3D Position Estimation of Objects from a Monocular Camera using a Set of Known 3D Points on an Underlying Surface

• US 20170098129A1
• Filed: 12/15/2016
• Published: 04/06/2017
• Est. Priority Date: 07/29/2013
• Status: Active Grant

• Current Assignee: Waymo LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Lo, Wan-Yen, Ferguson, David I.
• Granted Patent: US 10,198,641 B2
• Time in Patent Office: Days
• CPC Class Codes: B60R 11/04 Mounting of cameras operative during drive; Arrangement of controls thereof relative to the vehicle
• CPC Class Codes: B60R 2300/301 combining image information with other obstacle sensor information, e.g. using RADAR/LIDAR/SONAR sensors for estimating risk of collision
• CPC Class Codes: B60R 2300/8093 for obstacle warning
• CPC Class Codes: B60W 30/00 Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
• 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: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/0238 using obstacle or wall sensors G05D1/0246 and G05D1/0289 take precedence; lidar systems designed for anti-collision purposes G01S17/93
• CPC Class Codes: G05D 1/0246 using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/0274 using mapping information stored in a memory device navigation using map-matching G01C21/30
• CPC Class Codes: G06F 18/24 Classification techniques
• CPC Class Codes: G06T 15/20 Perspective computation
• CPC Class Codes: G06T 15/205 Image-based rendering
• CPC Class Codes: G06T 17/10 Constructive solid geometry [CSG] using solid primitives, e.g. cylinders, cubes
• CPC Class Codes: G06T 2207/30256 Lane; Road marking
• CPC Class Codes: G06T 7/50 Depth or shape recovery
• CPC Class Codes: G06T 7/70 Determining position or orientation of objects or cameras camera calibration G06T7/80
• 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

Abstract

Disclosed herein are methods and systems for determining a location of an object within an environment. An example method may include determining a three-dimensional (3D) location of a plurality of reference points in an environment, receiving a two-dimensional (2D) image of a portion of the environment that contains an object, selecting certain reference points from the plurality of reference points that form a polygon when projected into the 2D image that contains at least a portion of the object, determining an intersection point of a ray directed toward the object and a 3D polygon formed by the selected reference points, and based on the intersection point of the ray directed toward the object and the 3D polygon formed by the selected reference points, determining a 3D location of the object in the environment.

Claims

1. A computer-implemented method, comprising:

receiving a two-dimensional (2D) image of a portion of an environment from a camera on an autonomous vehicle, wherein the image contains an object;

retrieving previously stored three-dimensional (3D) locations of a plurality of reference points on an underlying surface of the environment;

projecting the plurality of reference points into the 2D image based on the previously stored 3D locations of the plurality of reference points and a location of the camera on the autonomous vehicle;

selecting reference points from the plurality of projected reference points such that the selected reference points form a polygon that surrounds the object in the 2D image;

determining an intersection point of a ray directed toward the object and a 3D polygon formed by the previously stored 3D locations of the selected reference points;

determining a location of the object in the environment based on the determined intersection point; and

providing instructions to control the autonomous vehicle based on the determined location of the object in the environment.

2. The method of claim 1, wherein selecting the reference points comprises selecting three reference points that form a triangle that surrounds the object in the 2D image.

3. The method of claim 2, wherein determining the intersection point of the ray directed toward the object and the 3D polygon formed by the previously stored 3D locations of the selected reference points comprises:

determining a plane that contains the previously stored 3D locations of the three selected reference points; and

determining the intersection point based on a single ray-plane intersection between the ray directed toward the object and the plane that contains the previously stored 3D locations of the three selected reference points.

4. The method of claim 1, wherein the ray directed toward the object comprises a ray directed toward a center pixel of the object.

5. The method of claim 1, wherein the underlying surface of the environment for which the 3D locations of the plurality of reference points were previously stored comprises a road in the environment.

6. The method of claim 5, wherein the object comprises a tire of a different vehicle, and wherein the method further comprises determining a location of the different vehicle based on the location of the object in the environment.

7. The method of claim 1, further comprising:

retrieving previously stored 3D locations of one or more continuous curves in the environment; and

determining the 3D locations of the plurality of reference points by sampling the one or more continuous curves at one or more intervals.

8. The method of claim 7, wherein the one or more continuous curves comprise a first lane boundary and a second lane boundary of a road within the environment, and wherein selecting the reference points comprises selecting at least one reference point on the first lane boundary and at least one reference point on the second lane boundary.

9. The method of claim 8, wherein selecting the reference points comprises selecting two reference points on the first lane boundary and one reference point on the second lane boundary to form a triangle that surrounds the object in the 2D image.

10. The method of claim 1, wherein selecting the reference points comprises sequentially considering individual reference points of the plurality based on distance to the object within the 2D image.

11. A vehicle, comprising:

a camera configured to capture a two-dimensional (2D) image of a portion of an environment, wherein the image contains an object; and

a computing system configured to;

retrieve previously stored three-dimensional (3D) locations of a plurality of reference points on an underlying surface of the environment;

project the plurality of reference points into the 2D image based on the previously stored 3D locations of the plurality of reference points and a location of the camera on the vehicle;

select reference points from the plurality of projected reference points such that the selected reference points form a polygon that surrounds the object in the 2D image;

determine an intersection point of a ray directed toward the object and a 3D polygon formed by the previously stored 3D locations of the selected reference points;

determine a location of the object in the environment based on the determined intersection point; and

provide instructions to control the vehicle based on the determined location of the object in the environment.

12. The vehicle of claim 11, wherein the computing system is configured to select the reference points by selecting three reference points that form a triangle that surrounds the object in the 2D image.

13. The vehicle of claim 12, wherein the computing system is configured to determine the intersection point of the ray directed toward the object and the 3D polygon formed by the previously stored 3D locations of the selected reference points by:

determining a plane that contains the previously stored 3D locations of the three selected reference points; and

determining the intersection point based on a single ray-plane intersection between the ray directed toward the object and the plane that contains the previously stored 3D locations of the three selected reference points.

14. The vehicle of claim 11, wherein the underlying surface of the environment for which the 3D locations of the plurality of reference points were previously stored comprises a road in the environment.

15. The vehicle of claim 11, wherein the computing system is further configured to:

retrieve previously stored 3D locations of one or more continuous curves in the environment; and

determine the 3D locations of the plurality of reference points by sampling the one or more continuous curves at one or more intervals.

16. A non-transitory computer readable medium having stored therein instructions, that when executed by a computing system, cause the computing system to perform functions comprising:

receiving a two-dimensional (2D) image of a portion of an environment from a camera on an autonomous vehicle, wherein the image contains an object;

retrieving previously stored three-dimensional (3D) locations of a plurality of reference points on an underlying surface of the environment;

projecting the plurality of reference points into the 2D image based on the previously stored 3D locations of the plurality of reference points and a location of the camera on the autonomous vehicle;

selecting reference points from the plurality of projected reference points such that the selected reference points form a polygon that surrounds the object in the 2D image;

determining an intersection point of a ray directed toward the object and a 3D polygon formed by the previously stored 3D locations of the selected reference points;

determining a location of the object in the environment based on the determined intersection point; and

providing instructions to control the autonomous vehicle based on the determined location of the object in the environment.

17. The non-transitory computer readable medium of claim 16, wherein selecting the reference points comprises selecting three reference points that form a triangle that surrounds the object in the 2D image.

18. The non-transitory computer readable medium of claim 17, wherein determining the intersection point of the ray directed toward the object and the 3D polygon formed by the previously stored 3D locations of the selected reference points comprises:

determining a plane that contains the previously stored 3D locations of the three selected reference points; and

determining the intersection point based on a single ray-plane intersection between the ray directed toward the object and the plane that contains the previously stored 3D locations of the three selected reference points.

19. The non-transitory computer readable medium of claim 16, wherein the ray directed toward the object comprises a ray directed toward a center pixel of the object.

20. The non-transitory computer readable medium of claim 16, wherein the underlying surface of the environment for which the 3D locations of the plurality of reference points were previously stored comprises a road in the environment.