Method and apparatus for generating map geometry based on a received image and probe data

• US 10,223,816 B2
• Filed: 02/13/2015
• Issued: 03/05/2019
• 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: Dorum, Ole Henry, Endres, Ian
• Primary Examiner(s): Drennan, Barry
• Assistant Examiner(s): Robinson, Terrell
• Application Number:
  US14/622,026
• Publication Number:
  US 20160239983A1
• Time in Patent Office: 1,481 Days
• Field of Search: 345440
• 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, by at least one processor, a first image associated with image location data, wherein the first image was captured by an image capturing device;

receiving, by the at least one processor, a plurality of instances of probe data, wherein (a) each instance of the probe data comprises probe location data determined by a positioning system of a mobile device, (b) the mobile device is coupled to a vehicle, and (c) the image capturing device is a distinct device from the mobile device and the image capturing device is not coupled to the vehicle;

associating, by the at least one processor, the plurality of instances of probe data with the first image based on the probe location data correlating to at least a portion of the image location data;

correlating, by the at least one processor, each instance of probe data with at least one pixel of the first image based on the image location data and the probe location data corresponding to the instance of probe data;

generating, by the at least one processor, a probe data density map indicating;

a probe data density of one or more pixels of the first image based on the correlation of the one or more instances of probe data with pixels of the first image, wherein the probe data density of a pixel of the first image corresponds to a number of instances of probe data that are correlated with the pixel;

categorizing, by the at least one processor, pixels of the first image based on the correlation of instances of probe data with the pixels and the probe data density map; and

generating, by the at least one processor;

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, further comprising;

determining a target center based on probe data density, and

wherein the categorizing pixels is based on the target center.

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

5. 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.

6. The method of claim 1, further comprising:

updating or generating map data based on the map geometry.

7. 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 associated with image location data, wherein the first image was captured by an image capturing device;

receive a plurality of instances of probe data, wherein (a) each instance of the probe data comprises probe location data determined by a positioning system of a mobile device, (b) the mobile device is coupled to a vehicle, and (c) the image capturing device is a distinct device from the mobile device and the image capturing device is not coupled to the vehicle;

associate the plurality of instances of probe data with the first image based on the probe location data correlating to at least a portion of the image location data;

correlate each instance of probe data with at least one pixel of the first image based on the image location data and the probe location data corresponding to the instance of probe data;

generate a probe data density map indicating a probe data density of one or more pixels of the first image based on the correlation of the one or more instances of probe data with pixels of the first image, wherein the probe data density of a pixel of the first image corresponds to a number of instances of probe data that are correlated with the pixel;

categorize pixels of the first image based on the correlation of instances of probe data with the pixels and the probe data density map; and

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

8. The apparatus of claim 7, 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.

9. The apparatus of claim 7, 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.

10. The apparatus of claim 7, wherein the categorizing pixels comprises categorizing pixels as target pixels and non-target pixels.

11. The apparatus of claim 7, 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.

12. The apparatus of claim 7, 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.

13. 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 associated with image location data, wherein the first image was captured by an image capturing device;

receive a plurality of instances of probe data, wherein (a) each instance of the probe data comprises probe location data determined by a positioning system of a mobile device, (b) the mobile device is coupled to a vehicle, and (c) the image capturing device is a distinct device from the mobile device and the image capturing device is not coupled to the vehicle;

associate the plurality of instances of probe data with the first image based on the probe location data correlating to at least a portion of the image location data;

correlate each instance of probe data with at least one pixel of the first image based on the image location data and the probe location data corresponding to the instance of probe data;

generate a probe data density map indicating a probe data density of one or more pixels of the first image based on the correlation of the one or more instances of probe data with pixels of the first image, wherein the probe data density of a pixel of the first image corresponds to a number of instances of probe data that are correlated with the pixel;

categorize pixels of the first image based on the correlation of instances of probe data with the pixels and the probe data density map; and

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

14. The computer program product of claim 13, 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.

15. The computer program product of claim 13, 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.

16. The computer program product of claim 13, wherein the categorizing pixels comprises categorizing pixels as target pixels and non-target pixels.

17. The computer program product of claim 13, 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.