CN114089362A - SLAM map construction method and related equipment - Google Patents
SLAM map construction method and related equipment Download PDFInfo
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- CN114089362A CN114089362A CN202111205757.0A CN202111205757A CN114089362A CN 114089362 A CN114089362 A CN 114089362A CN 202111205757 A CN202111205757 A CN 202111205757A CN 114089362 A CN114089362 A CN 114089362A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
Abstract
The invention discloses a SLAM map construction method and related equipment. The method comprises the following steps: acquiring environmental data through a radar sensor and a camera; constructing a local map according to the environment data; determining a cloud map through cloud data according to the environment data; and correcting the local map based on the cloud map to construct an SLAM map. The local map is corrected through the cloud map to create the SLAM map, and the SLAM map has the advantages of being good in real-time performance and high in precision. Meanwhile, the local map is corrected through the cloud map, the ADAS controller does not need to be upgraded when the vehicle function is upgraded, and the cloud map calculated through the cloud is used for correcting the body map, so that the function is upgraded, the upgrading cost is saved, and the flexibility of vehicle function upgrading is improved.
Description
Technical Field
The present disclosure relates to the field of SLAM maps, and more particularly, to a SLAM map construction method and related devices.
Background
With the increasing degree of intelligence of vehicle driving, SLAM (Simultaneous Localization and Mapping) is applied more frequently in automobiles. When the SLAM map is constructed in the automobile field, information acquisition is usually completed by a vehicle-end controller, a sensor and an image collector which are connected to the controller, the vehicle-end controller performs coordinate position positioning through a two-dimensional image of the surrounding environment obtained by the sensor and the image collector, and image information is fused through a computing device of a vehicle to construct the SLAM map for intelligent driving of the vehicle.
However, due to the limitation of the computing capability of the vehicle device, the constructed SLAM map has the problem of insufficient precision, and meanwhile, the upgrading of the intelligent driving mode of the vehicle is also limited.
Therefore, there is a need for a SLAM mapping method to at least partially solve the problems in the prior art.
Disclosure of Invention
In this summary, concepts in a simplified form are introduced that are further described in the detailed description. This summary of the invention is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
The embodiment of the application provides an SLAM map construction method and related equipment, and mainly aims to improve the precision of an SLAM map.
To at least partially solve the above problem, in a first aspect, the present invention provides a method for constructing a SLAM map, including:
acquiring environmental data through a radar sensor and a camera;
constructing a local map according to the environment data;
determining a cloud map through cloud data according to the environment data;
and correcting the local map based on the cloud map to construct an SLAM map.
Optionally, the environment data includes point cloud data and image data,
the above-mentioned environmental data of acquireing through radar sensor and camera includes:
acquiring the point cloud data through the radar sensor;
and acquiring the image data by using the camera.
Optionally, the obtaining of the local map and the cloud map according to the environment data includes:
constructing a local map by utilizing a neural network algorithm according to the point cloud data and the image data;
and acquiring a cloud map based on a map database by using the point cloud data and the image data.
Optionally, the image data includes a histogram of directional gradients of the image.
Optionally, the method further includes:
and updating the map database according to the point cloud data and the image data.
Optionally, the method further includes:
and when the communication between the vehicle and the cloud is abnormal, constructing the SLAM map through the local map.
Optionally, the method further includes:
establishing a reference coordinate system by taking the intersection point of the rear axle of the vehicle body and the center line of the vehicle body as a coordinate center;
and calibrating according to the relative positions of the radar sensor, the camera and the reference coordinate system.
In a second aspect, the present invention further provides a SLAM map building apparatus, including:
an acquisition unit: the system comprises a radar sensor and a camera, wherein the radar sensor and the camera are used for acquiring environmental data;
a first building unit: the local map is constructed according to the environment data;
a second building element: the system comprises a cloud map, a cloud data acquisition unit, a cloud storage unit and a data processing unit, wherein the cloud map is used for determining a cloud map through the cloud data according to the environment data;
a third building element: and the map updating module is used for modifying the local map based on the cloud map to construct an SLAM map.
In a third aspect, an electronic device includes: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the steps of the method for constructing a SLAM map according to any one of the first aspect described above when the computer program stored in the memory is executed.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the SLAM map construction method of any one of the above aspects.
In conclusion, the scheme acquires environmental data through the radar sensor and the camera; a local map is constructed according to the environment data, and the local map has the advantage of good real-time performance; and determining a cloud map through cloud data according to the environment data, wherein the cloud map has the advantage of high precision, and the local map is corrected based on the cloud map to construct an SLAM map. The cloud map has the advantages of high precision and long distance covered by the map, the local map is limited by the identification distance and the identification precision of the sensor, and the cloud map can identify the position of the vehicle according to the cloud data stored by the server so as to control the vehicle to carry out intelligent driving. The local map is corrected through the cloud map, so that the SLAM map for intelligent driving of the vehicle is constructed, the characteristics of good real-time performance of the local map and high precision of the cloud map can be integrated, and the SLAM map with higher precision and better real-time performance is constructed for controlling the vehicle to run. Meanwhile, the local map is corrected through the cloud map, the ADAS controller does not need to be upgraded when the vehicle function is upgraded, and the cloud map calculated through the cloud is used for correcting the body map, so that the function is upgraded, the upgrading cost is saved, and the flexibility of vehicle function upgrading is improved.
Additional advantages, objects, and features of the SLAM map construction method of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the specification. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a schematic flow chart of a method for constructing a SLAM map according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of an SLAM map building apparatus according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an SLAM map building electronic device according to an embodiment of the present application.
Detailed Description
The embodiment of the application provides an SLAM map construction method and related equipment. Meanwhile, the local map is corrected through the cloud map, the ADAS controller does not need to be upgraded when the vehicle function is upgraded, and the cloud map calculated through the cloud is used for correcting the body map, so that the function is upgraded, the upgrading cost is saved, and the flexibility of vehicle function upgrading is improved.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present application and in the drawings described above, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments.
Referring to fig. 1, a schematic flow chart of a method for constructing a SLAM map according to an embodiment of the present application may specifically include:
to at least partially solve the above problem, in a first aspect, the present invention provides a method for constructing a SLAM map, including:
s110, acquiring environmental data through a radar sensor and a camera;
specifically, radar sensors and cameras are arranged on the front portion, the rear portion and the side portion of an automobile body, and data of the environment around the running of the automobile are obtained. The radar sensor is used for obtaining the distance of object for the car, utilizes the camera to obtain the picture, discerns the object that includes in the picture, confirms the kind of object, and the object can include: vehicles, pedestrians, trees, sidewalks, non-motorized vehicles, and the like.
S120, constructing a local map according to the environment data;
specifically, distance information acquired by a radar sensor and image information acquired by a camera are transmitted to an ADAS controller (Advanced Driver Assistance System), and the ADAS controller establishes a local map according to environment data including the distance information and the image information. It will be appreciated that the accuracy of the sensor identification and the level of operation of the ADAS controller are factors that limit the accuracy and accuracy of the local map.
S130, determining a cloud map through cloud data according to the environment data;
specifically, network connection is established with a server through a T-BOX, environment data including distance information and image information are transmitted to the server through TSP (Telematics Service provider) Service, the server determines a cloud map of a vehicle according to the cloud data, and the cloud map is obtained by calculation of the server side and is far higher than a local map in precision.
And S140, correcting the local map based on the cloud map to construct an SLAM map.
Specifically, the local map has the advantage of good real-time performance, such as: the pedestrians pass through the vehicle temporarily, the local map can effectively identify the suddenly appearing pedestrians, and the signals are sent to the ADAS controller to complete emergency avoidance. The cloud map has the advantages of high precision and long distance covered by the map, the local map is limited by the identification distance and the identification precision of the sensor, and the cloud map can identify the position of the vehicle according to the cloud data stored by the server so as to control the vehicle to carry out intelligent driving. The local map is corrected through the cloud map, so that the SLAM map for intelligent driving of the vehicle is constructed, the characteristics of good real-time performance of the local map and high precision of the cloud map can be integrated, and the SLAM map with higher precision and better real-time performance is constructed for controlling the vehicle to run.
Further, when the functions of the vehicle are expanded by adding the sensors to the vehicle, the local map is obtained based on the operation of the ADAS controller, the ADAS controller does not need to be updated at the moment, only the data of the added sensors need to be transmitted to the cloud server, and the cloud map with more characteristics is created by the server, so that the flexible upgrade of the vehicle is realized.
It will be appreciated that the correction of the map may be done either locally on the vehicle or remotely on the server.
In summary, the embodiment acquires the environment information through the radar sensor and the camera, establishes the local map at the vehicle end based on the environment information, establishes the cloud map at the cloud end, and corrects the local map through the cloud map to establish the SLAM map, and the SLAM map has the advantages of good real-time performance and high precision. Meanwhile, the local map is corrected through the cloud map, the ADAS controller does not need to be upgraded when the vehicle function is upgraded, and the cloud map calculated through the cloud is used for correcting the body map, so that the function is upgraded, the upgrading cost is saved, and the flexibility of vehicle function upgrading is improved.
In some examples, the environmental data includes point cloud data and image data,
the above-mentioned environmental data of acquireing through radar sensor and camera includes:
acquiring the point cloud data through the radar sensor;
and acquiring the image data by using the camera.
Specifically, the method comprises the steps of acquiring point cloud data of an environment through a radar sensor, acquiring the distance between a vehicle and an object in the environment where the vehicle is located, acquiring image data of the environment through a camera, and identifying the type of the object in the environment through the image data.
In some examples, the obtaining the local map and the cloud map according to the environment data includes:
constructing a local map by utilizing a neural network algorithm according to the point cloud data and the image data;
specifically, the structure of the neural network is determined, objects in the environment possibly encountered by the vehicle are used as training samples to train a neural network model, point cloud data and image data are fused based on the trained neural network model, and a local map is constructed.
And acquiring a cloud map based on a map database by using the point cloud data and the image data.
Specifically, the point cloud data and the image data are uploaded to a cloud server, the cloud server compares the information contained in the point cloud data and the image data with a map database to generate a cloud map, and the coverage range of the cloud map established at the moment is larger than the coverage range of the local map due to the fact that delay possibly exists in transmission between the cloud and the local end, so that the cloud map can continuously correct the local map, and the SLAM map can be smoothly constructed.
In some examples, the image data includes a histogram of directional gradients of the image.
Specifically, Histogram of Oriented Gradient (HOG) features are a feature descriptor used for object detection in computer vision and image processing. The contour of an object in the environment can be accurately judged through the direction gradient histogram, and the influence of light on the identification precision is reduced, so that the accuracy of object identification is improved.
In some examples, the method further comprises:
and updating the map database according to the point cloud data and the image data.
Specifically, the environment information of the vehicle at the moment can be clearly represented through a point cloud map acquired by a radar and image data acquired by a camera, and the server can update the map database of the cloud according to the environment information acquired by the vehicle. Before updating, however, the environmental information needs to be screened and processed, so as to avoid the influence of the temporary objects on the map updating. For example: after the vehicle arrives at the environment information, the environment information is uploaded to a server, the server compares the information contained in the environment information with the data contained in the map database, and the condition that the environment information comprises a pedestrian is detected, and the map database is not updated because the pedestrian exists temporarily and has high randomness; when a wall is detected in the environment information, the information included in the map database does not include the wall, and the server updates the map of the position of the wall.
In conclusion, the map database can be updated through the environment information acquired by the vehicle
In some examples, the method further comprises:
and when the communication between the vehicle and the cloud is abnormal, constructing the SLAM map through the local map.
Specifically, when the communication between the vehicle and the cloud is abnormal, the environment data cannot be uploaded to the cloud server, the cloud server cannot create a cloud map, and the local map cannot be modified by using the cloud map to create the SLAM map. In this case, the SLAM map is constructed using the local map to provide the vehicle with intelligent driving, preventing the occurrence of an accident.
In summary, when the communication between the vehicle and the cloud is abnormal, the cloud cannot construct a cloud map so as to modify the local map to establish the SLAM map, and at the moment, the SLAM map is constructed through the local map for intelligent driving of the vehicle, so that the possibility of danger of the vehicle is reduced.
In some examples, the method further comprises:
establishing a reference coordinate system by taking the intersection point of the rear axle of the vehicle body and the center line of the vehicle body as a coordinate center;
and calibrating according to the relative positions of the radar sensor, the camera and the reference coordinate system.
Specifically, the environmental information of the vehicle can be acquired more comprehensively by arranging the radar sensors and the cameras at the front part, the rear part and the side part of the automobile body, a reference coordinate system is established by the intersection point of the rear shaft of the automobile body and the center line of the automobile body, all the radar sensors and the cameras are calibrated through the position relation between the coordinate system of the radar sensors and the camera and the reference coordinate system, the environmental information of the vehicle acquired by the radar sensors and the camera relative to the reference coordinate system is obtained, the information acquired by the radar sensors and the camera is fused, and therefore the SLAM map of the vehicle is constructed.
Further, when the vehicle turns, the intersection point of the rear axle of the vehicle body and the center line of the vehicle is approximate to the center of the turning of the vehicle, and the point is used as the coordinate origin of the reference coordinate system, so that the influence of the turning of the vehicle on the accuracy of SLAM map construction can be reduced.
In conclusion, by establishing a reference coordinate system by taking the intersection point of the rear axle of the vehicle body and the center line of the vehicle body as a coordinate center to calibrate the sensor, the influence of vehicle turning on the precision of the SLAM map can be reduced.
In a second aspect, the present invention further provides a SLAM map building apparatus, including:
referring to fig. 2, an embodiment of an SLAM map building apparatus in the embodiment of the present application may include:
the acquisition unit 21: the system comprises a radar sensor and a camera, wherein the radar sensor and the camera are used for acquiring environmental data;
the first construction unit 22: the local map is constructed according to the environment data;
the second building unit 23: the system comprises a cloud map, a cloud data acquisition unit, a cloud storage unit and a data processing unit, wherein the cloud map is used for determining a cloud map through the cloud data according to the environment data;
third building element 24: and the map updating module is used for modifying the local map based on the cloud map to construct an SLAM map.
As shown in fig. 3, an electronic device 300 is further provided in the embodiments of the present application, which includes a memory 310, a processor 320, and a computer program 311 stored on the memory 320 and executable on the processor, and when the computer program 311 is executed by the processor 320, the steps of any method for implementing the aforementioned SLAM map construction are implemented.
Since the electronic device described in this embodiment is a device used for implementing a SLAM map building apparatus in this embodiment, based on the method described in this embodiment, a person skilled in the art can understand the specific implementation manner of the electronic device of this embodiment and various variations thereof, so that how to implement the method in this embodiment by the electronic device is not described in detail herein, and as long as the person skilled in the art implements the device used for implementing the method in this embodiment, the device falls within the scope of protection intended by this application.
In a specific implementation, the computer program 311 may implement any of the embodiments corresponding to fig. 1 when executed by a processor.
It should be noted that, in the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to relevant descriptions of other embodiments for parts that are not described in detail in a certain embodiment.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Embodiments of the present application further provide a computer program product, where the computer program product includes computer software instructions, and when the computer software instructions are run on a processing device, the processing device is caused to execute a flow of SLAM map construction as in the corresponding embodiment of fig. 1.
The computer program product includes one or more computer instructions. The procedures or functions according to the embodiments of the present application are all or partially generated when the computer program instructions are loaded and executed on a computer. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). A computer-readable storage medium may be any available medium that a computer can store or a data storage device, such as a server, a data center, etc., that is integrated with one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method of the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. A SLAM mapping method, comprising:
acquiring environmental data through a radar sensor and a camera;
constructing a local map according to the environment data;
determining a cloud map through cloud data according to the environment data;
and correcting the local map based on the cloud map to construct an SLAM map.
2. The method of claim 1, wherein the environmental data comprises point cloud data and image data,
acquire environmental data through radar sensor and camera, include:
acquiring the point cloud data through the radar sensor;
and acquiring the image data by using the camera.
3. The method of claim 2, wherein the obtaining a local map and a cloud map from the environment data comprises:
constructing a local map by utilizing a neural network algorithm according to the point cloud data and the image data;
and acquiring a cloud map based on a map database by using the point cloud data and the image data.
4. The method of claim 3, wherein the image data comprises a histogram of oriented gradients of the image.
5. The method of claim 3, further comprising:
updating the map database with the point cloud data and the image data.
6. The method of claim 1, further comprising:
and when the communication between the vehicle and the cloud is abnormal, constructing an SLAM map through a local map.
7. The method of claim 1, further comprising:
establishing a reference coordinate system by taking the intersection point of the rear axle of the vehicle body and the center line of the vehicle body as a coordinate center;
and calibrating according to the relative positions of the radar sensor, the camera and the reference coordinate system.
8. A SLAM mapping apparatus, comprising:
an acquisition unit: the system comprises a radar sensor and a camera, wherein the radar sensor and the camera are used for acquiring environmental data;
a first building unit: for constructing a local map from the environmental data;
a second building element: the system comprises a cloud map, a database and a database, wherein the cloud map is used for determining a cloud map through cloud data according to the environment data;
a third building element: the map updating method comprises the steps of correcting the local map based on the cloud map to construct a SLAM map.
9. An electronic device, comprising: memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor is adapted to implement the steps of the SLAM mapping method according to any of claims 1-7 when executing the computer program stored in the memory.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that: the computer program when executed by a processor implements the steps of the SLAM mapping method of any of claims 1-7.
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