External-world recognition system
US-11195349-B2 · Dec 7, 2021 · US
US11852489B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-11852489-B2 |
| Application number | US-201917254539-A |
| Country | US |
| Kind code | B2 |
| Filing date | Jun 3, 2019 |
| Priority date | Jun 22, 2018 |
| Publication date | Dec 26, 2023 |
| Grant date | Dec 26, 2023 |
A practical reading order for non-experts. Skip the full description unless you need deep technical detail.
What the patent document calls the invention.
A short plain-language summary of the technical disclosure.
Who owns or filed the patent and who is credited as inventor.
Filing, priority, publication, and grant dates set the timeline.
The legal scope of protection — read this for what is actually claimed.
Technology tags used to group this patent with similar filings.
Prior art links and similar publications in this corpus.
Official abstract text for this publication.
A method for determining the position of a vehicle involves determining a plurality of position hypotheses by comparing landmark objects detected by a sensor system of the vehicle with landmark objects stored in a map, in particular in a map section. By analyzing all position hypotheses and filtering out all false information using probabilistic analysis, a position hypothesis with an integrity value is determined. A position hypothesis with a position accuracy sufficient in a predetermined way for determining the position of the vehicle is determined by filtering according to predetermined limit values.
Opening claim text (preview).
The invention claimed is: 1. A method for landmark-based positioning of a vehicle, the method comprising: detecting, by a sensor system of the vehicle, sensor landmark objects in an environment of the vehicle, wherein the sensor system is an image sensor system capturing the sensor landmarks objects in an environment of the vehicle or the sensor system is a LIDAR sensor system measuring reflections from the sensor landmark objects; forming associations between the sensor landmark objects and map landmark objects by comparing the sensor landmark objects with the map landmark objects stored in a section of a map; determining a plurality of position hypotheses as assumptions for the vehicle position based on the formed associations; determining, for each position hypothesis, an integrity value using probabilistic analysis of each of the position hypotheses; filtering the position hypotheses according to integrity values of the position hypotheses to determine a subset of position hypotheses, wherein position hypothesis of the subset of position hypotheses have integrity values fulfilling a predetermined requirement; determining a probability distribution of the vehicle position based on the subset of the position hypotheses; and determining, using the probability distribution of the vehicle position, a position of the vehicle on the map. 2. The method of claim 1 , wherein a most probable position of the vehicle is determined as the vehicle position from the determined probability distribution of the vehicle position and a predetermined permissible error rate, and an upper error limit is determined for the vehicle position. 3. The method of claim 2 , wherein the determined vehicle position including the determined upper error limits of the vehicle position and a position of the vehicle determined by a global navigation system including an upper error limit of the position of the vehicle determined by a global navigation system are merged with each other. 4. The method of claim 1 , wherein sensor landmark objects already passed by the vehicle are also used for the comparison of the sensor landmark objects and the map landmark objects. 5. The method of claim 1 , wherein sensor landmark objects detected by the sensor system of the vehicle are used for the comparison of the sensor and map landmark objects over a predetermined time period and/or over a predetermined route length. 6. The method of claim 1 , wherein the vehicle includes an automated driving operation, the method further comprising: operating the vehicle in the automated driving operation based on the determined position of the vehicle on the map. 7. The method of claim 1 , wherein the filtering of the position hypotheses is performed by a localization filter. 8. The method of claim 7 , wherein the localization filter is a histogram filter. 9. The method of claim 1 , further comprising: determining a satellite-based position of the vehicle using a global navigation satellite system; determining whether an integrity of the satellite-based position and an integrity of the determined position of the vehicle on the map are both within an upper error limit; and merging the satellite-based position and an integrity of the determined position of the vehicle on the map responsive to determining that the integrity of the satellite-based position and the integrity of the determined position of the vehicle on the map are both within an upper error limit.
with correlation of data from several navigational instruments · CPC title
Instruments for performing navigational calculations (G01C21/24, G01C21/26 take precedence) · CPC title
Data obtained from position sensors only, e.g. from inertial navigation · CPC title
by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement · CPC title
Related publications grouped by family.
Answers are generated from the same data shown on this page.