Systems and methods of correlating satellite position data with terrestrial features

• US 10,002,109 B2
• Filed: 06/09/2017
• Issued: 06/19/2018
• Est. Priority Date: 06/24/2016
• Status: Active Grant

• Current Assignee: Freeport-Mcmoran Corporation (Freeport-Mcmoran Copper & Gold Incorporated)
• Original Assignee: Freeport-Mcmoran Corporation (Freeport-Mcmoran Copper & Gold Incorporated)
• Inventors: Walker, Mary Amelia, Catron, Robert, Vaughan, Brian, Lu, Hung Jung
• Primary Examiner(s): TSE, YOUNG TOI
• Application Number:
  US15/618,594
• Publication Number:
  US 20170371837A1
• Time in Patent Office: 375 Days
• Field of Search: 375142, 375150, 375259, 375343, 342107-109, 3423572, 34235722, 34235723, 370350, 455427, 4554561, 708 5, 708813
• CPC Class Codes: G01S 19/40 Correcting position, velocity or attitude
• CPC Class Codes: G01S 19/42 Determining position
• CPC Class Codes: G01S 19/50 whereby the position solution is constrained to lie upon a particular curve or surface, e.g. for locomotives on railway tracks
• CPC Class Codes: G06F 17/15 Correlation function computation including computation of convolution operations arithmetic circuits for sum of products per se, e.g. multiply-accumulators G06F7/5443; digital filters, e.g. FIR, IIR, adaptive filters H03H17/00

Abstract

A method of correlating satellite position data with terrestrial features may include: Using a geometric snapping algorithm to correlate the satellite position data and terrestrial survey data and snap the satellite position data to the terrestrial features; determining whether the satellite position data can be snapped to unique terrestrial features; and using a hybrid space-time snapping algorithm to correlate the satellite position data and terrestrial survey data and snap the satellite position data to unique terrestrial features when the satellite position data cannot be snapped to unique terrestrial features.

Claims

1. A method of correlating satellite position data with terrestrial features, the locations of the terrestrial features being given by terrestrial survey data, comprising:

using a geometric snapping algorithm to correlate the satellite position data and the terrestrial survey data and snap the satellite position data to the terrestrial features;

determining whether the satellite position data can be snapped to unique terrestrial features; and

using a hybrid space-time snapping algorithm to correlate the satellite position data and the terrestrial survey data and snap the satellite position data to the unique terrestrial features when the satellite position data cannot be snapped to the unique terrestrial features,

wherein said using the geometric snapping algorithm comprises;

defining a two-dimensional grid comprising a plurality of grid points at defined locations;

for a plurality of locations (x, y) defined by the satellite position data, rounding the satellite position data to the nearest grid point of the defined two-dimensional grid to create an amplitude data table, each rounded satellite position data point in the amplitude data table defining a reference grid point value (gx, gy);

for a plurality of locations (rx, ry) of the terrestrial features given by the terrestrial survey data, matching the terrestrial survey data to at least four adjacent grid points (gx1, gy1), (gx2, gy2), (gx3, gy3), and (gx4, gy4) of the defined two-dimensional grid to create a terrestrial coordinate table;

merging the amplitude data table and the terrestrial coordinate table based on the reference grid point values (gx, gy) to form a merged table;

searching the merged table to identify the grid point with the minimum distance between the (x,y) location and the (rx, ry) location, the identified grid point comprising a snapping point; and

snapping the (x,y) location to the snapping point.

2. The method of claim 1, wherein the terrestrial features comprise a plurality of trails, wherein the satellite position data comprises data collected over time as a vehicle traverses at least one trail, and wherein said using the hybrid space-time snapping algorithm comprises using a spatial-temporal score to snap the satellite position data to the trail actually traversed by the vehicle.

3. The method of claim 2, wherein said using the spatial-temporal score comprises determining each of a spatial-proximity value, a spatial-evenness value, and a temporal-evenness value.

4. The method of claim 2, further comprising defining a patience window within which said using said hybrid space-time snapping algorithm will snap the satellite position data to the unique terrestrial features.

5. The method of claim 4, wherein said defining the patience window comprises defining a patience window having a time span of about 10 seconds.

6. The method of claim 2, wherein said using the hybrid space-time algorithm further comprises:

defining a prime path as any path with more than a predetermined time of satellite position data fixes being the sole choice of snappable points; and

snapping satellite data fixes to said prime path.

7. The method of claim 6, wherein the predetermined time is 10 seconds.

8. The method of claim 6, wherein said using the hybrid space-time algorithm further comprises:

defining a periapsis time stamp as corresponding to a time stamp associated with a satellite position data fix that is closest in distance to a snappable road coordinate point; and

snapping the satellite data fixes based on the periapsis time stamp.

9. The method of claim 6, further comprising:

determining if multiple points from the same path are available to be snapped at the same time; and

snapping only a closest point of said multiple points.

10. The method of claim 1, wherein said defining the two-dimensional grid comprises defining a two-dimensional grid of squares.

11. The method of claim 10, wherein said defining the two-dimensional grid of squares comprises defining the two-dimensional grid of squares wherein the side of each square grid of the two-dimensional grid of squares corresponds to a terrestrial length of about 300 feet (about 91 m).

12. The method of claim 10, wherein said using the geometric snapping algorithm further comprises snapping the satellite position data to the terrestrial features only when the distance between the (x,y) location and the (rx, ry) location is equal to or less than a terrestrial distance of about 150 feet (about 46 m).

13. The method of claim 1, wherein the at least four adjacent grid points (gx1, gy1), (gx2, gy2), (gx3, gy3), and (gx4, gy4) of the defined two-dimensional grid comprise southwest, northwest, southeast, and northeast terrestrial grid points, respectively.

14. The method of claim 1, wherein each location of the terrestrial features is defined by a terrestrial coordinate system comprising:

a start call-point;

an end call-point;

a distance to start call-point; and

a distance to end call-point.

15. The method of claim 14, wherein the distances to the start and end call-points are integers.

16. The method of claim 15, wherein at least some of the distances comprise curvilinear distances and wherein said geometric snapping algorithm measures the curvilinear distances along Bezier curves.

17. A method of correlating satellite position data with terrestrial features, the locations of the terrestrial features being given by terrestrial survey data, wherein the terrestrial features comprise a plurality of trails and wherein the satellite position data comprises data collected over time as a vehicle traverses at least one trail, comprising:

using a geometric snapping algorithm to correlate the satellite position data and the terrestrial survey data and snap the satellite position data to the terrestrial features;

determining whether the satellite position data can be snapped to unique terrestrial features; and

using a hybrid space-time snapping algorithm to correlate the satellite position data and the terrestrial survey data and snap the satellite position data to the unique terrestrial features when the satellite position data cannot be snapped to the unique terrestrial features,

wherein said using the hybrid space-time snapping algorithm comprises using a spatial-temporal score to snap the satellite position data to the trail actually traversed by the vehicle, wherein said using the spatial-temporal score comprises determining each of a spatial-proximity value, a spatial-evenness value, and a temporal-evenness value, and wherein the spatial-proximity value (SPV) is given by the following equation;

SPV=exp(−

½

*(average error distance/30)²).

18. The method of claim 17, wherein the spatial-evenness value is the ratio of an effective number of snapping error distances and a number of snapping error distances.

19. The method of claim 18, wherein the effective number of snapping error distances is determined from an Inverse Herfindahl Index of the number of snapping error distances.

20. The method of claim 17, wherein the temporal-evenness value is determined from a distribution of time differences between consecutive snapped trail points.

21. A method of correlating satellite position data with terrestrial features, the locations of the terrestrial features being given by terrestrial survey data, wherein the terrestrial features comprise a plurality of trails and wherein the satellite position data comprises data collected over time as a vehicle traverses at least one trail, comprising:

using a geometric snapping algorithm to correlate the satellite position data and the terrestrial survey data and snap the satellite position data to the terrestrial features;

determining whether the satellite position data can be snapped to unique terrestrial features; and

using a hybrid space-time snapping algorithm to correlate the satellite position data and the terrestrial survey data and snap the satellite position data to the unique terrestrial features when the satellite position data cannot be snapped to the unique terrestrial features,

wherein said using the hybrid space-time snapping algorithm comprises using a spatial-temporal score to snap the satellite position data to the trail actually traversed by the vehicle, wherein said using the spatial-temporal score comprises determining each of a spatial-proximity value, a spatial-evenness value, and a temporal-evenness value, and wherein said using the spatial-temporal score comprises determining the third root of the product of the spatial-proximity value, the spatial-evenness value, and the temporal-evenness value.

22. A non-transitory computer-readable storage medium having computer-executable instructions embodied thereon that, when executed by at least one computer processor cause the processor to:

use a geometric snapping algorithm to correlate satellite position data and terrestrial survey data and snap the satellite position data to terrestrial features, wherein the geometric snapping algorithm;

defines a two-dimensional grid comprising a plurality of grid points at defined locations;

for a plurality of locations (x, y) defined by the satellite position data, rounds the satellite position data to the nearest grid point of the defined two-dimensional grid to create an amplitude data table, each rounded satellite position data point in the amplitude data table defining a reference grid point value (gx, gy);

for a plurality of locations (rx, ry) of the terrestrial features given by the terrestrial survey data, matches the terrestrial survey data to at least four adjacent grid points (gx1, gy1), (gx2, gy2), (gx3, gy3), and (gx4, gy4) of the defined two-dimensional grid to create a terrestrial coordinate table;

merges the amplitude data table and the terrestrial coordinate table based on the reference grid point values (gx, gy) to form a merged table;

searches the merged table to identify the grid point with the minimum distance between the (x, y) location and the (rx, ry) location, the identified grid point comprising a snapping point; and

snaps the (x,y) location to the snapping point;

determine whether the satellite position data can be snapped to unique terrestrial features; and

use a hybrid space-time snapping algorithm to correlate the satellite position data and the terrestrial survey data and snap the satellite position data to the unique terrestrial features when the satellite position data cannot be snapped to the unique terrestrial features.

23. A position correlation system, comprising:

a computer processor;

a terrestrial survey database operatively associated with said computer processor, said terrestrial survey database comprising terrestrial survey data associated with terrestrial features in a defined operations area;

a satellite position database operatively associated with said computer processor, said satellite position database comprising satellite position data associated with movement of at least one object within the defined operations area;

a user interface operatively associated with said computer processor, said user interface allowing a user to interface with said computer processor;

a geometric snapping algorithm operatively associated with said computer processor, said geometric snapping algorithm correlating the satellite position data and the terrestrial survey data and snapping the satellite position data to the terrestrial features, said correlating and said snapping further comprising;

defining a two-dimensional grid comprising a plurality of grid points at defined locations;

for a plurality of locations (x, y) defined by the satellite position data, rounding the satellite position data to the nearest grid point of the defined two-dimensional grid to create an amplitude data table, each rounded satellite position data point in the amplitude data table defining a reference grid point value (gx, gy);

for a plurality of locations (rx, ry) of the terrestrial features given by the terrestrial survey data, matching the terrestrial survey data to at least four adjacent grid points (gx1, gy1), (gx2, gy2), (gx3, gy3), and (gx4, gy4) of the defined two-dimensional grid to create a terrestrial coordinate table;

merging the amplitude data table and the terrestrial coordinate table based on the reference grid point values (gx, gy) to form a merged table;

searching the merged table to identify the grid point with the minimum distance between the (x,y) location and the (rx, ry) location, the identified grid point comprising a snapping point; and

snapping the (x,y) location to the snapping point; and

a hybrid space-time snapping algorithm operatively associated with said computer processor, said hybrid space-time snapping algorithm correlating the satellite position data and the terrestrial survey data and snapping the satellite position data to unique terrestrial features, said computer processor utilizing said geometric snapping algorithm and said hybrid space-time snapping algorithm to correlate the satellite position data and the terrestrial position data, said computer processor further utilizing said hybrid space-time snapping algorithm when the satellite position data cannot otherwise be snapped to the unique terrestrial features, said computer processor producing output data relating to the snapped satellite position data and transferring the output data to the user interface.