Inertial navigation system for mobile objects with constraints

• US 20030216865A1
• Filed: 04/11/2003
• Published: 11/20/2003
• Est. Priority Date: 04/12/2002
• Status: Active Grant

• Current Assignee: ENSCO, Incorporated (Teris LLC)
• Original Assignee: ENSCO, Incorporated (Teris LLC)
• Inventors: Gaines, Harry T., Riewe, Frederick Eugene
• Granted Patent: US 6,826,478 B2
• Time in Patent Office: Days
• US Class Current: 701/220
• CPC Class Codes: B61L 2205/04 Satellite based navigation systems, e.g. global positioning system [GPS]
• CPC Class Codes: B61L 25/021 Measuring and recording of train speed
• CPC Class Codes: B61L 25/025 Absolute localisation, e.g. providing geodetic coordinates
• CPC Class Codes: B61L 25/026 Relative localisation, e.g. using odometer
• CPC Class Codes: G01C 21/1652 with ranging devices, e.g. LIDAR or RADAR
• CPC Class Codes: G01S 19/47 the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial

Abstract

An aided inertial navigation system and method for navigating a mobile object having constraints comprising an inertial measurement unit, a processor, and an error correction device. The inertial measurement unit provides acceleration data and/or angular velocity data of the mobile object. The processor is adapted to receive the acceleration data and/or angular velocity data, and to provide output data with position output indicative of position of the mobile object. The error correction device receives as input, state and dynamics information and auxiliary input data including map information associated with the path, speed data, wheel-angle data and discrete data. The error correction device provides as output, state corrections to the processor that enhance accuracy of the position output. The state corrections are used by the processor to estimate position of the mobile object based on the constraints to the mobile object and the map information associated with the path.

Claims

1. An aided inertial navigation system (AINS) for navigating a mobile object, said AINS comprising:

an inertial measurement unit adapted to provide acceleration data and angular velocity data of said mobile object, said mobile object having constraints which constrain mobility of said mobile object to a path;

a processor adapted to receive said acceleration data and angular velocity data from said inertial measurement unit, and to provide output data with position output indicative of position of said mobile object; and

an error correction device adapted to receive as input, state and dynamics information of said mobile object, and provide as output, state corrections to said processor;

wherein said processor enhances said position output based on said state corrections and said constraints to said mobile object to increase accuracy of said position output.

2. The AINS of claim 1, wherein said error correction device is further adapted to receive auxiliary input data including at least one of positional input data, map information associated with said path, speed data, wheel-angle data, and discrete data.

3. The AINS of claim 2, wherein said state corrections provided to said processor are based on said auxiliary input data as well as said state and dynamics information.

4. The AINS of claim 1, wherein said inertial measurement unit includes at least one of an accelerometer that provides said acceleration data, and a gyroscope that provides said angular velocity data.

5. The AINS of claim 4, wherein said inertial measurement unit includes at least one accelerometer and at least one gyroscope.

6. The AINS of claim 1, wherein said processor is an inertial navigation and sensor compensation unit and said output data further includes a velocity output indicative of speed of said mobile object, and an attitude output indicative of direction of movement of said mobile object.

7. The AINS of claim 1, wherein said processor is an inertial navigation and sensor compensation unit, and said output data further includes an accuracy output indicative of accuracy of said position output.

8. The AINS of claim 7, wherein said accuracy output is expressed as a confidence interval for distance along said path.

9. The AINS of claim 7, wherein said accuracy output is expressed as at least one of a confidence circle and a confidence ellipse.

10. The AINS of claim 1, wherein said error correction device is a Kalman filter.

11. The AINS of claim 10, wherein said Kalman filter is further adapted to receive auxiliary input data including at least one of positional input data, map information associated with said path, speed data, wheel-angle data, and discrete data.

12. The AINS of claim 11, wherein said positional input data is provided by at least one of Global Positioning System, Differential GPS, ultrasonic positioning system, and radio-frequency positioning system.

13. The AINS of claim 11, wherein said speed data is provided by at least one of an odometer, wheel tachometer, and Doppler radar.

14. The AINS of claim 11, wherein said wheel angle data is provided by at least one of wheel angle sensor and truck angle sensor.

15. The AINS of claim 11, wherein said discrete data is provided by at least one of a transponder and a rail detector.

16. The AINS of claim 10, wherein said Kalman filter is provided with zero-azimuth-change observations when said mobile object is stationary.

17. The AINS of claim 2, wherein said map information includes coordinates of a series of map points marking at least one map segment.

18. The AINS of claim 17, wherein said processor is further adapted to calculate a maximum distance error between a segment of said path and said at least one map segment.

19. The AINS of claim 17, wherein said map information further includes along-path distances between said series of map points.

20. The AINS of claim 1, wherein said mobile object is an automobile, and said path is defined by a road.

21. The AINS of claim 1, wherein said vehicle is at least one of a railcar and a trolley, and said path is defined by a track.

22. The AINS of claim 1, wherein said mobile object is a mobile probe, and said path is defined by a pipe.

23. An aided inertial navigation system (AINS) for navigating a mobile object, said AINS comprising:

an inertial measurement unit adapted to provide acceleration data and angular velocity data of said mobile object, said mobile object having constraints which constrain mobility of said mobile object to a path;

a processor adapted to receive said acceleration data and angular velocity data from said inertial measurement unit, and to provide a plurality of output data for navigating said mobile object, said plurality of output data with position output indicative of position of said mobile object; and

an error correction device adapted to;

receive as input, state and dynamics information of said mobile object and auxiliary input data including at least one of positional input data, map information associated with said path, speed data, wheel angle data and discrete data; and

provide as output, state corrections to said processor, said state corrections being determined based on said auxiliary input data;

wherein said processor enhances accuracy of said position output indicative of position of said mobile object based on said state corrections and said constraints to said mobile object.

24. An aided inertial navigation system (AINS) for navigating a mobile object, said AINS comprising:

an inertial measurement unit adapted to provide acceleration data and angular velocity data of said mobile object;

a processor adapted to receive said acceleration data and angular velocity data from said inertial measurement unit, and to provide output data having position output indicative of position of said mobile object; and

a Kalman filter adapted to receive as input, state and dynamics information of said mobile object, and provide as output, state corrections to said processor, said Kalman filter being provided with zero-azimuth-change observations when said mobile object is stationary;

wherein said processor enhances said position output based on said state corrections to increase accuracy of said position output.

25. A method for navigating a mobile object comprising the steps of:

constraining mobility of said mobile object to a path;

monitoring acceleration and angular velocity of said mobile object;

providing acceleration data and angular velocity data;

determining output data having at least position output indicative of position of said mobile object based on said acceleration data and angular velocity data; and

enhancing accuracy of said position output indicative of position of said mobile object based on said constraints to mobility of said mobile object.

26. The method of claim 25, further including the step of providing map information associated with said path, wherein said step of enhancing accuracy of said position output is further based on said map information.

27. The method of claim 25, wherein said step of enhancing accuracy of said position output is further based on state and dynamics information of said mobile object, said state and dynamics information being derived from said acceleration data and angular velocity data.

28. The method of claim 25, further including the steps of determining velocity and attitude of said mobile object, and providing a velocity output indicative of speed of said mobile object and an attitude output indicative of direction of said mobile object.

29. The method of claim 25, further including the step of determining accuracy of said position output, and wherein said output data further includes an accuracy output indicative of accuracy of said position output.

30. The method of claim 25, wherein said step of enhancing accuracy of said position output includes the step of generating state corrections using a Kalman filter based on at least one of positional input data, speed data, map information, wheel angle data, and discrete data.

31. The method of claim 30, further including the step of determining smoothly varying distance along path for use as said speed data.

32. The method of claim 30, wherein said map information includes coordinates of a series of map points marking at least one map segment, and along-path distances between said series of points.

33. The method of claim 32, further including the steps of determining where position of said mobile object coincides with a known map point and further enhancing accuracy of said position output based on said known map point.

34. The method of claim 32, further including the steps of monitoring along path distance and comparing said along path distance to length of said at least one map segment to determine accuracy of said map information.

35. The method of claim 32, further including the step of calculating a maximum distance error between a segment of said path and said at least one map segment.

36. The method of claim 30, further including the step of providing said Kalman filter with zero-azimuth-change observations when said mobile object is stationary.

37. A method for navigating a mobile object comprising the steps of:

constraining mobility of said mobile object to a path;

providing map information associated with said path;

monitoring acceleration and angular velocity of said mobile object;

providing acceleration data and angular velocity data;

determining output data with position output indicative of position of said mobile object based on said acceleration data and angular velocity data for navigating said mobile object; and

enhancing accuracy of said position output based on constraints to mobility of said mobile object and said map information associated with said path.

38. A method for navigating a mobile object comprising the steps of:

monitoring acceleration and angular velocity of said mobile object and providing acceleration data and angular velocity data;

determining output data with position output indicative of position of said mobile object for navigating said mobile object based on said acceleration data and angular velocity data;

providing a Kalman filter adapted to receive as input, state and dynamics information of said mobile object, and providing state corrections as outputs; and

providing said Kalman filter with zero-azimuth-change observations when said mobile object is stationary to enhance accuracy of said position output indicative of position of said mobile object.

39. The method of claim 38, further including the step of determining whether said mobile object is stationary.

40. The method of claim 39, further including the steps of establishing an azimuth reference value by sampling and saving a current azimuth value.

41. The method of claim 40, further including the step of establishing an azimuth reference error in said Kalman filter error state which is same as said azimuth reference value.

42. The method of claim 41, further including the step of adjusting an error state covariance matrix to accurately reflect said azimuth reference error.