System and method for weighted correction of an eyepoint position

• US 7,257,519 B2
• Filed: 08/02/2002
• Issued: 08/14/2007
• Est. Priority Date: 08/02/2002
• Status: Active Grant

• Current Assignee: Rockwell Collins Simulation and Training Solutions, LLC (Rtx Corporation)
• Original Assignee: Evans & Sutherland Computer Corp. (Elevate Entertainment, Inc.)
• Inventors: Cosman, Michael A.
• Primary Examiner(s): Shah; Kamini
• Assistant Examiner(s): ALHIJA, SAIF A
• Application Number:
  US10/211,220
• Publication Number:
  US 20040032418A1
• Time in Patent Office: 1,838 Days
• Field of Search: 703/1, 703/2, 375/316
• US Class Current: 703/2
• CPC Class Codes: G06T 15/20 Perspective computation
• CPC Class Codes: G06T 17/00 Three dimensional [3D] modelling, e.g. data description of 3D objects
• CPC Class Codes: G06T 7/33 using feature-based methods
• CPC Class Codes: G09B 25/00 Models for purposes not provided for in G09B23/00, e.g. full-sized devices for demonstration purposes model vehicles, tracks therefor, models in the nature of toys A63H

Abstract

A method is provided for correlating imprecise source data in a local coordinate system to a precise global eyepoint position for a global coordinate system within which the local coordinate system is located. The method includes the step of identifying key correlation points for the local coordinate system which has modeling inaccuracies. Global coordinates are also identified for each of the key correlation points. Another step is computing delta values for each of the key correlation points that represent the difference between key correlation points'"'"' local system coordinates and the global coordinate system'"'"'s coordinate values for the key correlation points. An additional step is modifying the global eyepoint position based on delta values for selected key correlation points as the global eyepoint position moves in relation to the local coordinate system within the global coordinate system.

Claims

1. A method for correlating imprecise source data in a local coordinate system to a precise global eyepoint position for a global coordinate system within which the local coordinate system is located within a computer simulated environment, comprising the steps of:

identifying key correlation points for the local coordinate system, which has modeling inaccuracies;

identifying global coordinates for each of the key correlation points;

computing delta values for each of the key correlation points that represent the difference between key correlation points'"'"' local system coordinates and the global coordinate system'"'"'s coordinate values for the key correlation points;

and modifying the global eyepoint position based on delta values for selected key correlation points as the global eyepoint position moves in relation to the local coordinate system and the global coordinate system;

displaying an image of the computer simulated environment to a user based on the modified global eyepoint position.

2. A method as in claim 1, further comprising the step of modifying the global eyepoint position based on weighted delta values for key correlation points.

3. A method as in claim 1, further comprising the step of adding a portion of the computed delta values to the eyepoint position based on the eyepoint'"'"'s comparative distance from key correlation points.

4. A method for correcting a global eyepoint within a local coordinate system that has modeling variations as compared to a global coordinate system into which the local coordinate system has been inserted for a computer simulated environment, comprising the steps of:

identifying key correlation points for the local coordinate system which has modeling inaccuracies;

identifying global eyepoint coordinates for each of the key correlation points;

creating a polygon mesh to connect at least the key correlation points to form at least one polygon having vertices;

computing delta values for each of the polygon vertices that represent the difference between the global eyepoint coordinates and the larger scale terrain'"'"'s coordinate values in relation to the key correlation points;

and modifying the position of the global eyepoint based on weighted delta values for polygon vertices as the global eyepoint moves in relation to the triangles;

displaying an image of the computer simulated environment to a user based on the modified global eyepoint position.

5. A method as in claim 4, further comprising the step of identifying exterior correlation points at a distance from the perimeter of the local coordinate system.

6. A method as in claim 5, further comprising the step of creating a polygon mesh to connect the key correlation points and exterior correlation points to form polygons having vertices.

7. A method as in claim 6, wherein the step of creating a polygon mesh further comprises the step of creating a triangular mesh to connect the key correlation points and exterior correlation points to form triangles having triangle vertices.

8. A method for correcting a host position within inset terrains that have modeling variations as compared to a larger scale terrain for a computer simulated environment, comprising the steps of:

identifying key correlation points for each inset terrain and host coordinates for each of the key correlation points, where the key correlation points are host independent;

selecting exterior correlation points at a distance from the perimeter of the inset terrain;

creating a polygon mesh to connect the key correlation points and exterior correlation points to form polygons having vertices;

computing delta values for each polygon vertex that represent the difference between the host coordinates and the larger scale terrain'"'"'s coordinate values in relation to the key correlation points;

and modifying the position of a host eyepoint based on weighted delta values for polygon vertices as the host eyepoint passes through the polygons;

displaying an image of the computer simulated environment to a user based on the modified host eyepoint position.

9. A method as in claim 8, wherein the step of creating a polygon mesh further comprises the step of creating a triangular mesh to connect the key correlation points and exterior correlation points to form triangles having triangle vertices.

10. A method as in claim 8, further comprising the step of identifying key correlation points for inset terrains that represent airports.

11. A method as in claim 9, further comprising the step of identifying key correlation points such as correlation points selected from the group of correlation points consisting of ends of runways, critical intersections and road boundaries.

12. A method as in claim 8, wherein the step of computing delta values further comprises converting resultant delta values into equivalent X, Y delta values based on the latitude of the airport.

13. A method as in claim 8, wherein the step of modifying the position of the host eyepoint further comprises the step of modifying the position of the host eyepoint by using linear weightings to modify the host eyepoint.

14. A method as in claim 13, further comprising the step of modifying the position of the host eyepoint linearly so that there is there is substantial agreement between a host coordinate and latitude-longitude and heading values when the key correlation points are crossed.

15. A method as in claim 8, wherein the step of modifying the position of the host eyepoint further comprises the step of modifying the position of the host eyepoint by using barycentric weightings.

16. A method as in claim 8, wherein the step of modifying the position of the host eyepoint further comprises the step of modifying the position of the host eyepoint by using higher order weightings to modify the host eyepoint while preserving first order continuity at triangle boundaries.

17. A method as in claim 16, wherein the step of modifying the position of the host eyepoint further comprises the step of modifying the position of the host eyepoint by applying quadratic corrections.

18. A method for correcting a host position within inset airports that include modeling discrepancies as compared to a global model for a computer simulated environment, comprising the steps of:

identifying key correlation points for each airport that are host independent and host coordinates for each of the key correlation points;

selecting exterior correlation points at a distance from the perimeter of the inset terrain;

creating a triangular mesh to connect the key correlation points and exterior correlation points to form triangles having triangle vertices;

computing delta values for each triangle vertex, that represent the difference between the host coordinates and latitude-longitude and heading values in the global model;

and interpolating the position of a host eyepoint linearly between key correlation points based on the delta values for triangle vertices as the host eyepoint passes from one triangle'"'"'s boundaries to another;

displaying an image of the computer simulated environment to a user based on the modified host eyepoint position.

19. A method as in claim 18, wherein the step of computing delta values further comprises converting resultant lat-long delta values into equivalent X, Y delta values based on the latitude of the airport.

20. A method as in claim 18, further comprising the step of interpolating the position of the host eyepoint linearly so that there is there is substantial agreement between a host coordinate and latitude-longitude and heading values when the key correlation points are crossed.

21. A method as in claim 18, further comprising the step of identifying key correlation points such as correlation points selected from the group of correlation points consisting of ends of runways, critical intersections and road boundaries.

22. A method as in claim 18, wherein the step of interpolating the position of the host eyepoint further comprises the step of interpolating the position of the host eyepoint using barycentric weightings to modify the host eyepoint.

23. A method for correcting a host position within inset airports that include modeling discrepancies as compared to a global model for a computer simulated environment, comprising the steps of:

identifying key correlation points for each inset terrain that are host independent;

identifying host coordinates for each of these correlation points;

selecting exterior correlation points at a distance from the center of the inset terrain;

creating a triangular mesh to connect the key correlation points and exterior correlation points to form triangles having triangle vertices;

computing delta values for each triangle vertex that represent the difference between the host coordinates and internal latitude-longitude and heading values;

and interpolating the position of a host eyepoint based on the weighted delta values for triangle vertices as the host eyepoint passes from one triangle'"'"'s boundaries to another;

displaying an image of the computer simulated environment to a user based on the modified host eyepoint position.

24. A method as in claim 23, wherein the step of interpolating the position of the host eyepoint further comprises the step of interpolating the position of the host eyepoint by using barycentric weightings to modify the host eyepoint.

25. A method as in claim 23, further comprising the step of subtracting an eyepoint lat-long position from a global airport lat-long origin to form delta relative to the global airport origin.

26. A method as in claim 25, further comprising the step of converting the delta relative to the global airport origin into X-Y coordinates based on the latitude of the airport in the global model, which forms an airport-relative eyepoint.

27. A method as in claim 23, further comprising the step of creating 2D vectors from the eyepoint to each triangle vertex.

28. A method as in claim 27, further comprising the steps of:

computing a 2D cross product of each vector; and

testing the 2D cross product to determine whether the eyepoint is within a given triangle.

29. A method as in claim 23, further comprising the step of computing a 2D cross product of each vector to provide a value that represents twice the area of the sub-triangle formed by the eyepoint and the triangle edge.

30. A method as in claim 29, further comprising the step of computing the 2D cross product for each triangle vertex in the triangle within which the eyepoint is located.

31. A method as in claim 30, further comprising the step of dividing the 2D cross product of each vector by the sum of the three 2D cross products to create three barycentric weights.

32. A method as in claim 31, further comprising the step of computing the instantaneous correction factors for the eyepoint by multiplying each barycentric weight by its associated correction delta values and then summing the delta values.

33. A method as in claim 32, further comprising the step of adding the correction delta values for the X, Y, Z and heading to the host eyepoint.

34. A method as in claim 33, further comprising the step of applying a corrected host eyepoint in the global model.