SYSTEMS AND METHODS FOR ALIGNING CROWDSOURCED SPARSE MAP DATA

• US 20180023961A1
• Filed: 07/21/2017
• Published: 01/25/2018
• Est. Priority Date: 07/21/2016
• Status: Active Grant

• Current Assignee: MobilEye Vision Technologies Ltd. (Intel Corporation)
• Original Assignee: MobilEye Vision Technologies Ltd. (Intel Corporation)
• Inventors: Fridman, Ofer
• Granted Patent: US 10,365,658 B2
• Time in Patent Office: Days
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2552/10 Number of lanes
• CPC Class Codes: B60W 2552/25 Road altitude
• CPC Class Codes: B60W 2552/30 Road curve radius
• CPC Class Codes: B60W 2552/35 Road bumpiness, e.g. potholes
• CPC Class Codes: B60W 2552/50 Barriers
• CPC Class Codes: B60W 2552/53 Road markings, e.g. lane marker or crosswalk
• CPC Class Codes: B60W 2554/20 Static objects
• CPC Class Codes: B60W 2554/4026 Cycles
• CPC Class Codes: B60W 2554/4029 Pedestrians
• CPC Class Codes: B60W 2554/4041 Position
• CPC Class Codes: B60W 2554/60 Traversable objects, e.g. speed bumps or curbs
• CPC Class Codes: B60W 2554/802 Longitudinal distance
• CPC Class Codes: B60W 2554/804 Relative longitudinal speed
• CPC Class Codes: B60W 60/0016 of the vehicle or its occupants
• CPC Class Codes: G01C 21/28 with correlation of data from several navigational instruments
• CPC Class Codes: G01C 21/3822 Road feature data, e.g. slope data
• 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: G05D 1/0231 using optical position detecting means position-fixing by using electromagnetic waves other than radio waves, e.g. optical position detecting means G01S5/16
• CPC Class Codes: G05D 1/0246 : using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/0251 : extracting 3D information from a plurality of images taken from different locations, e.g. stereo vision stereoscopic image analysis H04N13/00; depth recovery from images G06T7/593
• CPC Class Codes: G05D 1/0274 : using mapping information stored in a memory device navigation using map-matching G01C21/30
• CPC Class Codes: G05D 1/0278 : using satellite positioning signals, e.g. GPS
• CPC Class Codes: G06T 11/60 : Editing figures and text; Combining figures or text
• CPC Class Codes: G06T 2207/10024 : Color image
• CPC Class Codes: G06T 2207/10032 : Satellite or aerial image; Remote sensing
• CPC Class Codes: G06T 2207/30256 : Lane; Road marking
• CPC Class Codes: G06T 7/11 : Region-based segmentation
• CPC Class Codes: G06T 7/12 : Edge-based segmentation
• CPC Class Codes: G06T 7/337 : involving reference images or patches
• CPC Class Codes: G06T 7/37 : using transform domain methods
• CPC Class Codes: G06T 7/70 : Determining position or orientation of objects or cameras camera calibration G06T7/80
• CPC Class Codes: G06T 7/74 : involving reference images or patches
• CPC Class Codes: G06V 20/56 : exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G06V 20/588 : Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
• CPC Class Codes: G08G 1/096708 : where the received information might be used to generate an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096725 : where the received information generates an automatic action on the vehicle control
• CPC Class Codes: H04N 13/239 : using two 2D image sensors having a relative position equal to or related to the interocular distance H04N13/243 takes precedence
• CPC Class Codes: H04N 2013/0081 : Depth or disparity estimation from stereoscopic image signals
• CPC Class Codes: H04N 23/90 : Arrangement of cameras or camera modules, e.g. multiple cameras in TV studios or sports stadiums
• CPC Class Codes: H04N 7/18 : Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast

Abstract

Systems and methods are provided for aligning crowdsourced map data. In one implementation, a method of determining a line representation of a road surface feature extending along a road segment may include receiving a first set of drive data including position information associated with the road surface feature, receiving a second set of drive data including position information associated with the road surface feature, segmenting the first set of drive data into first drive patches and segmenting the second set of drive data into second drive patches, longitudinally aligning the first set of drive data with the second set of drive data within corresponding patches, and determining the line representation of the road surface feature based on the longitudinally aligned first and second drive data in the first and second draft patches.

Claims

1. A method of determining a line representation of a road surface feature extending along a road segment, the line representation of the road surface feature being configured for use in autonomous vehicle navigation, the method comprising:

receiving, by a server, a first set of drive data including position information associated with the road surface feature, the position information being determined based on analysis of images of the road segment;

receiving, by a server, a second set of drive data including position information associated with the road surface feature, the position information being determined based on analysis of images of the road segment;

segmenting the first set of drive data into first drive patches and segmenting the second set of drive data into second drive patches;

longitudinally aligning the first set of drive data with the second set of drive data within corresponding patches; and

determining the line representation of the road surface feature based on the longitudinally aligned first and second drive data in the first and second draft patches.

2. The method of claim 1, wherein the road surface feature includes a road edge or a lane marking.

3. The method of claim 1, wherein determining the line representation includes an alignment of the line representation with global coordinates based on GPS data acquired as part of at least one of the first set of drive data or the second set of drive data.

4. The method of claim 1, wherein determining the line representation further includes determining and applying a set of average transformations where each of the average transformations is based on transformations determined that link data from the first set of drive data across sequential patches and the link data from the second set of drive data across sequential patches.

5. The method of claim 1, further including overlaying the line representation of the road surface feature on at least one geographical image.

6. The method of claim 5, wherein the geographical image is a satellite image.

7. The method of claim 1, wherein the first set of data and the second set of data further include position information associated with a plurality of landmarks.

8. A system for determining a line representation of a road surface feature extending along a road segment, the line representation of the road surface feature being configured for use in autonomous vehicle navigation, the system comprising:

at least one processing device configured to;

receive a first set of drive data including position information associated with the road surface feature, the position information being determined based on analysis of images of the road segment;

receive a second set of drive data including position information associated with the road surface feature, the position information being determined based on analysis of images of the road segment;

segment the first set of drive data into first drive patches and segment the second set of drive data into second drive patches;

longitudinally align the first set of drive data with the second set of drive data within corresponding patches; and

determine the line representation of the road surface feature based on the longitudinally aligned first and second drive data in the first and second draft patches.

9. The system of claim 8, wherein the road surface feature includes a road edge or a lane marking.

10. The system of claim 8, wherein determining the line representation includes an alignment of the line representation with global coordinates based on GPS data acquired as part of at least one of the first set of drive data or the second set of drive data.

11. The system of claim 8, wherein determining the line representation further includes determining and applying a set of average transformations where each of the average transformations is based on transformations determined that link data from the first set of drive data across sequential patches and the link data from the second set of drive data across sequential patches.

12. The system of claim 8, further including overlaying the line representation of the road surface feature on at least one geographical image.

13. The system of claim 8, wherein the first set of data and the second set of data further include position information associated with a plurality of landmarks.

14. A non-transitory, computer-readable medium storing instructions that, when executed by at least one processing device, cause the device to:

receive a first set of drive data including position information associated with the road surface feature, the position information being determined based on analysis of images of the road segment;

receive a second set of drive data including position information associated with the road surface feature, the position information being determined based on analysis of images of the road segment;

segment the first set of drive data into first drive patches and segmenting the second set of drive data into second drive patches;

longitudinally align the first set of drive data with the second set of drive data within corresponding patches; and

determine the line representation of the road surface feature based on the longitudinally aligned first and second drive data in the first and second draft patches.

15. The non-transitory, computer-readable medium of claim 14, wherein the road surface feature includes a road edge or a lane marking.

16. The non-transitory, computer-readable medium of claim 14, wherein determining the line representation includes an alignment of the line representation with global coordinates based on GPS data acquired as part of at least one of the first set of drive data or the second set of drive data.

17. The non-transitory, computer-readable medium of claim 14, wherein determining the line representation further includes determining and applying a set of average transformations where each of the average transformations is based on transformations determined that link data from the first set of drive data across sequential patches and the link data from the second set of drive data across sequential patches.

18. The non-transitory, computer-readable medium of claim 14, further including overlaying the line representation of the road surface feature on at least one geographical image.

19. The non-transitory, computer-readable medium of claim 18, wherein the geographical image is a satellite image.

20. The non-transitory, computer-readable medium of claim 14, wherein the first set of data and the second set of data further include position information associated with a plurality of landmarks.