CN112382116A - Method and system for acquiring point cloud map of vehicle - Google Patents
Method and system for acquiring point cloud map of vehicle Download PDFInfo
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- CN112382116A CN112382116A CN202011263911.5A CN202011263911A CN112382116A CN 112382116 A CN112382116 A CN 112382116A CN 202011263911 A CN202011263911 A CN 202011263911A CN 112382116 A CN112382116 A CN 112382116A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
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- 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
- G01C21/30—Map- or contour-matching
- G01C21/32—Structuring or formatting of map data
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
- G08G1/133—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams within the vehicle ; Indicators inside the vehicles or at stops
- G08G1/137—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams within the vehicle ; Indicators inside the vehicles or at stops the indicator being in the form of a map
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Abstract
本发明提供一种用于获取车辆的点云地图的方法和系统，涉及车辆的自动驾驶技术领域。方法包括：采集所述车辆的预设行驶路线上是否存在GPS和/或RTK信号丢失的障碍路段；若是，在所述车辆与所述障碍路段的起点之间的距离大于预设值时，采用RTK获取所述车辆在所述预设行驶路线上的绝对坐标，及采用SLAM获取所述车辆在所述预设行驶路线上的相对坐标；根据所述绝对坐标和所述相对坐标得到所述车辆在所述障碍路段的行驶轨迹从而得到所述车辆在所述预设行驶路线上的点云地图。本发明提供的方法可以在局部路段无GPS和RTK信号的场景下为自动驾驶车辆提供点云地图。
The invention provides a method and a system for acquiring a point cloud map of a vehicle, and relates to the technical field of automatic driving of vehicles. The method includes: collecting whether there is an obstacle road segment with GPS and/or RTK signal loss on the preset driving route of the vehicle; if so, when the distance between the vehicle and the starting point of the obstacle road segment is greater than a preset value, adopting RTK obtains the absolute coordinates of the vehicle on the preset travel route, and uses SLAM to obtain the relative coordinates of the vehicle on the preset travel route; obtain the vehicle according to the absolute coordinates and the relative coordinates A point cloud map of the vehicle on the preset driving route is obtained from the driving track of the obstacle road section. The method provided by the present invention can provide a point cloud map for an automatic driving vehicle in a scene where there is no GPS and RTK signals in a local road section.
Description
Technical Field
The invention relates to the technical field of intelligent driving of vehicles, in particular to a method and a system for acquiring a point cloud map of a vehicle.
Background
The automatic driving technology has been more and more emphasized by people, wherein the high-precision positioning technology (reaching centimeter level) is the core and basic function of automatic driving, the automatic driving vehicle cannot walk without being positioned, the automatic driving vehicle without being positioned with high precision has no safety guarantee, and point cloud positioning is the most accurate positioning means at present and is not neglected.
The premise of point cloud positioning is to create a high-precision point cloud map, and to provide global absolute coordinates for positioning, in an open scene, the global absolute coordinates can be rapidly acquired by generally using an RTK (real-time kinematic carrier phase difference technology), but in a scene without RTK signals or poor RTK signals, the global coordinates cannot be acquired, so that the point cloud map cannot be created, and the point cloud map cannot be used for point cloud positioning.
Disclosure of Invention
It is an object of a first aspect of the invention to provide a method that can provide a point cloud map for autonomous vehicles in a scenario where local road segments are free of GPS and RTK signals.
It is a further object of the first aspect of the invention to provide a method of providing an accurate point cloud map for an autonomous vehicle.
It is an object of a second aspect of the present invention to provide a system that can provide a point cloud map for autonomous vehicles in a scenario where there are no GPS and RTK signals on local road segments.
According to the first aspect described above, the present invention provides a method for acquiring a point cloud map of a vehicle, comprising:
acquiring whether a GPS and/or RTK signal lost obstacle road section exists on a preset driving route of the vehicle;
if so, when the distance between the vehicle and the starting point of the obstacle road section is larger than a preset value, acquiring an absolute coordinate of the vehicle on the preset running route by adopting RTK, and acquiring a relative coordinate of the vehicle on the preset running route by adopting SLAM;
and obtaining the driving track of the vehicle on the obstacle road section according to the absolute coordinates and the relative coordinates so as to obtain a point cloud map of the vehicle on the preset driving route.
Optionally, obtaining the driving track of the vehicle on the obstacle road section according to the absolute coordinate and the relative coordinate to obtain the point cloud map of the vehicle on the preset driving route includes:
aligning the absolute coordinates and the relative coordinates according to a timestamp to obtain a conversion matrix on a track with a distance between the vehicle and the starting point of the obstacle road section being greater than zero and less than or equal to the preset value;
and on the obstacle road section, converting the absolute coordinates and the relative coordinates according to the conversion matrix to obtain a driving track of the vehicle on the obstacle road section so as to obtain a point cloud map of the vehicle on the preset driving route.
Optionally, on a track where a distance between the vehicle and the start point of the obstacle road segment is greater than zero and less than or equal to the preset value, aligning the absolute coordinate and the relative coordinate according to a timestamp to obtain a transformation matrix includes:
and aligning the absolute coordinates and the relative coordinates according to a timestamp to enable the coordinates given by the absolute coordinates and the relative coordinates at the same time to be at the same position, thereby obtaining the conversion matrix.
Optionally, the following method is adopted to obtain the transformation matrix:
selecting a certain track point P of the vehicle on a track with the distance between the vehicle and the starting point of the obstacle road section being larger than zero and smaller than the preset value;
the absolute coordinates are: prtk＝(x，y)；
The relative coordinates are: pslam＝(u，k)；
Then
Convert the matrix into
Optionally, on the obstacle section, converting the absolute coordinates and the relative coordinates according to the conversion matrix to obtain a driving track of the vehicle on the obstacle section so as to obtain a point cloud map of the vehicle on the preset driving route includes:
and converting the absolute coordinates on the obstacle road section according to the conversion matrix to obtain the running track of the vehicle on the obstacle road section based on the relative coordinates of the track, wherein the distance between the vehicle and the starting point of the obstacle road section is larger than zero and smaller than or equal to the preset value.
Optionally, the obstacle section comprises a tunnel and an underground garage.
Optionally, when the distance between the vehicle and the starting point of the obstacle road segment is greater than a preset value, acquiring the absolute coordinate of the vehicle on the preset driving route by using RTK, and acquiring the relative coordinate of the vehicle on the preset driving route by using SLAM includes:
stopping the vehicle at a certain point where the distance between the vehicle and the starting point of the obstacle section is greater than a preset value, and starting or restarting the RTK and the SLAM to synchronize the RTK with the operation starting point of the SLAM.
Optionally, the selection of the preset value is required to be combined with the running speed of the vehicle.
Optionally, the preset value is 1 km.
According to the second aspect, the invention further provides a system for acquiring a point cloud map of a vehicle, which comprises a control device, wherein the control device comprises a memory and a processor, the memory stores a control program, and the control program is used for realizing the method for acquiring the point cloud map of the vehicle when being executed by the processor.
The method for acquiring the point cloud map of the vehicle is generally suitable for automatically driving the vehicle, the vehicle plans a driving route according to the point cloud map provided by the GPS or the RTK on a road section with good GPS or RTK signals, the GPS and RTK signals are monitored by a sensor on the vehicle in the whole process of driving the vehicle, when an obstacle road section with lost GPS and/or RTK signals on the current driving route of the vehicle is predicted, an absolute coordinate and a relative coordinate are acquired by two means when the distance between the vehicle and the starting point of the obstacle road section is larger than a preset value, namely the absolute coordinate on the preset driving route is acquired by the RTK and the relative coordinate on the preset driving route is acquired by an SLAM (synchronous positioning and mapping), the accurate relative coordinate of the vehicle at each moment can be acquired by the SLAM, and then the driving track of the vehicle on the obstacle road section is acquired according to the absolute coordinate and the relative coordinate, so that the point cloud driving track of the vehicle on the preset route The map can be used for supplementing the obstacle road section, the problem that the point cloud map cannot be created under the condition of losing RTK and/or GPS scenes is solved, the using range of point cloud positioning is expanded, and the robustness of the point cloud positioning is improved.
Further, before the vehicle enters the obstacle section, it is necessary to ensure that each coordinate point to be converted is in a one-to-one correspondence relationship as much as possible when performing coordinate conversion, that is, it is considered that the information values output at the same time through the RTK and the SLAM represent the same position, so that it is necessary to time-align the relative coordinate output by the SLAM and the absolute coordinate output by the RTK according to their respective time stamps, and use the relative coordinate output by the SLAM and the absolute coordinate output by the RTK after time alignment as the same position value at the same time, and based on this data pair, it is possible to calculate a conversion matrix between the local coordinate and the global coordinate
Therefore, the accuracy of the finally obtained point cloud map can be ensured.
The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments thereof, taken in conjunction with the accompanying drawings.
Drawings
Some specific embodiments of the invention will be described in detail hereinafter, by way of illustration and not limitation, with reference to the accompanying drawings. The same reference numbers in the drawings identify the same or similar elements or components. Those skilled in the art will appreciate that the drawings are not necessarily drawn to scale. In the drawings:
FIG. 1 is a block flow diagram of a method for obtaining a point cloud map of a vehicle, according to one embodiment of the invention;
FIG. 2 is a block flow diagram of a method for obtaining a point cloud map of a vehicle, according to another embodiment of the invention;
fig. 3 is a block diagram of a system for acquiring a point cloud map of a vehicle according to one embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
FIG. 1 is a block flow diagram of a method for obtaining a point cloud map of a vehicle, according to one embodiment of the invention. As shown in fig. 1, the present invention provides a method for obtaining a point cloud map of a vehicle, generally comprising:
s10: acquiring whether a GPS and/or RTK signal lost obstacle road section exists on a preset driving route of a vehicle;
s20: if so, when the distance between the vehicle and the starting point of the obstacle road section is larger than a preset value, acquiring absolute coordinates of the vehicle on a preset driving route by adopting RTK (real time kinematic), and acquiring relative coordinates of the vehicle on the preset driving route by adopting SLAM (SLAM);
s30: and obtaining the driving track of the vehicle on the obstacle road section according to the absolute coordinates and the relative coordinates so as to obtain a point cloud map of the vehicle on a preset driving route.
The method for acquiring the point cloud map of the vehicle provided by the embodiment is generally suitable for an automatic driving vehicle, the vehicle plans a driving route according to the point cloud map provided by the GPS or RTK on a road section with good GPS or RTK signals, the GPS and RTK signals are monitored by a sensor on the vehicle in the whole process of driving the vehicle, when an obstacle road section with lost GPS and/or RTK signals on the current driving route of the vehicle is predicted, an absolute coordinate and a relative coordinate are acquired by two means when the distance between the vehicle and the starting point of the obstacle road section is greater than a preset value, namely, the absolute coordinate on the preset driving route is acquired by the RTK and the relative coordinate on the preset driving route is acquired by an SLAM (synchronous positioning and mapping), the accurate relative coordinate of the vehicle at each moment can be acquired by the SLAM, and then the driving track of the vehicle on the obstacle road section is acquired according to the absolute coordinate and the relative coordinate, so that the points of the vehicle on the The cloud map can be used for supplementing the obstacle road section, the problem that the point cloud map cannot be created under the condition that RTK and/or GPS are lost is solved, the using range of point cloud positioning is expanded, and the robustness of point cloud positioning is improved.
In a preferred embodiment, the vehicle sequentially passes through the RTK signal good section, the RTK signal-free section (obstacle section), and the RTK signal good section according to the point cloud map created as described above.
Fig. 2 is a block flow diagram of a method for obtaining a point cloud map of a vehicle according to another embodiment of the invention. As shown in fig. 2, in a specific embodiment, obtaining a driving track of the vehicle on the obstacle section according to the absolute coordinates and the relative coordinates to obtain a point cloud map of the vehicle on the preset driving route includes:
s31: aligning the absolute coordinates and the relative coordinates according to the timestamp to obtain a conversion matrix on a track with the distance between the vehicle and the starting point of the obstacle road section being greater than zero and less than or equal to a preset value;
s32: and on the obstacle road section, converting the absolute coordinates and the relative coordinates according to the conversion matrix to obtain a driving track of the vehicle on the obstacle road section so as to obtain a point cloud map of the vehicle on a preset driving route.
In a more specific embodiment, on a trajectory where a distance between the vehicle and a start point of the obstacle section is greater than zero and less than or equal to a preset value, aligning the absolute coordinates and the relative coordinates according to the time stamp to obtain the conversion matrix includes:
and aligning the absolute coordinates and the relative coordinates according to the time stamp to enable the coordinates given by the absolute coordinates and the relative coordinates at the same time to be at the same position, thereby obtaining a conversion matrix.
Before the vehicle enters the obstacle section, it is necessary to ensure each waiting turn as much as possible when performing the coordinate conversionThe transformed coordinate points are in a one-to-one correspondence relationship, that is, the information values output at the same time through the RTK and the SLAM are considered to represent the same position, so that the relative coordinate output by the SLAM and the absolute coordinate output by the RTK need to be time-aligned according to the respective timestamps given by the relative coordinate output by the SLAM and the absolute coordinate output by the RTK, the relative coordinate output by the SLAM and the absolute coordinate output by the RTK after the time alignment are used to be considered as the same position value at the same time, and based on the data pair, the transformation matrix between the local coordinate and the
Therefore, the accuracy of the finally obtained point cloud map is ensured.
The automatic driving vehicle runs on a road section containing a normal GPS/RTK signal and a road section with an obstacle (lost GPS/RTK signal), executes a laser SLAM function, can acquire a transformation matrix from an SLAM local coordinate to an RTK global coordinate according to time alignment when the RTK signal is good, and can calculate the global coordinate corresponding to the SLAM local coordinate according to the transformation matrix when the vehicle enters the obstacle road section, thereby realizing the expected result of the invention.
In a further embodiment, the transformation matrix is obtained by:
selecting a certain track point P of the vehicle on a track with the distance between the vehicle and the starting point of the obstacle road section being larger than zero and smaller than a preset value;
the absolute coordinates are: prtk＝(x，y)；
The relative coordinates are: pslam＝(u，k)；
Then
From which a transformation matrix can be calculated according to the least squares method as
In a preferred embodiment, on the obstacle section, the step of converting the absolute coordinates and the relative coordinates according to the conversion matrix to obtain the driving track of the vehicle on the obstacle section so as to obtain the point cloud map of the vehicle on the preset driving route comprises the following steps:
and converting the absolute coordinates on the obstacle road section according to the conversion matrix to obtain the running track of the vehicle on the obstacle road section based on the relative coordinates of the track, wherein the distance between the vehicle and the starting point of the obstacle road section is greater than zero and less than or equal to a preset value.
After the transformation matrix is calculated, the accurate global coordinate of the relative coordinate obtained by SLAM under the condition that each point of the vehicle running has no GPS and/or RTK signal can be obtained, so that a point cloud map can be obtained.
In an alternative embodiment, the barrier section includes a tunnel and an underground garage.
In a further embodiment, the acquiring the absolute coordinates of the vehicle on the preset traveling route using the RTK and the acquiring the relative coordinates of the vehicle on the preset traveling route using the SLAM when the distance between the vehicle and the start point of the obstacle section is greater than a preset value includes:
and stopping the vehicle at a certain point where the distance between the vehicle and the starting point of the obstacle road section is greater than a preset value, and starting or restarting the RTK and the SLAM to synchronize the RTK with the running starting point of the SLAM.
The vehicle provided by the embodiment is an automatic driving vehicle calibrated by a sensor, the sensor required on the vehicle comprises a laser radar, a GPS/RTK and an IMU sensor, a required SLAM program can be installed on the vehicle or additionally installed on the vehicle, and the function of SLAM offline data processing can be realized only by ensuring that corresponding setting is carried out according to the parameter alignment of the sensor of the vehicle.
Preferably, the selection of the preset value is combined with the running speed of the vehicle. The preset value is required to ensure that enough storage space is provided for holding sensor data, the area where the sensor data is located is a SLAM ready area, good GPS or RTK signals are required to be in the area, the vehicle can perform circular motion in a small range, and the aim is to enable the laser SLAM to form a closed loop.
In a specific embodiment, the preset value is 1 km.
In a specific embodiment, when the SLAM collects data online, the method works according to the following steps:
moving the vehicle to a SLAM ready area;
all sensors and control equipment of the vehicle are restarted to be used as a starting point of movement, and sensor data are recorded;
making the vehicle do small-range circular motion for 3-5 circles;
starting to drive according to the planned route until reaching the terminal point;
and finishing recording and storing.
In a specific embodiment, when the SLAM is offline, the method works according to the following steps:
starting a SLAM program;
the SLAM generates all track points by using the point cloud data and the IMU data;
extracting a track of the GPS/RTK data, and aligning the SLAM track with the GPS/RTK track according to the timestamp;
solving a rotating speed matrix by using the aligned SLAM coordinate and the GPS/RTK global coordinate;
extracting SLAM track points in the obstacle road section, and converting the SLAM track points into global coordinates by using a conversion matrix;
and replacing the finally calculated global track of the obstacle road section into the whole running track according to the corresponding relation of the time stamps, and completing all conversion to obtain the point cloud map.
Fig. 3 is a block diagram of a system for acquiring a point cloud map of a vehicle according to one embodiment of the present invention. As shown in fig. 3, the present invention further provides a system for acquiring a point cloud map of a vehicle, which generally includes a control device 10, the control device 10 includes a memory 11 and a processor 12, the memory 11 stores a control program, and the control program is executed by the processor 12 to implement the method for acquiring a point cloud map of a vehicle according to any one of the embodiments. The processor 12 may be a Central Processing Unit (CPU), a digital processing unit, or the like. The processor 12 transceives data through the communication interface. The memory 11 is used for storing programs executed by the processor. The memory 11 is any medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, or a combination of memories. The above-described computing program may be downloaded from a computer-readable storage medium to a corresponding computing/processing device or to a computer or external storage device via a network (e.g., the internet, a local area network, a wide area network, and/or a wireless network).
Thus, it should be appreciated by those skilled in the art that while a number of exemplary embodiments of the invention have been illustrated and described in detail herein, many other variations or modifications consistent with the principles of the invention may be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Accordingly, the scope of the invention should be understood and interpreted to cover all such other variations or modifications.
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Application publication date: 20210219 |
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