Systems and methods for improved position determination of vehicles

• US 8,374,783 B2
• Filed: 02/28/2008
• Issued: 02/12/2013
• Est. Priority Date: 10/10/2007
• Status: Active Grant

• Current Assignee: Leica Geosystems AG (Hexagon AB)
• Original Assignee: Leica Geosystems AG (Hexagon AB)
• Inventors: Takac, Frank, Zelzer, Oliver, Kellar, William James
• Primary Examiner(s): Tarcza, Thomas
• Assistant Examiner(s): TISSOT, ADAM D
• Application Number:
  US12/039,601
• Publication Number:
  US 20090099774A1
• Time in Patent Office: 1,811 Days
• Field of Search: 701/216, 701/466, 701/408, 701/472, 701500-512, 701/519, 701/469, 701/535, 340988-996
• US Class Current: 701/466
• CPC Class Codes: A01B 79/005 Precision agriculture
• CPC Class Codes: G01C 21/28 with correlation of data from several navigational instruments
• CPC Class Codes: G01S 19/47 the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
• 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

Abstract

Systems and methods for determining a position of a vehicle are described. The system includes at least one GNSS sensor mounted to the vehicle for receiving GNSS signals of a global positioning system and at least one physical sensor mounted to the vehicle for generating physical data indicative of a physical parameter of at least a part of the vehicle. The system also includes a recursive statistical estimator, such as a Kalman Filter, in communication with the GNSS sensor(s) for seeding the recursive statistical estimator with an output of the GNSS sensor(s) to determine an estimated position of the vehicle. A data fusion module combines the estimated position and velocity of the vehicle with the physical data thus generating combined data, which is used to seed the recursive statistical estimator to determine an updated estimated position of the vehicle.

Claims

1. A method for determining a position of a mining, agricultural or rail vehicle including:

at least one GNSS sensor receiving GNSS signals of a global positioning system;

at least one physical sensor generating physical data indicative of at least one measured physical parameter of at least a part of the mining, agricultural or rail vehicle;

seeding a recursive statistical estimator with an output of the at least one GNSS sensor to determine an estimated position and velocity of the mining, agricultural or rail vehicle;

seeding the recursive statistical estimator with information based on a mining, agricultural or rail vehicle-specific movement limitation characteristic by defining a restriction for values of the at least one physical sensor;

combining the estimated position and velocity of the mining, agricultural or rail vehicle determined from the output of the at least one GNSS sensor with the physical data generated by the at least one physical sensor and with the mining, agricultural or rail vehicle-specific movement limitation characteristic to generate combined data; and

seeding the recursive statistical estimator with the combined data to determine an updated estimated position of the mining, agricultural or rail vehicle.

2. A method according to claim 1, wherein the physical parameter is one of the following:

position, latitude, longitude, altitude, attitude, heading, inclination, speed, acceleration, rate of angular rotation, magnetic field strength.

3. A method according to claim 1, wherein the recursive statistical estimator comprises one of the following:

a Kalman Filter, an extended filter, a complementary filter, an adaptive filter.

4. A method according to claim 1, further including the recursive statistical estimator using a dynamic model describing time-dependent effects on the at least one physical sensor for eliminating bias of the at least one physical sensor.

5. A method according to claim 4, wherein eliminating bias is based on a time series of data generated during movement of the mining, agricultural or rail vehicle.

6. A method according to claim 4, wherein the at least one physical sensor comprises a magnetometer and eliminating bias is based on a calculation of differences between GNSS-signal headings and physical data headings.

7. A method according to claim 4, wherein the at least one physical sensor comprises an angular rate sensor and eliminating bias is based on a best fit of a sequence of GNSS-data with a measured sequence of angular rates.

8. A method according to claim 1, further including seeding the recursive statistical estimator with three-dimensional environmental data.

18. A method according to claim 1, wherein the vehicle-specific movement limitation characteristic is associated with a limitation to the velocity of the vehicle.

19. A method according to claim 1, wherein the vehicle-specific movement limitation characteristic is associated with a track upon which the vehicle travels.

20. A method according to claim 1, wherein the vehicle-specific movement limitation characteristic limits a sharp edge in a trajectory of the vehicle.

21. A method according to claim 1, wherein the vehicle-specific movement limitation characteristic is associated with a known impossible movement of the vehicle.

9. A system for determining a position of a vehicle comprising:

at least one GNSS sensor mounted to the mining, agricultural or rail vehicle for receiving GNSS signals of a global positioning system;

at least one physical sensor mounted to the vehicle for generating physical data indicative of a physical parameter of at least a part of the mining, agricultural or rail vehicle;

three dimensional topographical environmental data describing the topography of the mining, agricultural or rail environment for seeding the recursive statistical estimator;

a recursive statistical estimator coupled to be in communication with the at least one GNSS sensor for seeding the recursive statistical estimator with an output of the at least one GNSS to determine an estimated position and velocity of the mining, agricultural or rail vehicle; and

a data fusion module for combining the estimated position and velocity of the vehicle derived from the GNSS signals of the global positioning system with the physical data derived from the physical data generated by the physical sensor and with the three dimensional topographical data describing the topography of the mining, agricultural or rail environment to generate combined data, wherein the recursive statistical estimator is seeded with the combined data to determine an updated estimated position of the mining, agricultural or rail vehicle.

10. A system according to claim 9, wherein the physical parameter is one of the following:

position, latitude, longitude, altitude, attitude, heading, inclination, speed, acceleration, rate of angular rotation, magnetic field strength.

11. A system according to claim 9, wherein the recursive statistical estimator comprises one of the following:

a Kalman Filter, an extended filter, a complementary filter, an adaptive filter.

12. A system according to claim 9, further comprising a dynamic model describing time-dependent effects on the at least one physical sensor for eliminating bias of the at least one physical sensor.

13. A system according to claim 12, wherein eliminating bias is based on a time series of data generated during movement of the vehicle mining, agricultural or rail.

14. A system according to claim 12, wherein the at least one physical sensor comprises a magnetometer and eliminating bias is based on a calculation of differences between GNSS-signal headings and physical data headings.

15. A system according to claim 12, wherein the at least one physical sensor comprises an angular rate sensor and eliminating bias is based on a best fit of a sequence of GNSS-data with a measured sequence of angular rates.

16. A system according to claim 10, further comprising information on mining, agricultural or rail vehicle-specific movement characteristics based on restricted values of the at least one physical sensor for seeding the recursive statistical estimator.

22. A system according to claim 9, wherein the three-dimensional environmental information includes the slope of terrain upon which the mining, agricultural or rail vehicle travels upon.

23. A system according to claim 9, wherein the mining, agricultural or rail vehicle is an agricultural vehicle and the three-dimensional environmental information includes the location of a rock to be avoided.

17. A processor for determining a position of a mining, agricultural or rail vehicle including:

computer readable program code components configured to cause reception of GNSS signals of a global positioning system;

computer readable program code components configured to cause generation of physical data indicative of at least one measured physical parameter of at least a part of the mining, agricultural or rail vehicle;

computer readable program code components configured to cause seeding a recursive statistical estimator with an output of at least one GNSS sensor to determine an estimated position and velocity of the mining, agricultural or rail vehicle;

computer readable program code components configured to cause combining the estimated position and velocity of the vehicle determined by the recursive statistical estimator based the output of at least one GNSS sensor with the physical data and a dynamic model that is based on an understanding of particular vehicle constraints of the mining, agricultural or rail vehicle as well as the mining agricultural or rail land surface the mining, agricultural or rail vehicle travels on to generate combined data; and

computer readable program code components configured to cause seeding the recursive statistical estimator with the combined data to determine an updated estimated position of the mining, agricultural or rail vehicle.