Positioning system and method for operating a mobile unit positioning system

Patent metadata

Field	Value
Publication number	US-12007238-B2
Application number	US-201916719617-A
Country	US
Kind code	B2
Filing date	Dec 18, 2019
Priority date	Dec 21, 2018
Publication date	Jun 11, 2024
Grant date	Jun 11, 2024

Abstract

A method for providing map data which include position information for first landmarks of a first landmark class, collecting environment data, and determining a position of the mobile unit. Training data are generated and stored for the first landmarks based on the position of the mobile unit, the collected environment data and the position information. Based on the training data, a first detector module is generated for detecting the first landmark class. The position determination system includes a memory unit for providing map data which include position information for first landmarks of a first landmark class, a data acquisition unit for collecting environment data, a localization unit for determining a position of the mobile unit, and a processing unit for generating and storing training data based on the position of the mobile unit, the collected environment data and the position information for the first landmarks.

First claim

The invention claimed is:

1. A method for operating a position determination system for a mobile unit that performs computational processing, the method comprising:
providing map data, which comprise position information for first landmarks of a first landmark class;
collecting environment data;
determining a position of the mobile unit;
generating and storing training data, wherein the training data is generated based on the position of the mobile unit, the collected environmental data and the position information for the first landmarks, wherein the training data is generated by performing a transformation such that the map data is in a coordinate system relative to the mobile unit, wherein portions of the collected environment data in which the first landmarks of the first landmark class are located are determined based on the position information for the first landmarks included in the environment data, whereby subsets of the collected environment data that include the first landmarks are determined and stored as the training data; and
generating a first detector module for detecting the first landmark class based on the training data,
wherein the first detector module is implemented in the mobile unit's computational processes,
wherein the generation of the first detection module is performed via a machine learning procedure that includes determining a correlation for the training data and testing the determined correlation to determine whether the correlation exceeds a threshold value, defined for evaluation of quality of the environment data to exclude environment data with insufficient recording quality and to ensure that the training data is generated based on environment data that is recorded at multiple locations, from multiple perspectives relative to landmarks and under multiple weather conditions,
wherein the correlation indicates patterns detected in different portions of the environmental data that include the first landmarks of the first landmark class, and
wherein the machine learning procedure uses pattern recognition to compare portions of the environment data that include the training data with each other and with negative examples without the first landmarks to determine correlations to ensure reliable recognition of the first landmarks as well as reliable recognition of environment data where the first landmark does not exist.

2. The method of claim 1 , wherein:
the map data also comprise position information for second landmarks of a second landmark class, wherein the second detector module is implemented in the mobile unit's computational processes; and
the second landmarks are detected by a second detector module for the second landmark class based on the environment data; and
the position of the mobile unit is determined based on the detected second landmarks.

3. The method of claim 1 , wherein:
the first landmarks are detected by the first detector module at a later time; and
the position of the mobile unit is determined based on the detected first landmarks.

4. The method of claim 1 , wherein:
the training data comprise a subset of the collected environment data;
the subset is generated based on the position information about the first landmarks.

5. The method of claim 1 , wherein the map data comprise additional information about landmarks of the first landmark class.

6. The method of claim 5 , wherein h additional information comprise a mapping of the first landmark class to a generic landmark class.

7. The method of claim 1 , wherein the first detector module is generated in response to the training data satisfying a quality criterion.

8. The method of claim 1 , wherein the first detector module is generated by a learning unit integrated in the mobile unit.

9. The method of claim 1 , wherein:
the training data are transferred to an external unit;
the first detector module is generated by the external unit; and
the first detector module is transmitted to the mobile unit.

10. The method of claim 1 , wherein:
an output is generated based on the training data; and
a user is prompted to confirm the training data.

11. The method of claim 1 , wherein the mobile unit is a transportation vehicle.

Assignees

• Volkswagen Ag

Inventors

• Wilbers Daniel
• Merfels Christian
• Rech Bernd
• Schaper Thilo
• Koch Niklas
• Jürgens Stefan
• Perdomo Lopez David
• Hungar Constanze
• Wappler Stefan

Classifications

• G01C21/3837

  Data obtained from a single source · CPC title

• G01C21/3811

  Point data, e.g. Point of Interest [POI] · CPC title

• G06N5/04

  Inference or reasoning models · CPC title

• G06N20/00

  Machine learning · CPC title

• G06V10/245

  by locating a pattern; Special marks for positioning · CPC title