METHOD FOR CREATING A UNIVERSALLY USEABLE FEATURE MAP

Abstract

A method for creating digital maps with the aid of a control unit. Measured data of surroundings are received during a measuring run. A SLAM method is carried out for ascertaining a trajectory of the measuring run based on the received measured data. The received measured data are transformed into a coordinate system of the trajectory. The transformed measured data are used for the purpose of creating an intensity map. Features are extracted from the intensity map and are stored in a feature map. A method for carrying out a localization, a control unit, a computer program as well as a machine-readable memory medium are also described.

Claims

1-12. (canceled)

13. A method for creating a digital map using a control unit, the method comprising the following steps: receiving measured data of surroundings during a measuring run; ascertaining, using a SLAM method, a trajectory of the measuring run based on the received measured data; transforming the received measured data into a coordinate system of the ascertained trajectory; creating an intensity may using the transformed measured data; and extracting features from the intensity map and storing the extracted features in a feature map.

14. The method as recited in claim 13, wherein the feature map is stored as the digital map or as a map layer of the digital map.

15. The method as recited in claim 13, wherein the received measured data are present as a point cloud and are assigned to a grid made up of a plurality of cells, median values of the measured data of each cell of the cells being formed for creating the intensity map.

16. The method as recited in claim 15, further comprising: creating an elevation map (from the received measured data, a weighted mean value being formed from the measured data of each cell and of adjacent cells for creating the elevation map.

17. The method as recited in claim 16, wherein pieces of information from the created elevation map are received and stored in the feature map for determining an elevation of the extracted features.

18. The method as recited in claim 13, wherein the extracted features are stored as universally ascertainable features in the feature map, the features being extracted and stored as geometric shapes and/or lines and/or points and/or point clouds.

19. A method for carrying out a localization using a control unit, the method comprising: receiving measured data of surroundings and a feature map; recognizing and extracting features in the received measured data; and ascertaining a position by comparing at least one of the extracted features with features stored in the feature map.

20. The method as recited in claim 19, wherein the received measured data are position data and are stored in a position diagram, the ascertained position in the case of successfully compared features being stored as a new measured value in the position diagram.

21. The method as recited in claim 19, wherein the measured data are ascertained by at least one sensor, which differs from at least one sensor, for creating the feature map.

22. A control unit configured to create a digital map using a control unit, the control unit configured to: receive measured data of surroundings during a measuring run; ascertain, using a SLAM method, a trajectory of the measuring run based on the received measured data; transform the received measured data into a coordinate system of the ascertained trajectory; create an intensity may using the transformed measured data; and extract features from the intensity map and store the extracted features in a feature map.

23. A non-transitory machine-readable memory medium on which is stored a computer program for creating a digital map using a control unit, the computer program, when executed by computer, causing the computer to perform the following steps: receiving measured data of surroundings during a measuring run; ascertaining, using a SLAM method, a trajectory of the measuring run based on the received measured data; transforming the received measured data into a coordinate system of the ascertained trajectory; creating an intensity may using the transformed measured data; and extracting features from the intensity map and storing the extracted features in a feature map.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Preferred exemplary embodiments of the present invention are explained in greater detail below with reference to highly simplified schematic representations.

[0030] FIG. 1 schematically shows a representation of an arrangement for illustrating an example method according to the present invention,

[0031] FIG. 2 schematically shows a diagram for illustrating the method for creating digital maps according to one exemplary embodiment of the present invention.

[0032] FIG. 3 schematically shows a diagram for illustrating the method for carrying out a localization according to one exemplary embodiment of the present invention.

[0033] FIG. 4 schematically shows an intensity map.

[0034] FIG. 5 schematically shows an elevation map.

[0035] FIG. 6 shows a perspective representation of a feature map.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

[0036] FIG. 1 schematically shows a representation of an arrangement 1 for illustrating a method 2, 4 according to an example embodiment of the present invention.

[0037] Arrangement 1 includes two vehicles 6, 8. Alternatively or in addition, arrangement 1 may include robots and/or additional vehicles. According to the exemplary embodiment represented, a first vehicle 6 is used for carrying out method 2 for creating digital maps, in particular, marking maps. Second vehicle 8 is schematically illustrated in order to illustrate a method 4 for carrying out a localization within the digital map.

[0038] First vehicle 6 includes a control unit 10, which is connected in a data-transferring manner to a machine-readable memory 12 and to a sensor 14. Sensor 14 may, for example, be a LIDAR sensor 14.

[0039] First vehicle 6 is able to scan surroundings U and to generate measured data with the aid of LIDAR sensor 14. The ascertained measured data may subsequently be received and evaluated by control unit 10. A feature map created by control unit 10 may be provided to other road users and to vehicle 8 via a communication link 16. The feature map may be stored in machine-readable memory medium 12.

[0040] Second vehicle 8 also includes a control unit 11. Control unit 11 is connected in a data-transferring manner to a machine-readable memory medium 13 and to a sensor 15. Sensor 15 according to the exemplary embodiment is a camera sensor 15 and is also able to ascertain measured data of surroundings U and to transfer them to control unit 11. Control unit 11 is able to extract features from the measured data of surroundings U and to compare them with features from the feature map, which have been received by control unit 11 via communication link 16.

[0041] A schematic diagram for illustrating method 2 for creating digital maps according to one exemplary embodiment is shown in FIG. 2.

[0042] In a first step 18, measured data of surroundings U are ascertained during a measuring run of first vehicle 6 and received by control unit 10. According to the exemplary embodiment, surroundings U are scanned with a LIDAR sensor 14.

[0043] In a subsequent step 19, a SLAM method is carried out during the measuring run based on the received measured data. A trajectory of first vehicle 6 is ascertained with the aid of the SLAM method.

[0044] The received measured data are transformed 20 into a coordinate system of the trajectory. Alternatively, the trajectory may be transformed into a coordinate system of the measured data. For example, the shared coordinate system may be a Cartesian coordinate system.

[0045] An intensity map 30 is created 21 based on the transformed measured data. Such an intensity map 30 is illustrated in FIG. 4. The measured data may be present, in particular, as a grid map including a plurality of cells 31, 31. Cells 31, 32 may, for example, be designed as pixels or as pixel groups. Each cell 31, 32 may include in accordance with the coordinate system a local assignment such as, for example, GPS coordinates.

[0046] An intensity is subsequently calculated for each cell 31, 32. For this purpose, a mean value is calculated for all measured values within respective cell 31, 32. An intensity map 30 is thus formed 21 from the calculated mean values.

[0047] An elevation map 40 is also created 22. Elevation map 40 is created from the weighted mean values and is shown in FIG. 5. The weighted mean values are calculated for the measured values within each cell 31 and for the measured data in the corresponding adjacent cells 32.

[0048] In one further step 23, features are extracted from intensity map 30. This may take place, for example, via an automated pattern recognition algorithm or manually by an employee. For example, transitions between bright and dark areas in intensity map 30 may be considered as possible patterns. Each feature may be assigned a profile based on elevation map 40.

[0049] The ascertained features are stored 24 according to their position within intensity map 30 in a feature map 60. Feature map 60 is schematically illustrated in FIG. 6. In this case, an exemplary LIDAR scan is superimposed with a plurality of features 62, 64, 66. Features 62, 64, 66 are designed by way of example as lane markings 62, roadway boundaries 64 and other markings on surface 66. Feature map 60 may, for example, be stored in machine-readable memory medium 12 and be provided via communication link 16.

[0050] FIG. 3 schematically shows a diagram for illustrating method 4 for carrying out a localization according to one exemplary embodiment. Method 4 is carried out, for example, by control unit 11 of second vehicle 8.

[0051] In a step 25, measured data of surroundings U are ascertained by sensor 15 and transferred to control unit 11. Feature map 60 is also received by control unit 11 via communication link 16. This may be converted by a position diagram localizer implemented in control unit 11.

[0052] The measured data in this case may be ascertained continuously or at defined temporal intervals and may be received by control unit 11. In addition, odometric measured data may be received by control unit 11.

[0053] In a further step 26, features 62, 64, 66 are extracted from the received measured data. Features 62, 64, 66 in this case are compared 27 with received feature map 60. In the comparison, the attempt is made to find features 62, 64, 66 detected on board on feature map 60. The odometrically ascertained measured data in this case may narrow down the search area within feature map 60. Since feature map 60 includes abstracted and therefore universally useable features 62, 64, 66, the measured data ascertained using camera sensor 15 may also be used for a localization.

[0054] If matches are found between the features ascertained on board with features 62, 64, 66 in feature map 60, the position of vehicle 8 may be corrected or updated 28.