METHOD AND APPARATUS FOR GENERATING MAP GEOMETRY BASED ON A RECEIVED IMAGE AND PROBE DATA

• US 20160239983A1
• Filed: 02/13/2015
• Published: 08/18/2016
• Est. Priority Date: 02/13/2015
• Status: Active Grant

• Current Assignee: HERE Global B.V. (Nokia Corporation)
• Original Assignee: HERE Global B.V. (Nokia Corporation)
• Inventors: Endres, Ian, Dorum, Ole Henry
• Granted Patent: US 10,223,816 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01C 11/04 Interpretation of pictures
• CPC Class Codes: G01C 21/3848 Data obtained from both position sensors and additional sensors
• CPC Class Codes: G01C 21/3852 Data derived from aerial or satellite images
• CPC Class Codes: G01C 21/3867 Geometry of map features, e.g. shape points, polygons or for simplified maps
• CPC Class Codes: G06T 11/20 Drawing from basic elements, e.g. lines or circles
• CPC Class Codes: G06V 20/182 Network patterns, e.g. roads or rivers

Abstract

A method, apparatus and computer program product are provided for generating map geometry based on a received image and probe data. A method is provided including receiving a first image and probe data associated with the first image, categorizing pixels of the first image based on the probe data, and generating a map geometry based on the pixel categorization of the first image.

Claims

1. A method comprising:

receiving a first image and probe data associated with the first image;

categorizing pixels of the first image based on the probe data; and

generating a map geometry based on pixel categorization of the first image.

2. The method of claim 1 further comprising:

receiving a second image;

categorizing pixels of the second image based on the categorization of the pixels of the first image; and

generating a map geometry based on pixel categorization of the second image.

3. The method of claim 1, wherein the categorizing pixels of the first image is based on probe data density.

4. The method of claim 3, further comprising;

determining a target center based on probe data density, and

wherein the categorizing pixels is based on the target center.

5. The method of claim 1, wherein the categorizing pixels comprises categorizing pixels as target pixels and non-target pixels.

6. The method of claim 1 further comprising:

determining a target confidence value of pixels of the first image based on the probe data, and

wherein the categorizing pixels is based on the target confidence value of the respective pixel satisfying a predetermined target confidence value threshold.

7. The method of any of claims 1-6, further comprising:

updating or generating map data based on the map geometry.

8. An apparatus comprising at least one processor and at least one memory including computer program code, the at least one memory and computer program code configured to, with the processor, cause the apparatus to at least:

receive a first image and probe data associated with the first image;

categorize pixels of the first image based on the probe data; and

generate a map geometry based on pixel categorization of the first image.

9. The apparatus of claim 8, wherein the at least one memory and the computer program code are further configured to:

receive a second image;

categorize pixels of the second image based on the categorization of the pixels of the first image; and

generate a map geometry based on pixel categorization of the second image.

10. The apparatus of claim 8, wherein the categorizing pixels of the first image is based on probe data density.

11. The apparatus of claim 10, wherein the at least one memory and the computer program code are further configured to:

determine a target center based on probe data density, and

wherein the categorizing pixels is based on the target center.

12. The apparatus of claim 8, wherein the categorizing pixels comprises categorizing pixels as target pixels and non-target pixels.

13. The apparatus of any of claims 8-12, wherein the at least one memory and the computer program code are further configured to:

determine a target confidence value of pixels of the first image based on the probe data, and

wherein the categorizing pixels is based on the target confidence value of the respective pixel satisfying a predetermined target confidence value threshold.

14. The apparatus of claim 8, wherein the at least one memory and the computer program code are further configured to:

update or generate map data based on the map geometry.

15. A computer program product comprising at least one non-transitory computer-readable storage medium having computer-executable program code portions stored therein, the computer-executable program code portions comprising program code instructions configured to:

receive a first image and probe data associated with the first image;

categorize pixels of the first image based on the probe data; and

generate a map geometry based on pixel categorization of the first image.

16. The computer program product of claim 15, wherein the computer-executable program code portions further comprise program code instructions configured to:

receive a second image;

categorize pixels of the second image based on the categorization of the pixels of the first image; and

generate a map geometry based on pixel categorization of the second image.

17. The computer program product of claim 15, wherein the categorizing pixels of the first image is based on probe data density.

18. The computer program product of claim 17, wherein the computer-executable program code portions further comprise program code instructions configured to:

determine a target center based on probe data density, and

wherein the categorizing pixels is based on the target center.

19. The computer program product claim 15, wherein the categorizing pixels comprises categorizing pixels as target pixels and non-target pixels.

20. The computer program product of claim 15, wherein the computer-executable program code portions further comprise program code instructions configured to:

determine a target confidence value of pixels of the first image based on the probe data, and

wherein the categorizing pixels is based on is based on the target confidence value of the respective pixel satisfying a predetermined target confidence value threshold.

21-28. -28. (canceled)