Visual odometry and pairwise alignment for high definition map creation

• US 10,598,489 B2
• Filed: 05/30/2019
• Issued: 03/24/2020
• Est. Priority Date: 12/30/2016
• Status: Active Grant

• Current Assignee: NVIDIA Corporation
• Original Assignee: DeepMap, Inc. (NVIDIA Corporation)
• Inventors: Zhang, Ronghua, Chen, Chen, Zeng, Di, Wheeler, Mark Damon
• Primary Examiner(s): Baker, Charlotte M
• Application Number:
  US16/427,037
• Publication Number:
  US 20190277632A1
• Time in Patent Office: 299 Days
• Field of Search: 382103
• CPC Class Codes: B60W 2552/00 Input parameters relating to infrastructure
• CPC Class Codes: B60W 2552/53 Road markings, e.g. lane marker or crosswalk
• CPC Class Codes: B60W 40/06 Road conditions
• CPC Class Codes: G01C 11/06 by comparison of two or more pictures of the same area
• CPC Class Codes: G01C 11/12 the pictures being supported in the same relative position as when they were taken
• CPC Class Codes: G01C 11/30 by triangulation
• CPC Class Codes: G01C 21/005 with correlation of navigation data from several sources, e.g. map or contour matching G01C21/30 takes precedence
• CPC Class Codes: G01C 21/3602 Input other than that of destination using image analysis, e.g. detection of road signs, lanes, buildings, real preceding vehicles using a camera
• CPC Class Codes: G01C 21/3635 Guidance using 3D or perspective road maps
• CPC Class Codes: G01C 21/3694 Output thereof on a road map
• CPC Class Codes: G01C 21/3848 Data obtained from both position sensors and additional sensors
• CPC Class Codes: G01C 21/3867 Geometry of map features, e.g. shape points, polygons or for simplified maps
• CPC Class Codes: G01S 17/89 for mapping or imaging
• CPC Class Codes: G01S 19/42 Determining position
• CPC Class Codes: G01S 19/46 the supplementary measurement being of a radio-wave signal type
• CPC Class Codes: G01S 19/47 the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
• CPC Class Codes: G05D 1/0246 using a video camera in combination with image processing means
• CPC Class Codes: G06T 17/05 Geographic models
• CPC Class Codes: G06T 17/20 Finite element generation, e.g. wire-frame surface description, tesselation
• CPC Class Codes: G06T 2200/04 involving 3D image data
• CPC Class Codes: G06T 2207/10021 : Stereoscopic video; Stereoscopic image sequence
• CPC Class Codes: G06T 2207/10028 : Range image; Depth image; 3D point clouds
• CPC Class Codes: G06T 2207/20048 : Transform domain processing
• CPC Class Codes: G06T 2207/30252 : Vehicle exterior; Vicinity of vehicle
• CPC Class Codes: G06T 2207/30256 : Lane; Road marking
• CPC Class Codes: G06T 2210/56 : Particle system, point based geometry or rendering
• CPC Class Codes: G06T 2215/12 : Shadow map, environment map
• CPC Class Codes: G06T 7/11 : Region-based segmentation
• CPC Class Codes: G06T 7/246 : using feature-based methods, e.g. the tracking of corners or segments
• CPC Class Codes: G06T 7/248 : involving reference images or patches
• CPC Class Codes: G06T 7/55 : from multiple images
• CPC Class Codes: G06T 7/593 : from stereo images
• CPC Class Codes: G06T 7/68 : of symmetry
• CPC Class Codes: G06T 7/70 : Determining position or orientation of objects or cameras camera calibration G06T7/80
• CPC Class Codes: G06T 7/73 : using feature-based methods
• CPC Class Codes: G06T 7/74 : involving reference images or patches
• CPC Class Codes: G06V 10/462 : Salient features, e.g. scale invariant feature transforms [SIFT]
• CPC Class Codes: G06V 10/758 : Involving statistics of pixels or of feature values, e.g. histogram matching
• CPC Class Codes: G06V 20/56 : exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G06V 20/58 : Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
• 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 1/20 : Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles

Abstract

As an autonomous vehicle moves through a local area, pairwise alignment may be performed to calculate changes in the pose of the vehicle between different points in time. The vehicle comprises an imaging system configured to capture image frames depicting a portion of the surrounding area. Features are identified from the captured image frames, and a 3-D location is determined for each identified feature. The features of different image frames corresponding to different points in time are analyzed to determine a transformation in the pose of the vehicle during the time period between the image frames. The determined poses of the vehicle are used to generate an HD map of the local area.

Claims

1. A method, comprising:

receiving a plurality of image frames depicting a local area surrounding a vehicle, each image frame corresponding to a point in time, and comprising at least a first image of the local area captured by a first camera and a second image of the local area captured by a second camera;

for each image frame of the plurality of image frames;

identifying a first set of feature points on the first image of the image frame, and a second set of feature points on the second image of the image frame;

determining features of the image frame associated with features within the local area, each feature corresponding to a first feature point of the first set of feature points matched with a second feature point of the second set of feature points; and

for each determined feature, determining a location of the feature based upon the first and second feature points of the feature;

determining a transformation between a first position of the vehicle at a first point in time corresponding to a first image frame of the plurality of image frames, and a second position of the vehicle at a second point in time corresponding to a second image frame of the plurality of image frames, by;

selecting a first subset of features of the first image frame;

identifying a second subset of features of the second image frame corresponding to the first subset of features, based upon a level of similarity between features of the first and second subsets;

determining the transformation based upon a transformation between the determined positions of features of the first and second subsets of features; and

generating a high definition map of the local area based on the transformation, the high definition map for use in driving by one or more autonomous vehicles.

2. The method of claim 1, further comprising rectifying the first image and the second image of the image frame such that the first image and the second image are coplanar, and wherein the first and second feature points are not matched to correspond to a feature in response to a determination that a location of the first feature point on the first image and a location of the second feature point on the second image do not correspond to the same row of the first and second images.

3. The method of claim 1, further comprising discarding a feature in response to a determination that the determined location of the feature corresponds to a distance from the vehicle exceeding a threshold distance.

4. The method of claim 1, wherein determining the transformation between the first and second positions further comprises:

for each of a plurality of iterations;

selecting a different first subset of features of the first image frame;

identifying the second subset of features of the second image frame corresponding to the selected first subset of features;

determining a candidate transformation between the determined positions of corresponding features of the first and second subsets of features;

for each feature of first image frame, matching the feature with a feature of the second image frame, based upon applying the determined candidate transformation to the feature to generate a transformed feature; and

determining a metric for the determined candidate transformation, based upon the features of the first image frame and their determined matching features of the second image frame; and

selecting a candidate transformation of the plurality of iterations having a highest metric.

5. The method of claim 4, wherein determining the transformation between the first and second positions further comprises:

recalculating the transformation between the first and second positions, based upon the features of the first image frame and their determined matching features associated with the selected candidate transformation.

6. The method of claim 4, wherein matching the feature of the first image frame with a feature of the second image frame comprises determining a feature of the second image frame associated with a descriptor with at least a threshold similarity to a descriptor of the feature of the first image frame, that is nearest to a location the transformed feature.

7. The method of claim 4, wherein the metric for the candidate transformation is based upon a number of features of the first image frame having a matching feature of the second image frame within a threshold distance.

8. The method of claim 4, wherein the metric for the candidate transformation is based upon a volume of space covering locations of features of the first image frame that have a matching the second image frame within a threshold distance.

9. The method of claim 4, wherein determining the transformation between the first and second positions further comprises:

identifying an initial guess of the transformation; and

for each candidate transformation of the plurality of iterations, discarding the determined candidate transformation if the candidate transformation deviates from the initial guess by over a threshold amount.

10. The method of claim 1, wherein each feature of the first subset of features is selected to be at least a threshold distance from the remaining features of the first subset of features.

11. The method of claim 1, wherein the transformation is determined using a PnP (Perspective-n-Point) algorithm.

12. A computer-readable storage medium storing instructions that when executed causes one or more processors to:

receive a plurality of image frames depicting a local area of a vehicle, each image frame corresponding to a point in time, and comprising at least a first image of the local area captured by a first camera and a second image of the local area captured by a second camera;

for each image frame of the plurality of image frames;

identify a first set of feature points on the first image of the image frame, and a second set of feature points on the second image of the image frame;

determine features of the image frame associated with features within the local area, each feature corresponding to a first feature point of the first set of feature points matched with a second feature point of the second set of feature points; and

for each determined feature, determine a location of the feature based upon the first and second feature points of the feature;

determine a transformation between a first position of the vehicle at a first point in time corresponding to a first image frame of the plurality of image frames, and a second position of the vehicle at a second point in time corresponding to a second image frame of the plurality of image frames, by;

selecting a first subset of features of the first image frame;

identifying a second subset of features of the second image frame corresponding to the first subset of features, based upon a level of similarity between features of the first and second subsets;

determining the transformation based upon a transformation between the determined positions of features of the first and second subsets of features; and

generate a high definition map of the local area based on the transformation, the high definition map for use in driving by one or more autonomous vehicles.

13. The computer-readable storage medium of claim 12, wherein the instructions further cause the one or more processors to rectify the first image and the second image of the image frame such that the first image and the second image are coplanar, and wherein the first and second feature points are not matched to correspond to a feature in response to a determination that a location of the first feature point on the first image and a location of the second feature point on the second image do not correspond to the same row of the first and second images.

14. The computer-readable storage medium of claim 12, wherein the instructions further cause the one or more processors to discard a feature in response to a determination that the determined location of the feature corresponds to a distance from the vehicle exceeding a threshold distance.

15. The computer-readable storage medium of claim 12, wherein the instructions further cause the one or more processors to determine the transformation between the first and second positions by:

for each of a plurality of iterations;

selecting a different first subset of features of the first image frame;

identifying the second subset of features of the second image frame corresponding to the selected first subset of features;

determining a candidate transformation between the determined positions of corresponding features of the first and second subsets of features;

for each feature of first image frame, matching the feature with a feature of the second image frame, based upon applying the determined candidate transformation to the feature to generate a transformed feature; and

determining a metric for the determined candidate transformation, based upon the features of the first image frame and their determined matching features of the second image frame; and

selecting a candidate transformation of the plurality of iterations having a highest metric.

16. The computer-readable storage medium of claim 15, wherein the instructions further cause the one or more processors to determine the transformation between the first and second positions by:

recalculating the transformation between the first and second positions, based upon the features of the first image frame and their determined matching features associated with the selected candidate transformation.

17. The computer-readable storage medium of claim 15, wherein matching the feature of the first image frame with a feature of the second image frame comprises determining a feature of the second image frame associated with a descriptor with at least a threshold similarity to a descriptor of the feature of the first image frame, that is nearest to a location the transformed feature.

18. The computer-readable storage medium of claim 15, wherein the metric for the candidate transformation is based upon a number of features of the first image frame having a matching feature of the second image frame within a threshold distance.

19. The computer-readable storage medium of claim 15, wherein the metric for the candidate transformation is based upon a volume of space covering locations of features of the first image frame that have a matching the second image frame within a threshold distance.

20. The computer-readable storage medium of claim 15, wherein determining the transformation between the first and second positions further comprises:

identifying an initial guess of the transformation; and

for each candidate transformation of the plurality of iterations, discarding the determined candidate transformation if the candidate transformation deviates from the initial guess by over a threshold amount.

21. The computer-readable storage medium of claim 12, wherein each feature of the first subset of features is selected to be at least a threshold distance from the remaining features of the first subset of features.

22. The computer-readable storage medium of claim 12, wherein the transformation is determined using a PnP (Perspective-n-Point) algorithm.

23. A computer system for generating a high definition map of a local area based upon sensor readings of a vehicle traveling through the local area, the computer system comprising:

one or more computer processors; and

a tangible computer-readable storage medium containing computer program code that when executed causes one or more processors to;

receive a plurality of image frames depicting a local area of a vehicle, each image frame corresponding to a point in time, and comprising at least a first image of the local area captured by a first camera and a second image of the local area captured by a second camera;

for each image frame of the plurality of image frames;

identify a first set of feature points on the first image of the image frame, and a second set of feature points on the second image of the image frame;

determine features of the image frame associated with features within the local area, each feature corresponding to a first feature point of the first set of feature points matched with a second feature point of the second set of feature points; and

for each determined feature, determine a location of the feature based upon the first and second feature points of the feature;

determine a transformation between a first position of the vehicle at a first point in time corresponding to a first image frame of the plurality of image frames, and a second position of the vehicle at a second point in time corresponding to a second image frame of the plurality of image frames, by;

selecting a first subset of features of the first image frame;

identifying a second subset of features of the second image frame corresponding to the first subset of features, based upon a level of similarity between features of the first and second subsets;

determining the transformation based upon a transformation between the determined positions of features of the first and second subsets of features; and

generate a high definition map of the local area based on the transformation, the high definition map for use in driving by one or more autonomous vehicles.

24. The computer system of claim 23, wherein the instructions further cause the one or more processors to rectify the first image and the second image of the image frame such that the first image and the second image are coplanar, and wherein the first and second feature points are not matched to correspond to a feature in response to a determination that a location of the first feature point on the first image and a location of the second feature point on the second image do not correspond to the same row of the first and second images.

25. The computer system of claim 23, wherein the instructions further cause the one or more processors to discard a feature in response to a determination that the determined location of the feature corresponds to a distance from the vehicle exceeding a threshold distance.

26. The computer system of claim 23, wherein the one or more processors are further configured to determine the transformation between the first and second positions by:

for each of a plurality of iterations;

selecting a different first subset of features of the first image frame;

identifying the second subset of features of the second image frame corresponding to the selected first subset of features;

determining a candidate transformation between the determined positions of corresponding features of the first and second subsets of features;

for each feature of first image frame, matching the feature with a feature of the second image frame, based upon applying the determined candidate transformation to the feature to generate a transformed feature; and

determining a metric for the determined candidate transformation, based upon the features of the first image frame and their determined matching features of the second image frame; and

selecting a candidate transformation of the plurality of iterations having a highest metric.

27. The computer system of claim 26, wherein the instructions further cause the one or more processors to determine the transformation between the first and second positions by:

recalculating the transformation between the first and second positions, based upon the features of the first image frame and their determined matching features associated with the selected candidate transformation.

28. The computer system of claim 26, wherein matching the feature of the first image frame with a feature of the second image frame comprises determining a feature of the second image frame associated with a descriptor with at least a threshold similarity to a descriptor of the feature of the first image frame, that is nearest to a location the transformed feature.

29. The computer system of claim 26, wherein the metric for the candidate transformation is based upon a number of features of the first image frame having a matching feature of the second image frame within a threshold distance.

30. The computer system of claim 26, wherein the metric for the candidate transformation is based upon a volume of space covering locations of features of the first image frame that have a matching the second image frame within a threshold distance.

31. The computer system of claim 26, wherein determining the transformation between the first and second positions further comprises:

identifying an initial guess of the transformation; and

for each candidate transformation of the plurality of iterations, discarding the determined candidate transformation if the candidate transformation deviates from the initial guess by over a threshold amount.

32. The computer system of claim 23, wherein each feature of the first subset of features is selected to be at least a threshold distance from the remaining features of the first subset of features.

33. The computer system of claim 23, wherein the transformation is determined using a PnP (Perspective-n-Point) algorithm.