WO2023087802A1 - 自动驾驶定位方法、***、测试方法、设备及存储介质 - Google Patents

自动驾驶定位方法、***、测试方法、设备及存储介质 Download PDF

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WO2023087802A1
WO2023087802A1 PCT/CN2022/111845 CN2022111845W WO2023087802A1 WO 2023087802 A1 WO2023087802 A1 WO 2023087802A1 CN 2022111845 W CN2022111845 W CN 2022111845W WO 2023087802 A1 WO2023087802 A1 WO 2023087802A1
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positioning
automatic driving
data
test
integrated navigation
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PCT/CN2022/111845
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English (en)
French (fr)
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张婷
潘定海
原诚寅
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北京国家新能源汽车技术创新中心有限公司
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Publication of WO2023087802A1 publication Critical patent/WO2023087802A1/zh

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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
    • 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
    • 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
    • G01C21/1652—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 ranging devices, e.g. LIDAR or RADAR
    • 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
    • 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
    • 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

Definitions

  • the present invention relates to the technical field of automatic driving positioning, in particular to an automatic driving positioning method, system, testing method, equipment and storage medium.
  • Positioning is the basic module of automatic driving.
  • a key prerequisite for the continuous safe and reliable operation of the automatic driving system is that the positioning system of the vehicle must output sufficiently high-precision position and position-related information in real time and stably, including the longitude, latitude, and speed of the vehicle. , pitch angle, heading angle, update frequency, etc. Once this information cannot be obtained in a timely and accurate manner, the vehicle will not be able to determine its own position, and must immediately exit the automatic driving mode, and the driver will take over the control of the vehicle.
  • GNSS and IMU integrated navigation system are commonly used positioning solutions in automatic driving systems.
  • GNSS Global Navigation Satellite System
  • IMU intial navigation
  • IMU intial navigation
  • the integrated navigation system formed by the fusion of GNSS and IMU can take advantage of the advantages of the two navigation systems.
  • the current GNSS/IMU integrated navigation system still cannot achieve long-term and long-distance stable positioning.
  • a test method for automatic driving which leads to low reliability of integrated navigation and low positioning accuracy, proposes an automatic driving positioning method, system, testing method, equipment and storage medium.
  • the present invention provides an automatic driving positioning method, system, testing method, equipment and storage medium.
  • a positioning method for automatic driving comprising:
  • the acquired real-time laser point cloud data is processed by the laser SLAM algorithm, and then processed by the extended Kalman filter method;
  • the fusion output positioning data is obtained, and the input abnormal data is eliminated by using the fusion output positioning data to form a closed-loop processing.
  • the real-time laser point cloud data is processed by laser SLAM algorithm to generate position and heading angle data.
  • the fused output position, velocity, and attitude angle positioning data are generated, and the fused output positioning data is used to eliminate abnormal data in the input data to form a closed-loop process.
  • steps of removing abnormal data include:
  • Step 1 Determine the starting point of the fusion positioning output data, input position track and laser SLAM position track;
  • Step 2 Carry out interval resampling to the fusion positioning output data, input position track and laser SLAM position track in step 1 by linear interpolation method;
  • Step 3 Calculate the distance between equally spaced resampling points, extract and eliminate abnormal position data input and laser SLAM input;
  • Step 4 Based on the fusion output speed data, use the curve similarity analysis method to extract and eliminate the input abnormal speed data;
  • Step 5 Based on the attitude angle data of the fusion output, the curve similarity analysis method is used to extract and eliminate the abnormal angle data of the input and laser SLAM input.
  • the invention provides an automatic driving positioning system, comprising: an integrated navigation equipment module, a laser radar module, an automatic driving controller module and an automatic driving positioning module,
  • the integrated navigation device module is used to provide the position, speed and attitude angle data set to the automatic driving positioning module
  • the laser radar module is used to provide real-time laser point cloud data to the automatic driving positioning module;
  • the automatic driving positioning module is used to realize the automatic driving positioning method
  • the automatic driving controller module is used to run the automatic driving positioning module.
  • the automatic driving positioning module adopts an automatic driving positioning program based on integrated navigation and laser SLAM fusion technology.
  • test method is suitable for the above-mentioned automatic driving positioning system, the test method includes:
  • the test of the automatic driving positioning system is completed through the combined test of the integrated navigation equipment module and the automatic driving positioning function module.
  • testing steps of the integrated navigation equipment module include:
  • Step 1 Test the positioning stability of the integrated navigation device in a static state
  • Step 2 Test the changes of different positioning states of the integrated navigation device in the motion state
  • Step 3 Based on the high-precision navigation and positioning system, test the positioning accuracy of the integrated navigation equipment
  • Step 4 Test the switching time between the fixed solution and the non-fixed solution of the integrated navigation device
  • Step 5 Test the change in the time of multiple cold starts of the integrated navigation device
  • Step 6 Test and analyze the packet loss rate and packet loss interval of the integrated navigation device.
  • testing steps of the automatic driving positioning function module include:
  • Step 1 In a site with excellent GNSS signals and complete fixed facilities, arrange a fixed navigation track to test the positioning accuracy of automatic driving and the update frequency of positioning data;
  • Step 2 Test the positioning accuracy and data processing time of automatic driving in the GNSS signal occlusion area.
  • the present invention also provides an automatic driving positioning device, which includes: a memory, a processor, and an automatic driving positioning program stored on the memory and running on the processor, the automatic driving positioning program is configured as The steps of the automatic driving positioning method as described in any one of the above are realized.
  • the present invention also provides an electronic storage medium, in which at least one instruction or at least one section of program is stored, and the at least one instruction or at least one section of program is loaded and executed by a processor to realize any of the above automatic Driving positioning method.
  • the present invention adopts a real-time closed-loop automatic driving positioning method based on the integration of integrated navigation and laser SLAM, which can make up for the defect that satellite signals are easily affected by the environment, further improve the reliability of integrated navigation, and effectively improve positioning accuracy;
  • the positioning method adopted by the automatic driving positioning system of the present invention is a closed-loop processing method, and the positioning data input by the combined navigation device and laser SLAM is processed by the extended Kalman filter, and the output result data will be used for the combined navigation device and
  • the screening of laser SLAM input positioning data, the direction verification of the validity of the input data, and the real-time laser positioning method based on deep learning are used to output laser positioning data with high accuracy and strong robustness, making the fusion positioning data more reliable;
  • the function-level test method of the automatic driving positioning system provided by the present invention is applicable to any automatic driving positioning system including integrated navigation equipment.
  • the present invention carries out automatic driving from the two aspects of integrated navigation equipment testing and positioning function testing
  • the test of the positioning system, and a set of test methods for integrated navigation equipment set up from the six aspects of static positioning data stability, positioning state, positioning accuracy, fixed solution switching time, cold start time, and packet loss, are for the testing of the automatic driving positioning system Verification-providing verification scheme, the test is easy to operate and has high reliability of test results.
  • Fig. 1 is a structural schematic diagram of the overall structure process of the present invention.
  • FIG. 2 is a schematic diagram of the flow structure of the automatic driving positioning system of the present invention.
  • FIG. 3 is a schematic structural diagram of a laser SLAM processing flow in an embodiment of the present invention.
  • FIG. 4 is a schematic diagram of a flow structure of fused positioning data and eliminating input abnormal data in an embodiment of the present invention.
  • Fig. 5 is a schematic structural diagram of the test flow of the integrated navigation device of the present invention.
  • the present invention provides a positioning method for automatic driving, including:
  • the acquired position, velocity, and attitude angle data sets of the integrated navigation equipment are processed by the extended Kalman filter method;
  • the real-time laser point cloud data obtained from the lidar is processed by the laser SLAM algorithm, and then processed by the extended Kalman filter method;
  • the real-time laser point cloud data is processed by the laser SLAM algorithm to generate position and heading angle data.
  • the laser point cloud data is processed by the laser SLAM algorithm to generate the position and heading angle.
  • the laser SLAM algorithm uses the deep learning model to learn the point cloud features, and outputs more accurate and robust positioning results.
  • the feature matching and pose The calculation process is combined with the real-time production of point cloud feature maps, which reduces the calculation time of laser positioning and has higher real-time performance, making the fusion results of laser positioning and integrated navigation more reliable.
  • the feature map can be combined with it to directly extract the local point cloud feature map, reducing the calculation time of laser positioning.
  • the real-time laser point cloud data is generated during the process of laser SLAM algorithm processing.
  • the real-time point cloud feature map can be combined with it to directly extract
  • the fusion output positioning data is obtained, and the input abnormal data is eliminated by using the fusion output positioning data to form a closed-loop processing.
  • the fused output position, velocity, and attitude angle positioning data are generated, and the fused output positioning data is used to eliminate abnormal data in the integrated navigation and laser SLAM positioning to form a closed-loop processing.
  • Figure 4 shows the process of eliminating input abnormal data with fusion positioning results.
  • the curve similarity analysis method is used to compare the change trend of V1 and V0 over time, and the abnormal speed data in the integrated navigation input speed V1 is eliminated;
  • the curve similarity analysis method is used to compare the change trend of A1, A2 and A0 over time, and the abnormal angle data in the attitude angle A1 input by integrated navigation and the attitude angle A2 input by laser SLAM are eliminated.
  • the invention provides an automatic driving positioning system, comprising: an integrated navigation equipment module, a laser radar module, an automatic driving controller module and an automatic driving positioning module,
  • the integrated navigation device module is used to provide the position, speed and attitude angle data set to the automatic driving positioning module
  • the laser radar module is used to provide real-time laser point cloud data to the automatic driving positioning module;
  • the automatic driving positioning module is used to realize the automatic driving positioning method
  • the automatic driving controller module is used to run the automatic driving positioning module.
  • the automatic driving positioning module adopts an automatic driving positioning program based on integrated navigation and laser SLAM fusion technology.
  • test method is suitable for the above-mentioned automatic driving positioning system, the test method includes:
  • the test of the automatic driving positioning system is completed through the combined test of the integrated navigation equipment module and the automatic driving positioning function module.
  • the tests for the integrated navigation aid module include:
  • Static positioning data stability test test the positioning stability of the integrated navigation device in a static state, and count the distribution of position data in a static state for a period of time.
  • Install the integrated navigation equipment on the vehicle park the vehicle in an open area, wait until the integrated navigation equipment can receive stable satellite signals, and after the equipment is in a stable state, record the received data within a period of time ⁇ T, and count the position data x, y According to the distribution of x and y, calculate the range, variance, standard deviation and covariance of x and y, and use the following formula to analyze the degree of dispersion of the location data.
  • R x x max -x min
  • R y y max -y min
  • x max , x min , y max , y min are respectively the maximum value of x, the minimum value of x, The maximum value of y, the minimum value of y.
  • n is the number of data records
  • Positioning state test test the changes of different positioning states of the combined navigation device in the motion state.
  • the device receives data, and counts fixed solution, floating point solution, pseudorange solution, single point solution and other positioning states trajectories, calculate the proportion of trajectories in different positioning states at different vehicle speeds, and analyze the stability of the positioning of the integrated navigation device in the moving state.
  • Positioning accuracy test take the high-precision navigation and positioning system as the benchmark, compare and analyze the position error of the integrated navigation equipment and the reference positioning system on the same track line, and test the positioning accuracy of the integrated navigation equipment.
  • a fixed navigation track is arranged in an open test site, and the vehicle equipped with the integrated navigation device to be tested drives along the navigation track line, and the received positioning data set ⁇ x, y ⁇ is recorded;
  • a high-precision navigation and positioning system is installed at the same location, and the received reference positioning data set ⁇ x0, y0 ⁇ is recorded in the same way to form a reference trajectory curve R0.
  • Fixed solution switching time test test the switching time between the fixed solution and the non-fixed solution of the integrated navigation device.
  • the vehicle installed with the integrated navigation device to be tested drives along the specified trajectory at different speeds, records the received data content, records the time points from fixed solution to non-fixed solution, and from non-fixed solution to fixed solution, and calculates the time difference.
  • Cold start time test test the change in the time of repeated cold starts of the combined navigation device.
  • Select a site with a good positioning signal for the integrated navigation device to be tested turn off the power of the device by unplugging the power, wait for a period of time (for example, 5 minutes), power on the device and receive data, and record the time from power on to the first observation with a fixed solution T. Power off, repeat the same operation several times, record the time T, and calculate the maximum value, minimum value, average value and standard deviation of T.
  • Packet loss test test and analyze the packet loss rate and packet loss interval of the integrated navigation device, including data comparison test and data integrity test.
  • the vehicle installed with the integrated navigation device to be tested drives along the specified track at different speeds, records the data content sent by the device through the communication interface, compares the data content received and recorded inside the device, and analyzes and calculates the packet loss rate at different speeds , check the interval of packet loss in the driving track;
  • Data integrity test According to the data content sent by the integrated navigation device through the communication interface, the time continuity is counted, the integrity of the data itself is checked, the packet loss rate at different speeds is calculated, and the packet loss interval in the driving track is checked.
  • the test of the automatic driving positioning function module includes:
  • GNSS Global Navigation Satellite System
  • Control point use high-precision measuring instruments, such as total stations, etc., measure the precise position coordinates (X0, Y0) of the control points, turn on the vehicle's automatic driving positioning module, and drive the vehicle through the tunnel at different speeds.
  • Control point record the position data (X, Y) and time of the control point output by the automatic driving system, calculate the error of (X0, Y0) and (X, Y), analyze the change of the error over time, and calculate the distribution of the error The maximum, minimum, and average values of the error.
  • the present invention also provides an automatic driving positioning device, which includes: a memory, a processor, and an automatic driving positioning program stored on the memory and running on the processor, the automatic driving positioning program is configured as The steps of the automatic driving positioning method as described in any one of the above are realized.
  • the present invention also provides an electronic storage medium, in which at least one instruction or at least one section of program is stored, and the at least one instruction or at least one section of program is loaded and executed by a processor to realize any of the above automatic Driving positioning method.
  • the integrated navigation of the present invention adopts GNSS and IMU integrated navigation.
  • the present invention adopts a real-time closed-loop automatic driving positioning system based on the integration of integrated navigation and laser SLAM, which can make up for the defect that satellite signals are easily affected by the environment, further improve the reliability of integrated navigation, and effectively improve the positioning accuracy.
  • the positioning data input by the integrated navigation equipment and laser SLAM is processed by the extended Kalman filter, and the output result data will be used to screen the input positioning data of the integrated navigation equipment and laser SLAM, and reversely verify the validity of the input positioning data.
  • using a real-time laser positioning method based on deep learning to output laser positioning data with high accuracy and strong robustness making the result of fusion positioning more reliable and optimizing the overall performance of the positioning system.
  • test and positioning function test are two aspects to carry out the test of the automatic driving positioning system, and conduct a relatively comprehensive test on the automatic driving positioning system.
  • the test operation is simple and the reliability is high.

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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)
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Abstract

一种自动驾驶定位方法、***、测试方法、设备及存储介质，涉及自动驾驶定位技术领域，该方法采取组合导航和激光SLAM输出的定位数据经扩展卡尔曼滤波处理后生成的融合数据，通过将实时激光定位和组合导航融合的定位结果反向验证输入数据，即利用该融合数据剔除组合导航和激光SLAM定位中的异常数据，形成实时闭环的定位***，再通过测试方法测试自动驾驶定位***，提供自动驾驶定位***的新的测试验证方案。该方法能够有效提高定位精度，提升组合导航的可靠性。

Description

自动驾驶定位方法、***、测试方法、设备及存储介质 技术领域
本发明涉及自动驾驶定位技术领域，特别涉及一种自动驾驶定位方法、***、测试方法、设备及存储介质。
背景技术
汽车自动驾驶是近年来的一项前沿研究技术，需要在没有人为操纵的情况下完整、安全、有效地驾驶汽车。定位是自动驾驶的基础模块，自动驾驶***能够持续安全可靠运行的一个关键前提是车辆的定位***必须实时稳定地输出足够高精度的位置和与位置相关的信息，包括车辆的经度、纬度、速度、俯仰角、航向角、更新频率等。一旦这些信息无法及时精确地获取，车辆就无法确定自身位置，必须立即退出自动驾驶模式，由驾驶员接管车辆操控。
要实现汽车自动驾驶***，必须采用高精定位导航技术，GNSS和IMU组合导航***是自动驾驶***中常用的定位方案，GNSS(全球导航卫星***)可以提供精确的绝对定位，但是在局部区域卫星信号丢失或者微弱时会导致定位信息延迟而造成车辆失控。IMU(惯性导航)虽然可以不依托外在信息，无惧极端环境提供稳定的位置和速度信息，但是存在定位误差随时间积累、长时间内不能保证足够的导航精度的问题。GNSS和IMU融合形成的组合导航***可以发挥两种导航***的优势。但是，在复杂场景下，受到信号遮挡和多径效应的影响，目前的GNSS/IMU组合导航***仍然无法实现长时间、长距离的稳定定位。
因此，针对现有技术，因无法在全部地区实现准确实时的定位，定位精度、***健壮性等各方面也仍然具有很大的提升空间的情况下，本发明的目的在于 针对自动驾驶定位缺乏***的测试方法，导致组合导航可靠性低、定位精度低而提出的一种自动驾驶定位方法、***、测试方法、设备及存储介质。
发明内容
为了解决现有技术存在的缺漏，本发明提供了一种自动驾驶定位方法、***、测试方法、设备及存储介质。
本发明的技术内容如下：
一种自动驾驶定位方法，包括：
输入位置、速度、姿态角数据集合以及实时激光点云数据；
将获取的位置、速度、姿态角数据集合经扩展卡尔曼滤波法处理；
将获取的实时激光点云数据经激光SLAM算法处理，再经扩展卡尔曼滤波法处理；
经扩展卡尔曼滤波法处理后获得融合输出定位数据，利用融合输出定位数据剔除输入的异常数据，形成闭环处理。
进一步地，所述实时激光点云数据经激光SLAM算法处理生成位置、航向角数据。
进一步地，经扩展卡尔曼滤波法处理后生成融合输出的位置、速度、姿态角定位数据，使用该融合输出定位数据剔除输入数据中的异常数据，形成闭环处理。
进一步地，剔除异常数据的步骤包括：
步骤一：确定融合定位输出数据、输入位置轨迹和激光SLAM位置轨迹的起始点；
步骤二：采用线性插值法对步骤一中的融合定位输出数据、输入位置轨迹 和激光SLAM位置轨迹进行间隔重采样；
步骤三：计算等间隔重采样点之间的距离，提取并剔除输入和激光SLAM输入的异常位置数据；
步骤四：基于融合输出的速度数据，采用曲线相似性分析方法，提取并剔除输入的异常速度数据；
步骤五：基于融合输出的姿态角数据，采用曲线相似性分析方法，提取并剔除输入和激光SLAM输入的异常角度数据。
本发明提供了一种自动驾驶定位***，包括：组合导航设备模块、激光雷达模块、自动驾驶控制器模块及自动驾驶定位模块，
所述组合导航设备模块，用于向所述自动驾驶定位模块提供位置、速度、姿态角数据集合；
所述激光雷达模块，用于向所述自动驾驶定位模块提供实时激光点云数据；
所述自动驾驶定位模块，用于实现自动驾驶定位方法；
所述自动驾驶控制器模块，用于运行所述自动驾驶定位模块。
进一步地，所述自动驾驶定位模块采取基于组合导航和激光SLAM融合技术的自动驾驶定位程序。
本发明提供了一种测试方法，所述测试方法适用于上述的自动驾驶定位***，所述测试方法包括：
通过组合导航设备模块及自动驾驶定位功能模块的组合测试完成自动驾驶定位***的测试。
进一步地，所述组合导航设备模块的测试步骤包括：
步骤一：测试组合导航设备在静止状态下的定位稳定性；
步骤二：测试组合导航设备在运动状态下的不同定位状态的变化；
步骤三：以高精度导航定位***为基准，测试组合导航设备的定位精度；
步骤四：测试组合导航设备的固定解和非固定解的互相切换时间；
步骤五：测试组合导航设备重复多次冷启动的时间的变化；
步骤六：测试及分析组合导航设备的丢包率和丢包区间。
进一步地，所述自动驾驶定位功能模块的测试步骤包括：
步骤一：在GNSS信号优良、固定设施完备的场地中，布置固定的导航轨道，测试自动驾驶定位精度和定位数据更新频率；
步骤二：在GNSS信号遮挡区域测试自动驾驶定位精度和数据处理时间。
本发明还提供了一种自动驾驶定位设备，所述设备包括：存储器、处理器及存储在所述存储器上并在所述处理器上运行的自动驾驶定位程序，所述自动驾驶定位程序配置为实现如上述中任一项所述的自动驾驶定位方法的步骤。
本发明还提供了一种电子存储介质，所述存储介质中存储有至少一条指令或至少一段程序，所述至少一条指令或至少一段程序由处理器加载并执行以实现如上述任一项的自动驾驶定位方法。
本发明至少包括以下有益效果：
(1)本发明采用一种基于组合导航和激光SLAM相融合的实时闭环的自动驾驶定位方法，能够弥补卫星信号易受环境影响的缺陷，进一步地提升组合导航的可靠性，有效提高定位精度；
(2)本发明的自动驾驶定位***采用的定位方法为闭环的处理方法，组合导航设备和激光SLAM输入的定位数据经扩展卡尔曼滤波处理后输出的结果数据， 又会用于组合导航设备和激光SLAM输入定位数据的筛选，方向验证输入数据的有效性，同时采用基于深度学习的实时激光定位方法，输出具有高准确性和强鲁棒性的激光定位数据，使得融合定位的数据更加可靠；
(3)本发明提供的自动驾驶定位***的功能级测试方法，该测试方法适用于任何包含组合导航设备的自动驾驶定位***，本发明从组合导航设备测试、定位功能测试两大方面开展自动驾驶定位***的测试，并且从静态定位数据稳定性、定位状态、定位精度、固定解切换时间、冷启动时间、丢包六个方面设置的一套组合导航设备测试方法，为自动驾驶定位***的测试验证提供型的验证方案，该测试操作方便、测试结果可靠性较高。
附图说明
图1为本发明的整体结构流程结构示意图。
图2为本发明的自动驾驶定位***流程结构示意图。
图3为本发明的实施例中的激光SLAM处理流程结构示意图。
图4为本发明的实施例中的融合定位数据剔除输入异常数据的流程结构示意图。
图5为本发明的组合导航设备测试流程结构示意图。
具体实施方式
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
结合图1-5所示，本发明提供了一种自动驾驶定位方法，包括：
输入组合导航设备的位置、速度、姿态角数据集合以及激光雷达的实时激光点云数据；
组合导航的位置数据集合为P1＝{(x 10,y 10,z 10),…,(x 1i,y 1i,z 1i),…}，速度数据集合为V1＝{(v x10,v y10,v z10),…,(v x1i,v y1i,v z1i),…}，姿态角数据集合为A1＝{(α 10,β 10,γ 10),…,(α 1i,β 1i,γ 1i),…}。
将获取组合导航设备的位置、速度、姿态角数据集合经扩展卡尔曼滤波法处理；
将获取激光雷达的实时激光点云数据经激光SLAM算法处理，再经扩展卡尔曼滤波法处理；
所述实时激光点云数据经激光SLAM算法处理生成位置、航向角数据。
激光点云数据经过激光SLAM算法处理后生成位置、航向角，激光SLAM算法使用深度学习模型来学习点云特征，输出更加准确和更强鲁棒性的定位结果，同时，将特征匹配和位姿计算的过程和实时制作点云特征地图结合在一起，减少了激光定位的计算时间，实时性更高，使得激光定位和组合导航融合的结果更加可靠。
激光SLAM算法的处理流程如图3所示，实时激光点云数据经过剔除噪声点和离群点、点云降采样、提取PointNet特征后，生成实时点云特征图，将之进行相邻点云特征匹配，计算粗略位姿，再基于粗略位姿提取局部点云特征地图，然后经过Flann特征匹配、Ransac剔除错误点对处理后，得到正确的特征匹配点对，再基于此计算位姿变换关系，输出精确位姿，精确位姿包括位置集合P2＝{(x 20,y 20,z 20),…,(x 2i,y 2i,z 2i),…}，航向角集合A2＝{γ 20,…,γ 2i,…}，同时，基于 精确位姿，转换点云特征图，生成精确的点云特征图，并拼接出完整点云特征图，生成点云特征地图，已生成的点云特征地图在实时激光点云数据处理的过程中，可以与之结合直接进行提取局部点云特征地图，减少激光定位的计算时间，实时激光点云数据在进行激光SLAM算法处理的过程中，所生成的实时点云特征图可以直接进行Flann特征匹配、Ransac剔除错误点对处理，也可以直接转换点云特征图的位姿。
经扩展卡尔曼滤波法处理后获得融合输出定位数据，利用融合输出定位数据剔除输入的异常数据，形成闭环处理。
经扩展卡尔曼滤波法处理后生成融合输出的位置、速度、姿态角定位数据，使用该融合输出定位数据剔除组合导航和激光SLAM定位中的异常数据，形成闭环处理。
采用扩展卡尔曼滤波方法，以组合导航设备的输出数据为初始值，以组合导航和激光SLAM输出的定位数据为观测量，综合输出位置集合P0＝{(x 00,y 00,z 00),…,(x 0i,y 0i,z 0i),…}、速度集合V0＝{(v x00、v y00、x z00),…,(v x0i,v y0i,v z0i),…}、姿态角集合A0＝{(α 00,β 00,γ 00),…,(α 0i,β 0i,γ 0i),…}。
以融合定位结果剔除输入异常数据的流程如图4所示，
比较P1、P2和P0的轨迹相似性，通过曲线相似性分析方法剔除P1和P2中的异常值，步骤如下：
确认融合定位输出数据集P0的起始位置P 00＝(x 00,y 00,z 00)、基于组合导航定位数据P1和激光SLAM数据P2，计算组合导航位置轨迹和激光SLAM位置轨迹中距离P 00最短的位置点P 10＝(x 10,y 10,z 10)和P 20＝(x 20,y 20,z 20)，以P 10和P 20作为组合导航位置轨迹和激光SLAM位置轨迹的起始点；
采用线性插值的方法对P0、P1、P2进行等间隔重采样，以间隔d的采样点表示轨迹集合P0、P1、P2，分别以P0’、P1’和P2’表示，P0’＝{P 00,P 01,…,P 0j,…}，P1’＝{P 10,P 11,…,P 1j,…}，P2’＝{P 20,P 21,…,P 2j,…}，其中，P 0j＝P 00+j*d，P 1j＝P 10+j*d，P 2j＝P 20+j*d；
计算P0上各点分别和P1、P2对应点的距离DT1＝{dist(P 00,P 10),dist(P 01,P 11),…,dist(P 0j,P 1j),…}、DT2＝{dist(P 00,P 20),dist(P 01,P 21),…,dist(P 0j,P 2j),…}。设置确定距离阈值T D，记录DT1和DT2中超过T D的距离值所对应的P1’和P2’中的点，P1和P2中在这些点位置附近的点即为组合导航和激光SLAM输入的异常定位数据，剔除异常数据；
基于融合输出的速度数据，采用曲线相似性分析方法，比较V1和V0随时间的变化趋势，剔除组合导航输入速度V1中的异常速度数据；
基于融合输出的姿态角数据，采用曲线相似性分析方法，比较A1、A2与A0随时间的变化趋势，剔除组合导航输入姿态角A1和激光SLAM输入的姿态角A2中的异常角度数据。
本发明提供了一种自动驾驶定位***，包括：组合导航设备模块、激光雷达模块、自动驾驶控制器模块及自动驾驶定位模块，
所述组合导航设备模块，用于向所述自动驾驶定位模块提供位置、速度、姿态角数据集合；
所述激光雷达模块，用于向所述自动驾驶定位模块提供实时激光点云数据；
所述自动驾驶定位模块，用于实现自动驾驶定位方法；
所述自动驾驶控制器模块，用于运行所述自动驾驶定位模块。
进一步地，所述自动驾驶定位模块采取基于组合导航和激光SLAM融合技术的自动驾驶定位程序。
本发明提供了一种测试方法，所述测试方法适用于上述的自动驾驶定位***，所述测试方法包括：
通过组合导航设备模块及自动驾驶定位功能模块的组合测试完成自动驾驶定位***的测试。
组合导航设备模块的测试包括：
1、静态定位数据稳定性测试：测试组合导航设备在静止状态下的定位稳定性，统计静止状态下一段时间内位置数据的分布情况。
在车辆上安装好组合导航设备，将车辆静止停放到一空旷地带，待组合导航设备能接收到稳定的卫星信号，设备状态稳定后，记录一段时间ΔT内的接收数据，统计位置数据x、y的分布情况，计算x、y的极差、方差、标准差和协方差，通过下式计算，分析位置数据的离散程度。
x、y的极差：R x＝x max-x min，R y＝y max-y min，其中，x max、x min、y max、y min分别为x的最大值、x的最小值、y的最大值、y的最小值。
x的方差、标准差：
Figure PCTCN2022111845-appb-000001
y的方差、标准差：
Figure PCTCN2022111845-appb-000002
其中，
Figure PCTCN2022111845-appb-000003
为x的平均值，
Figure PCTCN2022111845-appb-000004
为y的平均值，n为数据记录数；
x、y的协方差：
Figure PCTCN2022111845-appb-000005
2、定位状态测试：测试组合导航设备在运动状态下的不同定位状态的变化。
选定测试场地和运行轨迹，将安装有组合导航的同一车辆以不同的车速沿 着指定轨迹行驶，设备接收数据，统计固定解、浮点解、伪距解、单点解等不同定位状态下的运行轨迹，计算不同车速下不同定位状态的轨迹所占比例，分析在运动状态下组合导航设备定位的稳定性。
3、定位精度测试：以高精度导航定位***为基准，对比分析在相同轨道线上的组合导航设备和基准定位***的位置误差，测试组合导航设备的定位精度。
在空旷的测试场地中布置固定的导航轨道，安装有待测组合导航设备的车辆沿导航轨道线行驶，记录接收的定位数据集合{x,y}；拆除待测组合导航设备，在同一车辆的相同位置安装高精度导航定位***，按照同样的方式记录接收的基准定位数据集合{x0,y0}，形成基准轨迹曲线R0。计算{x,y}中每个定位点到R0的距离，形成集合{d}，即为定位误差，设定误差统计区间的范围，计算落入每个区间的数量，统计d的分布情况。计算{d}中的最大值d max，最小值d min和平均值
Figure PCTCN2022111845-appb-000006
4、固定解切换时间测试：测试组合导航设备的固定解和非固定解的互相切换时间。
安装有待测组合导航设备的车辆以不同速度沿着指定轨迹行驶，记录接收的数据内容，记录固定解到非固定解、非固定解到固定解的时间点，并计算时间差。
5、冷启动时间测试：测试组合导航设备重复多次冷启动的时间的变化。
选择待测组合导航设备定位信号优良的场地，采用拔电方式关闭设备电源，等待一段时间(如，5分钟)，给设备通电并接收数据，记录从通电到第一次观测有固定解的时间T。断电，再重复多次相同操作，记录时间T，计算T的最大 值、最小值、平均值、标准差。
6、丢包测试：测试及分析组合导航设备的丢包率和丢包区间，包括数据对比测试和数据完整性测试。
数据对比测试：安装有待测组合导航设备的车辆以不同速度沿着指定轨迹行驶，记录设备通过通讯接口发送的数据内容，对比设备内部接收记录的数据内容，分析计算不同速度时的丢包率，查看行驶轨迹中丢包的区间；
数据完整性测试：根据组合导航设备通过通讯接口发送的数据内容，统计时间连续性，检查数据自身的完整性，计算不同速度时的丢包率，查看行驶轨迹中丢包的区间。
自动驾驶定位功能模块的测试包括：
(1)选择GNSS(Global Navigation Satellite System)信号优良、固定设施较完备的场地中，布置固定的导航轨道，测试自动驾驶定位精度和定位数据更新频率。
开启车辆自动驾驶定位模块，以不同的速度沿导航轨道线行驶，记录接收的定位数据内容和时间，计算不同速度下的数据更新频率f；然后在同一车辆上安装高精度导航定位***，按照同样的方式记录接收的数据内容，作为基准定位数据，参照组合导航设备的定位精度测试方法，计算自动驾驶定位数据和基准数据的误差，统计误差分布情况，计算误差的最大值、最小值和平均值。
(2)在隧道等GNSS信号遮挡区域测试自动驾驶定位精度和数据处理时间。
在隧道中设置控制点，使用高精度测量仪器，如全站仪等，测量控制点的精确位置坐标(X0、Y0)，开启车辆自动驾驶定位模块，驾驶车辆以不同的速度通过隧道，途中经过控制点，记录自动驾驶***输出的控制点位置数据(X、Y)和时间，计算(X0、Y0)和(X、Y)的误差，分析误差随时间的变化，统计误差分 布情况，并计算误差的最大值、最小值和平均值。
在自动驾驶定位模块开始接收传感器数据和输出定位数据处加上时间戳，计算输入输出数据的时间差ΔPT，记录Δτ时间内的ΔPT值，统计ΔPT的分布情况。
本发明还提供了一种自动驾驶定位设备，所述设备包括：存储器、处理器及存储在所述存储器上并在所述处理器上运行的自动驾驶定位程序，所述自动驾驶定位程序配置为实现如上述中任一项所述的自动驾驶定位方法的步骤。
本发明还提供了一种电子存储介质，所述存储介质中存储有至少一条指令或至少一段程序，所述至少一条指令或至少一段程序由处理器加载并执行以实现如上述任一项的自动驾驶定位方法。
本发明的组合导航采用的是GNSS和IMU组合导航。
本发明采用基于组合导航和激光SLAM相融合的实时闭环的自动驾驶定位***，能够弥补卫星信号易受环境影响的缺陷，进一步提升组合导航的可靠性，有效提高定位精度，自动驾驶定位***中的组合导航设备和激光SLAM输入的定位数据经扩展卡尔曼滤波处理后输出的结果数据，又会用于组合导航设备和激光SLAM输入定位数据的筛选，反向验证输入的定位数据的有效性，同时，采用基于深度学习的实时激光定位方法，输出具有高准确性和强鲁棒性的激光定位数据，使得融合定位的结果更加可靠，优化定位***综合性能。
同时提供一种针对自动驾驶定位缺乏***的测试方法，建立一套适用于各种组合导航设备的自动驾驶定位***，与现有自动驾驶***采取的***级测试方法不同，本发明从组合导航设备测试、定位功能测试两大方面开展自动驾驶定位***的测试，对自动驾驶定位***进行相对全面的测试，测试操作简单，可靠性较高。
以上所述仅为本发明的实施例，并非因此限制本发明的专利范围，凡是利用本发明说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本发明的专利保护范围内。

Claims (10)

  1. 一种自动驾驶定位方法，其特征在于：包括：
    输入位置、速度、姿态角数据集合以及实时激光点云数据；
    将获取的位置、速度、姿态角数据集合经扩展卡尔曼滤波法处理；
    将获取的实时激光点云数据经激光SLAM算法处理，再经扩展卡尔曼滤波法处理；
    经扩展卡尔曼滤波法处理后获得融合输出定位数据，利用融合输出定位数据剔除输入的异常数据，形成闭环处理。
  2. 根据权利要求1所述的一种自动驾驶定位方法，其特征在于：所述实时激光点云数据经激光SLAM算法处理生成位置、航向角数据。
  3. 根据权利要求1所述的一种自动驾驶定位方法，其特征在于：所述定位方法包括：经扩展卡尔曼滤波法处理后生成融合输出的位置、速度、姿态角定位数据，使用该融合输出定位数据剔除输入数据中的异常数据，形成闭环处理。
  4. 根据权利要求3所述的一种自动驾驶定位方法，其特征在于：所述融合输出定位数据剔除输入的异常数据的步骤包括：
    步骤一：确定融合定位输出数据、输入位置轨迹和激光SLAM位置轨迹的起始点；
    步骤二：采用线性插值法对步骤一中的融合定位输出数据、输入位置轨迹和激光SLAM位置轨迹进行间隔重采样；
    步骤三：计算等间隔重采样点之间的距离，提取并剔除输入和激光SLAM输入的异常位置数据；
    步骤四：基于融合输出的速度数据，采用曲线相似性分析方法，提取并剔 除输入的异常速度数据；
    步骤五：基于融合输出的姿态角数据，采用曲线相似性分析方法，提取并剔除输入和激光SLAM输入的异常角度数据。
  5. 一种自动驾驶定位***，其特征在于：包括：组合导航设备模块、激光雷达模块、自动驾驶控制器模块及自动驾驶定位模块，
    所述组合导航设备模块，用于向所述自动驾驶定位模块提供位置、速度、姿态角数据集合；
    所述激光雷达模块，用于向所述自动驾驶定位模块提供实时激光点云数据；
    所述自动驾驶定位模块，用于实现自动驾驶定位方法；
    所述自动驾驶控制器模块，用于运行所述自动驾驶定位模块。
  6. 根据权利要求5所述的一种自动驾驶定位***，其特征在于：所述自动驾驶定位模块采取基于组合导航和激光SLAM融合技术的自动驾驶定位程序，以实现自动驾驶定位***的闭环处理。
  7. 一种测试方法，其特征在于：所述测试方法适用于权利要求5或6的自动驾驶定位***，所述测试方法包括：
    通过组合导航设备模块及自动驾驶定位功能模块的组合测试完成自动驾驶定位***的测试。
  8. 根据权利要求7所述的一种测试方法，其特征在于：
    所述组合导航设备模块的测试步骤包括：
    测试组合导航设备在静止状态下的定位稳定性；
    测试组合导航设备在运动状态下的不同定位状态的变化；
    以高精度导航定位***为基准，测试组合导航设备的定位精度；
    测试组合导航设备的固定解和非固定解的互相切换时间；
    测试组合导航设备重复多次冷启动的时间的变化；
    测试及分析组合导航设备的丢包率和丢包区间；
    所述自动驾驶定位功能模块的测试步骤包括：
    在GNSS信号优良、固定设施完备的场地中，布置固定的导航轨道，测试自动驾驶定位精度和定位数据更新频率；
    在GNSS信号遮挡区域测试自动驾驶定位精度和数据处理时间。
  9. 一种自动驾驶定位设备，其特征在于：所述设备包括：存储器、处理器及存储在所述存储器上并在所述处理器上运行的自动驾驶定位程序，所述自动驾驶定位程序配置为实现如上述权利要求1-4中任一项所述的自动驾驶定位方法的步骤。
  10. 一种电子存储介质，其特征在于：所述存储介质中存储有至少一条指令或至少一段程序，所述至少一条指令或至少一段程序由处理器加载并执行以实现如上述权利要求1-4任一项的自动驾驶定位方法。
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