Method and system for mapping and locating a vehicle based on radar measurements

1. A method for mapping a surroundings of at least one vehicle and for locating the at least one vehicle, the method comprising:

ascertaining, by at least one radar sensor of the at least one vehicle, measurement data of the surroundings of the vehicle;

aggregating the measurement data of the at least one radar sensor to reduce a measurement data density of the ascertained measurement data and to reduce a storage space requirement of the measurement data;

comparing the aggregated measurement data with existing aggregated measurement data;

optimizing, based on the comparison between the aggregated measurement data and the existing aggregated measurement data, the aggregated measurement data by reducing measurement errors;

generating or updating, based on the optimized aggregated measurement data, a map; and

locating the at least one vehicle on the generated or updated map by comparing the ascertained measurement data with the generated or updated map;

wherein the ascertained measurement data pointing to ghost targets are deleted, by applying a density-based spatial cluster analysis or a k-means algorithm to the ascertained measurement data,

wherein in the aggregating, directions of transmitted and received radar waves are used to combine the ascertained measurement data into measurement point clouds, which are groups of measurement points, wherein the groups are formed into formed groups as a function of a receiving angle of reflected radar waves, and

wherein distances between the formed groups are measured or calculated, and the distances are compared to the distances of the existing aggregated measurement data to reduce deviations of the ascertained measurement data.

2. The method as recited in claim 1 , wherein the measurement data of the at least one radar sensor are aggregated by a cluster analysis.

3. The method as recited in claim 1 , wherein the measurement data of the at least one radar sensor are filtered.

4. The method as recited in 1 , wherein the aggregated measurement data form nodes, and connecting paths between the nodes are formed and compared for the comparison with the existing aggregated measurement data.

5. The method as recited in claim 4 , wherein at least one node is formed based on at least one measurement data cloud.

6. The method as recited in claim 4 , wherein the nodes represent measurement locations of the at least one radar sensor and the connecting paths between the measurement locations are ascertained odometrically.

7. The method as recited in claim 1 , wherein the aggregated measurement data are compared with measurement data of at least one second sensor for reducing measurement errors.

8. The method as recited in claim 1 , wherein the map is updated by superimposing optimized aggregated measurement data.

9. The method as recited in claim 1 , wherein the generated map is compressed.

10. The method as recited in claim 1 , wherein the generated map is compressed by cluster formation, each cluster of the generated map being assigned a time stamp and older clusters being replaced by corresponding current clusters when the map is updated.

11. The method as recited in claim 10 , wherein the compressed map is analyzed for detecting objects.

12. The method as recited in claim 1 , wherein the generated map is linked to at least one geographic map.

13. A system for mapping a surroundings of at least one vehicle and for locating the at least one vehicle, comprising:

at least one vehicle having at least one radar sensor to ascertain measurement data;

at least one external and/or internal processing unit to generate or update a map based on the ascertained measurement data and to locate the at least one vehicle based on the ascertained measurement data;

wherein the at least one external and/or internal processing unit is configured to aggregate the measurement data of the at least one radar sensor to reduce a measurement data density of the ascertained measurement data and to reduce a storage space requirement of the measurement data, and to compare the aggregated measurement data with existing aggregated measurement data,

wherein based on the comparison between the aggregated measurement data and the existing aggregated measurement data, the processing unit is further configured to optimize the aggregated measurement data by reducing measurement errors, and generate or update the map based on the optimized aggregated measurement data,

wherein the ascertained measurement data pointing to ghost targets are deleted, by applying a density-based spatial cluster analysis or a k-means algorithm to the ascertained measurement data,

wherein in the aggregating, directions of transmitted and received radar waves are used to combine the ascertained measurement data into measurement point clouds, which are groups of measurement points, wherein the groups are formed into formed groups as a function of a receiving angle of reflected radar waves, and

wherein distances between the formed groups are measured or calculated, and the distances are compared to the distances of the existing aggregated measurement data to reduce deviations of the ascertained measurement data.