CN116797680A

CN116797680A - Method, device and equipment for building map and computer readable storage medium

Info

Publication number: CN116797680A
Application number: CN202210252086.1A
Authority: CN
Inventors: 冯路
Original assignee: Beijing Co Wheels Technology Co Ltd
Current assignee: Beijing Co Wheels Technology Co Ltd
Priority date: 2022-03-15
Filing date: 2022-03-15
Publication date: 2023-09-22

Abstract

The present disclosure relates to a method, apparatus, device, and computer-readable storage medium for mapping, where the present disclosure segments received data by receiving a plurality of data units sent by a vehicle, each data unit including a two-dimensional image of current point cloud data and characteristic point cloud data in the current point cloud data, reducing a transmission amount of the data; fusing the two-dimensional image of the current point cloud data with a historical image corresponding to the two-dimensional image of the current point cloud data to obtain a fused image, so that the accuracy of the image is improved; and performing pose optimization on the characteristic point cloud data in the current point cloud data to obtain an optimized characteristic point cloud position, and adjusting the position of the fused image according to the optimized characteristic point cloud position to obtain a target image, so that the obtained target image is more accurate.

Description

Method, device and equipment for building map and computer readable storage medium

Technical Field

The present disclosure relates to the field of mapping technologies, and in particular, to a mapping method, apparatus, device, and computer readable storage medium.

Background

A point cloud is a map represented by a set of discrete points. The point cloud is just some points without logic independent, and can have hundreds to thousands of points, and has sparsity and disorder. When people see a cluster of point clouds, the objects represented by the point clouds can be identified according to life experience for many years.

Generally, cloud mapping is performed based on the total amount of point cloud data, but the total amount of point cloud data cannot be returned due to the limitation of bandwidth and flow of vehicles.

Disclosure of Invention

In order to solve the technical problems, the present disclosure provides a method, an apparatus, a device and a computer readable storage medium for mapping, so as to reduce transmission of mapping data and improve mapping accuracy.

In a first aspect, an embodiment of the present disclosure provides a method for mapping, including:

receiving a plurality of data units sent by a vehicle, wherein each data unit comprises a two-dimensional image of current point cloud data and characteristic point cloud data in the current point cloud data, and the current point cloud data is point cloud data acquired in the process that the vehicle runs on a route with a preset length;

for each data unit in the plurality of data units, acquiring a history image corresponding to the two-dimensional image of the current point cloud data, wherein the history image is a two-dimensional image of history point cloud data, and the current point cloud data and the history point cloud data correspond to a route with the same preset length;

Fusing the two-dimensional image of the current point cloud data with a historical image corresponding to the two-dimensional image of the current point cloud data to obtain a fused image;

performing pose optimization on the characteristic point cloud data in the current point cloud data to obtain an optimized characteristic point cloud position;

and adjusting the position of the fused image according to the optimized characteristic point cloud position to obtain a target image.

In some embodiments, the two-dimensional image of the current point cloud data is a two-dimensional image obtained after the current point cloud data is flattened, and the two-dimensional image is used for representing intensity information and height information of a target object corresponding to the current point cloud data.

In some embodiments, after receiving the plurality of data units transmitted by the vehicle, the method further comprises:

the characteristic point cloud data are ordered according to the first position information of the characteristic point cloud data, so that the characteristic point cloud data in the point cloud data collected in the process that the vehicle runs on a preset track are obtained, and the preset track comprises a plurality of routes with preset lengths.

In some embodiments, the method further comprises:

and optimizing characteristic point cloud data in the point cloud data acquired in the process of driving the vehicle on a preset track to obtain second position information of the characteristic point cloud data.

In some embodiments, optimizing feature point cloud data in point cloud data acquired during a vehicle traveling on a preset track includes:

and pre-integrating characteristic point cloud data in the point cloud data acquired in the process of the vehicle running on the preset track according to information output by an inertia measurement unit of the vehicle in the process of the vehicle running on the preset track.

In some embodiments, fusing the two-dimensional image of the current point cloud data and the historical image corresponding to the two-dimensional image of the current point cloud data to obtain a fused image, including:

and according to the second position information of the characteristic point cloud data, the two-dimensional image of the current point cloud data is overlapped to a historical image corresponding to the two-dimensional image of the current point cloud data, and a fused image is obtained.

In some embodiments, the method further comprises:

and segmenting the target image, and outputting the segmented target image to a third party platform, wherein the third party platform is used for labeling the segmented target image.

In a second aspect, an embodiment of the present disclosure provides a mapping apparatus, including:

The receiving module is used for receiving a plurality of data units sent by a vehicle, wherein each data unit comprises a two-dimensional image of current point cloud data and characteristic point cloud data in the current point cloud data, and the current point cloud data are point cloud data acquired in the process that the vehicle runs on a route with a preset length;

the acquisition module is used for acquiring a history image corresponding to the two-dimensional image of the current point cloud data aiming at each data unit in the plurality of data units, wherein the history image is a two-dimensional image of history point cloud data, and the current point cloud data and the history point cloud data correspond to a route with the same preset length;

the fusion module is used for fusing the two-dimensional image of the current point cloud data with the historical image corresponding to the two-dimensional image of the current point cloud data to obtain a fused image;

the optimization module is used for performing pose optimization on the characteristic point cloud data in the current point cloud data to obtain an optimized characteristic point cloud position;

and the adjusting module is used for adjusting the position of the fused image according to the optimized characteristic point cloud position to obtain a target image.

In a third aspect, an embodiment of the present disclosure provides an electronic device, including:

a memory;

a processor; and

a computer program;

wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method according to the first aspect.

In a fourth aspect, embodiments of the present disclosure provide a computer-readable storage medium having stored thereon a computer program for execution by a processor to implement the method of the first aspect.

In a fifth aspect, the presently disclosed embodiments also provide a computer program product comprising a computer program or instructions for execution by a processor to implement the method of the first aspect.

The method, the device, the equipment and the computer readable storage medium for constructing the map provided by the embodiment of the disclosure receive data in a segmented way by receiving a plurality of data units sent by a vehicle, wherein each data unit comprises a two-dimensional image of current point cloud data and characteristic point cloud data in the current point cloud data, and the current point cloud data is the point cloud data acquired in the process that the vehicle runs on a route with a preset length, so that the transmission quantity of the data is reduced; for each data unit in the plurality of data units, acquiring a history image corresponding to the two-dimensional image of the current point cloud data, wherein the history image is the two-dimensional image of the history point cloud data, the current point cloud data and the history point cloud data correspond to a route with the same preset length, and the position of the two-dimensional image of the current point cloud data in the history image is clear; fusing the two-dimensional image of the current point cloud data with a historical image corresponding to the two-dimensional image of the current point cloud data to obtain a fused image, so that the accuracy of the image is improved; performing pose optimization on the characteristic point cloud data in the current point cloud data to obtain an optimized characteristic point cloud position, and adjusting the position of the fused image according to the optimized characteristic point cloud position to obtain a target image; therefore, the position of the fused image is determined, further adjustment is carried out, the obtained target image is more accurate, the target image can be updated locally by the image construction method, the time is saved, the labor cost is reduced, the image construction efficiency is improved, and the user experience comfort level is improved.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.

In order to more clearly illustrate the embodiments of the present disclosure or the solutions in the prior art, the drawings that are required for the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.

FIG. 1 is a flow chart of a method of mapping provided in an embodiment of the present disclosure;

fig. 2 is a schematic diagram of data unit division according to an embodiment of the disclosure;

fig. 3 is a schematic diagram of data unit division according to an embodiment of the disclosure;

fig. 4 is a schematic diagram of a two-dimensional image of current point cloud data and a two-dimensional image of corresponding historical point cloud data according to an embodiment of the disclosure;

fig. 5 is a schematic diagram of data unit fusion provided in an embodiment of the present disclosure;

fig. 6 is a schematic structural diagram of a mapping apparatus according to an embodiment of the disclosure;

fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure.

Detailed Description

In order that the above objects, features and advantages of the present disclosure may be more clearly understood, a further description of aspects of the present disclosure will be provided below. It should be noted that, without conflict, the embodiments of the present disclosure and features in the embodiments may be combined with each other.

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced otherwise than as described herein; it will be apparent that the embodiments in the specification are only some, but not all, embodiments of the disclosure.

The embodiments of the present disclosure provide a method for mapping, which is described below in connection with specific embodiments.

Fig. 1 is a flowchart of a method for mapping according to an embodiment of the disclosure. The method can be applied to application scenes of cloud mapping, and it can be understood that the mapping method provided by the embodiment of the disclosure can also be applied to other scenes. The following describes a method for mapping shown in fig. 1, which comprises the following specific steps:

s101, receiving a plurality of data units sent by a vehicle, wherein each data unit comprises a two-dimensional image of current point cloud data and characteristic point cloud data in the current point cloud data, and the current point cloud data are acquired in the process that the vehicle runs on a route with a preset length.

Radar (radio detection and ranging, radar), known collectively as "radio detection and ranging", also known as "radio positioning", is a method of radio finding objects and determining their spatial position. Radar is an electronic device that detects a target using electromagnetic waves. The radar emits electromagnetic waves to irradiate the target and receives echoes thereof, thereby obtaining information such as the distance from the target to the electromagnetic wave emission point, the distance change rate (radial velocity), the azimuth, the altitude and the like.

The radar measurement speed principle is that the radar generates a frequency Doppler effect according to the relative motion between the radar and a target. The target echo frequency received by the radar is different from the radar transmitting frequency, and the difference between the target echo frequency and the radar transmitting frequency is called Doppler frequency. The radar distance measurement principle is to measure the time difference between the transmitted pulse and the echo pulse, and the electromagnetic wave propagates at the speed of light, so that the distance between the radar and the target can be converted.

The point cloud is also called a point data set of the appearance surface of the product obtained by a measuring instrument in reverse engineering, the number of points obtained by a three-dimensional coordinate measuring machine is usually small, and the distance between the points is also large, namely a sparse point cloud; the point cloud obtained by using the three-dimensional laser scanner or the photographic scanner has larger and denser point number, and is called dense point cloud.

The Real-time dynamic (RTK) carrier phase difference technology is a difference method for processing the observed quantity of the carrier phases of two measuring stations in Real time, and the carrier phases acquired by a reference station are sent to a user receiver to calculate the difference and calculate the coordinates. The method is a new common satellite positioning measurement method, the previous static, quick static and dynamic measurement needs to be solved afterwards to obtain centimeter-level precision, the RTK is a measurement method capable of obtaining centimeter-level positioning precision in real time in the field, the method adopts a carrier phase dynamic real-time differential method, the method is a great milestone for GPS application, the appearance of the method is engineering lofting and landform mapping, and various control measurement brings new measurement principles and methods, so that the operation efficiency is greatly improved.

The RTK is arranged on the vehicle, so that the positioning is more accurate; the radar sensor is also arranged for extracting point cloud data acquired in the process of driving the vehicle on a route with a preset length, for example, the point cloud data can be 120 degrees forward of the driving track of the vehicle; when the vehicle runs the preset length on the preset track, the preset length may be divided, for example, the preset length is divided by taking 50 meters as a unit, and a plurality of data units are divided, it is understood that when the preset length is less than or equal to 50 meters, the preset length is not required to be divided, as shown in fig. 2, when the preset length is greater than 50 meters and the preset length is the whole multiple of 50 meters, the preset length is divided by taking 50 meters as one data unit, as shown in fig. 2, the preset length 20 is 200 meters, and the preset length is divided by taking 50 meters as one data unit, and is divided into a data unit 21, a data unit 22, a data unit 23 and a data unit 24. When the preset length is greater than 50 meters and the preset length is not the whole multiple of 50 meters, the data unit is divided by taking 50 meters as one data unit, until the last data unit is less than 50 meters, as shown in fig. 3, the preset length 30 is 180 meters, and the data unit is divided by taking 50 meters as one data unit, and is divided into the data unit 31, the data unit 32, the data unit 33 and the data unit 34. It is understood that the preset length may be other lengths, and the embodiment is divided in units of 50 meters, and in other embodiments, the preset length may be divided in other lengths, which is not limited in particular.

The two-dimensional image of the current point cloud data is a two-dimensional image obtained after the current point cloud data is flattened, and the two-dimensional image is used for representing the intensity information and the height information of the target object corresponding to the current point cloud data. The intensity information refers to information representing the intensity of the brightness of the target object, which is obtained after measurement by the measuring instrument. In this embodiment, the measuring instrument is a radar. The height information is information representing the height of the target object obtained after measurement by the measuring instrument.

The characteristic point cloud data in the current point cloud data refer to characteristic point cloud data in the current point cloud data extracted by a vehicle machine of a vehicle through a LOAM algorithm in the current point cloud data.

Alternatively, the vehicle may be another mobile carrier with a sensor, and the embodiment is not limited specifically.

Alternatively, the radar measurement may be other measurement modes such as laser measurement and photogrammetry, which are not limited in this embodiment.

S102, acquiring a historical image corresponding to the two-dimensional image of the current point cloud data aiming at each data unit in the plurality of data units, wherein the historical image is the two-dimensional image of the historical point cloud data, and the current point cloud data and the historical point cloud data correspond to the same route with the preset length.

And acquiring a history image corresponding to the two-dimensional image of the current point cloud data aiming at each data unit in the plurality of data units, wherein the history image is a two-dimensional image obtained by flattening the history point cloud data, and the current point cloud data and the history point cloud data correspond to a route with the same preset length.

As shown in fig. 4, the two-dimensional image 31 of the current point cloud data corresponds to the two-dimensional image 41 of the history point cloud data, the two-dimensional image 32 of the current point cloud data corresponds to the two-dimensional image 42 of the history point cloud data, the two-dimensional image 33 of the current point cloud data corresponds to the two-dimensional image 43 of the history point cloud data, and the two-dimensional image 34 of the current point cloud data corresponds to the two-dimensional image 44 of the history point cloud data.

It can be understood that when the cloud end has no history image corresponding to the two-dimensional image of the current point cloud data, the two-dimensional image of the current point cloud data can be used as the history image, the vehicle acquires the point cloud data for the second time on the route with the preset length, the cloud end receives a plurality of data units sent by the vehicle for the second time, and each data unit comprises the two-dimensional image of the current point cloud data acquired for the second time and the characteristic point cloud data in the current point cloud data acquired for the second time.

And S103, fusing the two-dimensional image of the current point cloud data with a historical image corresponding to the two-dimensional image of the current point cloud data to obtain a fused image.

And fusing the two-dimensional image of the current point cloud data with the historical image corresponding to the two-dimensional image of the current point cloud data to obtain a fused image.

As shown in fig. 5, the two-dimensional image 31 of the current point cloud data and the history image 41 corresponding to the two-dimensional image of the current point cloud data are fused to obtain a fused image 51; fusing the two-dimensional image 32 of the current point cloud data with the history image 42 corresponding to the two-dimensional image of the current point cloud data to obtain a fused image 52; fusing the two-dimensional image 33 of the current point cloud data with the historical image 43 corresponding to the two-dimensional image of the current point cloud data to obtain a fused image 53; the two-dimensional image 34 of the current point cloud data is fused with the history image 44 corresponding to the two-dimensional image of the current point cloud data to obtain a fused image 54.

It can be understood that when the cloud end has no history image corresponding to the two-dimensional image of the current point cloud data, the two-dimensional image of the current point cloud data acquired for the first time can be used as the history image, and the two-dimensional image of the current point cloud data acquired for the second time can be used as the two-dimensional image of the current point cloud data. And fusing the two-dimensional image of the first acquired current point cloud data with the two-dimensional image of the second acquired current point cloud data to obtain a fused image.

And S104, performing pose optimization on the characteristic point cloud data in the current point cloud data to obtain the optimized characteristic point cloud position.

Pose optimization (Pose Graph) is a mapping method based on least square nonlinear optimization, and uses a node-Edge (Vertex-Edge) Pose Graph mode to model a simultaneous localization mapping (SLAM) problem of a robot. The least squares nonlinear optimization problem is converted into graph optimization problem by constructing a model of a node-Edge (Vertex-Edge) pose graph.

And performing pose optimization on the characteristic point cloud data in the current point cloud data to obtain an optimized characteristic point cloud position, wherein it can be understood that the characteristic point cloud data in the current point cloud data is consistent with the two-dimensional image position of the current point cloud data.

And S105, adjusting the position of the fused image according to the optimized characteristic point cloud position to obtain a target image.

And adjusting the position of the fused image according to the optimized characteristic point cloud position to obtain a target image. Achieving optimization of target image on horizontal plane

According to the embodiment of the disclosure, the data is received in a segmented mode through receiving a plurality of data units sent by the vehicle, each data unit comprises a two-dimensional image of current point cloud data and characteristic point cloud data in the current point cloud data, the current point cloud data are acquired in the process that the vehicle runs on a route with a preset length, and the transmission quantity of the data is reduced; for each data unit in the plurality of data units, acquiring a history image corresponding to the two-dimensional image of the current point cloud data, wherein the history image is the two-dimensional image of the history point cloud data, the current point cloud data and the history point cloud data correspond to a route with the same preset length, and the position of the two-dimensional image of the current point cloud data in the history image is clear; fusing the two-dimensional image of the current point cloud data with a historical image corresponding to the two-dimensional image of the current point cloud data to obtain a fused image, so that the accuracy of the image is improved; performing pose optimization on the characteristic point cloud data in the current point cloud data to obtain an optimized characteristic point cloud position, and adjusting the position of the fused image according to the optimized characteristic point cloud position to obtain a target image; therefore, the position of the fused image is determined, further adjustment is carried out, the obtained target image is more accurate, the target image can be updated locally by the image construction method, the time is saved, the labor cost is reduced, the image construction efficiency is improved, and the user experience comfort level is improved.

In addition, since the embodiment of the disclosure can be updated locally or globally during the mapping process. Therefore, compared to the global update method in the prior art, the embodiment of the disclosure has higher flexibility.

The characteristic point cloud data on the routes with the preset lengths are ordered according to the first position information of the characteristic point cloud data, for example, when a network is not good, the received characteristic point cloud data are obtained according to time sequence, and the third section of characteristic point cloud data can be received faster than the second section of characteristic point cloud data, so that the characteristic point cloud data on the routes with the preset lengths are ordered according to the first position information of the characteristic point cloud data, the third section of characteristic point cloud data are placed behind the second section of characteristic point cloud data, the characteristic point cloud data in the point cloud data acquired in the process that a vehicle runs on a preset track are obtained, the characteristic point cloud data of the whole vehicle on the preset track are obtained, and the position correction of the whole track is convenient to achieve.

In some embodiments, the method further comprises: and optimizing characteristic point cloud data in the point cloud data acquired in the process of driving the vehicle on a preset track to obtain second position information of the characteristic point cloud data.

The method for optimizing the characteristic point cloud data in the point cloud data acquired in the process of driving the vehicle on the preset track comprises the following steps: and pre-integrating characteristic point cloud data in the point cloud data acquired in the process of the vehicle running on the preset track according to information output by an inertia measurement unit of the vehicle in the process of the vehicle running on the preset track.

An inertial measurement unit (Inertial Measurement Unit, IMU) is a device that measures the three-axis attitude angle (or angular rate) and acceleration of an object. Generally, an IMU includes three single-axis accelerometers and three single-axis gyroscopes, where the accelerometers detect acceleration signals of the object in the carrier coordinate system on three independent axes, and the gyroscopes detect angular velocity signals of the carrier relative to the navigation coordinate system, measure angular velocity and acceleration of the object in three-dimensional space, and calculate the attitude of the object based on the angular velocity and acceleration. IMUs are typically used for local integration during mapping to obtain relatively accurate local displacements and attitude changes.

IMU pre-integration belongs to front end part, and operation is performed immediately after IMU data are collected. Because IMUs have high sampling frequencies, typically 100Hz-1000Hz, the amount of data is very large, and when optimizing it is common practice to extract one data at intervals, such as every 1 second. However, in doing so, when we perform iterative solution calculations to update and adjust the values of displacement, velocity, and attitude (PVQ), once the previous PVQ is adjusted, such as the first second, all traces behind each are re-integrated, such as traces behind the first second. The purpose of pre-integration is to try to change the multiple integration process to only one integration, which can save a great deal of computation by applying a pre-integration model.

According to information output by an inertia measurement unit of a vehicle in the process of driving the vehicle on a preset track, carrying out pre-integration on characteristic point cloud data in point cloud data acquired in the process of driving the vehicle on the preset track, and obtaining the second position information of the characteristic point cloud data by knowing the real distance between two adjacent characteristic point cloud data and correcting in time through the pre-integration.

Because the PTK fails for a long time and causes the phenomena of fracture and jump between the characteristic point cloud data on the preset track, the embodiment of the disclosure corrects and optimizes the characteristic point cloud data of the vehicle on the preset track by taking the characteristic point cloud data as a rigid node whole, and further improves the accuracy of drawing.

and according to the second position information of the characteristic point cloud data, overlapping the two-dimensional image of the current point cloud data into a historical image corresponding to the two-dimensional image of the current point cloud data, taking the two-dimensional image of the current point cloud data as a reference, taking the historical image corresponding to the two-dimensional image of the current point cloud data as an auxiliary, obtaining a fused image, storing the position information of boundary nodes on the two-dimensional image of the current point cloud data and the historical image corresponding to the two-dimensional image of the current point cloud data, and adjusting adjacent nodes in the global image. The global image refers to a unique image on the preset track, for example, when the embodiment obtains the target image on the preset track, the target image may be updated to the global image.

According to the embodiment of the disclosure, the two-dimensional images of the current point cloud data are overlapped to the corresponding historical images for fusion, so that the accuracy of image construction is further improved, the image construction method has the capability of local updating, and the large-scale city image construction combining the vehicle-end data and the cloud image construction overall process is realized.

In some embodiments, the method further comprises: and segmenting the target image, and outputting the segmented target image to a third party platform, wherein the third party platform is used for labeling the segmented target image.

The target image is segmented, for example, a separation belt, a guardrail, a rod-shaped object, a traffic light, a traffic sign and the like can be segmented, then the segmented target image is output to a third party platform, the output mode can be a communication connection mode such as Bluetooth output and network output, and the third party platform is used for labeling the segmented target image. The third party platform can be a labeling platform or other platforms needing labeling.

Optionally, the third party platform marks the cut target image, which may be marking isolation belt, guard rail, rod, traffic light, traffic sign label, etc.

According to the embodiment of the disclosure, the segmented target image is marked through the third-party platform, so that the target image is more clear at a glance and is convenient to use.

Fig. 6 is a schematic structural diagram of a mapping apparatus according to an embodiment of the disclosure. The mapping means may be an electronic device as described in the above embodiments, or the mapping means may be a part or component in the electronic device. The mapping apparatus provided in the embodiments of the present disclosure may execute the processing flow provided in the embodiment of the mapping method, as shown in fig. 6, where the mapping apparatus 60 includes: the device comprises a receiving module 61, an acquiring module 62, a fusion module 63, an optimizing module 64 and an adjusting module 65; the receiving module 61 is configured to receive a plurality of data units sent by a vehicle, where each data unit includes a two-dimensional image of current point cloud data and feature point cloud data in the current point cloud data, and the current point cloud data is point cloud data collected during a process that the vehicle travels on a route with a preset length; an obtaining module 62, configured to obtain, for each of the plurality of data units, a history image corresponding to the two-dimensional image of the current point cloud data, where the history image is a two-dimensional image of history point cloud data, and the current point cloud data and the history point cloud data correspond to a route with a same preset length; the fusion module 63 is configured to fuse the two-dimensional image of the current point cloud data with a history image corresponding to the two-dimensional image of the current point cloud data, so as to obtain a fused image; the optimizing module 64 is configured to perform pose optimization on the feature point cloud data in the current point cloud data, so as to obtain an optimized feature point cloud position; and the adjusting module 65 is configured to adjust the position of the fused image according to the optimized feature point cloud position, so as to obtain a target image.

Optionally, the two-dimensional image of the current point cloud data is a two-dimensional image obtained after the current point cloud data is flattened, and the two-dimensional image is used for representing intensity information and height information of a target object corresponding to the current point cloud data.

Optionally, the mapping apparatus 60 further includes: the sorting module 66 is configured to sort the feature point cloud data according to the first location information of the feature point cloud data, so as to obtain feature point cloud data in the point cloud data collected during the running of the vehicle on a preset track, where the preset track includes a plurality of routes with preset lengths.

Optionally, the optimizing module 64 is further configured to optimize feature point cloud data in the point cloud data collected during the running of the vehicle on the preset track, so as to obtain second location information of the feature point cloud data.

Optionally, the optimizing module 64 is further configured to pre-integrate the characteristic point cloud data in the point cloud data collected during the vehicle driving on the preset track according to the information output by the inertia measurement unit of the vehicle during the vehicle driving on the preset track.

Optionally, the fusion module 63 is further configured to superimpose the two-dimensional image of the current point cloud data on the historical image corresponding to the two-dimensional image of the current point cloud data according to the second location information of the feature point cloud data, so as to obtain a fused image.

Optionally, the mapping apparatus 60 further includes: the output module 67 is configured to segment the target image, and output the segmented target image to a third party platform, where the third party platform is configured to label the segmented target image.

The device for mapping the embodiment shown in fig. 6 may be used to implement the technical solution of the above method embodiment, and its implementation principle and technical effects are similar, and are not described herein again.

Fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure. The electronic device provided in the embodiment of the present disclosure may execute the processing flow provided in the embodiment of the method for mapping, as shown in fig. 7, where the electronic device 70 includes: memory 71, processor 72, computer programs and communication interface 73; wherein the computer program is stored in the memory 71 and configured to be executed by the processor 72 for performing the method of mapping as described above.

In addition, the embodiment of the present disclosure also provides a computer readable storage medium having a computer program stored thereon, where the computer program is executed by a processor to implement the mapping method described in the foregoing embodiment.

Furthermore, embodiments of the present disclosure provide a computer program product comprising a computer program or instructions which, when executed by a processor, implement a method of mapping as described above.

It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having 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. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.

In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.

The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.

The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to:

In addition, the electronic device may also perform other steps in the method of mapping described above.

Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including, but not limited to, an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.

The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.

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. The 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.

It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, 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 only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

The foregoing is merely a specific embodiment of the disclosure to enable one skilled in the art to understand or practice the disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown and described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method of mapping, the method comprising:

2. The method according to claim 1, wherein the two-dimensional image of the current point cloud data is a two-dimensional image obtained by flattening the current point cloud data, and the two-dimensional image is used for representing intensity information and height information of a target object corresponding to the current point cloud data.

3. The method of claim 1, wherein after receiving the plurality of data units transmitted by the vehicle, the method further comprises:

4. A method according to claim 3, characterized in that the method further comprises:

5. The method according to claim 4, wherein optimizing the characteristic point cloud data among the point cloud data acquired during the travel of the vehicle on the preset trajectory includes:

6. The method of claim 4, wherein fusing the two-dimensional image of the current point cloud data with the historical image corresponding to the two-dimensional image of the current point cloud data to obtain the fused image comprises:

7. The method according to claim 1, wherein the method further comprises:

8. A mapping apparatus, the apparatus comprising:

9. An electronic device, comprising:

a memory;

a processor; and

a computer program;

wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of any of claims 1-7.

10. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method according to any of claims 1-7.