SYSTEMS, DEVICES, AND METHODS FOR AUTONOMOUS VEHICLE LOCALIZATION

• US 20190101649A1
• Filed: 12/22/2017
• Published: 04/04/2019
• Est. Priority Date: 10/03/2017
• Status: Active Grant

• Current Assignee: Aurora Operations Incorporated (Aurora Innovation, Inc.)
• Original Assignee: Uber Technologies, Inc.
• Inventors: Jensen, Kenneth
• Granted Patent: US 10,684,372 B2
• Time in Patent Office: Days
• CPC Class Codes: G01C 21/30 Map- or contour-matching
• CPC Class Codes: G01S 17/06 Systems determining position data of a target
• CPC Class Codes: G01S 17/46 Indirect determination of position data
• CPC Class Codes: G01S 17/89 for mapping or imaging
• CPC Class Codes: G01S 7/4808 Evaluating distance, position or velocity data
• CPC Class Codes: G01S 7/497 Means for monitoring or calibrating
• 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 1/024 in combination with a laser lasers per se H01S
• CPC Class Codes: G05D 1/0248 in combination with a laser lasers per se H01S
• CPC Class Codes: G05D 1/0257 using a radar radar systems designed for anti-collision purposes between land vehicles or between land vehicle and fixed obstacles G01S13/931
• CPC Class Codes: G05D 1/0268 using internal positioning means
• 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

Abstract

Disclosed are systems, methods, and computer-readable storage media to control a vehicle. In one aspect, a method includes capturing point-cloud data representative of a surrounding of an autonomous vehicle with one or more LIDAR sensors, identifying a point in the point cloud data as a non-matching point in response to the point having no corresponding point in a map used to determine a position of the autonomous vehicle, determining whether the non-matching point is to he used in a determination of an overlap score based on one or more comparisons of the point cloud data and the map, determining the overlap score in response to the determining whether the non-matching point is to be used in the determination of the overlap score, determining a position of the autonomous vehicle based on the overlap score and the map, and controlling the autonomous vehicle based on the position.

Claims

1. A method of controlling a vehicle, comprising:

capturing point-cloud data representative of a surrounding of an autonomous vehicle with one or more LIDAR sensors;

identifying a point in the point cloud data as a non-matching point based at least on the point having no corresponding point in a map used to determine a position of the autonomous vehicle;

determining that the non-matching point is to be used in a determination of an overlap score based on one or more comparisons of the point cloud data and the map;

determining the overlap score based at least partly on the determining that the non-matching point is to be used in the determination of the overlap score;

determining a position of the autonomous vehicle based on the overlap score and the map; and

controlling the autonomous vehicle based on the position.

2. The method of claim 1, wherein determining that the non-matching point is to be used in a determination of the overlap score comprises:

generating ray-tracing data representing a tracing of a ray from a laser source of the one or more LIDAR sensors to a destination point that intersects the non-matching point;

determining that the ray intersects a reference point in the map; and

in response to determining that the ray intersects the reference point in the map, including the non-matching point in the determination of the overlap score.

3. The method of claim 1, wherein determining that the non-matching point is to be used in a determination of the overlap score comprises:

determining that the non-matching point corresponds to a non-occluded point of the map; and

including the non-matching point in the determination of the overlap score in response to the non-matching point corresponding to a non-occluded point.

4. The method of claim 1, wherein determining the overlap score further comprises relating (i) a number of points that match to the map to (ii) a total number of points included in the determination of the overlap score.

5. The method of claim 1, further comprising:

determining a height of the one or more LIDAR sensors; and

adjusting the point-cloud data based on the height,

wherein determining that the non-matching point is to be used in the determination of the overlap score comprises determining that the non-matching point is to be used in the determination of the overlap score based on the adjusted point cloud data.

6. The method of claim 5, further comprising:

determining a height parameter for the map;

comparing the height parameter to the height of the one or more LIDAR sensors; and

adjusting the point-cloud data based on the comparison.

7. The method of claim 5, further comprising:

determining an angle of the one or more LIDAR sensors with respect to a reference plane; and

adjusting the point-cloud data based on the angle.

8. The method of claim 1, wherein controlling the autonomous vehicle based on the position comprises determining a motion plan for the autonomous vehicle;

and sending a signal to one or more of a steering controller, engine controller, or braking controller based on the motion plan.

9. A vehicle, comprising:

one or more light detection and ranging (LIDAR) sensors;

one or more hardware processors;

one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the one or more hardware processors cause the one or more hardware processors to perform operations, the operations comprising;

capturing point-cloud data representative of a surrounding of the vehicle;

identifying a point in the point cloud data as a non-matching point in response to the point having no corresponding point in a map used to determine a position of the vehicle;

determining that the non-matching point is to be used in a determination of an overlap score based on one or more comparisons of the point cloud data and the map;

determining the overlap score in response to the determining that the non-matching point is to be used in the determination of the overlap score,

determining a position of the vehicle based on the overlap score and the map; and

controlling the vehicle based on the position.

10. The vehicle of claim 9, wherein determining that the non-matching point is to be used in a determination of the overlap score comprises:

generating ray-tracing data representing a tracing of a ray from a laser source of the one or more LIDAR sensors to a destination point that intersects the non-matching point;

determining that the ray intersects a reference point in the map; and

in response to determining that the ray intersects the reference point in the map, including the non-matching point in the determination of the overlap score.

11. The vehicle of claim 9, wherein determining that the non-matching point is to be used in a determination of the overlap score comprises:

determining that the non-matching point corresponds to a non-occluded point of the map; and

including the non-matching point in the determination of the overlap score in response to the non-matching point corresponding to a non-occluded point.

12. The vehicle of claim 9, wherein determining the overlap score further comprises relating (i) a number of points that match to the map to (ii) a total number of points included in the determination of the overlap score.

13. The vehicle of claim 9, wherein the one or more tangible, non-transitory, computer readable media collectively store further instructions that when executed by the one or more hardware processors cause the one or more hardware processors to perform further operations, the further operations comprising:

determining a height of the one or more LIDAR sensors; and

adjusting the point-cloud data based on the height,

wherein determining that the non-matching point is to be used in the determination of the overlap score comprises determining that the non-matching point is to be used in the determination of the overlap score based on the adjusted point cloud data.

14. The vehicle of claim 13, wherein the one or more tangible, non-transitory, computer readable media collectively store further instructions that when executed by the one or more hardware processors cause the one or more hardware processors to perform further operations, the further operations comprising:

determining a height parameter for the map;

comparing the height parameter he height of the one or more LID AR sensors; and

adjusting the point-cloud data based on the comparison.

15. The vehicle of claim 13, wherein the one or more tangible, non-transitory, computer readable media collectively store further instructions that when executed by the one or more hardware processors cause the one or more hardware processors to perform further operations, the further operations comprising:

determining an angle of the one or more LIDAR sensors with respect to a reference plane; and

adjusting the point-cloud data based on the angle.

16. The vehicle of claim 9, wherein controlling the autonomous vehicle based on the position comprises determining a motion plan for the autonomous vehicle;

and sending a signal to one or more of a steering controller, engine controller, or braking controller based on the motion plan.

17. A computing system for a vehicle, comprising:

one or more hardware processors;

one or more tangible, non-transitory computer readable media that collectively store instructions that when executed by the one or more hardware processors cause the one or more hardware processors to perform operations, the operations comprising;

capturing point-cloud data representative of a surrounding of the vehicle with one or more LIDAR sensors;

identifying a point in the point cloud data as a non-matching point in response to the point having no corresponding point in a map used to determine a position of the vehicle;

determining that the non-matching point is to be used in a determination of an overlap score based on one or more comparisons of the point cloud data and the map;

determining the overlap score in response to the determining that the non-matching point is to be used in the determination of the overlap score;

determining a position of the vehicle based on the overlap score and the map; and

controlling the vehicle based on the position.

18. The computing system of claim 17, wherein determining that the non-matching point is to be used in a determination of the overlap score comprises:

generating ray-tracing data representing a tracing of a ray from a laser source of the one or more LIDAR sensors to a destination point that intersects the non-matching point;

determining that the ray intersects a reference point in the map; and

in response to determining that the ray intersects the reference point in the map, including the non-matching point in the determination of the overlap score.

19. The computing system of claim 17, wherein determining that the non-matching point is to be used in a determination of the overlap score comprises:

determining that the non-matching point corresponds to a non-occluded point of the map; and

including the non-matching point in the determination of the overlap score in response to the non-matching point corresponding to a non-occluded point.

20. The computing system of claim 17, wherein determining the overlap score further comprises relating (i) a number of points that match to the map to (ii) a total number of points included in the determination of the overlap score.