ASCERTAINING A STARTING POSITION OF A VEHICLE FOR A LOCALIZATION

Abstract

A method for ascertaining a starting position of a vehicle for a localization of the vehicle using a control unit. In the method, measurement data are received from an odometry sensor system and/or a GNSS sensor system of the vehicle, a first position and an uncertainty range of the first position are determined based on the measurement data received, at least one map section of a feature map containing a plurality of stored features is received, the map section having a position and extent which is superimposed on the first position and the uncertainty range, 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 measurement data received, a first starting position of the vehicle is ascertained by comparing the static features extracted from measurement data with features stored in the map section.

Claims

1-13. (canceled)

14. A method for ascertaining a starting position of a vehicle for a localization of the vehicle, by a control unit, the method comprising the following steps: receiving measurement data from an odometry sensor system of the vehicle and/or from a GNSS sensor system of the vehicle; determining, based on the measurement data received from the odometry sensor system and/or the GNSS sensor system, a first position and an uncertainty range of the first position; receiving at least one map section of a feature map containing a plurality of stored features, the map section having a position and extent which is superimposed on the first position and the uncertainty range; receiving measurement data from a LiDAR sensor system and/or a radar sensor system and/or a camera sensor system, and extracting static features from the measurement data received from the LiDAR sensor system, and/or the radar sensor system and/or the camera sensor system; and ascertaining a first starting position of the vehicle by comparing the extracted static features with features stored in the map section.

15. The method as recited in claim 14, wherein at least one second starting position, temporally separated from the first starting position, is ascertained, the first starting position and the at least one second starting position being compared with positions ascertained from measurement data from the odometry sensor system, a deviation being calculated between the at least one second starting position and a position of the vehicle ascertained using the measurement data from the odometry sensor system, and a consistency check is carried out.

16. The method as recited in claim 14, wherein at least one second starting position, temporally separated from the first starting position, is ascertained, the first starting position and the at least one second starting position being combined with measurement data from the odometry sensor system to form trajectories, a goodness of fit being ascertained for each of the trajectories, a trajectory with a best goodness of fit being used or all trajectories being rejected.

17. The method as recited in claim 14, wherein extracted static features from prior measurements by the LiDAR sensor system and/or the radar sensor system and/or the camera sensor system are used to compare the extracted features with features stored in the map section.

18. The method as recited in claim 17, wherein extracted static features from the prior measurements are linked to the extracted static features from current measurements using measurement data from the odometry sensor system.

19. The method as recited in claim 14, wherein an optimization method is used to determine the first position.

20. The method as recited in claim 14, wherein the measurement data from the odometry sensor system and/or the GNSS sensor system of the vehicle are received continuously and the first position is determined continuously based on the measurement data received from the odometry sensor system and/or the GNSS sensor system of the vehicle.

21. The method as recited in claim 14, wherein the ascertained starting position is used to perform a road approval service.

22. A method for performing a localization, the method comprising the following steps: receiving a first starting position of a vehicle as an input variable and/or as a validation variable, the first starting position being ascertained by: receiving measurement data from an odometry sensor system of the vehicle and/or from a GNSS sensor system of the vehicle, determining, based on the measurement data received from the odometry sensor system and/or the GNSS sensor system, a first position and an uncertainty range of the first position, receiving at least one map section of a feature map containing a plurality of stored features, the map section having a position and extent which is superimposed on the first position and the uncertainty range, receiving measurement data from a LiDAR sensor system, and/or a radar sensor system and/or a camera sensor system, and extracting static features from the measurement data received from the LiDAR sensor system, and/or the radar sensor system and/or the camera sensor system, and ascertaining a first starting position of the vehicle by comparing the extracted static features with features stored in the map section.

23. The method as recited in claim 22, wherein the ascertaining of the starting position is carried out in parallel with the method for performing the localization.

24. A control unit configured to ascertain a starting position of a vehicle for a localization of the vehicle, by a control unit, the control unit configured to: receive measurement data from an odometry sensor system of the vehicle and/or from a GNSS sensor system of the vehicle; determine, based on the measurement data received from the odometry sensor system and/or the GNSS sensor system, a first position and an uncertainty range of the first position; receive at least one map section of a feature map containing a plurality of stored features, the map section having a position and extent which is superimposed on the first position and the uncertainty range; receive measurement data from a LiDAR sensor system and/or a radar sensor system and/or a camera sensor system, and extracting static features from the measurement data received from the LiDAR sensor system and/or the radar sensor system and/or the camera sensor system; and ascertain a first starting position of the vehicle by comparing the extracted static features with features stored in the map section.

25. A non-transitory machine-readable storage medium on which is stored a computer program for ascertaining a starting position of a vehicle for a localization of the vehicle, by a control unit, the computer program, when executed by a computer, causing the computer to perform the following steps: receiving measurement data from an odometry sensor system of the vehicle and/or from a GNSS sensor system of the vehicle; determining, based on the measurement data received, a first position and an uncertainty range of the first position; receiving at least one map section of a feature map containing a plurality of stored features, the map section having a position and extent which is superimposed on the first position and the uncertainty range; receiving measurement data from a LiDAR sensor system and/or a radar sensor system and/or a camera sensor system, and extracting static features from the measurement data received from the LiDAR sensor system, and/or the radar sensor system and/or the camera sensor system; and ascertaining a first starting position of the vehicle by comparing the extracted static features with features stored in the map section.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0036] FIG. 1 shows a schematic top view of a roadway to illustrate a determination of a first position of a vehicle in accordance with an example embodiment of the present invention.

[0037] FIG. 2 shows a schematic top view of a roadway from FIG. 1 and of a map section,

[0038] FIGS. 3, 4 show schematic diagrams to illustrate a consistency check, according to an example embodiment of the present invention.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

[0039] FIGS. 1 through 4 show schematic representations to illustrate a method for ascertaining a first starting position A of a vehicle 2 for a localization of vehicle 2 by a control unit 4.

[0040] Vehicle 2 has an odometry sensor system and/or a GNSS sensor system 6 as well as an additional sensor system 8 for a feature-based localization. Additional sensor system 8 may be designed as, for example, a LiDAR sensor system, a radar sensor system and/or a camera sensor system.

[0041] FIG. 1 shows a schematic top view of a roadway 10 to illustrate a determination of a first position P of vehicle 2. Vehicle 2 travels along roadway 10 in direction of travel F. In the exemplary embodiment shown, measurement data are collected during the journey by the odometry sensor system and GNSS sensor system 6. In this process, a plurality of measurement data 12 collected in chronological succession by the odometry sensor system and GNSS sensor system 6 are stored in order to determine first position P. Measurement data 12 from the odometry sensor system and GNSS sensor system 6 may optionally be smoothed so as to obtain optimized measurement data 14 from the ascertained measurement data 12. The ascertained measurement data 12 may be optimized by way of moving averages, for example.

[0042] There may be a maximum limit set for the amount of measurement data 12 ascertained, such that old measurement data are deleted automatically or overwritten by more recent measurement data. First position P may in this case be the last or most recent measurement by the odometry sensor system and GNSS sensor system 6 following optimization or smoothing.

[0043] FIG. 2 shows a schematic top view of a roadway 10 from FIG. 1 and of a map section 16. Map section 16 is part of a feature map and contains a plurality of features 18. In the exemplary embodiment shown, the feature map is a radar-specific feature map. Map section 16 has a position and an extent which is superimposed on or covers first position P and an optional uncertainty range of first position P.

[0044] Furthermore, extracted static features 20 from current measurements and static features 22 from prior measurements by the LiDAR sensor system, the radar sensor system and/or camera sensor system 8 are taken into account, such that an enlarged map section 16 is used to compare extracted static features 20, 22 with features 18 stored in map section 16.

[0045] The radar-specific feature map and map section 16 are stored in the form of map sections 16 which map the topology of the road network or of roadway 10. The at least one map section 16 may be transformed into a coordinate system for vehicle 2, which in the interests of clarity is not shown in FIG. 2. Furthermore, features 18 of map section 16 may be compared with the extracted static features 20, 22 and aligned with one another for a feature-based localization. Such an alignment may be carried out with a cost function and an optimization algorithm for the cost function.

[0046] 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, temporally separated from first starting position A, are ascertained.

[0047] In parallel with starting positions A, A2, A3, positions P, P2, P3 are ascertained using odometry sensor system 6 and compared with starting positions A, A2, A3. To this end, a difference D or a gap between positions P, P2, P3 and starting positions A, A2 may be calculated. The consistency check is successful if difference D is below a predefined threshold or limit.

[0048] In the exemplary embodiment shown, the first two starting positions A, A2 are consistent and correct. The last starting position A3 exhibits too great a deviation D from position P3 and is not consistent. In this case, the method for ascertaining the starting position A of vehicle 2 may be carried out again and subjected to a consistency check. By preference, the consistency check may present a number of successfully checked starting positions A, A2, A3 before the most recently checked starting position A3 is approved for a localization of vehicle 2.

[0049] FIG. 4 illustrates a technically more complex consistency check, which is based on multi-hypothesis tracking. A plurality of starting positions A, A2, A3 with a best match within map section 16 are taken into consideration. These starting positions A, A2, A3 are linked to matches from the last alignment of features 18, 20, 22. Measurement data 12 from odometry sensor system 6 are used to join starting positions A, A2, A3. Each of starting positions A, A2, A3 may have parallel starting positions AP within map section 16 at which extracted features 20, 22 match features 18 stored in map section 16. Such results of the feature-based localization may occur in periodic environments, such as freeway sections, for example. Measurement data 12 from odometry sensor system 6 are compared in the form of trajectories with respective starting positions A, A2, A3, AP, starting positions A, A2, A3, AP having the best goodness of fit being used for the further localization of vehicle 2.