CN114089365B - Automatic driving vehicle positioning method and device, electronic equipment and storage medium - Google Patents
Automatic driving vehicle positioning method and device, electronic equipment and storage medium Download PDFInfo
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- 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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- 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
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
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- 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
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
- G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
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- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
- G01S19/485—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an optical system or imaging system
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
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- G—PHYSICS
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- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0257—Control of position or course in two dimensions specially adapted to land vehicles using a radar
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Abstract
The application discloses a method and a device for positioning an automatic driving vehicle, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring laser point cloud data of the automatic driving vehicle at the current moment, matching positioning information of the automatic driving vehicle at the previous moment and a local point cloud map of the automatic driving vehicle at the previous moment; determining a local point cloud map at the current moment according to the matching positioning information at the previous moment and the local point cloud map at the previous moment; carrying out point cloud matching on the laser point cloud data at the current moment and the local point cloud map at the current moment by using a preset matching algorithm to obtain a point cloud matching result; and determining the matching and positioning information of the automatic driving vehicle at the current moment according to the point cloud matching result. The automatic driving vehicle positioning method can achieve real-time matching and positioning of the laser radar, improve the absolute positioning output frequency of point cloud matching, still can provide high-frequency absolute positioning information for the automatic driving vehicle under the condition that GNSS fails, and guarantees that a positioning track cannot drift.
Description
Technical Field
The present disclosure relates to the field of automatic driving technologies, and in particular, to a method and an apparatus for positioning an automatic driving vehicle, an electronic device, and a storage medium.
Background
An automatic driving positioning System generally uses positioning information acquired by a plurality of sensors to perform fusion positioning, so as to improve positioning accuracy, for example, an IMU (Inertial Measurement Unit), a GNSS (Global Navigation Satellite System), a odometer, and the like.
When the GNSS is adopted for fusion positioning, the short-time failure of GNSS signals can occur, and due to the lack of absolute position observation, the fusion positioning result can drift, which can have great influence on the planning control and safety of automatic driving. The point cloud data of the laser radar can be matched and positioned with a point cloud map which is made in advance, and since the point cloud map stores absolute information of positions including longitude and latitude coordinates or UTM (Universal Transverse Mercator Grid System) coordinates and the like, the absolute positioning information when GNSS signals are missing can be made up through laser point cloud data matching and positioning.
However, the frequency of the laser radar is generally about 10Hz, and the matching and positioning of the Point cloud map are directly performed by using an ICP (Iterative Closest Point) or NDT (Normal distribution Transform) method, which hardly achieves real-time performance, and further hardly meets the requirement for positioning real-time performance in an automatic driving scene.
Disclosure of Invention
The embodiment of the application provides a method and a device for positioning an automatic driving vehicle, electronic equipment and a storage medium, so as to improve the real-time performance of positioning the automatic driving vehicle.
The embodiment of the application adopts the following technical scheme:
in a first aspect, an embodiment of the present application provides an automatic driving vehicle positioning method, where the method includes:
acquiring laser point cloud data of the automatic driving vehicle at the current moment, matching positioning information of the automatic driving vehicle at the previous moment and a local point cloud map of the automatic driving vehicle at the previous moment;
determining a local point cloud map of the current moment according to the matching positioning information of the previous moment and the local point cloud map of the previous moment;
carrying out point cloud matching on the laser point cloud data at the current moment and the local point cloud map at the current moment by using a preset matching algorithm to obtain a point cloud matching result;
and determining the matching and positioning information of the automatic driving vehicle at the current moment according to the point cloud matching result.
Optionally, the determining the local point cloud map at the current time according to the matching positioning information at the previous time and the local point cloud map at the previous time includes:
comparing the matching positioning information at the previous moment with the boundary of the cuboid corresponding to the local point cloud map at the previous moment;
and determining the local point cloud map of the current moment according to the comparison result.
Optionally, the matching location information includes a matching location, and the determining the local point cloud map at the current time according to the comparison result includes:
under the condition that the distance between the matching position at the previous moment and any boundary of the cuboid corresponding to the local point cloud map at the previous moment is smaller than a preset distance threshold, the matching position at the previous moment is taken as an original point, and a preset point cloud segmentation algorithm is utilized to re-segment the global point cloud map to obtain the local point cloud map at the current moment;
and under the condition that the boundary distance between the matching position at the previous moment and each boundary of the cuboid corresponding to the local point cloud map at the previous moment is not smaller than the preset distance threshold, directly taking the local point cloud map at the previous moment as the local point cloud map at the current moment.
Optionally, the performing point cloud matching on the laser point cloud data at the current moment and the local point cloud map at the current moment by using a preset matching algorithm to obtain a point cloud matching result includes:
respectively carrying out voxel filtering processing on the laser point cloud data at the current moment and the local point cloud map at the current moment to obtain filtered laser point cloud data at the current moment and filtered local point cloud map at the current moment;
and performing point cloud matching on the filtered laser point cloud data at the current moment and the filtered local point cloud map at the current moment by using a preset matching algorithm to obtain a point cloud matching result.
Optionally, after acquiring the laser point cloud data of the current time of the autonomous vehicle, the method further comprises:
determining whether the laser point cloud data at the current moment is a first frame of laser point cloud data;
under the condition that the laser point cloud data at the current moment is a first frame of laser point cloud data, acquiring a global point cloud map and the position of a GNSS positioning signal corresponding to the first frame of laser point cloud data;
and according to the position of the GNSS positioning signal, partitioning the global point cloud map by using a preset point cloud partitioning algorithm to obtain the local point cloud map at the current moment.
Optionally, after determining the matching positioning information of the automatic driving vehicle at the current moment according to the point cloud matching result, the method further comprises:
and determining a local point cloud map at the next moment according to the matching positioning information at the current moment and the local point cloud map at the current moment.
In a second aspect, an embodiment of the present application further provides an automatic driving vehicle positioning device, where the device includes:
the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring laser point cloud data of the automatic driving vehicle at the current moment, matching positioning information of the previous moment and a local point cloud map of the previous moment;
the first determining unit is used for determining a local point cloud map at the current moment according to the matching positioning information at the previous moment and the local point cloud map at the previous moment;
the point cloud matching unit is used for carrying out point cloud matching on the laser point cloud data at the current moment and the local point cloud map at the current moment by using a preset matching algorithm to obtain a point cloud matching result;
and the second determining unit is used for determining the matching positioning information of the automatic driving vehicle at the current moment according to the point cloud matching result.
Optionally, the first determining unit is specifically configured to:
comparing the matching positioning information at the previous moment with the boundary of the cuboid corresponding to the local point cloud map at the previous moment;
and determining the local point cloud map of the current moment according to the comparison result.
In a third aspect, an embodiment of the present application further provides an electronic device, including:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to perform any of the methods described above.
In a fourth aspect, embodiments of the present application further provide a computer-readable storage medium storing one or more programs that, when executed by an electronic device including a plurality of application programs, cause the electronic device to perform any of the methods described above.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects: the automatic driving vehicle positioning method comprises the steps of firstly obtaining laser point cloud data of the automatic driving vehicle at the current moment, matching positioning information of the automatic driving vehicle at the previous moment and a local point cloud map of the automatic driving vehicle at the previous moment; then determining a local point cloud map of the current moment according to the matching positioning information of the previous moment and the local point cloud map of the previous moment; then, carrying out point cloud matching on the laser point cloud data at the current moment and the local point cloud map at the current moment by using a preset matching algorithm to obtain a point cloud matching result; and finally, determining the matching and positioning information of the automatic driving vehicle at the current moment according to the point cloud matching result. The automatic driving vehicle positioning method can achieve real-time matching and positioning of the laser radar, improve the absolute positioning output frequency of point cloud matching, still can provide high-frequency absolute positioning information for the automatic driving vehicle under the condition that GNSS fails, and guarantees that the positioning track of the automatic driving vehicle cannot drift.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic flow chart illustrating an exemplary method for locating an autonomous vehicle according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of an autonomous vehicle positioning process in an embodiment of the present application;
FIG. 3 is a schematic structural diagram of an autonomous vehicle positioning apparatus according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
The embodiment of the present application provides an automatic driving vehicle positioning method, and as shown in fig. 1, provides a flow schematic diagram of the automatic driving vehicle positioning method in the embodiment of the present application, where the method at least includes the following steps S110 to S140:
step S110, laser point cloud data of the current moment of the automatic driving vehicle, matching positioning information of the previous moment and a local point cloud map of the previous moment are obtained.
When the automatic driving vehicle positioning method provided by the embodiment of the application is used for positioning a vehicle, the laser point cloud data of the current moment obtained by scanning the laser radar of the vehicle is obtained, and in addition, the matching positioning information of the previous moment and the local point cloud map of the previous moment need to be further obtained.
The matching positioning information can be regarded as positioning information output after the laser radar point cloud data is matched with a point cloud map which is made in advance. Under the actual positioning scene, because the data volume contained in the global point cloud map which is made in advance is very large, if each positioning is matched with the global point cloud map, a large amount of time and resources are consumed, and the requirement of real-time positioning under the automatic driving scene cannot be met, so that the global point cloud map needs to be segmented according to the matching positioning information at the previous moment, and therefore the local point cloud data which needs to be concerned by the current positioning is obtained and is used as the basis of subsequent matching, the time and resources which need to be consumed by matching are greatly reduced, and the positioning real-time performance is improved.
And step S120, determining the local point cloud map at the current moment according to the matching positioning information at the previous moment and the local point cloud map at the previous moment.
The above-mentioned method of performing location matching based on the local point cloud map can improve the real-time performance of location to a certain extent, but if the global point cloud map is re-partitioned every time, it still increases a certain time consumption and resource waste, so after receiving the laser point cloud data of the current time, the embodiment of the application does not directly utilize the matching location information of the previous time to re-partition the global point cloud map, but determines whether re-partition is needed according to the spatial position relationship between the matching location information of the previous time and the local point cloud map of the previous time, thereby obtaining the local point cloud map of the current time.
And step S130, carrying out point cloud matching on the laser point cloud data at the current moment and the local point cloud map at the current moment by using a preset matching algorithm to obtain a point cloud matching result.
After the local point cloud map at the current moment is determined, point cloud matching is carried out on the laser point cloud data at the current moment and the local point cloud map at the current moment by using a preset matching algorithm such as an NDT algorithm, and therefore a point cloud matching result is obtained.
Compared with the traditional ICP algorithm, the NDT algorithm gives consideration to the requirements on efficiency and precision in the automatic driving scene. Of course, those skilled in the art may also flexibly select other matching algorithms according to actual requirements, and is not limited in detail herein.
And step S140, determining the matching positioning information of the automatic driving vehicle at the current moment according to the point cloud matching result.
After the point cloud matching is completed, the matching positioning information of the automatic driving vehicle at the current moment can be output, and the matching positioning information specifically comprises positioning information such as position and posture.
The automatic driving vehicle positioning method can achieve real-time matching and positioning of the laser radar, improve the absolute positioning output frequency of point cloud matching, still can provide high-frequency absolute positioning information for the automatic driving vehicle under the condition that GNSS fails, and guarantees that the positioning track of the automatic driving vehicle cannot drift.
In an embodiment of the application, the determining the local point cloud map at the current time according to the matching positioning information at the previous time and the local point cloud map at the previous time includes: comparing the matching positioning information at the previous moment with the boundary of the cuboid corresponding to the local point cloud map at the previous moment; and determining the local point cloud map of the current moment according to the comparison result.
The local point cloud map obtained by global point cloud map segmentation in the embodiment of the application can be regarded as a cuboid structure, and the size of the cuboid can be flexibly set according to actual requirements, for example, the size of the cuboid can be 150m × 150 m. When the local point cloud map at the current moment is determined according to the matching and positioning information at the previous moment and the local point cloud map at the previous moment, the matching and positioning information at the previous moment can be compared with each boundary of the cuboid corresponding to the local point cloud map, so that the relative position relation of the matching and positioning information at the previous moment and each boundary of the cuboid corresponding to the local point cloud map is determined, and whether the global point cloud map needs to be re-segmented at the current moment or not is further determined, and the local point cloud map at the current moment is obtained.
In one embodiment of the present application, the matching location information includes a matching location, and the determining the local point cloud map of the current time according to the comparison result includes: under the condition that the distance between the matching position at the previous moment and any boundary of the cuboid corresponding to the local point cloud map at the previous moment is smaller than a preset distance threshold, the matching position at the previous moment is taken as an original point, and a preset point cloud segmentation algorithm is utilized to re-segment the global point cloud map to obtain the local point cloud map at the current moment; and under the condition that the boundary distance between the matching position at the previous moment and each boundary of the cuboid corresponding to the local point cloud map at the previous moment is not smaller than the preset distance threshold, directly taking the local point cloud map at the previous moment as the local point cloud map at the current moment.
The matching positioning information of the embodiment of the application can comprise matching positions, under normal conditions, specific position points of the matching positions at the previous moment are located inside the cuboid corresponding to the local point cloud map at the previous moment, and based on the specific position points, when the local point cloud map at the current moment is determined, the embodiment of the application can further calculate the distance between each boundary of the cuboid corresponding to the matching positions at the previous moment and the local point cloud map at the previous moment, namely the vertical distance from the matching position points to six side faces of the cuboid.
Then, the calculated distance values are respectively compared with a preset distance threshold, and the size of the preset distance threshold can be flexibly set according to actual requirements, for example, set to 50m, and is not particularly limited herein. If at least one distance value is smaller than a preset distance threshold value, it is indicated that the matching position at the previous moment is closer to the boundary of the local point cloud map used at the previous moment, when the vehicle moves to the current moment, the actual position of the autonomous vehicle is likely to exceed the boundary of the local point cloud map at the previous moment, or the point cloud data contained in the local point cloud map at the previous moment cannot provide enough data support for the matching at the current moment, and at this moment, the point cloud map at the previous moment cannot be directly used as the local point cloud map at the current moment, but needs to be re-segmented. Specifically, the global point cloud map may be re-segmented by using the matching position at the previous time as an origin and using a preset point cloud segmentation algorithm, such as a CropBox method, to obtain the local point cloud map at the current time.
If the calculated distance values are not smaller than the preset distance threshold, it is indicated that the matching position at the previous moment is a certain distance away from the boundary of the local point cloud map used at the previous moment, so that when the vehicle moves to the current moment, the actual position of the automatically-driven vehicle does not exceed the boundary of the local point cloud map at the previous moment, or the point cloud data contained in the local point cloud map at the previous moment can still provide enough data support for the matching at the current moment, at the moment, the point cloud map at the previous moment can be directly used as the local point cloud map at the current moment without re-segmentation, thereby saving unnecessary segmentation operation to a great extent, and further improving the real-time performance of positioning.
It should be noted that, in the above embodiment, the local point cloud map at the "previous time" may be specifically understood as a local point cloud map at the "previous segmentation time", because, as in the foregoing embodiment, the global point cloud map is not re-segmented every positioning and matching, and thus the time of the received frame data of the laser point cloud and the segmentation time are not completely corresponding, for the convenience of understanding the embodiment of the present application, the following table 1 is taken as an example to describe:
TABLE 1
| Time of laser point cloud data (T) | Local point cloud map (G) | Local map division time (t) |
| T1 | G1 | t1 |
| T2 | G1 | t1 |
| T3 | G2 | t2 |
As can be seen from table 1 above, the time T when the laser point cloud data is received is not in a one-to-one correspondence relationship with the local map segmentation time T, because at the time T2, the global point cloud map is not re-segmented, so the segmentation time at this time is still the time T1, and when the local point cloud map at the time T2 is determined, the local point cloud map corresponding to the time T1 is obtained essentially.
In an embodiment of the application, the performing point cloud matching on the laser point cloud data at the current moment and the local point cloud map at the current moment by using a preset matching algorithm to obtain a point cloud matching result includes: respectively carrying out voxel filtering processing on the laser point cloud data at the current moment and the local point cloud map at the current moment to obtain filtered laser point cloud data at the current moment and filtered local point cloud map at the current moment; and performing point cloud matching on the filtered laser point cloud data at the current moment and the filtered local point cloud map at the current moment by using a preset matching algorithm to obtain a point cloud matching result.
Due to the fact that the amount of the point cloud data is large, and time is consumed for direct matching, in order to further improve the real-time performance of matching and locating, when the laser point cloud data at the current moment and the local point cloud map at the current moment are subjected to point cloud matching, the laser point cloud data at the current moment and the local point cloud map at the current moment can be subjected to Voxel filtering processing respectively, and the filtered laser point cloud data and the filtered local point cloud map are used for matching and locating.
The Voxel filtering uses a voxelization grid method to carry out down-sampling on the point cloud data, namely, the number of points is reduced, the point cloud data is reduced, and meanwhile, the shape characteristics of the point cloud are kept, so that the Voxel filtering method is very practical in improving the algorithm speed of registration, curved surface reconstruction, shape recognition and the like. According to the embodiment of the application, matching and positioning are carried out by using the laser point cloud data after the Voxel filtering and the local point cloud map, so that the matching time is saved, and the instantaneity of the laser point cloud matching and positioning is further improved.
In one embodiment of the present application, after obtaining laser point cloud data for a current time of the autonomous vehicle, the method further comprises: determining whether the laser point cloud data at the current moment is a first frame of laser point cloud data; under the condition that the laser point cloud data at the current moment is a first frame of laser point cloud data, acquiring a global point cloud map and the position of a GNSS positioning signal corresponding to the first frame of laser point cloud data; and according to the position of the GNSS positioning signal, partitioning the global point cloud map by using a preset point cloud partitioning algorithm to obtain the local point cloud map at the current moment.
After the laser point cloud data at the current moment is obtained, if the laser point cloud data at the current moment is the first frame of laser point cloud data received currently, the first frame of laser point cloud data does not have a corresponding local point cloud map at the previous segmentation moment, and does not have matching positioning information at the previous moment, so that the global point cloud map can be directly segmented by means of the GNSS position corresponding to the first frame of laser point cloud data, and the local point cloud map at the current moment is obtained.
In one embodiment of the present application, after determining the matching location information of the autonomous vehicle at the current time according to the point cloud matching result, the method further comprises: and determining a local point cloud map at the next moment according to the matching positioning information at the current moment and the local point cloud map at the current moment.
After the matching positioning information of the current moment is obtained, the local point cloud map of the next moment can be determined based on the matching positioning information of the current moment and the local point cloud map of the current moment, and therefore matching positioning of the next moment is achieved.
It should be noted that the automatic driving vehicle positioning method of the present application may be used with a GNSS fusion positioning method under the condition that the GNSS signal is valid, so as to achieve the dual positioning effect, and may also compensate the absolute positioning information under the condition that the GNSS signal is invalid, and the two are not in conflict, and there is no requirement for the order. Of course, if the operation resources are saved, GNSS fusion positioning may be preferentially adopted, and the automatic driving vehicle positioning method of the present application is triggered again when failure of GNSS signals is detected.
To facilitate understanding of the embodiments of the present application, a schematic diagram of an automated vehicle positioning process in the embodiments of the present application is provided as shown in fig. 2. Firstly, a pre-manufactured global point cloud map can be obtained, then after laser point cloud data at the current moment are received, whether the laser point cloud data at the current moment are first frame laser point cloud data or not is judged, if yes, a GNSS signal position corresponding to the first frame laser point cloud data needs to be further obtained, and then the pre-manufactured global point cloud map is segmented according to the GNSS signal position, so that a local point cloud map at the current moment is obtained.
If the laser point cloud data at the current moment is not the first frame of laser point cloud data, acquiring the matching and positioning information at the previous moment and the local point cloud map at the previous moment, then carrying out spatial comparison on the matching and positioning information at the previous moment and the local point cloud map at the previous moment, determining whether the global point cloud map needs to be re-segmented according to a comparison result, if so, obtaining the local point cloud map at the current moment through re-segmentation, and if not, directly taking the local point cloud map at the previous moment as the local point cloud map at the current moment.
After the local point cloud map at the current moment is obtained, Voxel filtering processing needs to be carried out on the local point cloud map at the current moment and laser point cloud data at the current moment, then point cloud matching is carried out on the filtered local point cloud map and the laser point cloud data, so that a point cloud matching result is obtained, and finally matching positioning information such as a matching position, a matching posture and the like at the current moment is output according to the point cloud matching result.
The automatic driving vehicle positioning process can realize real-time matching and positioning of the laser radar, improves the absolute positioning output frequency of point cloud matching, can still provide high-frequency absolute positioning information for the automatic driving vehicle under the condition that GNSS fails, ensures that the positioning track of automatic driving cannot drift, and improves the stability and reliability of positioning.
The embodiment of the present application further provides an automatic driving vehicle positioning device 300, as shown in fig. 3, which provides a schematic structural diagram of the automatic driving vehicle positioning device in the embodiment of the present application, where the device 300 includes: a first obtaining unit 310, a first determining unit 320, a point cloud matching unit 330, and a second determining unit 340, wherein:
a first obtaining unit 310, configured to obtain laser point cloud data of a current time of an autonomous vehicle, matching positioning information of a previous time, and a local point cloud map of the previous time;
a first determining unit 320, configured to determine a local point cloud map at the current time according to the matching positioning information at the previous time and the local point cloud map at the previous time;
the point cloud matching unit 330 is configured to perform point cloud matching on the laser point cloud data at the current time and the local point cloud map at the current time by using a preset matching algorithm to obtain a point cloud matching result;
and a second determining unit 340, configured to determine, according to the point cloud matching result, matching location information of the current time of the autonomous vehicle.
In an embodiment of the present application, the first determining unit 320 is specifically configured to: comparing the matching positioning information at the previous moment with the boundary of the cuboid corresponding to the local point cloud map at the previous moment; and determining the local point cloud map of the current moment according to the comparison result.
In an embodiment of the present application, the matching location information includes a matching location, and the first determining unit 320 is specifically configured to: under the condition that the distance between the matching position at the previous moment and any boundary of the cuboid corresponding to the local point cloud map at the previous moment is smaller than a preset distance threshold, the matching position at the previous moment is taken as an original point, and a preset point cloud segmentation algorithm is utilized to re-segment the global point cloud map to obtain the local point cloud map at the current moment; and under the condition that the boundary distance between the matching position at the previous moment and each boundary of the cuboid corresponding to the local point cloud map at the previous moment is not smaller than the preset distance threshold, directly taking the local point cloud map at the previous moment as the local point cloud map at the current moment.
In an embodiment of the present application, the point cloud matching unit 330 is specifically configured to: respectively carrying out voxel filtering processing on the laser point cloud data at the current moment and the local point cloud map at the current moment to obtain filtered laser point cloud data at the current moment and filtered local point cloud map at the current moment; and performing point cloud matching on the filtered laser point cloud data at the current moment and the filtered local point cloud map at the current moment by using a preset matching algorithm to obtain a point cloud matching result.
In one embodiment of the present application, the apparatus further comprises: a third determining unit configured to determine whether the laser point cloud data at the current time is a first frame of laser point cloud data; the second acquisition unit is used for acquiring a global point cloud map and the position of a GNSS positioning signal corresponding to the first frame of laser point cloud data under the condition that the laser point cloud data at the current moment is the first frame of laser point cloud data; and the segmentation unit is used for segmenting the global point cloud map by utilizing a preset point cloud segmentation algorithm according to the position of the GNSS positioning signal to obtain the local point cloud map at the current moment.
In one embodiment of the present application, the apparatus further comprises: and the fourth determining unit is used for determining the local point cloud map at the next moment according to the matching positioning information at the current moment and the local point cloud map at the current moment.
It can be understood that the above-mentioned positioning device for an autonomous vehicle can implement the steps of the positioning method for an autonomous vehicle provided in the foregoing embodiments, and the related explanations regarding the positioning method for an autonomous vehicle are applicable to the positioning device for an autonomous vehicle, and are not described herein again.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Referring to fig. 4, at a hardware level, the electronic device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 4, but that does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs the computer program to form the automatic driving vehicle positioning device on a logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:
acquiring laser point cloud data of the automatic driving vehicle at the current moment, matching positioning information of the automatic driving vehicle at the previous moment and a local point cloud map of the automatic driving vehicle at the previous moment;
determining a local point cloud map of the current moment according to the matching positioning information of the previous moment and the local point cloud map of the previous moment;
carrying out point cloud matching on the laser point cloud data at the current moment and the local point cloud map at the current moment by using a preset matching algorithm to obtain a point cloud matching result;
and determining the matching and positioning information of the automatic driving vehicle at the current moment according to the point cloud matching result.
The method performed by the autonomous vehicle positioning apparatus disclosed in the embodiment of fig. 1 may be implemented in or by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, etc. as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The electronic device may further execute the method executed by the positioning apparatus for an autonomous vehicle in fig. 1, and implement the functions of the positioning apparatus for an autonomous vehicle in the embodiment shown in fig. 1, which are not described herein again in this application.
Embodiments of the present application also provide a computer-readable storage medium storing one or more programs, where the one or more programs include instructions, which when executed by an electronic device including a plurality of application programs, enable the electronic device to perform the method performed by the automatic driving vehicle positioning apparatus in the embodiment shown in fig. 1, and are specifically configured to perform:
acquiring laser point cloud data of the automatic driving vehicle at the current moment, matching positioning information of the automatic driving vehicle at the previous moment and a local point cloud map of the automatic driving vehicle at the previous moment;
determining a local point cloud map of the current moment according to the matching positioning information of the previous moment and the local point cloud map of the previous moment;
carrying out point cloud matching on the laser point cloud data at the current moment and the local point cloud map at the current moment by using a preset matching algorithm to obtain a point cloud matching result;
and determining the matching and positioning information of the automatic driving vehicle at the current moment according to the point cloud matching result.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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 processor, 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.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that 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 an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
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 above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (8)
1. An autonomous vehicle positioning method, wherein the method comprises:
acquiring laser point cloud data of the automatic driving vehicle at the current moment, matching positioning information of the automatic driving vehicle at the previous moment and a local point cloud map of the automatic driving vehicle at the previous moment;
determining a local point cloud map of the current moment according to the matching positioning information of the previous moment and the local point cloud map of the previous moment;
carrying out point cloud matching on the laser point cloud data at the current moment and the local point cloud map at the current moment by using a preset matching algorithm to obtain a point cloud matching result;
determining matching positioning information of the automatic driving vehicle at the current moment according to the point cloud matching result;
the matching positioning information refers to positioning information which is output after laser point cloud data is matched with a prefabricated point cloud map, and the matching positioning information comprises a matching position;
the determining the local point cloud map at the current moment according to the matching positioning information at the previous moment and the local point cloud map at the previous moment comprises:
comparing the matching positioning information at the previous moment with the boundary of the cuboid corresponding to the local point cloud map at the previous moment;
and determining the local point cloud map of the current moment according to the comparison result.
2. The method of claim 1, wherein the determining the local point cloud map for the current time based on the comparison comprises:
under the condition that the distance between the matching position at the previous moment and any boundary of the cuboid corresponding to the local point cloud map at the previous moment is smaller than a preset distance threshold, the matching position at the previous moment is taken as an original point, and a preset point cloud segmentation algorithm is utilized to re-segment the global point cloud map to obtain the local point cloud map at the current moment;
and under the condition that the boundary distance between the matching position at the previous moment and each boundary of the cuboid corresponding to the local point cloud map at the previous moment is not smaller than the preset distance threshold, directly taking the local point cloud map at the previous moment as the local point cloud map at the current moment.
3. The method of claim 1, wherein the performing point cloud matching on the laser point cloud data at the current time and the local point cloud map at the current time by using a preset matching algorithm to obtain a point cloud matching result comprises:
respectively carrying out voxel filtering processing on the laser point cloud data at the current moment and the local point cloud map at the current moment to obtain filtered laser point cloud data at the current moment and filtered local point cloud map at the current moment;
and performing point cloud matching on the filtered laser point cloud data at the current moment and the filtered local point cloud map at the current moment by using a preset matching algorithm to obtain a point cloud matching result.
4. The method of claim 1, wherein after acquiring laser point cloud data for a current time of the autonomous vehicle, the method further comprises:
determining whether the laser point cloud data at the current moment is a first frame of laser point cloud data;
under the condition that the laser point cloud data at the current moment is a first frame of laser point cloud data, acquiring a global point cloud map and the position of a GNSS positioning signal corresponding to the first frame of laser point cloud data;
and according to the position of the GNSS positioning signal, partitioning the global point cloud map by using a preset point cloud partitioning algorithm to obtain the local point cloud map at the current moment.
5. The method of claim 1, wherein after determining the matching localization information for the current moment of the autonomous vehicle from the point cloud matching results, the method further comprises:
and determining a local point cloud map at the next moment according to the matching positioning information at the current moment and the local point cloud map at the current moment.
6. An autonomous vehicle positioning apparatus, wherein the apparatus comprises:
the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring laser point cloud data of the automatic driving vehicle at the current moment, matching positioning information of the previous moment and a local point cloud map of the previous moment;
the first determining unit is used for determining a local point cloud map at the current moment according to the matching positioning information at the previous moment and the local point cloud map at the previous moment;
the point cloud matching unit is used for carrying out point cloud matching on the laser point cloud data at the current moment and the local point cloud map at the current moment by using a preset matching algorithm to obtain a point cloud matching result;
the second determining unit is used for determining the matching positioning information of the automatic driving vehicle at the current moment according to the point cloud matching result;
the matching positioning information refers to positioning information which is output after laser point cloud data is matched with a prefabricated point cloud map, and the matching positioning information comprises a matching position;
the first determining unit is specifically configured to:
comparing the matching positioning information at the previous moment with the boundary of the cuboid corresponding to the local point cloud map at the previous moment;
and determining the local point cloud map of the current moment according to the comparison result.
7. An electronic device, comprising:
a processor; and
a memory arranged to store computer executable instructions which, when executed, cause the processor to perform the method of any of claims 1 to 5.
8. A computer readable storage medium storing one or more programs which, when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform the method of any of claims 1-5.
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| CN115265556A (en) * | 2022-07-30 | 2022-11-01 | 重庆长安汽车股份有限公司 | Method, apparatus, device and medium for positioning autonomous vehicle |
| CN115390086B (en) * | 2022-10-31 | 2023-03-31 | 智道网联科技(北京)有限公司 | Fusion positioning method and device for automatic driving, electronic equipment and storage medium |
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