CN113537314A - Longitudinal positioning method and device for unmanned vehicle, electronic equipment and storage medium - Google Patents
Longitudinal positioning method and device for unmanned vehicle, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention provides a longitudinal positioning method and device for an unmanned vehicle, electronic equipment and a storage medium. The method comprises the following steps: acquiring environmental data acquired by acquisition equipment arranged on a vehicle; extracting environmental features from the environmental data, wherein the environmental features comprise unit geometric features and combined features obtained by unit geometric feature coupling; according to at least one-dimensional attribute, giving a characteristic weight to the environmental characteristic; searching for best matching known features in global feature data according to the environment features and feature weights thereof, wherein the global feature data comprises known features in a global scope, and the known features comprise unit geometric features and combined features obtained by unit geometric feature coupling; performing a global longitudinal localization of the vehicle based on the location of the best matched known feature within a global scope. The invention avoids characteristic mismatching and correctly matches the characteristics by combining the characteristics, thereby improving the accuracy of longitudinal positioning of the unmanned vehicle.
Description
Technical Field
The invention relates to the field of unmanned driving, in particular to a longitudinal positioning method and device for an unmanned vehicle, electronic equipment and a storage medium.
Background
In autopilot control, especially of container trucks at ports, since port roads and environments are relatively simple, in the construction of port maps and feature acquisition matching, it is common to use simple basic geometric features for the feature recognition of entities. When the vehicle positioning in the port area is carried out, the acquired basic geometric features are matched with the basic geometric features stored in the port map, so that the vehicle global longitudinal positioning is realized. However, basic geometric features are prone to matching errors, leading to instances of positioning errors.
Therefore, the technical problem to be solved by the technical personnel in the field is urgent to avoid the characteristic mismatching and correctly match the characteristics so as to improve the accuracy of the longitudinal positioning of the unmanned vehicle.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a longitudinal positioning method and device for an unmanned vehicle, electronic equipment and a storage medium, so as to avoid characteristic mismatching and correctly match characteristics, thereby improving the accuracy of longitudinal positioning of the unmanned vehicle.
According to one aspect of the invention, there is provided an unmanned vehicle longitudinal positioning method comprising:
acquiring environmental data acquired by acquisition equipment arranged on a vehicle;
extracting environmental features from the environmental data, wherein the environmental features comprise unit geometric features and combined features obtained by unit geometric feature coupling;
according to at least one-dimensional attribute, giving a characteristic weight to the environmental characteristic;
searching for best matching known features in global feature data according to the environment features and feature weights thereof, wherein the global feature data comprises known features in a global scope, and the known features comprise unit geometric features and combined features obtained by unit geometric feature coupling;
performing a global longitudinal localization of the vehicle based on the location of the best matched known feature within a global scope.
In some embodiments of the present application, the unit geometric features include point features, edge features, and face features, and the combined features are obtained from a mutual constraint relationship of a plurality of unit geometric features.
In some embodiments of the present application, the plurality of constraint relationships of the combined features of the known features in the global feature data form a set of constraint relationships, and the single geometric feature in the environmental feature traverses the set of constraint relationships to obtain a combined feature matching the constraint relationships in the set of constraint relationships.
In some embodiments of the present application, the constraint relationship comprises a positional relationship between unit geometric features, the positional relationship being represented by feature relative distance and/or included angle between features.
In some embodiments of the present application, said assigning feature weights to said environmental features in dependence on at least one-dimensional attributes comprises one or more of:
determining a feature weight of at least one-dimensional attribute of the environmental feature according to a position relationship between the environmental feature and the vehicle;
determining a feature weight of at least one-dimensional attribute of the global feature from a relationship of the vehicle to the global feature data;
and determining the feature weight of at least one-dimensional attribute of the environmental feature according to the relationship between the environmental feature and the global feature data.
In some embodiments of the present application, the combined features are further obtained by coupling unit geometric features and marker features.
In some embodiments of the present application, said performing a global longitudinal localization of the vehicle based on the location of the best matched known feature within a global scope comprises:
and determining the position of the vehicle in the global scope according to the position of the best matched known feature in the global scope and the relative position of the best matched known feature and the vehicle.
According to yet another aspect of the present application, there is also provided an unmanned vehicle longitudinal positioning apparatus comprising:
the acquisition module is configured to acquire environmental data acquired by acquisition equipment arranged on a vehicle;
an extraction module configured to extract environmental features from the environmental data, the environmental features including unit geometric features and combined features obtained by unit geometric feature coupling;
a weighting module configured to assign a feature weight to the environmental feature according to at least one-dimensional attribute;
a matching module configured to search global feature data for a best matching known feature according to the environmental features and feature weights thereof, wherein the global feature data comprises known features in a global scope, and the known features comprise unit geometric features and combined features obtained by unit geometric feature coupling;
a localization module configured to perform a global longitudinal localization of the vehicle based on a location of the best matched known feature within a global scope.
According to still another aspect of the present invention, there is also provided an electronic apparatus, including: a processor; a storage medium having stored thereon a computer program which, when executed by the processor, performs the steps as described above.
According to yet another aspect of the present invention, there is also provided a storage medium having stored thereon a computer program which, when executed by a processor, performs the steps as described above.
Compared with the prior art, the invention has the advantages that:
the method comprises the steps of matching combined features obtained by coupling unit geometric features in environmental features with combined features of global feature data, searching best matched known features by combining weights of attributes, and executing global longitudinal positioning of the vehicle according to the positions of the best matched known features in a global range, so that feature multidimensional attributes and multi-feature coupling are increased as combined features, feature mismatching is avoided, the features are correctly matched, and accuracy of the longitudinal positioning of the unmanned vehicle is improved.
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The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.
FIG. 1 shows a flow chart of a method for longitudinal positioning of an unmanned vehicle according to an embodiment of the invention;
FIG. 2 illustrates a flow diagram for weighting features of the environment in terms of at least one-dimensional attributes, according to an embodiment of the invention;
FIG. 3 shows a schematic diagram of environmental features according to an embodiment of the invention;
FIG. 4 shows a schematic view of the longitudinal positioning of an unmanned vehicle according to an embodiment of the invention;
FIG. 5 shows a block diagram of an unmanned vehicle longitudinal positioning apparatus according to an embodiment of the present invention;
FIG. 6 schematically illustrates a computer-readable storage medium in an exemplary embodiment of the disclosure;
fig. 7 schematically illustrates an electronic device in an exemplary embodiment of the disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor devices and/or microcontroller devices.
In order to solve the drawbacks of the prior art, the present invention provides a method for longitudinally positioning an unmanned vehicle, as shown in fig. 1. Fig. 1 shows a flow chart of a method for longitudinal positioning of an unmanned vehicle according to an embodiment of the invention. The longitudinal positioning of the unmanned vehicle refers to the positioning of the vehicle in its direction of travel. Fig. 1 shows the following steps in total:
step S110: the environmental data collected by the collecting device arranged on the vehicle is obtained.
In particular, the acquisition device may include a vision sensor, a radar sensor, and the like. The environmental data may include one or more of image data, video data, lidar data, and the invention is not limited in this respect.
Step S120: and extracting environmental features from the environmental data, wherein the environmental features comprise unit geometric features and combined features obtained by unit geometric feature coupling.
In particular, the unit geometric features may include point features, edge features, and face features, and the present invention is not limited thereto. The point feature, the edge feature and the face feature are used as basic geometric data, and a unit geometric feature can be formed. From these unit geometric features, a combination of features can be obtained. The combined features can be obtained by coupling the same unit geometric features, for example, the combined features can be formed by coupling two edge features forming a set included angle; two surface features forming a set included angle can be coupled to form a combined feature; a combined feature may be formed by the coupling of two point features at a set distance. In some variations, the combined features may be obtained by coupling different unit geometric features, for example, the combined features may be formed by coupling edge features and surface features that form a set included angle; the point feature and the surface feature of a set distance can be coupled to form a combined feature; a combined feature may be formed by coupling a set distance of point features and line features. The invention is not so limited.
Further, in some embodiments of the present application, the combined features are also obtained by coupling unit geometric features and mark features.
a. In particular, various different point cloud feature identification steps may be employed. The following schematically describes the implementation of extracting line features and face features: extracting an ROI (region of interest) of laser data according to the vehicle pose and the type of a received task (container size); performing laser point cloud processing (outlier removal, downsampling, etc.); fitting features (e.g., line/plane features may use (weighted) least squares, RANSAC methods, etc.). Thus, the present application is not limited to the extraction of the line feature and the surface feature.
Step S130: and according to at least one-dimensional attribute, giving a characteristic weight to the environmental characteristic.
Specifically, the global feature is initially assigned with multi-dimensional attributes (for example, a U-shaped feature, which may have a feature perpendicular to the ground, the left is assigned with an inner attribute, and the right is assigned with an outer attribute, so that when two surfaces of the U-shaped feature extracted from the laser data are obtained, it can be determined whether the inner attribute is matched with the inner attribute, and the inner attribute can be further assigned with the outer attribute in the actual use process (for example, whether the inner attribute is matched with the inner attribute is visible, because the feature observed by the vehicle must be visible, the feature cannot be matched with the invisible global feature), so that in use, the method for assigning the environmental feature weight may be implemented by more than one method as shown in fig. 2: step S131: determining a feature weight (such as whether the feature is visible or not, relative distance between the feature and a vehicle and the like) of at least one-dimensional attribute of the environmental feature according to the position relation between the environmental feature and the vehicle; step S132: determining a feature weight of at least one-dimensional attribute of the global feature from a relationship of the vehicle to the global feature data; step S133: and determining the feature weight of at least one-dimensional attribute of the environmental feature according to the relationship between the environmental feature and the global feature data.
For example, whether the environmental feature is visible at the current position of the vehicle is taken as a visible attribute, and if the environmental feature is visible, the weight value of the environmental feature on the visible attribute is larger; if not, the environmental feature has a smaller weight on the attribute of being visible. For another example, the relative position relationship between the unit geometric features of the environmental features is used as an attribute, for example, when an attribute of an "L" structure is given to two edge features in advance, if the actually detected features of the two edge features in the environmental features are determined to be the "L" structure, the weight is larger; if the two edge features in the environment feature actually detected features are not in an "L" structure, the weight is smaller. The foregoing is merely an exemplary illustration of the calculation of the feature weights of the present invention, and the present invention is not limited thereto.
Further, the global feature data includes known features in a global scope, the known features including unit geometric features and combined features obtained by unit geometric feature coupling. In particular, the global features also have at least one-dimensional attributes. The global feature data may be established in advance based on the collection and extraction of the global context of the environmental features. The global scope may be, for example, the global scope of a port region. For example, the vehicle may traverse the port area according to a predetermined trajectory and perform real-time environmental data collection and extraction of the environmental features, so that the position of the vehicle in the port area may be determined according to the predetermined trajectory of the vehicle, and the position of the environmental feature in the port area may be determined by the relative position of the environmental feature collected by the vehicle and the vehicle, whereby the extracted environmental feature is stored as a known feature in association with the position of the environmental feature in the global environment. In each embodiment of the invention, the misrecognition and the mismatching of the unit geometric features are considered, so that the unit geometric features which are easy to be misrecognized and matched can be coupled into the combined features according to the constraint relation when the global feature data is established.
Further, a plurality of constraint relations of combined features of known features in the global feature data form a constraint relation set, and the single geometric feature in the environmental features traverses the constraint relation set to obtain the combined features matched with the constraint relations in the constraint relation set. Wherein the constraint relationship comprises a position relationship between unit geometric features, and the position relationship is represented by feature relative distance and/or included angle between features. The manner in which the constraint relationship is expressed is not intended to be limiting. In this embodiment, the constraint relationship of the combined features of the known features can be directly used for matching among the plurality of unit geometric features of the environmental feature. In other embodiments, a broader constraint relationship may also be formed according to a constraint relationship of a combination feature of known features, so that the broader constraint relationship is used for matching in a plurality of unit geometric features of the environmental features. For example, when the constraint relationship of the combined feature of the known feature is that the included angle between two edge features is 90 degrees, in order to avoid generating errors due to the influence of the acquisition angle, the acquisition performance and the like when acquiring the environmental feature, a broad constraint relationship may be set as two intersecting edge features, and whether the included angle between the two intersecting edge features is close to 90 degrees may be used as the feature weight of an attribute of the combined feature formed by the two intersecting edge features. The present invention can be implemented in many different ways, which are not described herein.
Step S140: and searching the best matched known feature in the global feature data according to the environment feature and the feature weight thereof.
Specifically, in this embodiment, the known feature of each global feature data may be associated with the storage of its feature weight. Therefore, the known features of the global feature data can be matched with the feature weights of the environment features, and the best matched known feature is obtained. Further, when the feature weight is multidimensional, the similarity between the environment feature and the known feature can be obtained through the matching calculation of the feature weight of the attribute of each dimension, so that the known feature with the highest similarity is taken as the best matched known feature. In other variations, the priorities of the attributes of the multidimensional attributes may be set, so that the matching and screening of the feature weights of the attributes with the highest priority are performed first to obtain a plurality of candidate known features, and the matching and screening of the feature weights are performed in sequence according to the priorities to finally obtain a best matched known feature. The present invention can be implemented in many different ways, which are not described herein.
Step S150: performing a global longitudinal localization of the vehicle based on the location of the best matched known feature within a global scope.
Specifically, step S150 may be implemented by determining the position of the vehicle within the global scope according to the position of the best matching known feature within the global scope and the relative position of the best matching known feature and the vehicle.
In the longitudinal positioning method of the unmanned vehicle, the combination feature obtained by coupling the unit geometric features in the environmental features is matched with the combination feature of the global feature data, meanwhile, the weight of the attribute is combined, the best matched known feature is searched, and the global longitudinal positioning of the vehicle is executed according to the position of the best matched known feature in the global range, so that the feature multidimensional attribute and the multi-feature coupling are added as the combination feature, the feature mismatching is avoided, the feature is correctly matched, and the accuracy of the longitudinal positioning of the unmanned vehicle is improved.
Referring now to fig. 3 and 4, fig. 3 illustrates a schematic diagram of environmental features according to an embodiment of the present invention; fig. 4 shows a schematic view of the longitudinal positioning of an unmanned vehicle according to an embodiment of the invention.
As shown in fig. 3, an edge feature 101, a face feature 102, an edge feature 103, and a mark feature 104 can be extracted and obtained from the environment data. The edge feature 101 and the face feature 102 and the position relationship therebetween can be set as required to form a combined feature; the edge feature 103 and the face feature 102 and the positional relationship therebetween are combined to form a combined feature; the edge feature 101 and the edge feature 103 and the positional relationship therebetween are combined to form a combined feature, and in each combined feature, a mark feature 104 may be further combined, so that the method may be applied to a scene as shown in fig. 4, and since the mark feature 104 corresponding to the edge feature 103A is visible to the vehicle 100, the difference that the vehicle cannot recognize the plurality of edge features 103A, 103B, 103C is avoided by combining the mark feature 104.
The above are merely a plurality of specific implementations of the method for longitudinal positioning of an unmanned vehicle according to the present invention, and each implementation may be implemented independently or in combination, and the present invention is not limited thereto. Furthermore, the flow charts of the present invention are merely schematic, the execution sequence between the steps is not limited thereto, and the steps can be split, combined, exchanged sequentially, or executed synchronously or asynchronously in other ways within the protection scope of the present invention.
The invention also provides a longitudinal positioning device of the unmanned vehicle, and fig. 5 shows a block diagram of the longitudinal positioning device of the unmanned vehicle according to the embodiment of the invention. The unmanned vehicle longitudinal location device 200 includes an acquisition module 210, an extraction module 220, a weighting module 230, a matching module 240, and a location module 250.
The obtaining module 210 is configured to obtain environmental data collected by a collecting device provided on a vehicle;
the extracting module 220 is configured to extract environmental features from the environmental data, wherein the environmental features comprise unit geometric features and combined features obtained by unit geometric feature coupling;
the weighting module 230 is configured to assign feature weights to the environmental features in accordance with at least one-dimensional attributes;
the matching module 240 is configured to search for a best matching known feature in global feature data according to the environmental features and feature weights thereof, wherein the global feature data comprises known features in a global scope, and the known features comprise unit geometric features and combined features obtained by unit geometric feature coupling;
the localization module 250 is configured to perform a global longitudinal localization of the vehicle based on the location of the best matched known feature within a global scope.
In the longitudinal positioning device for the unmanned vehicle, the combination feature obtained by coupling the unit geometric features in the environmental features is matched with the combination feature of the global feature data, meanwhile, the weight of the attribute is combined, the best matched known feature is searched, and the global longitudinal positioning of the vehicle is executed according to the position of the best matched known feature in the global range, so that the feature multidimensional attribute and the multi-feature coupling are increased as the combination feature, the feature mismatching is avoided, the feature is correctly matched, and the accuracy of the longitudinal positioning of the unmanned vehicle is improved.
Fig. 5 is a schematic diagram showing the longitudinal positioning device 200 of the unmanned vehicle provided by the invention, and the modules are divided, combined and added without departing from the scope of the invention. The present invention provides a longitudinal positioning apparatus 200 for an unmanned vehicle, which can be implemented by software, hardware, firmware, plug-in and any combination thereof, and the present invention is not limited thereto.
In an exemplary embodiment of the disclosure, a computer-readable storage medium is also provided, on which a computer program is stored, which, when being executed by a processor for example, is adapted to carry out the steps of the method for longitudinal positioning of an unmanned vehicle as described in any of the above embodiments. In some possible embodiments, the various aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above-mentioned method for longitudinal positioning of an unmanned vehicle section of this description, when said program product is run on the terminal device.
Referring to fig. 6, a program product 800 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a 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.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, 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.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a 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 readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the tenant computing device, partly on the tenant device, as a stand-alone software package, partly on the tenant computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing devices may be connected to the tenant computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In an exemplary embodiment of the present disclosure, there is also provided an electronic device, which may include a processor, and a memory for storing executable instructions of the processor. Wherein the processor is configured to perform the steps of the method for longitudinal positioning of an unmanned vehicle in any of the above embodiments via execution of the executable instructions.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 7. The electronic device 600 shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 7, the electronic device 600 is embodied in the form of a general purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 that connects the various system components (including the storage unit 620 and the processing unit 610), a display unit 640, and the like.
Wherein the storage unit stores program code executable by the processing unit 610 to cause the processing unit 610 to perform steps according to various exemplary embodiments of the present invention as described in the above-mentioned unmanned vehicle longitudinal location method section of this specification. For example, the processing unit 610 may perform the steps as shown in fig. 1.
The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)6201 and/or a cache memory unit 6202, and may further include a read-only memory unit (ROM) 6203.
The memory unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a tenant to interact with the electronic device 600, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 650. Also, the electronic device 600 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via the bus 630. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above-mentioned method for locating the longitudinal position of the unmanned vehicle according to the embodiments of the present disclosure.
Compared with the prior art, the invention has the advantages that:
the method comprises the steps of matching combined features obtained by coupling unit geometric features in environmental features with combined features of global feature data, searching best matched known features by combining weights of attributes, and executing global longitudinal positioning of the vehicle according to the positions of the best matched known features in a global range, so that feature multidimensional attributes and multi-feature coupling are increased as combined features, feature mismatching is avoided, the features are correctly matched, and accuracy of the longitudinal positioning of the unmanned vehicle is improved.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. A method of longitudinal positioning of an unmanned vehicle, comprising:
acquiring environmental data acquired by acquisition equipment arranged on a vehicle;
extracting environmental features from the environmental data, wherein the environmental features comprise unit geometric features and combined features obtained by unit geometric feature coupling;
according to at least one-dimensional attribute, giving a characteristic weight to the environmental characteristic;
searching for best matching known features in global feature data according to the environment features and feature weights thereof, wherein the global feature data comprises known features in a global scope, and the known features comprise unit geometric features and combined features obtained by unit geometric feature coupling;
performing a global longitudinal localization of the vehicle based on the location of the best matched known feature within a global scope.
2. The method according to claim 1, wherein the unit geometric features include a point feature, an edge feature, and a face feature, and the combined feature is obtained from a mutual constraint relationship of a plurality of unit geometric features.
3. The method of claim 2, wherein the plurality of constraint relationships of the combined features of the known features in the global feature data form a set of constraint relationships, and wherein a single geometric feature in the environmental features traverses the set of constraint relationships to obtain a combined feature that matches a constraint relationship in the set of constraint relationships.
4. A method according to claim 2 or 3, wherein the constraint relationship comprises a positional relationship between unit geometric features, the positional relationship being represented by feature relative distance and/or included angle between features.
5. The method of claim 1, wherein said assigning feature weights to said environmental features in accordance with at least one-dimensional attributes comprises one or more of:
(1) determining a feature weight of at least one-dimensional attribute of the environmental feature according to a position relationship between the environmental feature and the vehicle;
(2) determining a feature weight of at least one-dimensional attribute of the global feature from a relationship of the vehicle to the global feature data;
and determining the feature weight of at least one-dimensional attribute of the environmental feature according to the relationship between the environmental feature and the global feature data.
6. The method for longitudinal localization of an unmanned vehicle as claimed in claim 1, wherein the combined features are further obtained by coupling unit geometric features and marker features.
7. The unmanned vehicle longitudinal location method of claim 1, wherein said performing a global longitudinal location of the vehicle based on the location of the best matching known feature within a global scope comprises:
and determining the position of the vehicle in the global scope according to the position of the best matched known feature in the global scope and the relative position of the best matched known feature and the vehicle.
8. An unmanned vehicle longitudinal positioning apparatus, comprising:
the acquisition module is configured to acquire environmental data acquired by acquisition equipment arranged on a vehicle;
an extraction module configured to extract environmental features from the environmental data, the environmental features including unit geometric features and combined features obtained by unit geometric feature coupling;
a weighting module configured to assign a feature weight to the environmental feature according to at least one-dimensional attribute;
a matching module configured to search global feature data for a best matching known feature according to the environmental features and feature weights thereof, wherein the global feature data comprises known features in a global scope, and the known features comprise unit geometric features and combined features obtained by unit geometric feature coupling;
a localization module configured to perform a global longitudinal localization of the vehicle based on a location of the best matched known feature within a global scope.
9. An electronic device, characterized in that the electronic device comprises:
a processor;
storage medium having stored thereon a computer program which, when executed by the processor, performs the method of any of claims 1 to 7.
10. A storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, performs the method according to any one of claims 1 to 7.
Priority Applications (1)
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107430191A (en) * | 2014-12-26 | 2017-12-01 | 赫尔环球有限公司 | Extract the feature geometry of the positioning for device |
| CN108876857A (en) * | 2018-07-02 | 2018-11-23 | 上海西井信息科技有限公司 | Localization method, system, equipment and the storage medium of automatic driving vehicle |
| CN109870167A (en) * | 2018-12-25 | 2019-06-11 | 四川嘉垭汽车科技有限公司 | Positioning and map creating method while the pilotless automobile of view-based access control model |
| CN110658539A (en) * | 2018-06-29 | 2020-01-07 | 比亚迪股份有限公司 | Vehicle positioning method, device, vehicle and computer readable storage medium |
| CN111307165A (en) * | 2020-03-06 | 2020-06-19 | 新石器慧通（北京）科技有限公司 | Vehicle positioning method and system and unmanned vehicle |
| CN112393735A (en) * | 2019-08-15 | 2021-02-23 | 纳恩博（北京）科技有限公司 | Positioning method and device, storage medium and electronic device |
| CN112595329A (en) * | 2020-12-25 | 2021-04-02 | 北京百度网讯科技有限公司 | Vehicle position determining method and device and electronic equipment |
-
2021
- 2021-06-30 CN CN202110738119.9A patent/CN113537314A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107430191A (en) * | 2014-12-26 | 2017-12-01 | 赫尔环球有限公司 | Extract the feature geometry of the positioning for device |
| CN110658539A (en) * | 2018-06-29 | 2020-01-07 | 比亚迪股份有限公司 | Vehicle positioning method, device, vehicle and computer readable storage medium |
| CN108876857A (en) * | 2018-07-02 | 2018-11-23 | 上海西井信息科技有限公司 | Localization method, system, equipment and the storage medium of automatic driving vehicle |
| CN109870167A (en) * | 2018-12-25 | 2019-06-11 | 四川嘉垭汽车科技有限公司 | Positioning and map creating method while the pilotless automobile of view-based access control model |
| CN112393735A (en) * | 2019-08-15 | 2021-02-23 | 纳恩博（北京）科技有限公司 | Positioning method and device, storage medium and electronic device |
| CN111307165A (en) * | 2020-03-06 | 2020-06-19 | 新石器慧通（北京）科技有限公司 | Vehicle positioning method and system and unmanned vehicle |
| CN112595329A (en) * | 2020-12-25 | 2021-04-02 | 北京百度网讯科技有限公司 | Vehicle position determining method and device and electronic equipment |
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