laser beam emitted by a laser radar sensor, and the light beam incident to the target object and reflected back is collected to form point cloud data. The current point cloud data can be a set of massive sampling point data on the surface of the target object in the surrounding environment of the point to be tested, and each sampling point data contains three-dimensional space coordinate information. The target objects involved in the current point cloud data may be static target objects including, for example, lane lines, road signs, or buildings, etc., where dynamic target objects such as vehicles, pedestrians, etc. are rejected.

According to embodiments of the present disclosure, the plurality of historical point cloud data in the map may be raw point cloud data collected with a lidar sensor onboard the autonomous vehicle. The respective collection mode and type of a plurality of historical point cloud data in the map are the same as those of the current point cloud data, so that the positioning result can be quickly and accurately obtained when point cloud positioning is carried out. Different from the current point cloud data, the plurality of historical point cloud data are more dense, and each two adjacent historical point cloud data are overlapped, so that splicing can be realized, full coverage of the point cloud data of a preset road section is formed, and the historical point cloud data corresponding to the point to be tested can be found in the plurality of historical point cloud data of the map at the point to be tested related to the current point cloud data collected at the preset road section.

According to the embodiment of the disclosure, the map not only comprises historical point cloud data, but also comprises geographic coordinate information of the position point, and a mapping relation between the historical point cloud data and the geographic coordinate information of the position point. Therefore, in the case where the historical point cloud data matching the current point cloud data is determined, the geographic coordinate information of the current point cloud data may be determined based on the mapping relationship between the historical point cloud data and the geographic coordinate information of the location point, that is, the geographic coordinate information at the error level of, for example, centimeters is used as the positioning result. But is not limited thereto. In embodiments of the present disclosure, the current point cloud data may also be used to characterize point cloud data of a target object in the current environment of the point to be tested. The plurality of historical point cloud data in the map may be used to characterize point cloud data of a target object in a historical environment of each of the plurality of points to be tested. The current point cloud data and the plurality of historical point cloud data can be matched based on the fact that the current environment represented by the current point cloud data is consistent with the historical environment represented by the historical point cloud data and does not change under the condition that the current point cloud data is matched with the plurality of historical point cloud data to obtain the positioning result used for representing successful positioning. And under the condition that the current environment represented by the current point cloud data is inconsistent with the historical environment represented by the historical point cloud data, determining that the current environment changes, such as changes of road change, road construction and the like. And the geographic coordinate information of the current point cloud data cannot be obtained based on the mapping relation between the historical point cloud data and the geographic coordinate information of the position point, so that the positioning result used for representing the positioning failure is obtained.

According to the embodiment of the disclosure, a road section formed by a plurality of to-be-tested points for obtaining a positioning result for representing positioning failure, for example, a plurality of to-be-tested points in which a current environment represented by current point cloud data is inconsistent with a historical environment represented by historical point cloud data, can be determined as a target road section. The target road segment may indicate that the availability of point cloud data for relative locations in the map is reduced and needs to be updated.

According to an embodiment of the present disclosure, the geographical location information of the target road segment may refer to geographical location information of a start point of the target road segment and geographical location information of an end point of the target road segment, but is not limited thereto, and may also refer to geographical location information including the geographical location information of the start point of the target road segment and the geographical location information of the end point of the target road segment, and respective geographical location information of a plurality of location points between the start point of the target road segment and the end point of the target road segment. The area to be updated of the map may be determined according to the geographical location information of the target link.

According to the embodiment of the present disclosure, the geographic location information may be obtained through an on-board INS (Inertial Navigation System), but is not limited to this, and may also be obtained through calculation of GNSS (Global Navigation Satellite System) data, IMU (Inertial Measurement Unit) data, and laser point cloud data of an autonomous vehicle, or may be located by using other Positioning systems, such as a GPS (Global Positioning System ) or a BDS (BeiDou Navigation Satellite System, BeiDou Satellite Navigation System), and the like. Taking GNSS data and differential GPS data as an example, in the running process of the autonomous vehicle, the differential GPS data is acquired in real time by a vehicle-mounted GPSs and the IMU data is acquired in real time by an inertial measurement unit, and then, the differential GPS data and a plurality of current point cloud data can be subjected to offline registration by using an ICP (Iterative Closest Points) algorithm to obtain the current geographic position information of the autonomous vehicle. Or comparing the acquired differential GPS data, IMU data and current point cloud data with a predetermined map, such as a high-precision map, to obtain the current geographical position information of the automatic driving vehicle. Because the positioning precision of the differential GPS data can reach centimeter level, the accuracy of the geographic position information determined by combining a plurality of current point cloud data and utilizing the ICP algorithm to perform off-line registration can be superior to centimeter level.

In addition, taking an example that the autonomous vehicle uses an INS (Inertial Navigation System) to perform positioning, the positioning sensor may include a gyroscope, an acceleration sensor, and the like, the sensing positioning data may include acceleration data, angular velocity data, and the like, and the INS may calculate positioning coordinate information of a next point of the autonomous vehicle from an initial position of the autonomous vehicle according to a continuously measured heading angle, velocity, and the like of the autonomous vehicle, so that the positioning coordinate information of the autonomous vehicle at each time, that is, geographic position information, may be continuously measured.

By utilizing the map data updating method provided by the embodiment of the disclosure, the current point cloud data acquired by the laser radar sensor is fully utilized, so that the automatic driving vehicle can be positioned in real time, and the environmental change in the preset road section can be timely and effectively detected based on the current point cloud data, so that the map can be timely updated, the map data updating period, such as the map updating period, the map updating cost and the map availability are reduced.

According to the embodiment of the present disclosure, in operation S220, when accurate geographical position information cannot be obtained by point cloud positioning, the geographical position information may be obtained by positioning in a positioning manner of the INS (Inertial Navigation System). The positioning can be performed by using the GPS or BDS positioning method to obtain the geographical location information. The positioning time stamp can be obtained by marking the time stamp for each positioning result while the received positioning result of each of the plurality of points to be tested from the automatic driving vehicle. And marking the received geographical position information of each of the plurality of points to be tested from the automatic driving vehicle with a timestamp to obtain a geographical timestamp. The geographical position information of the point to be tested can be determined based on the matching degree of the positioning time stamp and the geographical time stamp. Therefore, under the condition that the positioning result cannot be obtained in a point cloud positioning mode or the positioning result representing positioning failure is obtained in the point cloud positioning mode, the geographic position information of the target road section can be determined based on the positioning time stamp and the geographic time stamp.

According to the embodiment of the disclosure, taking an automatic driving vehicle as an example, the time of the geographic timestamp and the time of the positioning timestamp can be determined to be a group, and the point to be tested in the same group is mapped with the geographic position information, so that the geographic position information of the point to be tested is determined. But is not limited thereto. The time interval between the time of the geographic timestamp and the time of the positioning timestamp may also be determined as a group if the time interval is less than or equal to a preset time interval threshold. Therefore, the situation that the time of the geographic timestamp is different from the time of the positioning timestamp due to the fact that the acquisition frequency of the point cloud data acquired by the laser radar sensor on the automatic driving vehicle is inconsistent with the acquisition frequency of the positioning system can be avoided.

According to the embodiment of the disclosure, a map containing a plurality of historical point cloud data can be stored on an automatic driving vehicle, and a positioning result is determined by matching current point cloud data of a point to be tested, which is acquired by the automatic driving vehicle, with the plurality of historical point cloud data in the map. And then transmitting the positioning result to a server, and determining the target road section from the preset road section by the server based on the positioning result. But is not limited thereto. The method can also be used for storing a map containing a plurality of historical point cloud data in a server, receiving current point cloud data of a point to be tested, which is acquired by the automatic driving vehicle, and determining a positioning result on the server by matching the current point cloud data of the point to be tested with the plurality of historical point cloud data in the map.

According to the embodiment of the disclosure, under the condition that the automatic driving vehicles report data and the automatic driving vehicles interact with the server to perform the map data updating method, the automatic driving vehicles can be used for determining the positioning result based on the matching of the current point cloud data and the historical point cloud data in the map, so that the data transmission quantity is reduced, and the determination rate of the server for the target road section is improved.

According to the embodiment of the disclosure, the positioning result of each point to be tested can be determined by utilizing a point cloud positioning mode. For example, based on matching of the current point cloud data of the point to be tested with the multiple historical point cloud data in the map, a current point cloud feature vector of the current point cloud data and a historical point cloud feature vector of each of the multiple historical point cloud data in the map are extracted, and a similarity matching mode is used to determine a similarity between the current point cloud feature vector and each of the multiple historical point cloud feature vectors, so as to determine a positioning result. But is not limited thereto. The point cloud positioning method can also be as follows: and converting the laser reflection intensity data in the current point cloud data into current laser point cloud projection data in the horizontal plane, and converting the laser reflection intensity data of each of the plurality of historical point cloud data into historical laser point cloud projection data in the horizontal plane. And determining the similarity between the current laser point cloud projection data and each of the plurality of historical laser point cloud projection data by using a similarity matching mode, and further determining a positioning result. It is understood that any point cloud positioning method may be used as long as the positioning result is determined based on the current point cloud data and the plurality of historical point cloud data.

Fig. 3 schematically shows a flow chart for determining a target road segment according to another embodiment of the present disclosure.

As shown in fig. 3, the map data updating method may include operations S310 to S320, S331 to S332, and S340.

In operation S310, for each to-be-tested point of a plurality of to-be-tested points, a confidence of a positioning result of the to-be-tested point is determined.

In operation S320, the confidence is compared with a predetermined confidence.

In operation S331, in response to the confidence being less than or equal to the predetermined confidence threshold, the point to be tested is determined as a target point, resulting in a plurality of target points.

In operation S340, a target link is determined from a predetermined link based on a plurality of target points.

In operation S332, in response to the confidence being greater than the predetermined confidence threshold, the operation is stopped.

According to the embodiment of the disclosure, a point cloud positioning mode can be utilized to match a plurality of historical point cloud data with the point cloud data of the point to be tested, so that a plurality of matching degrees corresponding to the plurality of historical point cloud data one to one are obtained. And sequencing the matching degrees from high to low to obtain a sequencing result. And determining the matching degree arranged at the top as the confidence degree of the positioning result of the point to be tested based on the sequencing result.

According to the embodiment of the disclosure, the confidence may be compared with a predetermined confidence threshold, the point to be tested which is less than or equal to the predetermined confidence threshold is determined as the target point, that is, the point to be tested whose positioning fails is determined as the target point, and the target road section is determined from the predetermined road section based on a plurality of target points.

According to other embodiments of the present disclosure, the plurality of points to be tested may be determined according to an extending direction of the predetermined road segment, and the number of the target points is determined from the plurality of points to be tested, so as to obtain the total number of the target points. The total number of target points may be compared with a predetermined target point threshold, and in the case that the total number of target points is greater than or equal to the predetermined target point threshold, a link formed by a plurality of target points may be determined as a target link. In case the total number of target points is smaller than a predetermined target point threshold, the target points may be disregarded.

According to other embodiments of the present disclosure, in response to the confidence being less than or equal to the predetermined confidence threshold, the point to be tested may be determined to be the initial target point. An image book matching the initial target point is determined. The target point is determined based on the image data. Wherein the image data corresponding to the initial target point may be processed using an image recognition model to determine whether the initial target point is the target point. The image recognition model may be a model for recognizing characters in the image, and may further assist in determining whether the point to be tested is a target point based on the character "road construction ahead", so as to improve the accuracy thereof.

According to the embodiment of the present disclosure, the target road section may be determined based on the plurality of target points in the case where there is only the positioning result of each of the plurality of points to be tested, that is, there is only one positioning result set, according to the operation shown in fig. 3. And determining an update area of the map according to the geographical position information of the target road section. But is not limited thereto. And determining a target positioning result set based on the plurality of positioning result sets, and determining a target road section from the target positioning result set. And determining an updating area of the map according to the geographical position information of the target road section.

Fig. 4 schematically shows a flowchart of a map data update method according to another embodiment of the present disclosure.

As shown in fig. 4, the method may include operations S410 to S440, S451 to S452.

In operation S410, a plurality of positioning result sets are received.

According to an embodiment of the present disclosure, there are N autonomous vehicles traveling from a predetermined road section during a predetermined period of time, such as a day or week. The point cloud data of static target objects such as lane lines, road signs, buildings and the like on a preset road section can be respectively collected by utilizing a laser radar sensor on an automatic driving vehicle, so that N current point cloud data sets corresponding to N automatic driving vehicles one by one are obtained. And matching to obtain a positioning result set of the automatic driving vehicle, such as a 1 st positioning result set, a 2 nd positioning result set, a 3 rd positioning result set,', and an Nth positioning result set, by using a plurality of historical point cloud data of a positioning map loaded on the automatic driving vehicle. The server may receive respective positioning result sets from the N autonomous vehicles, resulting in N positioning result sets.

In operation S420, a plurality of positioning result sets are clustered.

According to an embodiment of the present disclosure, N positioning result sets may be clustered. And determining the target positioning result set with the target road section as a group, and determining the positioning result without the target road section as a group. For example, if the 1 st positioning result set, the 2 nd positioning result set, and the 3 rd positioning result set include the target road segment, the 1 st positioning result set, the 2 nd positioning result set, and the 3 rd positioning result set are respectively determined as the target positioning result sets.

In operation S430, the determined number of target road segments in the determined target positioning result set is calculated. The number of target positioning result sets containing the same target road section can be calculated and obtained as the determined number of the target road section under the condition that the same position is represented by using the geographic position information of the target road section.

In operation S440, the determined number of target links is compared with a predetermined number threshold.

In operation S451, in the case where the determination number is greater than or equal to the predetermined number threshold value, it is determined that the possibility that the surrounding environment of the target link has changed is high, and an operation of determining the area to be updated of the map according to the geographical location information of the target link may be performed based on this.

In operation S452, in case that the determined number is less than the predetermined number threshold, it may be that the lidar sensor on the autonomous vehicle has failed but the surrounding environment of the target road segment has changed, and then the subsequent operation may be stopped.

By using the determining method of the area to be updated of the map provided by the embodiment of the disclosure, the calculated amount can be reduced in a clustering manner, and the authenticity and the effectiveness of the area to be updated in a determined manner can be improved by counting the determined number of the target road sections.

According to the embodiments of the present disclosure, the assist determination may be made in conjunction with other operation data (i.e., link-related data) of the autonomous vehicle, such as link image data, in the case where the determined number is greater than the predetermined number threshold. For example, the received road segment image data from the autonomous vehicle may be tagged according to a road segment image timestamp. Road segment image data corresponding to any target point in the target road segment may be determined based on the road segment image timestamp and the geographic timestamp or the location timestamp. The image recognition model provided by the embodiment of the disclosure can be utilized to process the road segment image data, so as to further assist in determining whether the target road segment is an area to be updated of the map. But is not limited thereto. Road section driving track data can be formed according to the geographic position information obtained by the automatic driving vehicle. It is determined whether the link travel track data is the same track as the predetermined link. For example, a position a in the predetermined section is under construction and needs to be bypassed. In this case, the road section driving track data is different from the track of the predetermined road section, so that the determination of whether the target road section is the area to be updated of the map can be further assisted. Whether the target road section is the area to be updated of the map or not can be determined according to the road section image data of the automatic driving vehicle and the road section driving track data.

According to an embodiment of the present disclosure, in the case of determining a region to be updated of a map, an update operation of the map may be performed.

For example, in response to the determined target road segment, acquiring a plurality of target current point cloud data of a plurality of target points in the target road segment, wherein the plurality of target points are in one-to-one correspondence with the plurality of target current point cloud data; and updating a plurality of historical point cloud data to be updated of the area to be updated in the map by using the plurality of target current point cloud data.

According to an embodiment of the present disclosure, the historical point cloud data to be updated may refer to historical point cloud data corresponding to a target point. The point cloud data to be updated corresponding to the target current point cloud data may be determined using the geographical location information of the target point, and the historical point cloud data to be updated may be updated using the target current point cloud data. The updating method is not limited, and the updating method can be used as long as the area to be updated in the map can be updated by using the target current point cloud data.

By using the data processing method provided by the embodiment of the disclosure, the area to be updated in the map is updated by using the acquired target current point cloud data of the area to be updated, and the detection and update are performed by using the operation data acquired at ordinary times, so that the updating period is short and the cost is low.

Fig. 5 schematically shows a block diagram of a map data update apparatus according to an embodiment of the present disclosure.

As shown in fig. 5, the map data updating apparatus 500 may include a first determination module 510 and a second determination module 520.

The first determining module 510 is configured to determine a target road segment from a predetermined road segment based on a positioning result of each of a plurality of points to be tested of the predetermined road segment, where the positioning result of each of the plurality of points to be tested is obtained by matching current point cloud data of each of the plurality of points to be tested with a plurality of historical point cloud data in a map.

And a second determining module 520, configured to determine an area to be updated of the map according to the geographic location information of the target road segment.

According to an embodiment of the present disclosure, the first determination module may include a first determination unit, a second determination unit, and a third determination unit.

The first determining unit is used for determining the confidence degree of the positioning result of the point to be tested aiming at each point to be tested in the plurality of points to be tested.

And the second determining unit is used for determining the point to be tested as the target point in response to the fact that the confidence coefficient is smaller than or equal to the preset confidence coefficient threshold value, and obtaining a plurality of target points.

A third determination unit configured to determine a target link from the predetermined links based on the plurality of target points.

According to an embodiment of the present disclosure, the first determining unit may include a first determining subunit, a second determining subunit, and a third determining subunit.

And the first determining subunit is used for determining the point to be tested as the initial target point in response to the confidence coefficient being less than or equal to the preset confidence coefficient threshold value.

And a second determining subunit for determining image data matched with the initial target point.

A third determining subunit, configured to determine the initial target point as the target point based on the image data.

According to the embodiment of the disclosure, the map data updating device may further include a first obtaining module, a clustering module, and a calculating module.

The first obtaining module is used for obtaining a plurality of positioning result sets of a predetermined road section in a predetermined time period, wherein each positioning result set in the plurality of positioning result sets comprises a positioning result of each of a plurality of to-be-tested points.

And the clustering module is used for clustering the plurality of positioning result sets based on the positioning results to obtain a target positioning result set containing the target road section.

And the calculation module is used for calculating the determined number of the determined target road sections based on the target positioning result set so as to execute the operation of determining the area to be updated of the map according to the geographical position information of the target road sections under the condition that the determined number is greater than or equal to the predetermined number threshold.

According to an embodiment of the present disclosure, the second determination module may include a fourth determination unit, a fifth determination unit.

A fourth determination unit, configured to determine road segment association data matching the target road segment, where the road segment association data includes at least one of: road section image data and road section driving track data.

And the fifth determining unit is used for determining the area to be updated of the map based on the geographical position information of the target road section and the road section correlation data.

According to an embodiment of the present disclosure, the map data updating apparatus may further include a first receiving module and a second receiving module.

The first receiving module is used for receiving the positioning result of each of the plurality of points to be tested, wherein the positioning result comprises a positioning timestamp.

And the second receiving module is used for receiving the respective geographical position information of the plurality of points to be tested, wherein the geographical position information comprises a geographical timestamp.

According to an embodiment of the present disclosure, the geographical location information of the target road segment is determined based on the positioning timestamp and the geographical timestamp.

According to the embodiment of the disclosure, the map data updating device may further include a second obtaining module and an updating module.

And the second acquisition module is used for responding to the determined target road section and acquiring a plurality of target current point cloud data of a plurality of target points in the target road section, wherein the plurality of target points correspond to the plurality of target current point cloud data one to one.

And the updating module is used for updating a plurality of historical point cloud data to be updated in the area to be updated in the map by utilizing the plurality of target current point cloud data.

The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.

According to an embodiment of the present disclosure, an electronic device includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method as described above.

According to an embodiment of the present disclosure, a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method as described above.

According to an embodiment of the disclosure, a computer program product comprising a computer program which, when executed by a processor, implements the method as described above.

FIG. 6 illustrates a schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 6, the apparatus 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The calculation unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.

The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 601 performs the respective methods and processes described above, such as the map data update method. For example, in some embodiments, the map data update method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the map data updating method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the map data update method in any other suitable manner (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.

It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and the present disclosure is not limited herein.

The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A map data update method, comprising:

determining a target road section from a preset road section based on the positioning result of each of a plurality of to-be-tested points of the preset road section, wherein the positioning result of each of the plurality of to-be-tested points is obtained by matching the current point cloud data of each of the plurality of to-be-tested points with a plurality of historical point cloud data in the map; and

and determining the area to be updated of the map according to the geographical position information of the target road section.

2. The method of claim 1, wherein the determining a target road segment from the predetermined road segment based on the positioning result of each of the plurality of points to be tested of the predetermined road segment comprises:

determining the confidence degree of the positioning result of the point to be tested aiming at each point to be tested in the plurality of points to be tested;

in response to determining that the confidence is less than or equal to a preset confidence threshold, determining the point to be tested as the target point, and obtaining a plurality of target points; and

determining a target road segment from the predetermined road segment based on the plurality of target points.

3. The method of claim 2, wherein said determining the point to be tested as the target point in response to the confidence level being less than or equal to a predetermined confidence level threshold comprises:

in response to the confidence level being less than or equal to a predetermined confidence level threshold, determining the point to be tested as an initial target point;

determining image data matching the initial target point; and

determining the initial target point as the target point based on the image data.

4. The method of any of claims 1 to 3, further comprising:

acquiring a plurality of positioning result sets of the predetermined road section in a predetermined time period, wherein each positioning result set in the plurality of positioning result sets comprises a positioning result of each of the plurality of points to be tested;

clustering the plurality of positioning result sets based on the positioning result to obtain a target positioning result set containing the target road section; and

and calculating the determined number of the target road sections based on the target positioning result set, so as to execute the operation of determining the area to be updated of the map according to the geographical position information of the target road sections under the condition that the determined number is greater than or equal to a predetermined number threshold value.

5. The method according to any one of claims 1 to 4, wherein the determining a region to be updated of the map according to the geographical location information of the target road segment comprises:

determining road segment association data that matches the target road segment, wherein the road segment association data comprises at least one of: road section image data and road section driving track data; and

and determining the area to be updated of the map based on the geographical position information of the target road section and the road section association data.

6. The method of any of claims 1 to 5, further comprising:

receiving a positioning result of each of a plurality of points to be tested, wherein the positioning result comprises a positioning timestamp; and

receiving respective geographical position information of a plurality of points to be tested, wherein the geographical position information comprises a geographical timestamp;

wherein the geographic location information of the target road segment is determined based on the positioning timestamp and a geographic timestamp.

7. The method of any of claims 1 to 6, further comprising:

responding to the determined target road section, acquiring a plurality of target current point cloud data of a plurality of target points in the target road section, wherein the plurality of target points correspond to the plurality of target point current point cloud data one to one; and

and updating a plurality of historical point cloud data to be updated of the area to be updated in the map by using the plurality of target current point cloud data.

8. A map data update apparatus comprising:

the map locating device comprises a first determining module, a second determining module and a third determining module, wherein the first determining module is used for determining a target road section from a preset road section based on the locating result of each of a plurality of to-be-tested points of the preset road section, and the locating result of each of the plurality of to-be-tested points is obtained by matching the current point cloud data of each of the plurality of to-be-tested points with a plurality of historical point cloud data in the map; and

and the second determination module is used for determining the area to be updated of the map according to the geographical position information of the target road section.

9. The apparatus of claim 8, wherein the first determining means comprises:

the first determining unit is used for determining the confidence degree of the positioning result of the to-be-tested point aiming at each to-be-tested point in the to-be-tested points;

the second determining unit is used for determining the point to be tested as the target point in response to the fact that the confidence degree is smaller than or equal to a preset confidence degree threshold value, and obtaining a plurality of target points; and

a third determination unit configured to determine a target link from the predetermined link based on the plurality of target points.

10. The apparatus of claim 9, wherein the first determining unit comprises:

the first determining subunit is used for determining the point to be tested as an initial target point in response to the confidence coefficient being less than or equal to a preset confidence coefficient threshold value;

a second determining subunit configured to determine image data that matches the initial target point; and

a third determining subunit, configured to determine, based on the image data, the initial target point as the target point.

11. The apparatus of any of claims 8 to 10, further comprising:

a first obtaining module, configured to obtain multiple positioning result sets of the predetermined road segment in a predetermined time period, where each of the multiple positioning result sets includes a positioning result of each of the multiple points to be tested;

the clustering module is used for clustering the positioning result sets based on the positioning results to obtain a target positioning result set containing the target road section; and

and the calculation module is used for calculating the determined number of the target road sections on the basis of the target positioning result set so as to execute the operation of determining the area to be updated of the map according to the geographical position information of the target road sections under the condition that the determined number is greater than or equal to a predetermined number threshold value.

12. The method of any of claims 8 to 11, wherein the second determination module comprises:

a fourth determination unit, configured to determine road segment association data that matches the target road segment, where the road segment association data includes at least one of: road section image data and road section driving track data; and

and the fifth determining unit is used for determining the area to be updated of the map based on the geographical position information of the target road section and the road section association data.

13. The apparatus of any of claims 8 to 12, further comprising:

the device comprises a first receiving module, a second receiving module and a third receiving module, wherein the first receiving module is used for receiving the positioning result of each of a plurality of points to be tested, and the positioning result comprises a positioning timestamp; and

the second receiving module is used for receiving the geographical position information of each of the plurality of points to be tested, wherein the geographical position information comprises a geographical timestamp;

14. The apparatus of any of claims 8 to 13, further comprising:

the second acquisition module is used for responding to the determined target road section and acquiring a plurality of target current point cloud data of a plurality of target points in the target road section, wherein the plurality of target points correspond to the plurality of target point current point cloud data one to one; and

and the updating module is used for updating a plurality of historical point cloud data to be updated of the area to be updated in the map by utilizing the plurality of target current point cloud data.

15. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 7.

16. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1 to 7.

17. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1 to 7.