System and method for improving orientation data

• US 9,785,254 B2
• Filed: 10/12/2012
• Issued: 10/10/2017
• Est. Priority Date: 11/01/2011
• Status: Active Grant

• Current Assignee: Qualcomm, Inc.
• Original Assignee: Qualcomm, Inc.
• Inventors: Kulik, Victor, Ahuja, Disha, Puig, Carlos M.
• Primary Examiner(s): Breene, John
• Assistant Examiner(s): Islam, Mohammad
• Application Number:
  US13/651,343
• Publication Number:
  US 20130110439A1
• Time in Patent Office: 1,824 Days
• CPC Class Codes: G01B 21/22 for measuring angles or tapers; for testing the alignment of axes
• CPC Class Codes: G01C 17/38 Testing, calibrating, or compensating of compasses
• CPC Class Codes: G01C 21/1654 with electromagnetic compass
• CPC Class Codes: G06F 15/00 Digital computers in general details G06F1/00 – G06F13/00; Data processing equipment in general
• CPC Class Codes: G06F 2200/1637 Sensing arrangement for detection of housing movement or orientation, e.g. for controlling scrolling or cursor movement on the display of an handheld computer
• CPC Class Codes: G06F 3/0304 Detection arrangements using opto-electronic means constructional details of pointing devices not related to the detection arrangement using opto-electronic means G06F3/033; optical digitisers G06F3/042
• CPC Class Codes: G06F 3/0346 with detection of the device orientation or free movement in a 3D space, e.g. 3D mice, 6-DOF [six degrees of freedom] pointers using gyroscopes, accelerometers or tilt-sensors
• CPC Class Codes: G06F 3/038 Control and interface arrangements therefor, e.g. drivers or device-embedded control circuitry

Abstract

Aspects of the disclosure relate to computing technologies. In particular, aspects of the disclosure relate to mobile computing device technologies, such as systems, methods, apparatuses, and computer-readable media for improving orientation data. In some embodiments, a magnetic vector filter receives magnetometer data from a magnetometer and gyroscope data from a gyroscope and determines the magnetic vector using the magnetometer data and the gyroscope data in the magnetic vector filter. In other embodiments, a gravity vector filter receives accelerometer data and gyroscope data and determines the gravity vector using the accelerometer data and the gyroscope data in the gravity vector filter.

Claims

1. A method for obtaining orientation information for a computing device, the method comprising:

receiving magnetometer data at a first Kalman filter, wherein the magnetometer data comprises sensor output from a magnetometer;

receiving accelerometer data at a second Kalman filter, wherein the accelerometer data comprises sensor output from an accelerometer;

receiving gyroscope data at the first Kalman filter and the second Kalman filter, wherein the gyroscope data comprises sensor output from a gyroscope;

determining a magnetic vector by using the magnetometer data and the gyroscope data in the first Kalman filter;

determining a gravity vector by using the accelerometer data and the gyroscope data in the second Kalman filter;

changing a window size for the first Kalman filter based on a first type of environmental noise and changing a window size for the second Kalman filter based on a second type of environmental noise; and

transitioning the gyroscope to a low power state in response to determining that the computing device has transitioned to a stationary state using one or more of the gravity vector or the magnetic vector.

2. The method of claim 1, further comprising at least one filter parameter for each of the first Kalman filter and the second Kalman filter that is dynamically adjustable.

3. The method of claim 2, wherein the at least one filter parameter associated with the second Kalman filter comprises one or more of a signal noise, a measurement error, a sensor calibration error or a linear acceleration.

4. The method of claim 2, wherein the at least one filter parameter associated with the first Kalman filter comprises one or more of sensor calibration error or a magnetic transient field.

5. The method of claim 1, further comprising an expanded adaptive filter, wherein the expanded adaptive filter receives as inputs computed coordinates of the gravity vector for the computing device along with the gyroscope data and the accelerometer data.

6. The method of claim 1, further comprising determining based on the magnetometer data and the gyroscope data, whether a detected change in a magnetic field in the magnetic vector filter is a magnetic anomaly.

7. The method of claim 6, further comprising:

in response to determining that the detected change in the magnetic field is the magnetic anomaly, increasing the window size associated with the first Kalman filter.

8. The method of claim 6, further comprising:

in response to determining that the gyroscope did not measure a change during a period of time in which a change in a magnetic field was detected, determining that the detected change in the magnetic field is the magnetic anomaly.

10. The method of claim 6, further comprising:

in response to determining that the detected change in the magnetic field is the magnetic anomaly, disregarding filter input received from the magnetometer corresponding to the detected change in the magnetic field.

11. The method of claim 6, wherein determining the detected change is the magnetic anomaly comprises comparing the magnetometer data to a threshold.

12. The method of claim 11, further comprising calculating the threshold based at least in part on the gyroscope data.

9. The method of 6,

wherein the detected change in the magnetic field is determined not to be the magnetic anomaly when the received sensor output from the gyroscope correlates to a detected change in the magnetic field;

and

wherein the detected change in the magnetic field is determined to be the magnetic anomaly when the received sensor output from the gyroscope does not correlate to the detected change in the magnetic field.

13. A computing device for determining orientation, comprising:

a receiver coupled to a first Kalman filter and configured to;

receive magnetometer data at the first Kalman filter, wherein the magnetometer data comprises sensor output from a magnetometer;

receive gyroscope data at the first Kalman filter, wherein the gyroscope data comprises sensor output from a gyroscope; and

the first Kalman filter configured to;

determine a magnetic vector by using the magnetometer data and the gyroscope data in the first Kalman filter;

a receiver coupled to a second Kalman filter and configured to;

receive accelerometer data at the second Kalman filter, wherein the accelerometer data comprises sensor output from an accelerometer;

receive the gyroscope data at the second Kalman filter; and

the second Kalman filter configured to;

determine a gravity vector by using the accelerometer data and the gyroscope data in the second Kalman filter, wherein the gravity vector is used for determining the orientation of the computing device; and

one or more processors configured to;

change a window size for the first Kalman filter based on a first type of environmental noise;

change a window size for the second Kalman filter based on a second type of environmental noise; and

transition the gyroscope to a low power state in response to determining that the computing device has transitioned to a stationary state using one or more of the gravity vector or the magnetic vector.

14. The computing device of claim 13, further comprising at least one filter parameter for each of the first Kalman filter and the second Kalman filter that is dynamically adjustable.

15. The computing device of claim 14, wherein the at least one filter parameter associated with the second Kalman filter comprises one or more of a signal noise, a measurement error, a sensor calibration error or a linear acceleration.

16. The computing device of claim 14, wherein the at least one filter parameter associated with the first Kalman filter comprises one or more of sensor calibration error or a magnetic transient field.

17. The computing device of claim 13, further comprising an expanded adaptive filter, wherein the expanded adaptive filter receives as inputs computed coordinates of the gravity vector for the computing device along with the gyroscope data and the accelerometer data.

18. The computing device of claim 13, the first Kalman filter further configured to determine based on the magnetometer data and the gyroscope data, whether a detected change in a magnetic field in the first Kalman filter is a magnetic anomaly.

19. The computing device of claim 18, wherein the first Kalman filter is configured to determine the detected change is the magnetic anomaly by comparing the magnetometer data to a threshold.

20. The computing device of claim 19, wherein the first Kalman filter is configured to calculate the threshold based at least in part on the gyroscope data.

21. The computing device of claim 18, wherein the first Kalman filter is further configured to determine that the detected change in the magnetic field is the magnetic anomaly, in response to determining that the gyroscope did not measure a change during a period of time in which the change in the magnetic field was detected.

22. The computing device of claim 18,

wherein the detected change in the magnetic field is determined by the first Kalman vector filter not to be the magnetic anomaly when the received sensor output from the gyroscope correlates to a detected change in the magnetic field;

and

wherein the detected change in the magnetic field is determined by the first Kalman filter to be the magnetic anomaly when the received sensor output from the gyroscope does not correlate to the detected change in the magnetic field.

23. The computing device of claim 18, the first Kalman filter further configured to disregard filter input received from the magnetometer corresponding to the detected change in the magnetic field, in response to determining that the detected change in the magnetic field is the magnetic anomaly.

24. The computing device of claim 18, the one or more processors are further configured to increase the window size associated with the first Kalman filter in response to determining the detected change in the magnetic field is the magnetic anomaly.

25. A non-transitory computer readable storage medium, wherein the non-transitory computer readable storage medium comprises instructions executable by a processor, the instructions comprising instructions to:

receive magnetometer data at a first Kalman filter, wherein the magnetometer data comprises sensor output from a magnetometer;

receive accelerometer data at a second Kalman filter, wherein the accelerometer data comprises sensor output from an accelerometer;

receive gyroscope data at the first Kalman filter and the second Kalman filter, wherein the gyroscope data comprises sensor output from a gyroscope;

determine a magnetic vector by using the magnetometer data and the gyroscope data in the first Kalman;

determine a gravity vector by using the accelerometer data and the gyroscope data in the second Kalman filter;

change a window size for the first Kalman filter based on a first type of environmental noise and change a window size for the second Kalman filter based on a second type of environmental noise; and

transition the gyroscope to a low power state in response to determining that a computing device comprising the processor has transitioned to a stationary state using one or more of the gravity vector or the magnetic vector.

26. An apparatus, comprising:

means for receiving magnetometer data at a first Kalman filter, wherein the magnetometer data comprises sensor output from a magnetometer;

means for receiving accelerometer data at a second Kalman filter, wherein the accelerometer data comprises sensor output from an accelerometer;

means for receiving gyroscope data at the first Kalman filter and the second Kalman filter, wherein the gyroscope data comprises sensor output from a gyroscope;

means for determining a magnetic vector by using the magnetometer data and the gyroscope data in the first Kalman filter;

means for determining a gravity vector by using the accelerometer data and the gyroscope data in the second Kalman filter;

means for changing a window size for the first Kalman filter based on a first type of environmental noise and changing a window size for the second Kalman filter based on a second type of environmental noise; and

means for transitioning the gyroscope to a low power state in response to determining that the apparatus has transitioned to a stationary state using one or more of the gravity vector or the magnetic vector.

27. The apparatus of claim 26, further comprising at least one filter parameter for each of the first Kalman filter and the second Kalman filter that is dynamically adjustable.

28. The apparatus of claim 26, further comprising a means for determining based on the magnetometer data and the gyroscope data, whether a detected change in a magnetic field in the magnetic vector filter is a magnetic anomaly;

wherein the detected change in the magnetic field is determined not to be the magnetic anomaly when the received sensor output from the gyroscope correlates to a detected change in the magnetic field; and

wherein the detected change in the magnetic field is determined to be the magnetic anomaly when the received sensor output from the gyroscope does not correlate to the detected change in the magnetic field.

29. The apparatus of claim 26, further comprising:

means for disregarding filter input received from the magnetometer corresponding to the detected change in the magnetic field or means for increasing the window size associated with the first Kalman filter, in response to determining that the detected change in the magnetic field is a magnetic anomaly.