COLLISION AVOIDANCE METHOD AND SYSTEM USING STEREO VISION AND RADAR SENSOR FUSION

• US 20090292468A1
• Filed: 03/25/2009
• Published: 11/26/2009
• Est. Priority Date: 03/25/2008
• Status: Abandoned Application

• Current Assignee: Sarnoff Corporation (SRI International, Inc.)
• Original Assignee: Sarnoff Corporation (SRI International, Inc.)
• Inventors: Peng, Chang, Wu, Shunguang, Camus, Theodore
• Application Number:
  US12/410,602
• Publication Number:
  US 20090292468A1
• Time in Patent Office: Days
• US Class Current: 701/301
• CPC Class Codes: B60W 30/08 Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
• CPC Class Codes: G01S 13/726 Multiple target tracking
• CPC Class Codes: G01S 13/862 Combination of radar systems with sonar systems
• CPC Class Codes: G01S 13/865 Combination of radar systems with lidar systems
• CPC Class Codes: G01S 13/867 Combination of radar systems with cameras
• CPC Class Codes: G01S 13/931 of land vehicles
• CPC Class Codes: G01S 2013/93271 in the front of the vehicles
• CPC Class Codes: G08G 1/165 for passive traffic, e.g. including static obstacles, trees

Abstract

A system and method for fusing depth and radar data to estimate at least a position of a threat object relative to a host object is disclosed. At least one contour is fitted to a plurality of contour points corresponding to the plurality of depth values corresponding to a threat object. A depth closest point is identified on the at least one contour relative to the host object. A radar target is selected based on information associated with the depth closest point on the at least one contour. The at least one contour is fused with radar data associated with the selected radar target based on the depth closest point to produce a fused contour. Advantageously, the position of the threat object relative to the host object is estimated based on the fused contour. More generally, a method is provided for aligns two possibly disparate sets of 3D points.

Claims

1. A computer-implemented method for fusing depth and radar data to estimate at least a position of a threat object relative to a host object, the method being executed by at least one processor, comprising the steps of:

receiving a plurality of depth values corresponding to the threat object;

receiving radar data corresponding to at least the threat object;

fitting at least one contour to a plurality of contour points corresponding to the plurality of depth values;

identifying a depth closest point on the at least one contour relative to the host object;

selecting a radar target based on information associated with the depth closest point on the at least one contour;

fusing the at least one contour with radar data associated with the selected radar target to produce a fused contour, wherein fusing is based on the depth closest point on the at least one contour; and

estimating at least the position of the threat object relative to the host object based on the fused contour.

2. The method of claim 1, wherein the step of fusing the at least one contour with radar data associated with the selected radar target further comprises the steps of:

fusing ranges and angles of the radar data associated with the selected radar target and the depth closest point on the at least one contour to form a fused closest point; and

translating the at least one contour to the fused closest point to form the fused contour, wherein the fused closest point is invariant.

3. The method of claim 2, wherein the step of translating the at least one contour to the fused closest point to form the fused contour further comprises the step of translating the at least one contour along a line formed on the origin of a coordinate system centered on the host object and the depth closest point to an intersection of the line and an arc formed by rotation of a central point associated with a best candidate radar target location about the origin of the coordinate system, wherein the best candidate radar target is selected from a plurality of radar targets by comparing Mahalanobis distances from the depth closest point to each of the plurality of radar targets.

4. The method of claim 1, wherein the step of fitting at least one contour to a plurality of contour points corresponding to the depth values further comprises the steps of:

extracting the plurality of contour points from the plurality of depth values, and

fitting a rectangular model to the plurality of contour points.

5. The method of claim 4, wherein the step of fitting a rectangular model to the plurality of contour points further comprises the steps of:

fitting a single line segment to the plurality of contour points to produce a first candidate contour,

fitting two perpendicular line segments joined at one point to the plurality of contour points to produce a second candidate contour, and

selecting a final contour according to a comparison of weighted fitting errors of the first and second candidate contours.

6. The method of claim 5, wherein the single line segment of the first candidate contour is fit to the plurality of contour points such that a sum of perpendicular distances to the single line segment is minimized, and wherein the two perpendicular line segments of the second candidate contour is fit to the plurality of contour points such that the sum of perpendicular distances to the two perpendicular lines segments is minimized.

7. The method of claim 6, wherein at least one of the single line segment and the two perpendicular line segments are fit to the plurality of contour points using a linear least squares model.

8. The method of claim 6, wherein the two perpendicular line segments are fit to the plurality of contour points by:

finding a leftmost point (L) and a rightmost point (R) on the two perpendicular line segments,

forming a circle wherein the L and the R are points on a diameter of the circle and C is another point on the circle,

calculating perpendicular errors associated with the line segments LC and RC, and

moving C along the circle to find a best point (C′

) such that the sum of the perpendicular errors to the line segments LC and RC is the smallest.

9. The method of claim 1, further comprising the step of estimating location and velocity information associated with the selected radar target based at least on the radar data.

10. The method of claim 1, further comprising the step of tracking the fused contour using an Extended Kalman Filter.

11. A system for fusing depth and radar data to estimate at least a position of a threat object relative to a host object, wherein a plurality of depth values corresponding to the threat object are received from a depth sensor, and radar data corresponding to at least the threat object is received from a radar sensor, comprising:

a contour fitting module configured to fit at least one contour to a plurality of contour points corresponding to the plurality of depth values,

a depth-radar fusion module configured to;

identify a depth closest point on the at least one contour relative to the host object,

select a radar target based on information associated with the depth closest point on the at least one contour, and

fuse the at least one contour with radar data associated with the selected radar target based on the depth closest point on the at least one contour to produce a fused contour; and

a contour tracking module configured to estimate at least the position of the threat object relative to the host object based on the fused contour.

12. The system of claim 11, wherein the depth sensor is at least one of a stereo vision system comprising one of a 3D stereo camera and two monocular cameras calibrated to each other, an infrared imaging systems, light detection and ranging (LIDAR), a line scanner, a line laser scanner, Sonar, and Light Amplification for Detection and Ranging (LADAR).

13. The system of claim 11, wherein the at least the position of the threat object is fed to a collision avoidance implementation system.

14. The system of claim 11, wherein the at least the position of the threat object is the location, size, pose and motion parameters of the threat object.

15. The system of claim 11, wherein the host object and the threat object are vehicles.

16. The system of claim 11, wherein the said step of fusing the at least one contour with radar data associated with the selected radar target further comprises the steps of:

fusing ranges and angles of the radar data and the depth closest point on the at least one contour to form a fused closest point; and

translating the at least one contour to the fused closest point to form the fused contour, wherein the fused closest point is invariant.

17. The system of claim 16, wherein the step of translating the at least one contour to the fused closest point to form the fused contour further comprises the step of translating the at least one contour along a line formed by the origin of a coordinate system centered on the host object and the depth closest point to an intersection of the line and an arc formed by rotation of a central point associated with a best candidate radar target location about the origin of the coordinate system, wherein the best candidate radar target is selected from a plurality of radar targets by comparing Mahalanobis distances from the depth closest point to each of the plurality of radar targets.

18. A computer-readable medium storing computer code for fusing depth and radar data to estimate at least a position of a threat object relative to a host object, wherein the computer code comprises:

code for receiving a plurality of depth values corresponding to the threat object;

code for receiving radar data corresponding to at least the threat object;

code for fitting at least one contour to a plurality of contour points corresponding to the plurality of depth values;

code for identifying a depth closest point on the at least one contour relative to the host object;

code for selecting a radar target based on information associated with the depth closest point on the at least one contour;

code for fusing the at least one contour with radar data associated with the selected radar target based on the depth closest point on the at least one contour to produce a fused contour; and

code for estimating at least the position of the threat object relative to the host object based on the fused contour.

19. The computer-readable medium of claim 18, wherein the code for fusing the at least one contour with radar data associated with the selected radar target further comprises code for:

fusing ranges and angles of the radar data associated with the selected radar target and the depth closest point on the at least one contour to form a fused closest point and

translating the at least one contour to the fused closest point to form the fused contour, wherein the fused closest point is invariant.

20. The computer-readable medium of claim 19, wherein the code for translating the at least one contour to the fused closest point to form the fused contour further comprises code for translating the at least one contour along a line formed on the origin of a coordinate system centered on the host object and the depth closest point to an intersection of the line and an arc formed by rotation of a central point associated with a best candidate radar target location about the origin of the coordinate system, wherein the best candidate radar target is selected from a plurality of radar targets by comparing Mahalanobis distances from the depth closest point to each of the plurality of radar targets.

21. A computer-implemented method for estimating at least a position of a threat object relative to a host object, the method being executed by at least one processor, comprising the steps of:

receiving a first set of one or more 3D points corresponding to the threat object;

receiving a second set of one or more 3D points corresponding to at least the threat object;

selecting a first reference point in the first set;

selecting a second reference point in the second set;

performing a weighted average of a location of the first reference point and a location of the second reference point to form a location of a third fused point;

computing a 3D translation of the location of the first reference point to the location of the third fused point;

translating the first set of one or more 3D points according to the computed 3D translation; and

estimating at least the position of the threat object relative to the host object based on the translated first set of one or more 3D points.

22. The method of claim 21, wherein the first set of one or more 3D points is received from a first depth sensor comprising one of a stereo vision, radar, Sonar, LADAR, and LIDAR sensor.

23. The method of claim 22, wherein the first reference point is the closest point of the first depth sensor to the threat object.

24. The method of claim 21, wherein the second set of one or more 3D points is received from a second depth sensor comprising one of a stereo vision, radar, Sonar, LADAR, and LIDAR sensor.

25. The method of claim 24, wherein the second reference point is the closest point of the second depth sensor to the threat object.

26. A computer-readable medium storing computer code for estimating at least a position of a threat object relative to a host object, the method being executed by at least one processor, wherein the computer code comprises:

code for receiving a first set of one or more 3D points corresponding to the threat object;

code for receiving a second set of one or more 3D points corresponding to at least the threat object;

code for selecting a first reference point in the first set;

code for selecting a second reference point in the second set;

code for performing a weighted average of a location of the first reference point and a location of the second reference point to form a location of a third fused point;

code for computing a 3D translation of the location of the first reference point to the location of the third fused point;

code for translating the first set of one or more 3D points according to the computed 3D translation; and

code for estimating at least the position of the threat object relative to the host object based on the translated first set of one or more 3D points.