Systems and methods for mapping based on multi-journey data

Patent metadata

Field	Value
Publication number	US-11776280-B2
Application number	US-202117446618-A
Country	US
Kind code	B2
Filing date	Aug 31, 2021
Priority date	Jan 4, 2017
Publication date	Oct 3, 2023
Grant date	Oct 3, 2023

Abstract

A method performed by an apparatus is described. The method includes receiving map data that is based on first image data, second image data, and a similarity metric. The first image data can be received from a first vehicle and represent an object. The second image data can be received from a second vehicle and represent the object. The similarity metric can be associated with the object represented in the first image data and the object represented in the second image data. The method can also include storing, by a vehicle, the received map data and localizing the vehicle based on the stored map data.

First claim

What is claimed is:

1. A vehicle, comprising:
a memory; and
a processor coupled to the memory, wherein the processor is configured to:
receive map data, wherein the map data is based on first image data, second image data, and a similarity metric, wherein the first image data is received from a first vehicle and represents an object, wherein the second image data is received from a second vehicle and represents the object, and wherein the similarity metric is associated with the object represented in the first image data and the object represented in the second image data;
store the received map data; and
localize the vehicle based on the stored map data.

2. The vehicle of claim 1 , wherein the map data is based on an object cluster associated with the object represented in the first image data and the object represented in the second image data.

3. The vehicle of claim 2 , wherein the object cluster is based on feature points of the object represented in the first image data and feature points of the object represented in the second image data.

4. The vehicle of claim 3 , wherein the map data is based on a bundle adjustment that is based on the object cluster.

5. The vehicle of claim 2 , wherein the processor is configured to:
receive image data from a camera coupled to the vehicle; and
localize the vehicle based on the stored map data and the received image data.

6. The vehicle of claim 5 , wherein the received image data from the camera comprises the first image data, and wherein the processor is configured to transmit the first image data.

7. The vehicle of claim 5 , wherein the processor is configured to obtain local semantic information based on the localization and the stored map data.

8. The vehicle of claim 7 , wherein the first image data and the second image data consist of feature points.

9. The vehicle of claim 7 , wherein the first image data and the second image data include camera pose information.

10. The vehicle of claim 7 , wherein the received map data corresponds to a plurality of tiles.

11. The vehicle of claim 10 , further comprising at least one antenna for receiving radio frequency signals.

12. The vehicle of claim 11 , wherein the map data is transmitted to the vehicle using radio frequency signals.

13. The vehicle of claim 12 , wherein the object is a lane marker or a sign.

14. The vehicle of claim 13 , wherein the similarity metric is based on a type of the object.

15. The vehicle of claim 14 , wherein the similarity metric is based on a sign object type, wherein a second similarity metric is based on a lane marker object type, wherein the similarity metric is different than the second similarity metric, and wherein the map data is based on the second similarity metric.

16. A method, comprising:
receiving map data, wherein the map data is based on first image data, second image data, and a similarity metric, wherein the first image data is received from a first vehicle and represents an object, wherein the second image data is received from a second vehicle and represents the object, and wherein the similarity metric is associated with the object represented in the first image data and the object represented in the second image data;
storing, by a vehicle, the received map data; and
localizing the vehicle based on the stored map data.

17. The method of claim 16 , wherein the map data is based on an object cluster associated with the object represented in the first image data and the object represented in the second image data.

18. The method of claim 17 , wherein the object cluster is based on feature points of the object represented in the first image data and feature points of the object represented in the second image data.

19. The method of claim 18 , wherein the map data is based on a bundle adjustment that is based on the object cluster.

20. The method of claim 17 , further comprising:
receiving image data from a camera coupled to the vehicle; and
localizing the vehicle based on the stored map data and the received image data.

21. The method of claim 20 , wherein the received image data from the camera comprises the first image data, and further comprising transmitting the first image data.

22. The method of claim 20 , wherein further comprising obtaining local semantic information based on the localization and the stored map data.

23. The method of claim 22 , wherein the first image data and the second image data consist of feature points.

24. The method of claim 22 , wherein the first image data and the second image data include camera pose information.

25. The method of claim 22 , wherein the received map data corresponds to a plurality of tiles.

26. The method of claim 25 , wherein the map data is transmitted to the vehicle using radio frequency signals.

27. The method of claim 26 , wherein the object is a lane marker or a sign.

28. The method of claim 27 , wherein the similarity metric is based on a type of the object.

29. The method of claim 28 , wherein the similarity metric is based on a sign object type, wherein a second similarity metric is based on a lane marker object type, wherein the similarity metric is different than the second similarity metric, and wherein the map data is based on the second similarity metric.

Assignees

• Qualcomm Inc

Inventors

• Dabeer Onkar Jayant
• Somasundaram Kiran
• Gowaikar Radhika Dilip
• Sukhavasi Ravi Teja

Classifications

• G01C21/3841

  Data obtained from two or more sources, e.g. probe vehicles · CPC title

• G01C21/32

  Structuring or formatting of map data · CPC title

• G06V10/761

  Proximity, similarity or dissimilarity measures · CPC title

• G06V10/762

  using clustering, e.g. of similar faces in social networks · CPC title

• G06V20/588 Primary

  Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road · CPC title