EP4025872A1 - Method for determining a navigation state of a vehicle - Google Patents

Method for determining a navigation state of a vehicle

Info

Publication number
EP4025872A1
EP4025872A1 EP20745170.9A EP20745170A EP4025872A1 EP 4025872 A1 EP4025872 A1 EP 4025872A1 EP 20745170 A EP20745170 A EP 20745170A EP 4025872 A1 EP4025872 A1 EP 4025872A1
Authority
EP
European Patent Office
Prior art keywords
vehicle
navigation state
state
kalman filter
data
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
EP20745170.9A
Other languages
German (de)
French (fr)
Inventor
Dehao LIU
Jiehu HOU
Qin Shi
Christian Thiel
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.)
Aumovio Germany GmbH
Original Assignee
Continental Automotive Technologies 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 Continental Automotive Technologies GmbH filed Critical Continental Automotive Technologies GmbH
Publication of EP4025872A1 publication Critical patent/EP4025872A1/en
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
    • G01C21/1656—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 passive imaging devices, e.g. cameras
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
    • B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
    • B29C70/00—Shaping composites, i.e. plastics material comprising reinforcements, fillers or preformed parts, e.g. inserts
    • B29C70/68—Shaping composites, i.e. plastics material comprising reinforcements, fillers or preformed parts, e.g. inserts by incorporating or moulding on preformed parts, e.g. inserts or layers, e.g. foam blocks
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
    • B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
    • B29C70/00—Shaping composites, i.e. plastics material comprising reinforcements, fillers or preformed parts, e.g. inserts
    • B29C70/04—Shaping composites, i.e. plastics material comprising reinforcements, fillers or preformed parts, e.g. inserts comprising reinforcements only, e.g. self-reinforcing plastics
    • B29C70/28—Shaping operations therefor
    • B29C70/30—Shaping by lay-up, i.e. applying fibres, tape or broadsheet on a mould, former or core; Shaping by spray-up, i.e. spraying of fibres on a mould, former or core
    • 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
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
    • B29K—INDEXING SCHEME ASSOCIATED WITH SUBCLASSES B29B, B29C OR B29D, RELATING TO MOULDING MATERIALS OR TO MATERIALS FOR MOULDS, REINFORCEMENTS, FILLERS OR PREFORMED PARTS, e.g. INSERTS
    • B29K2025/00—Use of polymers of vinyl-aromatic compounds or derivatives thereof as moulding material
    • B29K2025/04—Polymers of styrene
    • B29K2025/06—PS, i.e. polystyrene
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
    • B29K—INDEXING SCHEME ASSOCIATED WITH SUBCLASSES B29B, B29C OR B29D, RELATING TO MOULDING MATERIALS OR TO MATERIALS FOR MOULDS, REINFORCEMENTS, FILLERS OR PREFORMED PARTS, e.g. INSERTS
    • B29K2067/00—Use of polyesters or derivatives thereof, as moulding material
    • B29K2067/003—PET, i.e. poylethylene terephthalate
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
    • B29L—INDEXING SCHEME ASSOCIATED WITH SUBCLASS B29C, RELATING TO PARTICULAR ARTICLES
    • B29L2031/00—Other particular articles
    • B29L2031/768—Protective equipment
    • 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

Definitions

  • the disclosure relates to a method for determining a navigation state of a vehi cle, for example a position of the vehicle, using a Kalman Filter.
  • VIO Visual inertial odometry
  • IMU inertial measurement unit
  • DoF degree-of- freedom
  • the MSCKF algorithm can provide accu rate position estimation in environments of rich texture. It must be noted that IMU and monocular cameras cannot provide an absolute position or heading angle of a vehicle. As a consequence, the use of a Multi-State Constraint Kalman Filter and also other VIO systems using data of IMUs and mo nocular cameras have some limitations. An essential limitation is called unobservability which means that a 3-DOF abso lute position and a 1-DOF heading angle are not observable by using a Multi- State Constraint Kalman Filter. Consequently, neither an absolute position nor a heading angle can be acquired from a Multi-State Constraint Kalman Filter. An other critical aspect is the sensitivity of the parameters.
  • Multi-State Constraint Kalman Filter When the parameters of the Filter algorithm are set inappropriately, the Multi-State Constraint Kalman Filter is prone to fail. Furthermore, a Multi-State Constraint Kalman Filter usually suffers from accumulating large amounts of error over time. Moreover, it must be considered that a Multi-State Constraint Kalman Filter cannot work in a tex tureless environment.
  • the problem to be solved by the invention is providing a reliable and robust method for determining a navigation state of a vehicle.
  • a navigation state of the vehicle is estimated by using a Kalman Filter.
  • the estimat ed navigation state includes estimated position data of the vehicle.
  • the estimated navigation state of the vehicle is corrected by using position data of the vehicle provided by a satellite navigation receiver of the ve- hide to provide a corrected navigation state of the vehicle.
  • the corrected naviga tion state includes corrected position data of the vehicle.
  • the problems caused by the use of a Multi-State Constraint Kalman Filter alone as described above can be overcome by fusing the estimated position data of the vehicle obtained from the Kalman Filter and the satellite navigation data of the vehicle obtained from the satellite navigation receiver.
  • the satellite navigation receiver may be a GPS receiver or another satellite navigation system receiver that provides position data of the vehicle by evaluating satellite navigation sig nals provided from GPS or any other satellite navigation system.
  • the above-described limitations of an algorithm using a Multi-State Constraint Kalman Filter alone can be avoided by fusing the estimated position data of the vehicle and the satellite navigation data of the vehicle, and by additionally adding some logical constraints.
  • These con strains may cover non-holonomic constraints as well as altitude constraints. Ac- cording to a possible embodiment the non-holonomic-constraints and the alti tude constraints can be calculated using an accelerometer and GPS measure ment data.
  • the proposed method for determining a navigation state of a vehicle by fusing MSCKF-based position data and satellite navigation data, for example GPS posi- tion data is applicable to large-scale and textureless scenes and has more exten sive applicability compared to conventional methods for determining the naviga tion state of a vehicle.
  • the proposed algorithm/method for determining a navi gation state of a vehicle will not suffer from unobservability errors and accumu lative errors.
  • the proposed method/algorithm for determining a navigation state of a vehicle is more accurate and robust than conventional known methods for determining a navigation state, for example a position, of a vehicle.
  • the method may be applicable in the field of road database, intelligent robotics, autonomous vehicle navigation and systems of BD-environment reconstruction.
  • the vehicle of which the navigation state is determined by the pro posed method may be configured as an autonomous driving robot/car. Moreo ver, the method for determining a navigation state of a vehicle is applicable in the field of autonomous unmanned aerial vehicle (UAV) navigation.
  • UAV unmanned aerial vehicle
  • Figure 1 shows a vehicle comprising a satellite navigation receiver, an inertial measurement unit, a camera system and a processor to control the steps of the method for determining the navigation state of the vehicle.
  • the method comprises an MSCKF processing step and a subsequent GPS correc tion step.
  • the navigation state of the vehicle 1 is estimated by using a Kalman Filter.
  • the estimated navigation state obtained by the Kalman Filter includes estimated position data of the vehicle.
  • a classic MSCKF algorithm can be used in the MSCKF pro- cessing step.
  • a classic MSCKF algorithm is described, for example, in Mourikis, Anastasios I. and S.l.
  • Roumeliotis "A Multi-State Constraint Kalman Filter for Vi sion-Aided Inertial Navigation", 22 (2007): 3565-3572 or Li, Mingyang, and Mourikis, Anastasios I.: "High-precision, consistent EKF-based visual-inertial odometry", International Journal of Robotics Research 32.6 (2013): 690-711
  • the estimated naviga tion state of the vehicle 1 is corrected by using position data of the vehicle pro vided by a satellite navigation receiver 10 of the vehicle.
  • the corrected naviga tion state includes corrected position data of the vehicle 1.
  • the estimated position data of the vehicle determined by the Kalman Filter and the satellite navigation data of the vehicle provided by the satellite navigation receiver 10 are fused to provide the corrected navigation state of the vehicle.
  • the satellite navigation receiver 10 is configured as a GPS receiver
  • available GPS measurement data may be used to correct the navi gation state estimated in the MSCKF processing step by the use of the Kalman Filter. Since the satellite navigation data, for example the GPS navigation data, provide absolute position information, the step of correcting the estimated navi gation state of the vehicle by using satellite navigation data eliminates the accu mulative errors and unobservability that the use of a conventional MSCKF algo rithm alone has no ability to address.
  • the Kalman Filter is configured to fuse data provided from an inertial measurement unit 20 of the vehicle 1 and data provid ed from a camera system 30 of the vehicle 1 for capturing an image of an envi ronmental scene of the vehicle 1.
  • the camera system 30 may include a monocu lar camera 31.
  • an uncertainty of the estimated navigation state of the vehicle can be determined. After having fused the data provided from the inertial meas urement unit 20 of the vehicle 1 and the data provided from the camera system 30, the estimated navigation state of the vehicle 1 and its corresponding uncer tainty can be output.
  • the Kalman Filter used for estimating the navigation state of the vehicle 1 is con figured as a Multi-State Constraint Kalman Filter.
  • the Multi-State Constraint Kalman Filter is configured to apply an extended Kalman Filter (EKF) based algorithm to provide the estimated navigation data of the vehicle 1.
  • EKF extended Kalman Filter
  • the method may comprise a constraints correction step.
  • some (logical) constraints may be used to provide an im proved corrected navigation state of the vehicle.
  • the improved corrected naviga tion state of the vehicle includes improved corrected position data of the vehicle 1.
  • the constraints may include at least one of non-holonomic-constraints and alti tude constraints.
  • the non-holonomic constraints as well as the altitude con straints may be calculated using an accelerometer and the satellite navigation data provided from the satellite navigation receiver 10 of the vehicle 1. The use of these constraints makes the navigation state estimation more stable and accu rate and enhances the robustness of the system.
  • an initialization step may be performed before the MSCKF processing step of estimating the navigation state of the vehicle by using the Kalman Filter. During the initialization step, an initial guess of the navigation state of the vehicle 1 and corresponding uncertainty may be determined based on data provided by the satellite navigation receiver 10 and data provided by the inertial measurement unit 20 of the vehicle.
  • the described steps of the method for determining the navigation state of the vehicle 1 may be executed by a control unit 40, for example a controller including a processor of the vehicle 1.
  • the MSCKF algorithm may be stored in a storage unit coupled to the control unit 40.

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Composite Materials (AREA)
  • Mechanical Engineering (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Automation & Control Theory (AREA)
  • Navigation (AREA)

Abstract

A method for determining a navigation state of a vehicle comprises a step of estimating the navigation state of the vehicle by using a Kalman Filter. The estimated navigation state includes estimated position data of the vehicle. The estimated navigation state of the vehicle is corrected by using position data of the vehicle provided by a satellite navigation receiver to provide a corrected navigation state of the vehicle. The corrected navigation state of the vehicle includes corrected position data of the vehicle.

Description

Method for determining a navigation state of a vehicle
Field of the invention The disclosure relates to a method for determining a navigation state of a vehi cle, for example a position of the vehicle, using a Kalman Filter.
Description of the related art
Visual inertial odometry (VIO) has become more and more popular in recent decades and naturally attracts increasing attention from researchers in the realm of intelligent robotics and autonomous driving. The main purpose of VIO is to determine the location and orientation of a vehicle in an unknown environment. An extended Kalman Filter-based VIO algorithm, named Multi-State Constraint Kalman Filter (MSCKF) attracts increasing interest because of its accuracy and real-time implementation on resource-restricted devices. The MSCKF algorithm fuses data provided from an inertial measurement unit (IMU) of six degree-of- freedom (DoF), provided, for example, from a S-axis gyroscope and a S-axis ac celerometer, and a monocular camera. The MSCKF algorithm can provide accu rate position estimation in environments of rich texture. It must be noted that IMU and monocular cameras cannot provide an absolute position or heading angle of a vehicle. As a consequence, the use of a Multi-State Constraint Kalman Filter and also other VIO systems using data of IMUs and mo nocular cameras have some limitations. An essential limitation is called unobservability which means that a 3-DOF abso lute position and a 1-DOF heading angle are not observable by using a Multi- State Constraint Kalman Filter. Consequently, neither an absolute position nor a heading angle can be acquired from a Multi-State Constraint Kalman Filter. An other critical aspect is the sensitivity of the parameters. When the parameters of the Filter algorithm are set inappropriately, the Multi-State Constraint Kalman Filter is prone to fail. Furthermore, a Multi-State Constraint Kalman Filter usually suffers from accumulating large amounts of error over time. Moreover, it must be considered that a Multi-State Constraint Kalman Filter cannot work in a tex tureless environment.
There is a desire to provide a method for determining a navigation state of a ve hicle in a reliable manner, especially, if the use of a Multi-State Constraint Kal man Filter alone provides insufficient accuracy for determining a navigation state, in particular a position of a vehicle.
Summary of the invention
The problem to be solved by the invention is providing a reliable and robust method for determining a navigation state of a vehicle.
Solutions of the problem are described in the independent claims. The depend ent claims relate to further improvements of the invention.
In an embodiment of a method for determining a navigation state of a vehicle, a navigation state of the vehicle is estimated by using a Kalman Filter. The estimat ed navigation state includes estimated position data of the vehicle. In a subse quent step, the estimated navigation state of the vehicle is corrected by using position data of the vehicle provided by a satellite navigation receiver of the ve- hide to provide a corrected navigation state of the vehicle. The corrected naviga tion state includes corrected position data of the vehicle.
The problems caused by the use of a Multi-State Constraint Kalman Filter alone as described above can be overcome by fusing the estimated position data of the vehicle obtained from the Kalman Filter and the satellite navigation data of the vehicle obtained from the satellite navigation receiver. The satellite navigation receiver may be a GPS receiver or another satellite navigation system receiver that provides position data of the vehicle by evaluating satellite navigation sig nals provided from GPS or any other satellite navigation system. According to a preferred embodiment, the above-described limitations of an algorithm using a Multi-State Constraint Kalman Filter alone can be avoided by fusing the estimated position data of the vehicle and the satellite navigation data of the vehicle, and by additionally adding some logical constraints. These con strains may cover non-holonomic constraints as well as altitude constraints. Ac- cording to a possible embodiment the non-holonomic-constraints and the alti tude constraints can be calculated using an accelerometer and GPS measure ment data.
The proposed method for determining a navigation state of a vehicle by fusing MSCKF-based position data and satellite navigation data, for example GPS posi- tion data, is applicable to large-scale and textureless scenes and has more exten sive applicability compared to conventional methods for determining the naviga tion state of a vehicle. The proposed algorithm/method for determining a navi gation state of a vehicle will not suffer from unobservability errors and accumu lative errors. Moreover, it has been shown that the proposed method/algorithm for determining a navigation state of a vehicle is more accurate and robust than conventional known methods for determining a navigation state, for example a position, of a vehicle. The method may be applicable in the field of road database, intelligent robotics, autonomous vehicle navigation and systems of BD-environment reconstruction.
In particular, the vehicle of which the navigation state is determined by the pro posed method may be configured as an autonomous driving robot/car. Moreo ver, the method for determining a navigation state of a vehicle is applicable in the field of autonomous unmanned aerial vehicle (UAV) navigation.
Additional features and advantages are set forth in the detailed description that follows. It is to be understood that both the foregoing general description and the following detailed description are merely exemplary and are intended to provide an overview or framework for understanding the nature and character of the claims.
Description of Drawings
In the following the invention will be described by way of example, without limi tation of the general inventive concept, on examples of embodiment with refer ence to the drawing.
Figure 1 shows a vehicle comprising a satellite navigation receiver, an inertial measurement unit, a camera system and a processor to control the steps of the method for determining the navigation state of the vehicle.
The method comprises an MSCKF processing step and a subsequent GPS correc tion step. In the MSCKF processing step, the navigation state of the vehicle 1 is estimated by using a Kalman Filter. The estimated navigation state obtained by the Kalman Filter includes estimated position data of the vehicle. According to a possible embodiment, a classic MSCKF algorithm can be used in the MSCKF pro- cessing step. A classic MSCKF algorithm is described, for example, in Mourikis, Anastasios I. and S.l. Roumeliotis: "A Multi-State Constraint Kalman Filter for Vi sion-Aided Inertial Navigation", 22 (2007): 3565-3572 or Li, Mingyang, and Mourikis, Anastasios I.: "High-precision, consistent EKF-based visual-inertial odometry", International Journal of Robotics Research 32.6 (2013): 690-711
In the subsequent satellite navigation data correction step, the estimated naviga tion state of the vehicle 1 is corrected by using position data of the vehicle pro vided by a satellite navigation receiver 10 of the vehicle. The corrected naviga tion state includes corrected position data of the vehicle 1.
According to a possible embodiment of the method for determining a navigation state of the vehicle 1, the estimated position data of the vehicle determined by the Kalman Filter and the satellite navigation data of the vehicle provided by the satellite navigation receiver 10 are fused to provide the corrected navigation state of the vehicle. When the satellite navigation receiver 10 is configured as a GPS receiver, available GPS measurement data may be used to correct the navi gation state estimated in the MSCKF processing step by the use of the Kalman Filter. Since the satellite navigation data, for example the GPS navigation data, provide absolute position information, the step of correcting the estimated navi gation state of the vehicle by using satellite navigation data eliminates the accu mulative errors and unobservability that the use of a conventional MSCKF algo rithm alone has no ability to address.
According to a possible embodiment, the Kalman Filter is configured to fuse data provided from an inertial measurement unit 20 of the vehicle 1 and data provid ed from a camera system 30 of the vehicle 1 for capturing an image of an envi ronmental scene of the vehicle 1. The camera system 30 may include a monocu lar camera 31. According to an embodiment of the method for determining the navigation state of the vehicle 1, an uncertainty of the estimated navigation state of the vehicle can be determined. After having fused the data provided from the inertial meas urement unit 20 of the vehicle 1 and the data provided from the camera system 30, the estimated navigation state of the vehicle 1 and its corresponding uncer tainty can be output.
The Kalman Filter used for estimating the navigation state of the vehicle 1 is con figured as a Multi-State Constraint Kalman Filter. According to a possible embod iment of the method, the Multi-State Constraint Kalman Filter is configured to apply an extended Kalman Filter (EKF) based algorithm to provide the estimated navigation data of the vehicle 1.
According to a possible embodiment of the method for determining the naviga tion state of the vehicle 1, the method may comprise a constraints correction step. In particular, some (logical) constraints may be used to provide an im proved corrected navigation state of the vehicle. The improved corrected naviga tion state of the vehicle includes improved corrected position data of the vehicle 1. In conclusion, since the satellite navigation receiver 10 can provide absolute position information of the vehicle 1, the problems that an MSCKF algorithm suf fers, if applied alone, can be overcome by fusing satellite navigation data, such as GPS navigation data, and by adding some (logical constraints).
The constraints may include at least one of non-holonomic-constraints and alti tude constraints. The non-holonomic constraints as well as the altitude con straints may be calculated using an accelerometer and the satellite navigation data provided from the satellite navigation receiver 10 of the vehicle 1. The use of these constraints makes the navigation state estimation more stable and accu rate and enhances the robustness of the system. According to a possible embodiment of the method for determining the naviga tion state of the vehicle 1, an initialization step may be performed before the MSCKF processing step of estimating the navigation state of the vehicle by using the Kalman Filter. During the initialization step, an initial guess of the navigation state of the vehicle 1 and corresponding uncertainty may be determined based on data provided by the satellite navigation receiver 10 and data provided by the inertial measurement unit 20 of the vehicle.
The described steps of the method for determining the navigation state of the vehicle 1 may be executed by a control unit 40, for example a controller including a processor of the vehicle 1. The MSCKF algorithm may be stored in a storage unit coupled to the control unit 40.

Claims

Claims
1. A method for determining a navigation state of a vehicle, comprising: estimating the navigation state of the vehicle (1) by using a Kalman Filter, the estimated navigation state including estimated position data of the ve hicle (1), and correcting the estimated navigation state of the vehicle (1) by using posi tion data of the vehicle (1) provided by a satellite navigation receiver (10) of the vehicle to provide a corrected navigation state of the vehicle (1), the corrected navigation state including corrected position data of the vehicle (1).
2. The method of claim 1, comprising: fusing the estimated position data of the vehicle (1) and the satellite navi gation data of the vehicle (1).
3. The method of claim 1 or 2, wherein the Kalman Filter is configured as a Multi-State Constraint Kalman Filter.
4. The method of claim 3, wherein the Multi-State Constraint Kalman Filter is configured to apply an Extended Kalman Filter based algorithm to provide the estimated naviga tion data of the vehicle (1).
5. The method of any of the claims 1 to 4, wherein the Kalman Filter is configured to fuse data provided from an Iner tial Measurement Unit (20) of the vehicle (1) and data provided from a camera system (BO) of the vehicle (1) for capturing an image of an envi ronmental scene of the vehicle (1).
6. The method of claim 5, wherein the camera system (30) includes a monocular camera (31).
7. The method of any of the claims 1 to 6, comprising: determining an uncertainty of the estimated navigation state of the vehicle
(1).
8. The method of any of the claims 1 to 7, comprising: using some constraints to provide an improved corrected navigation state of the vehicle (1), the improved corrected navigation state of the vehicle (1) including improved corrected position data of the vehicle (1).
9. The method of claim 8, wherein the constraints include at least one of nonholonomic-constraints and altitude constraints.
10. The method of any of the claims 5 to 9, comprising: providing an initial guess of the navigation state of the vehicle (1) and cor responding uncertainty based on data provided by the satellite navigation receiver (10) and data provided by the Inertial Measurement Unit (20).
EP20745170.9A 2019-07-26 2020-07-22 Method for determining a navigation state of a vehicle Pending EP4025872A1 (en)

Applications Claiming Priority (2)

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PCT/EP2020/070673 WO2021018689A1 (en) 2019-07-26 2020-07-22 Method for determining a navigation state of a vehicle

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Family Cites Families (6)

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Publication number Priority date Publication date Assignee Title
US7228230B2 (en) * 2004-11-12 2007-06-05 Mitsubishi Denki Kabushiki Kaisha System for autonomous vehicle navigation with carrier phase DGPS and laser-scanner augmentation
US8757548B2 (en) * 2007-04-30 2014-06-24 The Boeing Company Apparatus for an automated aerial refueling boom using multiple types of sensors
WO2015013418A2 (en) * 2013-07-23 2015-01-29 The Regents Of The University Of California Method for processing feature measurements in vision-aided inertial navigation
US9658070B2 (en) * 2014-07-11 2017-05-23 Regents Of The University Of Minnesota Inverse sliding-window filters for vision-aided inertial navigation systems
US10267924B2 (en) * 2017-01-04 2019-04-23 Qualcomm Incorporated Systems and methods for using a sliding window of global positioning epochs in visual-inertial odometry
US10107627B2 (en) * 2017-03-08 2018-10-23 Northrop Grumman Systems Corporation Adaptive navigation for airborne, ground and dismount applications (ANAGDA)

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