CN113587937A - Vehicle positioning method and device, electronic equipment and storage medium - Google Patents
Vehicle positioning method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN113587937A CN113587937A CN202110726889.1A CN202110726889A CN113587937A CN 113587937 A CN113587937 A CN 113587937A CN 202110726889 A CN202110726889 A CN 202110726889A CN 113587937 A CN113587937 A CN 113587937A
- Authority
- CN
- China
- Prior art keywords
- point cloud
- cloud data
- vehicle
- information
- determining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 62
- 238000012545 processing Methods 0.000 claims abstract description 18
- 238000004590 computer program Methods 0.000 claims description 13
- 238000000605 extraction Methods 0.000 claims description 5
- 239000000126 substance Substances 0.000 claims description 2
- 238000004891 communication Methods 0.000 description 8
- 238000010586 diagram Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000013473 artificial intelligence Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000012217 deletion Methods 0.000 description 1
- 230000037430 deletion Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
- G01C21/1656—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments with passive imaging devices, e.g. cameras
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/06—Systems determining position data of a target
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/86—Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
- G01S13/865—Combination of radar systems with lidar systems
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/86—Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
- G01S13/867—Combination of radar systems with cameras
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
- G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
- G01S17/06—Systems determining position data of a target
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- 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
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- 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
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
- G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Electromagnetism (AREA)
- Automation & Control Theory (AREA)
- Navigation (AREA)
Abstract
The application discloses a vehicle positioning method, a vehicle positioning device, electronic equipment and a storage medium, and relates to the field of computers, in particular to the fields of automatic driving and intelligent transportation. The specific implementation scheme is as follows: acquiring first point cloud data currently acquired by a vehicle and current information of each obstacle; processing the first point cloud data according to the information of each obstacle to obtain second point cloud data; and determining the current positioning information of the vehicle according to the second point cloud data and the type of the current driving road section of the vehicle. According to the method, the first point cloud data are processed according to the information of the obstacle, so that the interference of the point cloud of the obstacle is reduced, the vehicle is positioned by utilizing the processed point cloud data and the type of the road section where the vehicle is currently located, the type of the road section is considered, and the positioning accuracy can be improved.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for positioning a vehicle, an electronic device, and a storage medium.
Background
An automatic driving automobile is also called an unmanned automobile, a computer driving automobile or a wheeled mobile robot, and is an intelligent automobile which realizes unmanned driving through a computer system. The first step of automatic driving is positioning, and path planning can be carried out only by knowing the position of the driver, so that the driver can control the automobile to run.
Therefore, how to improve the positioning accuracy of the vehicle is an urgent problem to be solved.
Disclosure of Invention
The application provides a vehicle positioning method and device, electronic equipment and a storage medium.
According to an aspect of the present application, there is provided a positioning method of a vehicle, including:
acquiring first point cloud data currently acquired by a vehicle and current information of each obstacle;
processing the first point cloud data according to the information of each obstacle to obtain second point cloud data;
and determining the current positioning information of the vehicle according to the second point cloud data and the type of the current driving road section of the vehicle.
According to another aspect of the present application, there is provided a positioning apparatus of a vehicle, including:
the first acquisition module is used for acquiring first point cloud data acquired by the vehicle at present and information of each current obstacle;
the second acquisition module is used for processing the first point cloud data according to the information of each obstacle so as to acquire second point cloud data;
and the first determining module is used for determining the current positioning information of the vehicle according to the second point cloud data and the type of the road section where the vehicle is currently located.
According to another aspect of the present application, there is provided an electronic device including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the above embodiments.
According to another aspect of the present application, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method according to the above-described embodiments.
According to another aspect of the present application, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method according to the above embodiments.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present application, nor do they limit the scope of the present application. Other features of the present application will become apparent from the following description.
Drawings
The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
fig. 1 is a schematic flowchart of a vehicle positioning method according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart illustrating another vehicle positioning method according to an embodiment of the present disclosure;
FIG. 3 is a schematic flow chart illustrating another vehicle positioning method according to an embodiment of the present disclosure;
FIG. 4 is a schematic flow chart illustrating another vehicle positioning method according to an embodiment of the present disclosure;
FIG. 5 is a schematic structural diagram of a positioning device of a vehicle according to an embodiment of the present disclosure;
fig. 6 is a block diagram of an electronic device for implementing a positioning method of a vehicle according to an embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
A positioning method, a device, an electronic apparatus, and a storage medium of a vehicle of the embodiments of the present application are described below with reference to the drawings.
Fig. 1 is a schematic flowchart of a vehicle positioning method according to an embodiment of the present application.
The vehicle positioning method according to the embodiment of the present application may be performed by the vehicle positioning apparatus according to the embodiment of the present application, and the apparatus may be configured with an electronic device, such as a vehicle-mounted terminal, to process current point cloud data according to information of each obstacle, and position a vehicle by using the processed point cloud data and a type of a current driving road section, so as to improve positioning accuracy.
As shown in fig. 1, the vehicle positioning method includes:
In the present application, a variety of sensors may be deployed on a vehicle, such as LiDAR (LiDAR), RADAR (RADAR), cameras, inertial sensors, and the like. During the driving process of the vehicle, the point cloud data of the current driving environment can be collected through the deployed laser radar, and the point cloud data is called as first point cloud data for convenience of distinguishing. The first point cloud data may include three-dimensional coordinate information, laser reflection intensity, and other information.
The vehicle may further be provided with a sensing module, and the sensing module may perform recognition processing on data acquired by a sensor disposed on the vehicle, such as currently acquired point cloud data, an image, and the like, so as to determine information of each obstacle in the current driving environment of the vehicle. In the present application, information of each obstacle in the current driving environment of the vehicle, which is output by the sensing module, may also be obtained, where the information of the obstacle may include a type, a position, a driving speed, and the like of the obstacle.
And 102, processing the first point cloud data according to the information of each obstacle to acquire second point cloud data.
Many obstacles may exist around the vehicle, and the obstacles may generate interference point cloud, which causes the failure or mismatching of the acquired first point cloud data and the point cloud positioning map, thereby affecting the positioning result.
In the application, the point cloud data of some obstacles, for example, the point cloud data corresponding to pedestrians, can be deleted from the first point cloud data according to the information of each obstacle, so as to obtain the processed point cloud data, that is, the second point cloud data.
And 103, determining the current positioning information of the vehicle according to the second point cloud data and the type of the current driving road section of the vehicle.
In practical applications, the types of road sections traveled by vehicles are different, and the number, types, etc. of surrounding obstacles may also be different, thereby having different effects on positioning results. For example, in a road with a wide view field, the number of obstacles is small, and in an urban area, a high-rise building stands, and pedestrians are also large, so that the number of obstacles is large.
According to the method and the device, the road section where the vehicle is located and the type of the road section where the vehicle is located can be obtained according to the positioning information of the vehicle at the previous moment, or the type of the road section where the vehicle is located can be determined according to the collected image. And then, determining the current positioning information of the vehicle according to the second point cloud data and the type of the current driving road section. The positioning information may include a position, a lane, a driving direction, and the like of the vehicle on the current driving road segment.
As an example, in the case that the type of the road segment where the vehicle is currently located is the specified type of road segment, the positioning information of the vehicle in the first direction is determined according to the second point cloud data. The specified type road sections can be road sections with an open road surface and shelters, such as tunnels, bridges and the like; the first direction may refer to a lateral direction, a direction perpendicular to a vehicle traveling direction.
For example, the specified type road section is a tunnel, and because the characteristics of the collected point cloud data are not obvious in the tunnel scene, the distance between the vehicle and the two sides of the road can be determined according to the second point cloud data, and the current driving lane of the vehicle can be determined according to the distance between the vehicle and the two sides of the road.
In addition, when the road segment where the vehicle is currently located is the specified type of road segment, the positioning information of the vehicle in the second direction may also be output, but the confidence of the positioning information in the second direction may automatically decrease. Wherein the second direction may refer to a traveling direction of the vehicle.
As another example, in the case that the road segment where the vehicle is currently located is a non-specified type road segment, the positioning information of the vehicle in the first direction and the second direction is determined according to the second point cloud data.
For example, when the vehicle runs in an urban area, the current lane, the position of the running road section, the running direction and the like of the vehicle can be determined according to the second point cloud data because the point cloud features are obvious.
According to the method and the device, the positioning information in the corresponding direction is determined according to whether the current driving road section of the vehicle is the road section of the specified type and the second point cloud data, so that the vehicle can be positioned in different scenes.
It can be understood that the vehicle positioning method of the present application can be applied to an autonomous vehicle, and can also be applied to a general vehicle.
In practice, the vehicle may use a variety of positioning methods. After the positioning information of the vehicle is obtained, the positioning information can be fused with the positioning results determined by other positioning methods on the vehicle, so as to obtain the final positioning information of the vehicle. For example, the vehicle may also use a GNSS (Global Navigation Satellite System) to perform positioning, and then the positioning result of the GNSS and the positioning result determined by using the point cloud data may be fused, and the fused result is used as the current final positioning result of the vehicle.
In the embodiment of the application, the first point cloud data acquired currently by the vehicle and the current information of each obstacle are acquired; processing the first point cloud data according to the information of each obstacle to obtain second point cloud data; and determining the current positioning information of the vehicle according to the second point cloud data and the type of the current driving road section of the vehicle. Therefore, the first point cloud data are processed according to the information of the obstacles, the interference of the point clouds of the obstacles is reduced, the vehicle is positioned by utilizing the processed point cloud data and the type of the road section where the vehicle is currently located, the type of the road section is also considered, and the positioning precision can be improved.
In practical applications, there are many obstacles around the vehicle, and different obstacles have different influences on the positioning of the vehicle, for example, the interference generated by the point cloud data corresponding to the building is small, and pedestrians may generate the interference point cloud. In an embodiment of the application, each acquired obstacle information may include category information, position information, a point cloud enclosure frame, and the like, and the information may be utilized to determine a target obstacle, delete point cloud data corresponding to the target obstacle, and determine current positioning information of a vehicle by using second point cloud data obtained after processing and a type of a road section where the vehicle is currently located.
Fig. 2 is a schematic flow chart of another vehicle positioning method according to an embodiment of the present application.
As shown in fig. 2, the vehicle positioning method includes:
In the present application, the method for obtaining the first point cloud data and the current obstacle information is similar to the step 101, and therefore is not described again.
In this application, the sensing module on the vehicle may output the category information, the location information, the cloud enclosure frame, and the like of each obstacle, and thus the category information, the location information, the cloud enclosure frame, and the like of each obstacle may be acquired from the sensing module. Wherein the point cloud enclosure box is used for indicating the range of the obstacle point cloud; the position information of the obstacle may be a position of the obstacle with respect to the vehicle, or may be a position of the obstacle in the global coordinate system.
In practical application, there are many obstacles around the vehicle, and different obstacles have different effects on the positioning of the vehicle.
In the method and the device, the type information of the obstacles which can generate the interference point cloud can be stored in advance, and the type information of each obstacle around the vehicle at present is matched with the type information of the preset obstacles. When the type information of any current obstacle is matched with the preset obstacle type information, it can be determined that any obstacle is a target obstacle. Therefore, the obstacles capable of generating the interference point cloud, namely the target obstacles, can be determined from the current obstacles.
For example, the preset obstacle type information includes a pedestrian, another vehicle, a fence, and the like, and if a current obstacle type around the vehicle is a pedestrian, the obstacle is a target obstacle.
According to the method and the device, the approximate position of the point cloud data corresponding to the target obstacle in the first point cloud data can be determined according to the position information of the target obstacle, and the point cloud data corresponding to the target obstacle in the first point cloud data can be determined according to the approximate position and the point cloud surrounding frame.
And 204, deleting the point cloud data corresponding to the target obstacle in the first point cloud data to obtain second point cloud data.
After the point cloud data corresponding to the target obstacle is determined, the point cloud data corresponding to the target obstacle can be deleted from the first point cloud data, and second point cloud data are obtained after deletion.
It can be understood that, if a plurality of target obstacles are provided, the point cloud data corresponding to each obstacle may be deleted from the first point cloud data to reduce the interference point clouds generated by the obstacles.
In addition, the vehicle positioning function corresponding to the running road surface of the vehicle is not large, and the point cloud data corresponding to the road surface in the first point cloud data can be determined according to the characteristics of the point cloud data corresponding to the road surface, for example, the coordinates of a plurality of adjacent point clouds in the height direction are the same. And then, deleting the point cloud data corresponding to the target obstacle and the point cloud data corresponding to the road surface from the first point cloud data, and acquiring second point cloud data. So that the amount of calculation can be reduced.
And step 205, determining the current positioning information of the vehicle according to the second point cloud data and the type of the current driving road section of the vehicle.
In the present application, step 205 is similar to step 103, and therefore will not be described herein again.
In the embodiment of the application, the acquired information of each obstacle may include category information, position information and a point cloud enclosure, the first point cloud data is processed according to the information of each obstacle to acquire the second point cloud data, a target obstacle is determined from each obstacle according to the category information of each obstacle, the point cloud data corresponding to the target obstacle in the first point cloud data is determined according to the position information of the target obstacle and the point cloud enclosure, the point cloud data corresponding to the target obstacle in the first point cloud data is deleted from the first point cloud data, and the second point cloud data is acquired. Therefore, the obstacles which can generate interference point clouds are determined according to the category information and the position information point cloud surrounding frame of each obstacle, the point cloud data corresponding to the obstacles are deleted from the first point cloud data, and therefore the interference point clouds are reduced, and meanwhile the residual point cloud data can be guaranteed to be utilized to accurately position the vehicle.
Fig. 3 is a schematic flowchart of another vehicle positioning method according to an embodiment of the present disclosure.
As shown in fig. 3, the vehicle positioning method includes:
In the present application, steps 301 to 302 are similar to steps 101 to 102, and therefore are not described herein again.
And step 303, acquiring initial pose information of the vehicle.
In the application, the initial pose information may be a positioning result of the latest fusion positioning of the vehicle. The initial pose information may include three-dimensional coordinate information, a heading angle, and the like, where the heading angle may be used to indicate a direction of travel of the vehicle.
And step 304, determining a target search range and a search step length in the point cloud positioning map according to the initial pose information and the type of the current driving road section of the vehicle.
In the application, a target search range in the point cloud map can be determined according to the initial pose information, for example, a range of 10 × 10 meters with the initial position as the center in the point cloud map is used as the target search range.
Due to the fact that the road sections of different types and the number of objects around the vehicle are different, in order to improve the positioning accuracy, the search step length corresponding to the type of the current driving road section can be determined according to the type of the current driving road section and the search step length corresponding to the type of the preset road section. For example, the open area is longer than the search step in the urban area.
For example, the target search range is 10 × 10m, and the search step size may be 10 cm.
Or, the target search range may also be determined according to the initial pose information and the type of the current driving road segment, for example, the target search range in an open area is large, and the target search range in an urban area is small. And then, determining the search step length according to the type of the current driving road section.
And 305, extracting each third point cloud data to be matched from a target search range in the point cloud positioning map based on the search step length.
In the method and the device, a plurality of small ranges can be obtained from a target search range based on the search step length, the point cloud data corresponding to each small range can be used as third point cloud data to be matched, and the range size of each third point cloud data is the same as that of the second point cloud data.
And step 306, determining the current positioning information of the vehicle according to the first matching degree of the second point cloud data and each third point cloud data.
After the third point cloud data to be matched is obtained, each point in the second point cloud data may be matched with each point in each third point cloud data to obtain a first matching degree between the second point cloud data and each third point cloud data. For example, a distance error or distance between each point in the second point cloud data and a corresponding point on the third point cloud data may be calculated, and then the sum of the squares of the errors may be smaller, which indicates that the better the match between the second point cloud data and the third point cloud data in the point cloud map.
After the first matching degree of the second point cloud data and each third point cloud data is obtained, the positioning information of the vehicle can be determined according to the third point cloud data with the first matching degree larger than a preset threshold value, or the positioning information of the vehicle can be determined according to the third point cloud data with the highest matching degree.
In the method, if each first matching degree is smaller than a preset threshold, the search step length can be reduced, the third point cloud data to be matched are extracted from the target search range based on the new search step length, and then the positioning information of the vehicle is determined according to the matching degree between the second point cloud data and each third point cloud data.
It should be noted that, in the present application, a plurality of different search step lengths may also be set, third point cloud data to be matched may be extracted from the target search range based on each search step length, a first matching degree between the second point cloud data and each third point cloud data is calculated, and then the third point cloud data with the highest matching degree may be selected to determine the position of the vehicle.
In the embodiment of the application, when the current positioning information of the vehicle is determined according to the second point cloud data and the type of the current driving road section of the vehicle, a target search range and a search step length in the point cloud positioning map can be determined according to the initial pose information and the type of the current driving road section of the vehicle, each third point cloud data to be matched is extracted based on the search step length, and the positioning information of the vehicle is determined according to the first matching degree of the second point cloud data and each third point cloud data. Therefore, the target search range and the search step length are determined according to the type of the current driving road section of the vehicle, so that the target search range and the search step length are matched with the driving road section, and the positioning precision is improved.
Fig. 4 is a schematic flow chart of another vehicle positioning method according to an embodiment of the present application.
As shown in fig. 4, the vehicle positioning method further includes:
The first matching degrees between the second point cloud data and each third point cloud data do not satisfy a preset condition, for example, the first matching degrees are all smaller than a preset threshold, and it may be that the target search range is relatively large, and the target search range may be updated according to each first matching degree. For example, an area surrounded by a plurality of third point cloud data having a relatively high first matching degree may be set as a new target search range.
It is understood that, in the present application, the target search range can be dynamically adjusted according to actual needs.
And step 402, extracting fourth point cloud data to be matched from the updated target search range in the point cloud positioning map based on the search step length.
According to the method and the device, the fourth point cloud data to be matched can be extracted from the updated target search range in the point cloud positioning map according to the search step length determined based on the type of the driving road section.
Alternatively, the search step size may be updated according to the updated target search range, for example, if the updated target search range becomes smaller, the search step size may be reduced. And then, extracting fourth point cloud data to be matched from the updated target search range in the point cloud positioning map according to the updated search step length. Therefore, the search step length is updated based on the updated target search range, so that the search step length can better meet the positioning search requirement, and the positioning precision is improved.
And 403, determining a second matching degree of the second point cloud data and each fourth point cloud data.
In the present application, step 403 is similar to the above-mentioned method for determining the first matching degree, and therefore, the description thereof is omitted here.
And step 404, returning to execute the operation of updating the target search range when each second matching degree does not meet the preset condition until at least one second matching degree in the second matching degrees meets the preset condition, and determining the current positioning information of the vehicle according to the at least one second matching degree.
In this application, if each second matching degree does not satisfy the preset condition, the target search range may be continuously updated until at least one second matching degree in the second matching degrees satisfies the preset condition, and the current location information of the vehicle is determined according to the at least one second matching degree, for example, the location information of the current vehicle may be determined according to fourth point cloud data with the highest second matching degree.
In the embodiment of the application, under the condition that each first matching degree does not meet the preset condition, the target search range can be updated according to the first matching degree, and the second point cloud data and the updated target search range are continuously matched by using the search step length, so that the current positioning information of the vehicle can be accurately determined.
In order to realize the above embodiments, the embodiments of the present application further provide a positioning device for a vehicle. Fig. 5 is a schematic structural diagram of a vehicle positioning device according to an embodiment of the present application.
As shown in fig. 5, the vehicle positioning apparatus 500 includes:
a first obtaining module 510, configured to obtain first point cloud data currently collected by a vehicle and information of current obstacles;
a second obtaining module 520, configured to process the first point cloud data according to the information of each obstacle to obtain second point cloud data;
the first determining module 530 is configured to determine current location information of the vehicle according to the second point cloud data and a type of a road section where the vehicle is currently located.
In a possible implementation manner of the embodiment of the present application, the information of each obstacle includes category information, location information, and a point cloud enclosure, and the second obtaining module 520 is configured to:
determining a target obstacle from the obstacles according to the category information of each obstacle;
determining point cloud data corresponding to the target obstacle in the first point cloud data according to the position information of the target obstacle and a point cloud surrounding frame;
and deleting the point cloud data corresponding to the target obstacle in the first point cloud data to obtain the second point cloud data.
In a possible implementation manner of the embodiment of the present application, the first determining module 530 is configured to:
determining positioning information of the vehicle in a first direction according to the second point cloud data under the condition that the current driving road section of the vehicle is a road section of a specified type;
and/or the presence of a gas in the gas,
and determining the positioning information of the vehicle in the first direction and the second direction according to the second point cloud data under the condition that the current driving road section of the vehicle is a non-specified type road section.
In a possible implementation manner of this embodiment of the present application, the first determining module 530 includes:
a first acquisition unit configured to acquire initial pose information of the vehicle;
the first determining unit is used for determining a target searching range and a searching step length in the point cloud positioning map according to the initial pose information and the type of the road section where the vehicle is currently located;
the extraction unit is used for extracting each third point cloud data to be matched from a target search range in the point cloud positioning map based on the search step length;
and the second determining unit is used for determining the current positioning information of the vehicle according to the first matching degree of the second point cloud data and each third point cloud data.
In a possible implementation manner of the embodiment of the present application, the apparatus may further include:
the updating module is used for updating the target searching range according to each first matching degree under the condition that each first matching degree does not meet the preset condition;
the extraction unit is further used for extracting fourth point cloud data to be matched from the updated target search range in the point cloud positioning map based on the search step length;
the apparatus may further comprise: the second determining module is used for determining a second matching degree of the second point cloud data and each fourth point cloud data;
the second determining unit is further configured to, when each of the second matching degrees does not satisfy a preset condition, return to perform the operation of updating the target search range until at least one of the second matching degrees satisfies the preset condition, and determine the current positioning information of the vehicle according to the at least one second matching degree.
In a possible implementation manner of the embodiment of the present application, the extraction unit is configured to:
updating the search step length according to the updated target search range;
and extracting fourth point cloud data to be matched from the updated target search range in the point cloud positioning map based on the updated search step length.
It should be noted that the explanation of the embodiment of the vehicle positioning method is also applicable to the vehicle positioning device of the embodiment, and therefore, the explanation is not repeated herein.
In the embodiment of the application, the first point cloud data acquired currently by the vehicle and the current information of each obstacle are acquired; processing the first point cloud data according to the information of each obstacle to obtain second point cloud data; and determining the current positioning information of the vehicle according to the second point cloud data and the type of the current driving road section of the vehicle. Therefore, the first point cloud data are processed according to the information of the obstacles, the interference of the point clouds of the obstacles is reduced, the vehicle is positioned by utilizing the processed point cloud data and the type of the road section where the vehicle is currently located, the type of the road section is also considered, and the positioning precision can be improved.
There is also provided, in accordance with an embodiment of the present application, an electronic device, a readable storage medium, and a computer program product.
FIG. 6 illustrates a schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present application that are described and/or claimed herein.
As shown in fig. 6, the device 600 includes a computing unit 601 which can perform various appropriate actions and processes in accordance with a computer program stored in a ROM (Read-Only Memory) 602 or a computer program loaded from a storage unit 608 into a RAM (Random Access Memory) 603. In the RAM 603, various programs and data required for the operation of the device 600 can also be stored. The calculation unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An I/O (Input/Output) interface 605 is also connected to the bus 604.
A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing Unit 601 include, but are not limited to, a CPU (Central Processing Unit), a GPU (graphics Processing Unit), various dedicated AI (Artificial Intelligence) computing chips, various computing Units running machine learning model algorithms, a DSP (Digital Signal Processor), and any suitable Processor, controller, microcontroller, and the like. The calculation unit 601 performs the respective methods and processes described above, such as the positioning method of the vehicle. For example, in some embodiments, the vehicle location method may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the positioning method of the vehicle described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured by any other suitable means (e.g. by means of firmware) to perform the positioning method of the vehicle.
Various implementations of the systems and techniques described here above may be realized in digital electronic circuitry, Integrated circuitry, FPGAs (Field Programmable Gate arrays), ASICs (Application-Specific Integrated circuits), ASSPs (Application Specific Standard products), SOCs (System On Chip, System On a Chip), CPLDs (Complex Programmable Logic devices), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present application may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a RAM, a ROM, an EPROM (Electrically Programmable Read-Only-Memory) or flash Memory, an optical fiber, a CD-ROM (Compact Disc Read-Only-Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a Display device (e.g., a CRT (Cathode Ray Tube) or LCD (Liquid Crystal Display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), internet, and blockchain Network.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The Server may be a cloud Server, which is also called a cloud computing Server or a cloud host, and is a host product in a cloud computing service system, so as to solve the defects of high management difficulty and weak service expansibility in a conventional physical host and a VPS (Virtual Private Server). The server may also be a server of a distributed system, or a server incorporating a blockchain.
According to an embodiment of the present application, there is also provided a computer program product, which when executed by an instruction processor in the computer program product, performs the positioning method of the vehicle proposed by the above-mentioned embodiment of the present application.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present application can be achieved, and the present invention is not limited herein.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (15)
1. A method of locating a vehicle, comprising:
acquiring first point cloud data currently acquired by a vehicle and current information of each obstacle;
processing the first point cloud data according to the information of each obstacle to obtain second point cloud data;
and determining the current positioning information of the vehicle according to the second point cloud data and the type of the current driving road section of the vehicle.
2. The method of claim 1, wherein the information of each obstacle includes category information, location information, and a point cloud bounding box, and the processing the first point cloud data to obtain second point cloud data according to the information of each obstacle comprises:
determining a target obstacle from the obstacles according to the category information of each obstacle;
determining point cloud data corresponding to the target obstacle in the first point cloud data according to the position information of the target obstacle and a point cloud surrounding frame;
and deleting the point cloud data corresponding to the target obstacle in the first point cloud data to obtain the second point cloud data.
3. The method of claim 1, wherein the determining the current positioning information of the vehicle according to the second point cloud data and the type of the road section where the vehicle is currently located comprises:
determining positioning information of the vehicle in a first direction according to the second point cloud data under the condition that the current driving road section of the vehicle is a road section of a specified type;
and/or the presence of a gas in the gas,
and determining the positioning information of the vehicle in the first direction and the second direction according to the second point cloud data under the condition that the current driving road section of the vehicle is a non-specified type road section.
4. The method of claim 1, wherein the determining the current positioning information of the vehicle according to the second point cloud data and the type of the road section where the vehicle is currently located comprises:
acquiring initial pose information of the vehicle;
determining a target search range and a search step length in the point cloud positioning map according to the initial pose information and the type of the current driving road section of the vehicle;
extracting each third point cloud data to be matched from a target search range in the point cloud positioning map based on the search step length;
and determining the current positioning information of the vehicle according to the first matching degree of the second point cloud data and each third point cloud data.
5. The method of claim 4, further comprising:
under the condition that each first matching degree does not meet a preset condition, updating the target search range according to each first matching degree;
extracting fourth point cloud data to be matched from the updated target search range in the point cloud positioning map based on the search step length;
determining a second matching degree of the second point cloud data and each fourth point cloud data;
and under the condition that each second matching degree does not meet a preset condition, returning to execute the operation of updating the target search range until at least one second matching degree in the second matching degrees meets the preset condition, and determining the current positioning information of the vehicle according to the at least one second matching degree.
6. The method of claim 5, wherein the extracting, based on the search step size, each fourth point cloud data to be matched from the updated target search range in the point cloud positioning map comprises:
updating the search step length according to the updated target search range;
and extracting fourth point cloud data to be matched from the updated target search range in the point cloud positioning map based on the updated search step length.
7. A positioning device for a vehicle, comprising:
the first acquisition module is used for acquiring first point cloud data acquired by the vehicle at present and information of each current obstacle;
the second acquisition module is used for processing the first point cloud data according to the information of each obstacle so as to acquire second point cloud data;
and the first determining module is used for determining the current positioning information of the vehicle according to the second point cloud data and the type of the road section where the vehicle is currently located.
8. The apparatus of claim 7, wherein the information of each obstacle comprises category information, location information, and a cloud bounding box, the second obtaining module to:
determining a target obstacle from the obstacles according to the category information of each obstacle;
determining point cloud data corresponding to the target obstacle in the first point cloud data according to the position information of the target obstacle and a point cloud surrounding frame;
and deleting the point cloud data corresponding to the target obstacle in the first point cloud data to obtain the second point cloud data.
9. The apparatus of claim 7, wherein the first determining means is configured to:
determining positioning information of the vehicle in a first direction according to the second point cloud data under the condition that the current driving road section of the vehicle is a road section of a specified type;
and/or the presence of a gas in the gas,
and determining the positioning information of the vehicle in the first direction and the second direction according to the second point cloud data under the condition that the current driving road section of the vehicle is a non-specified type road section.
10. The apparatus of claim 7, wherein the first determining means comprises:
a first acquisition unit configured to acquire initial pose information of the vehicle;
the first determining unit is used for determining a target searching range and a searching step length in the point cloud positioning map according to the initial pose information and the type of the road section where the vehicle is currently located;
the extraction unit is used for extracting each third point cloud data to be matched from a target search range in the point cloud positioning map based on the search step length;
and the second determining unit is used for determining the current positioning information of the vehicle according to the first matching degree of the second point cloud data and each third point cloud data.
11. The apparatus of claim 10, further comprising:
the updating module is used for updating the target searching range according to each first matching degree under the condition that each first matching degree does not meet the preset condition;
the extraction unit is further used for extracting fourth point cloud data to be matched from the updated target search range in the point cloud positioning map based on the search step length;
the device further comprises:
the second determining module is used for determining a second matching degree of the second point cloud data and each fourth point cloud data;
the second determining unit is further configured to, when each of the second matching degrees does not satisfy a preset condition, return to perform the operation of updating the target search range until at least one of the second matching degrees satisfies the preset condition, and determine the current positioning information of the vehicle according to the at least one second matching degree.
12. The apparatus of claim 11, wherein the decimation unit is to:
updating the search step length according to the updated target search range;
and extracting fourth point cloud data to be matched from the updated target search range in the point cloud positioning map based on the updated search step length.
13. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-6.
14. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-6.
15. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110726889.1A CN113587937A (en) | 2021-06-29 | 2021-06-29 | Vehicle positioning method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110726889.1A CN113587937A (en) | 2021-06-29 | 2021-06-29 | Vehicle positioning method and device, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113587937A true CN113587937A (en) | 2021-11-02 |
Family
ID=78244910
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110726889.1A Pending CN113587937A (en) | 2021-06-29 | 2021-06-29 | Vehicle positioning method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113587937A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114326730A (en) * | 2021-12-28 | 2022-04-12 | 北京百度网讯科技有限公司 | Method, apparatus, electronic device, medium for determining parking path |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108931786A (en) * | 2018-05-17 | 2018-12-04 | 北京智行者科技有限公司 | Curb detection device and method |
| CN110850439A (en) * | 2020-01-15 | 2020-02-28 | 奥特酷智能科技(南京)有限公司 | High-precision three-dimensional point cloud map construction method |
| CN110967008A (en) * | 2019-11-25 | 2020-04-07 | 江铃汽车股份有限公司 | Automatic driving full scene positioning method for commercial vehicle |
| CN111854780A (en) * | 2020-06-10 | 2020-10-30 | 恒大恒驰新能源汽车研究院(上海)有限公司 | Vehicle navigation method, device, vehicle, electronic equipment and storage medium |
| CN112526477A (en) * | 2019-09-19 | 2021-03-19 | 北京百度网讯科技有限公司 | Method and apparatus for processing information |
| CN112622923A (en) * | 2019-09-24 | 2021-04-09 | 北京百度网讯科技有限公司 | Method and device for controlling a vehicle |
| CN112711027A (en) * | 2020-12-08 | 2021-04-27 | 北京市首都公路发展集团有限公司 | Tunnel internal transverse positioning method based on laser radar point cloud data |
| CN112817026A (en) * | 2021-01-29 | 2021-05-18 | 西人马帝言(北京)科技有限公司 | Method, device and equipment for determining pose of moving object and storage medium |
| CN112949366A (en) * | 2020-04-01 | 2021-06-11 | 北京京东乾石科技有限公司 | Obstacle identification method and device |
| CN112988922A (en) * | 2019-12-16 | 2021-06-18 | 长沙智能驾驶研究院有限公司 | Perception map construction method and device, computer equipment and storage medium |
-
2021
- 2021-06-29 CN CN202110726889.1A patent/CN113587937A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108931786A (en) * | 2018-05-17 | 2018-12-04 | 北京智行者科技有限公司 | Curb detection device and method |
| CN112526477A (en) * | 2019-09-19 | 2021-03-19 | 北京百度网讯科技有限公司 | Method and apparatus for processing information |
| CN112622923A (en) * | 2019-09-24 | 2021-04-09 | 北京百度网讯科技有限公司 | Method and device for controlling a vehicle |
| CN110967008A (en) * | 2019-11-25 | 2020-04-07 | 江铃汽车股份有限公司 | Automatic driving full scene positioning method for commercial vehicle |
| CN112988922A (en) * | 2019-12-16 | 2021-06-18 | 长沙智能驾驶研究院有限公司 | Perception map construction method and device, computer equipment and storage medium |
| CN110850439A (en) * | 2020-01-15 | 2020-02-28 | 奥特酷智能科技(南京)有限公司 | High-precision three-dimensional point cloud map construction method |
| CN112949366A (en) * | 2020-04-01 | 2021-06-11 | 北京京东乾石科技有限公司 | Obstacle identification method and device |
| CN111854780A (en) * | 2020-06-10 | 2020-10-30 | 恒大恒驰新能源汽车研究院(上海)有限公司 | Vehicle navigation method, device, vehicle, electronic equipment and storage medium |
| CN112711027A (en) * | 2020-12-08 | 2021-04-27 | 北京市首都公路发展集团有限公司 | Tunnel internal transverse positioning method based on laser radar point cloud data |
| CN112817026A (en) * | 2021-01-29 | 2021-05-18 | 西人马帝言(北京)科技有限公司 | Method, device and equipment for determining pose of moving object and storage medium |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114326730A (en) * | 2021-12-28 | 2022-04-12 | 北京百度网讯科技有限公司 | Method, apparatus, electronic device, medium for determining parking path |
| CN114326730B (en) * | 2021-12-28 | 2024-04-05 | 北京百度网讯科技有限公司 | Method, device, electronic equipment and medium for determining parking path |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP3505869B1 (en) | Method, apparatus, and computer readable storage medium for updating electronic map | |
| CN112560680A (en) | Lane line processing method and device, electronic device and storage medium | |
| CN112558072B (en) | Vehicle positioning method, device, system, electronic equipment and storage medium | |
| CN113706704B (en) | Method and equipment for planning route based on high-precision map and automatic driving vehicle | |
| CN113378693A (en) | Target generation detection system and method and device for detecting target | |
| CN114506343A (en) | Trajectory planning method, device, equipment, storage medium and automatic driving vehicle | |
| CN113635912A (en) | Vehicle control method, device, equipment, storage medium and automatic driving vehicle | |
| CN114829971A (en) | Laser radar calibration method and device and storage medium | |
| CN115855084A (en) | Map data fusion method and device, electronic equipment and automatic driving product | |
| CN112859829A (en) | Vehicle control method and device, electronic equipment and medium | |
| CN113722342A (en) | High-precision map element change detection method, device and equipment and automatic driving vehicle | |
| CN113587937A (en) | Vehicle positioning method and device, electronic equipment and storage medium | |
| CN116499487B (en) | Vehicle path planning method, device, equipment and medium | |
| CN116659524A (en) | Vehicle positioning method, device, equipment and storage medium | |
| CN114689061A (en) | Navigation route processing method and device of automatic driving equipment and electronic equipment | |
| CN113276888B (en) | Riding method, device, equipment and storage medium based on automatic driving | |
| CN113469045B (en) | Visual positioning method and system for unmanned integrated card, electronic equipment and storage medium | |
| CN113450794B (en) | Navigation broadcasting detection method and device, electronic equipment and medium | |
| CN115790621A (en) | High-precision map updating method and device and electronic equipment | |
| CN115583254A (en) | Path planning method, device and equipment and automatic driving vehicle | |
| CN114998863A (en) | Target road identification method, target road identification device, electronic equipment and storage medium | |
| CN114779705A (en) | Method, device, electronic equipment and system for controlling automatic driving vehicle | |
| CN114036247A (en) | High-precision map data association method and device, electronic equipment and storage medium | |
| CN114170300A (en) | High-precision map point cloud pose optimization method, device, equipment and medium | |
| CN114581869A (en) | Method and device for determining position of target object, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |