CN110906941A - Construction method and system of automatic driving map for long-distance tunnel - Google Patents

Construction method and system of automatic driving map for long-distance tunnel Download PDF

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CN110906941A
CN110906941A CN201911218787.8A CN201911218787A CN110906941A CN 110906941 A CN110906941 A CN 110906941A CN 201911218787 A CN201911218787 A CN 201911218787A CN 110906941 A CN110906941 A CN 110906941A
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odometer
track
trajectory
information
gps
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CN110906941B (en
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邓海林
王维
韩升升
赵哲
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Suzhou Zhijia Technology Co Ltd
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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
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Automation & Control Theory (AREA)
  • Navigation (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

The invention discloses a method and a system for constructing an automatic driving map for a long-distance tunnel, wherein the method for constructing the automatic driving map comprises the following steps: step S1: acquiring initial GPS pose information and vehicle wheel speed information of a tunnel; step S2: processing the initial GPS pose information and the vehicle wheel speed information to obtain a odometer track; step S3: establishing a track adjusting model, and optimizing the odometer track through the track adjusting model to obtain an optimal odometer track; step S4: and constructing an automatic driving map according to the optimal milemeter track.

Description

Construction method and system of automatic driving map for long-distance tunnel
Technical Field
The present invention relates to a map construction method and system, and in particular, to a map construction method and system for an automatic driving in a long-distance tunnel.
Background
In automobile automatic driving, the construction of a high-precision map is a very critical ring in the whole automatic driving system. Accurate and consistent high-precision maps can help other modules of the whole automatic driving system to obtain accurate attitude information and provide more local environment information. In a long-distance tunnel scene, an automobile cannot obtain an accurate GPS positioning signal, so that a directly obtained GPS track is difficult to be directly used for constructing a high-precision map required by the system.
In the prior art, more sensor information is generally prone to be introduced to assist in building a more accurate vehicle motion track, but for a long-distance tunnel, due to an error accumulation effect, a scheme of an odometer mode cannot theoretically obtain an accurate and consistent tunnel motion track, and if more sensor data is introduced into an optimization problem, problems of large memory consumption or even incapability of accommodating, incapability of guaranteeing reliability of an optimization result and the like may be faced, for example, in chinese patents with publication numbers CN109991636A and CN109443348A, visual feature information is introduced to solve map building in a scene where GPS signals are not good, but due to extremely high similarity of visual information in the tunnel, a closed-loop optimization mode cannot be guaranteed to obtain a correct optimization track.
Therefore, it is urgently needed to develop a method and a system for constructing an automatic driving map for a long-distance tunnel, which overcome the above defects.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a method for constructing an automatic driving map for a long-distance tunnel, wherein the method comprises the following steps:
step S1: acquiring initial GPS pose information and vehicle wheel speed information of a tunnel;
step S2: processing the initial GPS pose information and the vehicle wheel speed information to obtain a odometer track;
step S3: establishing a track adjusting model, and optimizing the odometer track through the track adjusting model to obtain an optimal odometer track;
step S4: and constructing an automatic driving map according to the optimal milemeter track.
In the above construction method, in step S1, the GPS pose information and the wheel speed information of the vehicle at each of the front and rear sections of the tunnel entering scene are collected.
In the above construction method, step S2 includes:
step S21: starting time t of the milemeter0The GPS pose information is set as the origin of coordinates of the odometer trajectory;
step S22: according to the starting time t0Calculating the GPS pose information to obtain the GPS pose information at each moment;
step S23: and integrating and obtaining the odometer track according to the GPS pose information at all times.
In the above construction method, step S3 includes:
step S31: establishing an optimization loss function, wherein the optimization loss function L is as follows:
L(T)＝Lprior(T)+Lodom(T)，
wherein T is the pose trajectory to be optimized, LpriorTo a priori loss term, LodomLoss of relative pose;
step S32: and adjusting the attitude trajectory by using a nonlinear optimization method according to the initial parameters of the loss function L of the odometry trajectory to obtain the optimal odometry trajectory.
In the above construction method, in the step S4, the automatic driving map is obtained through point cloud extraction, point cloud alignment, and area point cloud map generation according to the optimal odometer trajectory.
The invention also provides a construction system of an automatic driving map for a long-distance tunnel, which comprises the following steps:
the information acquisition unit is used for acquiring initial GPS pose information of the tunnel and wheel speed information of the vehicle;
the odometer track obtaining unit is used for processing the initial GPS pose information and the vehicle wheel speed information to obtain an odometer track;
the optimization unit is used for establishing a track adjustment model and optimizing the odometer track through the track adjustment model to obtain an optimal odometer track;
and the map construction unit is used for constructing an automatic driving map according to the optimal milemeter track.
In the above construction system, the information acquisition unit acquires the GPS pose information and the wheel speed information of the vehicle at each of the front and rear sections of the tunnel scene.
The above construction system, wherein the odometer trajectory obtaining unit includes:
an origin setting module for setting the starting time t of the odometer0The GPS pose information is set as the origin of coordinates of the odometer trajectory;
a calculation module for calculating the start time t0Calculating the GPS pose information to obtain the GPS pose information at each moment;
and the integration module is used for integrating and obtaining the odometer track according to the GPS pose information at all times.
The above construction system, wherein the optimization unit comprises:
an optimization loss function establishing module for establishing an optimization loss function, wherein the optimization loss function L is as follows:
L(T)＝Lprior(T)+Lodom(T)，
wherein T is the pose trajectory to be optimized, LpriorTo a priori loss term, LodomLoss of relative pose;
and the adjusting module is used for adjusting the posture trajectory by using a nonlinear optimization method according to the initial parameters of the loss function L of the odometer trajectory so as to obtain the optimal odometer trajectory.
In the above construction system, the map construction unit obtains the automatic driving map by point cloud extraction, point cloud alignment, and area point cloud map generation according to the optimal odometer trajectory.
Aiming at the prior art, the invention has the following effects: the construction method and the system are simple and effective, are suitable for long-distance GPS track correction, only use initial GPS pose information and wheel speed information as all input, and can ensure that a continuous and consistent high-precision map which is enough to be used by the whole automatic driving system is obtained.
Drawings
FIG. 1 is a flow chart of a construction method of the present invention;
FIG. 2 is a flowchart illustrating the substeps of step S2 in FIG. 1;
FIG. 3 is a flowchart illustrating the substeps of step S3 in FIG. 1;
FIG. 4 is a schematic structural diagram of a build system;
wherein, the reference numbers:
information acquisition unit 11
Odometer trajectory acquisition unit 12
Optimization unit 13
Map construction unit 14
Origin setting module 121
Calculation Module 122
Integration module 123
Optimization loss function building block 131
Adjusting module 132
Detailed Description
The detailed description and technical description of the present invention are further described in the context of a preferred embodiment, but should not be construed as limiting the practice of the present invention.
Generally, GPS pose signals are completely unavailable in a tunnel scene, but according to research and observation, the posture information output by a GPS in the tunnel can be used for obtaining a smoother odometer track, and based on the observation, an automatic driving map construction method for a long-distance tunnel is provided.
Referring to fig. 1-3, fig. 1 is a flow chart of a construction method of the present invention; FIG. 2 is a flowchart illustrating the substeps of step S2 in FIG. 1; fig. 3 is a flowchart illustrating a substep of step S3 in fig. 1. As shown in fig. 1 to 3, the construction method of the present invention includes the steps of:
step S1: and acquiring initial GPS pose information of the tunnel and wheel speed information of the vehicle.
Specifically, a vehicle with a GPS device passes through a tunnel scene once, and initial GPS position and attitude information and vehicle wheel speed information of GPS signals before and after entering and exiting the tunnel scene are accurately acquired by the GPS device, wherein the GPS position and attitude information comprises position information and orientation information.
Step S2: and processing the initial GPS pose information and the vehicle wheel speed information to obtain a odometer track.
In step S2, the method includes:
step S21: starting time t of the milemeter0Setting the GPS pose information as the origin of coordinates of the odometer track;
step S22: according to the starting time t0Calculating the GPS pose information to obtain the GPS pose information at each moment;
step S23: and integrating and obtaining the odometer track according to the GPS pose information at all times.
Specifically, the odometer start time t0Initial pose P of0Setting the coordinate origin as the coordinate origin of the odometer track, and if the pose information of the odometer track at the time ti is PiThen at time ti+1Odometer pose Pi+1Then through GPS pose GiThe direction vector D of the vehicle orientation in the world coordinate system obtained in the step (c)iCombined with tiWheel speed information s of vehicle at timeiObtaining: pi+1＝Pi+Di*siAnd calculating GPS position and pose information at each moment, and integrating the GPS position and pose information at all moments to obtain the odometry track.
Step S3: and establishing a track adjusting model, and optimizing the odometer track through the track adjusting model to obtain the optimal odometer track.
In step S3, the method includes:
step S31: establishing an optimization loss function, wherein the optimization loss function L is as follows:
L(T)＝Lprior(T)+Lodom(T)，
wherein T is the pose trajectory to be optimized, LpriorTo a priori loss term, LodomIs a loss of relative pose.
Specifically, the GPS pose variance threshold is set to ξtStructural optimization lossesFunction L (T) ═ Lprior+LodomAnd T represents the pose trajectory to be optimized, where LpriorRepresenting the GPS-supplied a priori loss term, Lprior＝Lposition+LorientationBy location priors Lposition＝(T(x,y,z)-Twheel(x,y,z))2And attitude prior Lorientation＝(T(roll,pitch,yaw)-Twheel(roll,pitch,yaw))2And forming a set of three directional angle information, wherein T (x, y, z) is Cartesian coordinate information of the attitude T, and T (roll, pitch, yaw) is three directional angle information of the attitude T.
It should be noted that the position prior constraint LpositionPose variance ξ of GPS trajectory only when that time is>ξtIs added to the loss function L; l isodomFor relative pose loss, constraining the relative pose between two adjacent time points, for two time points tiAnd ti+1L ofodom(ti，ti+1)＝((ΔT(i,i+1)) -(ΔTwheel(i,i+1)))2Wherein Δ T(i,i+1)＝Ti-Ti+1。
It is worth noting that the method automatically judges whether the position of the GPS is introduced as the prior position constraint according to the GPS variance information ξ, and ensures that the tunnel entrance and exit position has accurate GPS signals as the constraint, so that the optimized track can be kept consistent with the original GPS position at the position with good GPS signals.
Step S32: and adjusting the attitude trajectory by using a nonlinear optimization method according to the initial parameters of the loss function L of the odometry trajectory to obtain the optimal odometry trajectory.
It should be noted that the invention can obtain the continuous high-precision map of the autopilot long-distance tunnel under the condition of only using the GPS pose information and the wheel speed information, and the L added into the constructed loss function LodomThe existence of the loss term can ensure the smooth and continuous whole track after optimization.
In particular, the odometer trajectory TwheelAs initial parameters of the loss function L, and using a nonlinear optimization method to adjust the attitude trajectory T to the maximumThe loss function L is minimized, and the optimal odometer track T is finally obtainedcorrected。
Step S4: and constructing an automatic driving map according to the optimal milemeter track.
Specifically, in step S4, an automatic driving map is obtained by point cloud extraction, point cloud alignment, and area point cloud map generation according to the optimal odometer trajectory, and the automatic driving map is shown as 5.
The invention uses the corrected GPS track to construct a high-precision automatic driving map by quickly correcting the inaccurate and discontinuous GPS track in the tunnel.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a construction system according to the present invention. As shown in fig. 4, the construction system of the automatic driving map for a long-distance tunnel of the present invention includes:
the information acquisition unit 11 is used for acquiring initial GPS pose information of a tunnel and wheel speed information of a vehicle;
an odometer track obtaining unit 12, configured to process the initial GPS pose information and the vehicle wheel speed information to obtain an odometer track;
the optimization unit 13 is used for establishing a track adjustment model and optimizing the odometer track through the track adjustment model to obtain an optimal odometer track; and
and the map construction unit 14 constructs an automatic driving map according to the optimal odometer track.
The information acquisition unit 11 acquires the GPS pose information and the wheel speed information of the vehicle at each section before and after entering the tunnel scene.
Further, the odometer trajectory obtaining unit 12 includes:
an origin setting module 121 for setting the starting time t of the odometer0The GPS pose information is set as the origin of coordinates of the odometer trajectory;
a calculation module 122, based on the start time t0Calculating the GPS pose information to obtain the GPS pose information at each moment;
and the integration module 123 integrates and obtains the odometer track according to the GPS pose information at all times.
Still further, the optimization unit 13 includes:
an optimization loss function establishing module 131, which establishes an optimization loss function, where the optimization loss function L is:
L(T)＝Lprior(T)+Lodom(T)，
wherein T is the pose trajectory to be optimized, LpriorTo a priori loss term, LodomLoss of relative pose; (ii) a
The adjusting module 132 adjusts the posture trajectory by using a nonlinear optimization method according to the initial parameters of the loss function L of the odometer trajectory to obtain the optimal odometer trajectory.
Further, the map building unit 14 obtains the automatic driving map through point cloud extraction, point cloud alignment, and area point cloud map generation according to the optimal odometer trajectory.
The invention has the following effects:
1) the method is simple and effective and is suitable for correcting the long-distance GPS track;
2) only initial GPS pose information and wheel speed information are used as all inputs, and a continuous and consistent high-precision map which is enough for the whole automatic driving system can be obtained.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (10)

1. A construction method of an automatic driving map for a long-distance tunnel is characterized by comprising the following steps:
step S1: acquiring initial GPS pose information and vehicle wheel speed information of a tunnel;
step S2: processing the initial GPS pose information and the vehicle wheel speed information to obtain a odometer track;
step S3: establishing a track adjusting model, and optimizing the odometer track through the track adjusting model to obtain an optimal odometer track;
step S4: and constructing an automatic driving map according to the optimal milemeter track.
2. The constructing method according to claim 1, wherein the GPS pose information and the vehicle wheel speed information of the respective sections before and after entering the tunnel scene are collected in step S1.
3. The constructing method according to claim 1, wherein the step S2 includes:
step S21: starting time t of the milemeter0The GPS pose information is set as the origin of coordinates of the odometer trajectory;
step S22: according to the starting time t0Calculating the GPS pose information to obtain the GPS pose information at each moment;
step S23: and integrating and obtaining the odometer track according to the GPS pose information at all times.
4. The constructing method according to claim 1, wherein the step S3 includes:
step S31: establishing an optimization loss function, wherein the optimization loss function L is as follows:
L(T)＝Lprior(T)+Lodom(T)，
wherein T is the pose trajectory to be optimized, LpriorTo a priori loss term, LodomLoss of relative pose;
step S32: and adjusting the attitude trajectory by using a nonlinear optimization method according to the initial parameters of the loss function L of the odometry trajectory to obtain the optimal odometry trajectory.
5. The construction method according to claim 1, wherein the automatic driving map is obtained by point cloud extraction, point cloud alignment, and area point cloud map generation according to the optimal odometer trajectory in the step S4.
6. A construction system of an automatic driving map for a long-distance tunnel, characterized by comprising:
the information acquisition unit is used for acquiring initial GPS pose information of the tunnel and wheel speed information of the vehicle;
the odometer track obtaining unit is used for processing the initial GPS pose information and the vehicle wheel speed information to obtain an odometer track;
the optimization unit is used for establishing a track adjustment model and optimizing the odometer track through the track adjustment model to obtain an optimal odometer track;
and the map construction unit is used for constructing an automatic driving map according to the optimal milemeter track.
7. The construction system according to claim 5, wherein the information acquisition unit acquires the GPS pose information and the wheel speed information of the vehicle at each section before and after entering a tunnel scene.
8. The build system of claim 5 wherein the odometer trajectory acquisition unit comprises:
an origin setting module for setting the starting time t of the odometer0The GPS pose information is set as the origin of coordinates of the odometer trajectory;
a calculation module for calculating the start time t0Calculating the GPS pose information to obtain the GPS pose information at each moment;
and the integration module is used for integrating and obtaining the odometer track according to the GPS pose information at all times.
9. The build system of claim 5, wherein the optimization unit comprises:
an optimization loss function establishing module for establishing an optimization loss function, wherein the optimization loss function L is as follows:
L(T)＝Lprior(T)+Lodom(T)，
wherein T is the pose trajectory to be optimized, LpriorTo a priori loss term, LodomLoss of relative pose;
and the adjusting module is used for adjusting the posture trajectory by using a nonlinear optimization method according to the initial parameters of the loss function L of the odometer trajectory so as to obtain the optimal odometer trajectory.
10. The construction system of claim 5, wherein the map construction unit obtains the autopilot map from the optimal odometer trajectory by point cloud extraction, point cloud alignment, and regional point cloud map generation.
CN201911218787.8A 2019-12-03 2019-12-03 Automatic driving map construction method and system for long-distance tunnel Active CN110906941B (en)

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