WO2021077622A1 - 定位方法、定位系统及汽车 - Google Patents

定位方法、定位系统及汽车 Download PDF

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WO2021077622A1
WO2021077622A1 PCT/CN2019/130854 CN2019130854W WO2021077622A1 WO 2021077622 A1 WO2021077622 A1 WO 2021077622A1 CN 2019130854 W CN2019130854 W CN 2019130854W WO 2021077622 A1 WO2021077622 A1 WO 2021077622A1
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positioning
credibility
subsystem
weight coefficient
positioning subsystem
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PCT/CN2019/130854
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English (en)
French (fr)
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宋聚宝
原诚寅
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北京新能源汽车技术创新中心有限公司
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Priority to US17/770,300 priority Critical patent/US20220390621A1/en
Publication of WO2021077622A1 publication Critical patent/WO2021077622A1/zh

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    • 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
    • G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
    • G01S19/485—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an optical system or imaging system
    • 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
    • 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
    • G01C21/183—Compensation of inertial measurements, e.g. for temperature effects
    • G01C21/188—Compensation of inertial measurements, e.g. for temperature effects for accumulated errors, e.g. by coupling inertial systems with absolute positioning systems
    • 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
    • G01C21/20—Instruments for performing navigational calculations
    • 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
    • G01S17/95—Lidar systems specially adapted for specific applications for meteorological use
    • 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
    • G01S19/396—Determining accuracy or reliability of position or pseudorange measurements
    • 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
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/14—Receivers specially adapted for specific applications
    • 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
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • a positioning method characterized in that it comprises:
  • the main filter that obtains the federated Kalman filter feeds back to the sub-filters of each federated Kalman filter, respectively, according to the global data, the second information that each positioning subsystem participates in the fusion operation and assigns weight coefficients;
  • the first information distribution weight coefficient and the second information distribution weight coefficient determine the final information distribution weight coefficient for each positioning subsystem to participate in the fusion operation;
  • the fusion operation is performed by the federated Kalman filter according to the final information of each subsystem and the weight coefficient is allocated, and the final positioning result is output.
  • the weight coefficient is allocated to the first information of the fusion operation.
  • the first information distribution weight coefficient of each of the positioning subsystems and the second information distribution weight coefficient of each of the subsystems are added together and the average value is taken as the final information of each positioning subsystem Assign weight coefficient.
  • the positioning method according to claim 1 characterized in that, based on the historical statistical data of the operating parameter status of each positioning subsystem, the maximum value of the operating parameters in the historical statistical data of each positioning subsystem is calculated. The percentage of the good state and the worst state obtains the credibility of the different states of each of the subsystems.
  • the multiple positioning subsystems include a combined navigation positioning subsystem , The laser point cloud positioning subsystem and the camera vision positioning subsystem, each of the sub-filters is used to filter the positioning data output by one of the positioning subsystems.
  • the credibility of the integrated navigation and positioning subsystem corresponding to different locations is set, and the credibility of the integrated navigation and positioning subsystem is the same as that of the The degree of obscuration of buildings is negatively related
  • the reliability of the laser point cloud positioning subsystem corresponding to different locations is set, and the reliability of the laser point cloud positioning subsystem is set It is positively correlated with the obvious degree of the features of the surrounding objects;
  • the credibility of the laser point cloud positioning subsystem corresponding to different locations is set, and the credibility of the camera visual positioning subsystem is the same as that of the The degree of light change is positively correlated.
  • the internal parameter mode comprises:
  • the reliability of the integrated navigation and positioning subsystem corresponding to different GNSS signal stability and the number of connected satellites is set.
  • the reliability of the system is positively correlated with the stability of the GNSS signal and the number of connected satellites;
  • the credibility of the laser point cloud positioning subsystem corresponding to different point cloud matching degrees is set; the credibility of the laser point cloud positioning subsystem is equal to The point cloud matching degree is positively correlated;
  • the reliability of the laser point cloud positioning subsystem corresponding to different PM2.5 values is set according to the PM2.5 value obtained by the external PM2.5 measurement sensor, wherein the reliability of the laser point cloud positioning subsystem is the same as that of the PM2. .5 value negative correlation;
  • the credibility of the camera vision positioning subsystem corresponding to different light brightness values is set according to the light brightness value acquired by the external light sensor, wherein the credibility of the camera vision positioning subsystem is positively correlated with the light brightness value.
  • a positioning system characterized by comprising: multiple positioning subsystems, a federated Kalman filter, a credibility database and a credibility evaluation module, the federated Kalman filter comprising a main filter and a plurality of sub-systems filter;
  • the credibility database is used to store the credibility data tables of each positioning subsystem, wherein each credibility data table stores the credibility of each positioning subsystem in different states;
  • the credibility evaluation module is used to obtain the real-time credibility of each positioning subsystem from the corresponding credibility data table according to the real-time positioning data of each positioning subsystem, and to obtain the real-time credibility of each positioning subsystem according to the real-time positioning data of each positioning subsystem.
  • the real-time credibility obtains the first information distribution weight coefficient of each positioning subsystem participating in the fusion operation of the federated Kalman filter;
  • the main filter is configured to feed back to each of the sub-filters respectively the second information allocation weight coefficient that each positioning subsystem participates in the fusion operation according to the global data;
  • the credibility evaluation module determines the final information distribution weight coefficient for each positioning subsystem to participate in the fusion operation according to the first information distribution weight coefficient and the second information distribution weight coefficient;
  • the federated Kalman filter allocates weight coefficients according to the final information of each subsystem to perform the fusion operation and output a final positioning result.
  • the credibility evaluation module assigns the weight coefficient of the first information of each of the positioning subsystems to the weight coefficient of each of the subsystems.
  • the second information distribution weight coefficients are added together and the average value is taken as the final information distribution weight coefficient of each positioning subsystem.
  • the credibility evaluation module is preset with a credibility threshold of each of the positioning subsystems, and when the real-time credibility of the subsystem is When the degree of reliability is greater than or equal to the corresponding credibility threshold, the subsystem participates in the fusion operation; when the real-time credibility of the subsystem is less than the corresponding credibility threshold, the subsystem Does not participate in the fusion operation.
  • the multiple subsystems include an integrated navigation positioning subsystem, a laser point cloud positioning subsystem, and a camera vision positioning subsystem, and each of the sub-filters is used to filter positioning data output by one positioning subsystem.
  • An automobile characterized by comprising the positioning system according to any one of claims 10 to 14.

Abstract

一种定位方法、定位系统及汽车，方法包括：获取每个定位子系统在不同状态下的可信度并生成可信度数据表；根据每个定位子系统的实时定位数据从对应的可信度数据表中获取实时可信度；根据每个定位子系统的实时可信度求取每个定位子系统参与联邦卡尔曼滤波器融合运算的第一信息分配权重系数；通过主滤波器根据全局数据分别向每个子滤波器反馈每个定位子系统参与融合运算的第二信息分配权重系数；根据第一信息分配权重系数和第二信息分配权重系数确定每个定位子系统参与融合运算的最终信息分配权重系数；通过联邦卡尔曼滤波器根据每个子系统的最终信息分配权重系数进行融合运算并输出最终的定位结果。该方法有效优化各个定位子系统的信息分配权重，进而提高系统定位精度并提高定位系统的鲁棒性。

Description

无标题
1、一种定位方法，其特征在于，包括：
获取多个不同的定位子系统中每个所述定位子系统在不同状态下的可信度并生成每个定位子系统可信度数据表；
根据每个定位子系统的实时定位数据从对应的所述可信度数据表中获取每个定位子系统的实时可信度；
根据每个定位子系统的所述实时可信度求取每个定位子系统参与联邦卡尔曼滤波器融合运算的第一信息分配权重系数；
获取所述联邦卡尔曼滤波器的主滤波器根据全局数据分别向每个所述联邦卡尔曼滤波器的子滤波器反馈每个定位子系统参与融合运算的第二信息分配权重系数；根据所述第一信息分配权重系数和所述第二信息分配权重系数确定每个定位子系统参与融合运算的最终信息分配权重系数；
通过所述联邦卡尔曼滤波器根据每个所述子系统的所述最终信息分配权重系数进行所述融合运算并输出最终的定位结果。
2、根据权利要求1所述的定位方法，其特征在于，根据每个定位子系统的所述实时可信度求取每个定位子系统参与联邦卡尔曼滤波器融合运算的第一信息分配权重系数包括：
求取每个定位子系统的所述实时可信度与所述多个子系统的所述实时可信度之和的百分比，将每个所述百分比作为每个定位子系统参与联邦卡尔曼滤波器融合运算的所述第一信息分配权重系数。
3、根据权利要求1所述的定位方法，其特征在于，根据所述第一信息分配权重系数和所述第二信息分配权重系数确定每个定位子系统参与融合运算的最终信息分配权重系数包括：
将每个所述定位子系统的所述第一信息分配权重系数与每个所述子系统的所述第二信息分配权重系数相加后取平均值作为每个定位子系统的所 述最终信息分配权重系数。
4、根据权利要求1所述的定位方法，其特征在于，在所述获取每个所述定位子系统在不同状态下的可信度之后还包括：
分别设置每个所述定位子系统的可信度阈值，当所述子系统的所述实时可信度大于等于对应的所述可信度阈值时，所述子系统参与所述融合运算；当所述子系统的所述实时可信度小于对应的所述可信度阈值时，所述子系统不参与所述融合运算。
5、根据权利要求1所述的定位方法，其特征在于，基于每个定位子系统运行参数状态的历史统计数据，通过计算每个所述定位子系统的所述历史统计数据中运行参数的最好状态与最差状态的百分比获得每个所述子系统不同状态的可信度。
6、根据权利要求1所述的定位方法，其特征在于，所述获取每个所述定位子系统在不同状态下的可信度包括：
通过高精地图方式、内部参数方式、外部参数方式的至少其中之一获取所述多个定位子系统在不同状态下的可信度；其中，所述多个定位子系统包括组合导航定位子系统、激光点云定位子系统和相机视觉定位子系统，每个所述子滤波器用于对一个所述定位子系统输出的定位数据进行滤波。
7、根据权利要求6所述的定位方法，其特征在于，所述高精地图方式包括：
根据所述高精地图获取在不同地点的外部环境信息中的建筑物遮挡程度设置所述组合导航定位子系统对应不同地点的可信度，所述组合导航定位子系统的可信度与所述建筑物遮挡程度负相关；
根据所述高精地图获取在不同地点的外部环境信息中的周围物体特征明显程度设置所述激光点云定位子系统对应不同地点的可信度，所述激光点云定位子系统的可信度与所述周围物体特征明显程度正相关；
根据所述高精地图获取在不同地点的外部环境信息中的光线变化程度 设置所述激光点云定位子系统对应不同地点的可信度，所述相机视觉定位子系统的可信度与所述光线变化程度正相关。
8、根据权利要求6所述的定位方法，其特征在于，所述内部参数方式包括：
根据所述组合导航定位子系统中的GNSS信号稳定程度及连接卫星个数设置所述组合导航定位子系统对应不同GNSS信号稳定程度及不同连接卫星个数的可信度，所述组合导航定位子系统的可信度与所述GNSS信号稳定程度及连接卫星个数正相关；
根据所述所述激光点云定位子系统中的点云匹配度设置所述激光点云定位子系统对应不同点云匹配度的可信度；所述激光点云定位子系统的可信度与所述点云匹配度正相关；
根据所述相机视觉定位子系统中的特征匹配度设置所述相机视觉定位子系统对应不同特征匹配度的可信度，所述相机视觉定位子系统的可信度与所述特征匹配度正相关。
9、根据权利要求6所述的定位方法，其特征在于，所述外部参数方式包括：
根据外部PM2.5测量传感器获取的PM2.5值设置所述激光点云定位子系统对应不同PM2.5值的可信度，其中所述激光点云定位子系统的可信度与所述PM2.5值负相关；
根据外部光线传感器获取的光线明亮值设置所述相机视觉定位子系统对应不同光线明亮值的可信度，其中所述相机视觉定位子系统的可信度与所述光线明亮值正相关。
10、一种定位系统，其特征在于，包括：多个定位子系统、联邦卡尔曼滤波器、可信度数据库和可信度评价模块，所述联邦卡尔曼滤波器包括主滤波器和多个子滤波器；
所述可信度数据库用于存储每个定位子系统可信度数据表，其中每个 所述可信度数据表中存储有每个所述定位子系统在不同状态下的可信度；
所述可信度评价模块用于根据每个定位子系统的实时定位数据从对应的所述可信度数据表中获取每个定位子系统的实时可信度，并根据每个定位子系统的所述实时可信度求取每个定位子系统参与联邦卡尔曼滤波器融合运算的第一信息分配权重系数；
所述主滤波器用于根据全局数据分别向每个所述子滤波器反馈每个定位子系统参与融合运算的第二信息分配权重系数；
所述所述可信度评价模块根据所述第一信息分配权重系数和所述第二信息分配权重系数确定每个定位子系统参与融合运算的最终信息分配权重系数；
所述联邦卡尔曼滤波器根据每个所述子系统的所述最终信息分配权重系数进行所述融合运算并输出最终的定位结果。
11、根据权利要求10所述的定位系统，其特征在于，所述可信度评价模块通过计算每个定位子系统的所述实时可信度与所述多个子系统的所述实时可信度之和的百分比，以及将每个所述百分比作为每个定位子系统参与联邦卡尔曼滤波器融合运算的所述第一信息分配权重系数。
12、根据权利要求10所述的定位系统，其特征在于，所述可信度评价模块将每个所述定位子系统的所述第一信息分配权重系数与每个所述子系统的所述第二信息分配权重系数相加后取平均值作为每个定位子系统的所述最终信息分配权重系数。
13、根据权利要求10所述的定位系统，其特征在于，所述可信度评价模块预设有每个所述定位子系统的可信度阈值，当所述子系统的所述实时可信度大于等于对应的所述可信度阈值时，所述子系统参与所述融合运算；当所述子系统的所述实时可信度小于对应的所述可信度阈值时，所述子系统不参与所述融合运算。
14、根据权利要求10所述的定位系统，其特征在于，还包括高精地图 和惯性传感器，所述高精地图和所述惯性传感器用于提供绝对位置信息；
所述多个子系统包括组合导航定位子系统、激光点云定位子系统和相机视觉定位子系统，每个所述子滤波器用于对一个所述定位子系统输出的定位数据进行滤波。
15、一种汽车，其特征在于，包括根据权利要求10至14任意一项所述的定位系统。

Claims (1)

  1. Figure PCTCN2019130854-appb-100001
    Figure PCTCN2019130854-appb-100002
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PCT/CN2019/130854 2019-10-22 2019-12-31 定位方法、定位系统及汽车 WO2021077622A1 (zh)

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