EP4172564A1 - Déterminer une position de départ d'un véhicule pour sa localisation - Google Patents

Déterminer une position de départ d'un véhicule pour sa localisation

Info

Publication number
EP4172564A1
EP4172564A1 EP21732875.6A EP21732875A EP4172564A1 EP 4172564 A1 EP4172564 A1 EP 4172564A1 EP 21732875 A EP21732875 A EP 21732875A EP 4172564 A1 EP4172564 A1 EP 4172564A1
Authority
EP
European Patent Office
Prior art keywords
starting position
measurement data
sensor system
vehicle
determined
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP21732875.6A
Other languages
German (de)
English (en)
Inventor
Renlin LI
Georg Krause
Timo Nachstedt
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Robert Bosch GmbH
Original Assignee
Robert Bosch GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Robert Bosch GmbH filed Critical Robert Bosch GmbH
Publication of EP4172564A1 publication Critical patent/EP4172564A1/fr
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—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
    • G01C21/26—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
    • 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
    • G01C21/34—Route searching; Route guidance
    • G01C21/36—Input/output arrangements for on-board computers
    • G01C21/3602—Input other than that of destination using image analysis, e.g. detection of road signs, lanes, buildings, real preceding vehicles using a camera
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G06T7/74—Determining position or orientation of objects or cameras using feature-based methods involving reference images or patches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10028—Range image; Depth image; 3D point clouds
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10032—Satellite or aerial image; Remote sensing
    • G06T2207/10044—Radar image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30248—Vehicle exterior or interior
    • G06T2207/30252—Vehicle exterior; Vicinity of vehicle

Definitions

  • the invention relates to a method for determining a starting position of a vehicle for localization of the vehicle.
  • the invention also relates to a method for performing a localization, a control device, a computer program and a machine-readable storage medium.
  • the vehicle In order to use digital maps with precise lanes, the vehicle must be able to determine its own position with lane accuracy.
  • the position of the vehicle can be determined by localization.
  • the localization of the vehicle position requires a precise initial position or starting position, since the localization is usually based on iterative processes. For example, measurement data from an odometry sensor system are used to determine the vehicle position based on the initial position.
  • the object on which the invention is based can be seen in proposing a precise method for determining an initial position or starting position for an iterative vehicle localization, which can be implemented with inexpensive sensors.
  • a method for determining a starting position of a vehicle for localization of the vehicle by a control device is provided.
  • the method can be carried out by an initialization module of the control device in the form of hardware and / or software.
  • measurement data are received from an odometry sensor system and / or a GNSS sensor system of the vehicle.
  • the odometry sensors and / or GNSS sensors can be connected to the control unit to conduct data.
  • a first position and an uncertainty range of the first position are determined based on the received measurement data.
  • at least one map section of a feature card with a large number of stored features is received.
  • the map section preferably has a position and extent which overlap the first position and the uncertainty region of the first position.
  • the uncertainty area can be designed in the form of an ellipse, a circle or in the form of a polygon.
  • the position can be central or eccentric in the uncertainty area.
  • measurement data are received from a LIDAR sensor system, a radar sensor system and / or a camera sensor system and static features are extracted from the received measurement data.
  • static features can depict static objects, such as buildings, lane boundaries, lane markings, trees, street signs, lane courses and the like.
  • the static features can be present in the form of a signature or a characteristic measurement data grid.
  • a starting position of the vehicle is then determined by comparing the static features extracted from the measurement data with the features stored in the map section.
  • the method can be used to determine an initial position or starting position of the vehicle that is accurate to the lane and serves as the basis for an iterative localization method.
  • the starting position can also be configured as a starting pose with a position and orientation of the vehicle.
  • the option of using radar sensors enables the starting position to be determined even in poor weather conditions, such as rain or fog. Furthermore, the starting position can also be precisely determined in areas without a GNSS signal.
  • the starting position can in particular be used to implement automated or partially automated driving functions. Driving functions of this type can, for example, access lane-accurate navigation and have trajectory planning.
  • the method for determining the starting position is essentially determined by three main steps. In one step, measurement data from the odometry sensors and / or the GNSS sensors of the vehicle are used separately or as a sensor data fusion in order to determine the first position.
  • a corresponding map section can be loaded which covers the first position and its uncertainty area.
  • the loaded map section can be used for feature-based localization using measurement data from a LIDAR sensor system, a radar sensor system and / or a camera sensor system.
  • a control device is provided, the control device being set up to carry out the method.
  • the control device can be, for example, a control device on the vehicle, a control device external to the vehicle, or a server unit external to the vehicle, such as a cloud system, for example.
  • control device can have a localization module and / or an initialization module.
  • control device can carry out the method for determining the starting position of a vehicle and / or the method for performing a localization.
  • a computer program which comprises commands which, when the computer program is executed by a computer or a control device, cause the computer or a control device to execute the method according to the invention.
  • a machine-readable storage medium is provided on which the computer program according to the invention is stored.
  • the vehicle can be assisted, partially automated, highly automated and / or fully automated or can be operated without a driver.
  • the vehicle can be, for example, a passenger car, a truck, a robotaxi, and the like.
  • the vehicle is not limited to running on roads. Rather, the vehicle can also be used as a Watercraft, aircraft, such as a transport drone, and the like can be configured.
  • At least one second starting position is determined at a time spaced apart from the first starting position, the first starting position and the at least one second starting position being compared with positions determined from measurement data from the odometry sensor system.
  • a deviation between the at least one second starting position and a position of the vehicle determined by measurement data from the odometry sensor system is preferably calculated and a consistency check is carried out.
  • This consistency check can be implemented as a final step of the method, which ensures reliable results of the initialization.
  • this consistency check can guarantee the correctness of the starting position in periodic environments, such as a forest section or a motorway, with a large number of identical road boundaries or static features.
  • the consistency check can be implemented in a technically particularly simple manner if the first of the two successive alignment results or starting positions is propagated by means of measurement data from the odometry sensor system up to the point in time of the second alignment result and then the difference with the actual second alignment result is calculated. If the difference is below a certain threshold, the starting positions can be considered to be consistent.
  • At least one second starting position is determined at a time spaced apart from the first starting position, the first starting position and the at least one second starting position being linked with measurement data from the odometry sensor system to form trajectories.
  • a goodness of fit is determined for each trajectory, where a trajectory with the highest quality of fit is used or all trajectories are discarded. If the starting positions are discarded, the method is carried out again in order to determine a starting position which passes the consistency check. This means that multi-hypothesis tracking can be used. This measure can further increase the precision of the starting position, particularly in periodic environments.
  • extracted static features from older measurements of the LIDAR sensor system, the radar sensor system and / or the camera sensor system are used to carry out a comparison of the extracted features with the features stored in the map section.
  • the map section can be enlarged according to the distance between a current measurement and the older measurement. This can be achieved by using measurement data from the odometry sensor system.
  • the older measurement data and the current measurement data can preferably have static features which are spatially separated from one another and which, combined with the features stored in the map section, can be compared in order to determine the first starting position. As a result, a larger data cloud can be used to determine the first starting position. This measure can take place, for example, instead of the consistency check.
  • the extracted features from the older measurements are coupled with the extracted features from current measurements using measurement data from the odometry sensor system.
  • the older static features which were ascertained a few seconds earlier, for example, can be linked to currently ascertained static features over the distance covered by the vehicle.
  • the distance covered can be traced using the odometry sensors.
  • an enlarged road geometry with corresponding features can be used to resolve ambiguities or ambiguities in periodic surroundings, such as, for example, motorways or forest sections.
  • an optimization method is used to determine the first position. For example, a data structure based on a sliding graph with measurement data from the odometry sensors and the GNSS sensors of the vehicle received over a period of time, and the first position can be determined therefrom. This measure can eliminate or reduce measurement uncertainties, fluctuations and jitter when determining the first position.
  • the measurement data from the odometry sensor system and / or the GNSS sensor system of the vehicle are continuously received. Furthermore, the first position is continuously determined based on the received measurement data. This measure can ensure the availability of the first position for determining the first starting position.
  • the determined starting position is used to carry out a road clearance service.
  • the method can be used to release a street or a street section for certain automated or partially automated driver assistance functions.
  • the redundant use of the measurement data from the odometry sensors and the static features can prevent manipulation of the starting position, for example through so-called GNSS spoofing.
  • a method for performing a localization is provided, a first starting position determined by a method according to the invention for determining a starting position of a vehicle being received as an input variable and / or as a validation variable.
  • a vehicle can also be localized with low-cost vehicle sensors compared to a digital map.
  • the method for performing the localization can implement a continuous comparison of static features from measurement data from a LIDAR sensor system, a radar sensor system and / or a camera sensor system of the vehicle with the features stored in the digital map. This can be done, for example, by a localization module of the control device. Since the method for performing the localization is designed iteratively, the vehicle can be localized particularly precisely by providing the precise starting position.
  • the search area for the feature-based localization can be limited to the at least one map section and the computation requirement can be reduced by the first position.
  • the method for determining the starting position or initial position is carried out in parallel with the method for performing the localization.
  • the initialization module of the control device can also run in the background during the localization.
  • the output of the initialization module can be used as an additional status check of the localizer output. If a difference between the starting position and the vehicle position determined by the localization is determined which exceeds a limit value, a new starting position can be output for the localization module and the localization of the vehicle can be initiated again.
  • FIG. 1 shows a schematic plan view of a roadway to illustrate a determination of a first position of a vehicle
  • FIG. 2 shows a schematic plan view of a roadway from FIG. 1 and a map section
  • FIGS. 1 to 4 show schematic representations to illustrate a method for determining a first starting position A of a vehicle 2 for localization of the vehicle 2 by a control device 4.
  • the vehicle 2 has an odometry sensor system and / or a GNSS sensor system 6 and an additional sensor system 8 for feature-based localization.
  • the additional sensor system 8 can be configured, for example, as a LIDAR sensor system, a radar sensor system and / or a camera sensor system.
  • FIG. 1 shows a schematic top view of a roadway 10 to illustrate a determination of a first position P of the vehicle 2.
  • the vehicle 2 travels on the roadway 10 in the direction of travel F.
  • measurement data are recorded by the odometry sensors and the GNSS sensors while driving 6 collected.
  • a plurality of measurement data 12 from the odometry sensor system and the GNSS sensor system 6 collected one after the other are stored in order to determine the first position P.
  • the measurement data 12 of the odometry sensor system and the GNSS sensor system 6 can optionally be smoothed so that optimized measurement data 14 result from the measurement data 12 determined.
  • the determined measurement data 12 can be optimized, for example, by means of moving averages.
  • the determined measurement data 12 can have a maximum number, so that old measurement data are automatically deleted or overwritten by more recent measurement data.
  • the first position P can be configured as the last or most recent measurement of the odometry sensor system and the GNSS sensor system 6 after optimization or smoothing.
  • FIG. 2 shows a schematic top view of a roadway 10 from FIG. 1 and of a map section 16.
  • the map section 16 is part of a feature map and has a multiplicity of features 18.
  • the feature map is a radar-specific feature map.
  • the map section 16 has a position and an extent which overlays or covers the first position P and an optional uncertainty region of the first position P.
  • extracted static features 20 from current measurements and static features 22 from older measurements of the LIDAR sensor system, the radar sensor system and / or the camera sensor system 8 are taken into account, so that an enlarged map section 16 is used to carry out a comparison of extracted static features 20, 22 with features 18 stored in the map section 16.
  • the radar-specific feature map and the map section 16 are stored in the form of map sections 16 which depict the topology of the road network or the roadway 10.
  • the at least one map section 16 can be transformed into a coordinate system of the vehicle 2, which is not shown in FIG. 2 for the sake of clarity.
  • the features 18 of the map section 16 can be compared with the extracted static features 20, 22 and aligned with one another for a feature-based localization. Such an alignment can be realized with a cost function and an optimization algorithm for the cost function.
  • FIG. 3 and FIG. 4 show schematic diagrams to illustrate a consistency check.
  • FIG. 3 shows a technically simplified consistency check.
  • a plurality of second starting positions A2, A3 spaced apart in time from the first starting position A are determined.
  • positions P, P2, P3 are determined by means of the odometry sensor system 6 and compared with the starting positions A, A2, A3. For this purpose, a difference D or a distance between the positions P, P2, P3 and the starting positions A, A2 can be calculated. The consistency check is successful if the difference D is below a predefined threshold value or limit value.
  • the first two starting positions A, A2 are consistent or correct.
  • the last starting position A3 has too great a deviation D from position P3 and is not consistent.
  • the method for determining the starting position A of the vehicle 2 can be carried out again and subjected to a consistency check.
  • the consistency check can preferably have a plurality of successfully checked starting positions A, A2, A3 before the last checked starting position A3 is released for localization of the vehicle 2.
  • FIG. 4 is based on multi-hypothesis tracking.
  • a plurality of starting positions A, A2, A3 with a best match within the map section 16 are taken into account. These starting positions A, A2, A3 are coupled with matches from the last alignment of the features 18, 20, 22.
  • the measurement data 12 of the odometry sensor system 6 are used to connect the starting positions A, A2, A3.
  • Each of the starting positions A, A2, A3 can have parallel starting positions AP within the map section 16, in which the extracted features 20,
  • the measurement data 12 of the odometry sensor system 6 are compared in the form of trajectories with the respective starting positions A, A2, A3, AP, the starting positions A, A2, A3, AP with the highest degree of adaptation being used for the further localization of the vehicle 2.

Abstract

L'invention concerne un procédé de détermination d'une position de départ d'un véhicule pour sa localisation au moyen d'un appareil de commande, dans lequel : des données de mesure sont reçues d'un système de capteurs odométriques et/ou d'un système de capteurs GNSS du véhicule, une première position et une plage d'incertitude de la première position sont déterminées sur la base des données de mesure reçues, au moins une section de carte d'une carte de caractéristiques comportant une pluralité de caractéristiques mémorisées est reçue, la section de carte présentant une position et une extension qui superpose la position et la plage d'incertitude, des données de mesure sont reçues d'un capteur LIDAR, d'un système de capteur radar et/ou d'un système de capteur de caméra et des caractéristiques statiques sont extraites des données de mesure reçues, une première position de départ du véhicule est déterminée en comparant les caractéristiques statiques extraites des données de mesure aux caractéristiques stockées dans la section de carte. L'invention concerne en outre un procédé de localisation, un dispositif de commande, un programme informatique et un support de stockage lisible par machine.
EP21732875.6A 2020-06-30 2021-06-14 Déterminer une position de départ d'un véhicule pour sa localisation Pending EP4172564A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102020208082.8A DE102020208082A1 (de) 2020-06-30 2020-06-30 Ermitteln einer Ausgangsposition eines Fahrzeugs für eine Lokalisierung
PCT/EP2021/065895 WO2022002564A1 (fr) 2020-06-30 2021-06-14 Déterminer une position de départ d'un véhicule pour sa localisation

Publications (1)

Publication Number Publication Date
EP4172564A1 true EP4172564A1 (fr) 2023-05-03

Family

ID=76483316

Family Applications (1)

Application Number Title Priority Date Filing Date
EP21732875.6A Pending EP4172564A1 (fr) 2020-06-30 2021-06-14 Déterminer une position de départ d'un véhicule pour sa localisation

Country Status (6)

Country Link
US (1) US20230204364A1 (fr)
EP (1) EP4172564A1 (fr)
JP (1) JP7458514B2 (fr)
CN (1) CN116324338A (fr)
DE (1) DE102020208082A1 (fr)
WO (1) WO2022002564A1 (fr)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115993089B (zh) * 2022-11-10 2023-08-15 山东大学 基于pl-icp的在线四舵轮agv内外参标定方法

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4732937B2 (ja) 2006-03-29 2011-07-27 パイオニア株式会社 位置検出装置、その方法、そのプログラム及びその記録媒体
DE102010033729B4 (de) 2010-08-07 2014-05-08 Audi Ag Verfahren und Vorrichtung zum Bestimmen der Position eines Fahrzeugs auf einer Fahrbahn sowie Kraftwagen mit einer solchen Vorrichtung
US9140792B2 (en) 2011-06-01 2015-09-22 GM Global Technology Operations LLC System and method for sensor based environmental model construction
US20130147661A1 (en) * 2011-12-07 2013-06-13 International Business Machines Corporation System and method for optical landmark identification for gps error correction
JP2015114126A (ja) 2013-12-09 2015-06-22 株式会社デンソー 自車位置検出装置
DE102014223363B4 (de) * 2014-11-17 2021-04-29 Volkswagen Aktiengesellschaft Verfahren und Vorrichtung zur Lokalisation eines Kraftfahrzeugs in einer ortsfesten Referenzkarte
DE102016004370A1 (de) 2016-04-09 2017-02-16 Daimler Ag Verfahren zur Positionsbestimmung von Fahrzeugen
JP6614124B2 (ja) 2016-12-21 2019-12-04 株式会社デンソー 緊急通報装置

Also Published As

Publication number Publication date
CN116324338A (zh) 2023-06-23
JP7458514B2 (ja) 2024-03-29
US20230204364A1 (en) 2023-06-29
DE102020208082A1 (de) 2021-12-30
WO2022002564A1 (fr) 2022-01-06
JP2023532729A (ja) 2023-07-31

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