SYSTEMS AND METHODS FOR ORIENTATION PREDICTION

• US 20160077166A1
• Filed: 09/12/2014
• Published: 03/17/2016
• Est. Priority Date: 09/12/2014
• Status: Active Application

• Current Assignee: Invensense Incorporated (TDK Corporation)
• Original Assignee: Invensense Incorporated (TDK Corporation)
• Inventors: Morozov, Alexey, Lin, Shang-Hung, Karahan, Sinan
• Application Number:
  US14/485,248
• Publication Number:
  US 20160077166A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01C 19/00 Gyroscopes; Turn-sensitive devices using vibrating masses; Turn-sensitive devices without moving masses; Measuring angular rate using gyroscopic effects
• CPC Class Codes: G01P 15/00 Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
• CPC Class Codes: G01R 33/0286 comprising microelectromechanical systems [MEMS] MEMS devices in general B81B
• CPC Class Codes: G06F 3/012 Head tracking input arrangements
• 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

Systems and methods are disclosed for predicting a future orientation of a device. A future motion sensor sample may be predicted using a plurality of motion sensor samples for the device up to a current time. After determining the current orientation of the device, the predicted motion sensor sample may be used to predict a future orientation of the device at one or more times.

Claims

1. A method for predicting a future orientation of a device configured to be moved by a user, comprising:

obtaining a plurality of motion sensor samples for the device up to a current time;

generating a quaternion representing a current orientation of the device;

predicting a future motion sensor sample, based at least in part, on the plurality of motion samples obtained up to the current time; and

generating a quaternion representing a predicted future orientation of the device by fusing the predicted future motion sensor sample with the current orientation quaternion.

2. The method of claim 1, further comprising predicting a plurality of predicted future motion sensor samples, wherein each motion sensor sample represents a successive future time and generating a plurality of quaternions representing predicted future orientations of the device, wherein each generated quaternion is derived by fusing one of the plurality of motion sensor samples with a preceding orientation quaternion.

3. The method of claim 1, wherein predicting a future motion sensor sample comprises predicting data from at least one of the group consisting of a gyroscope, an accelerometer and a magnetometer.

4. The method of claim 1, wherein predicting a future motion sensor sample comprises deriving a linear function from the plurality of motion sensor samples.

5. The method of claim 1, wherein predicting a future motion sensor sample comprises deriving a nonlinear function from the plurality of motion sensor samples.

6. The method of claim 1, wherein predicting a future motion sensor sample comprises providing a frequency domain representation of a differential equation corresponding to typical motion of the device receiving as inputs the plurality of motion sensor samples.

7. The method of claim 6, further comprising training the differential equation.

8. The method of claim 1, wherein predicting a future motion sensor sample comprises providing an artificial neural network representing typical motion of the device receiving as inputs the plurality of motion sensor samples.

9. The method of claim 8, further comprising training the artificial neural network.

10. The method of claim 1, wherein predicting a future motion sensor sample comprises combining a plurality of predictions obtained from the group consisting of deriving a linear function from the plurality of motion sensor samples, deriving a nonlinear function from the plurality of motion sensor samples, providing a frequency domain representation of a differential equation corresponding to typical motion of the device receiving as inputs the plurality of motion sensor samples and providing an artificial neural network representing typical motion of the device receiving as inputs the plurality of motion sensor samples.

11. The method of claim 1, wherein generating the quaternion representing a predicted future orientation of the device comprises integrating the predicted future motion sensor sample with the current orientation quaternion.

12. The method of claim 1, further comprising generating a graphical representation of a virtual environment using the predicted future orientation quaternion.

13. The method of claim 12, wherein the device is configured to track the motion of the user'"'"'s head.

14. A system for predicting orientation, comprising:

a device configured to be moved by a user outputting motion sensor data;

a data prediction block configured to receive a plurality of samples of the motion sensor data up to a current time and output a predicted future motion sensor sample;

a quaternion generator configured to output a quaternion representing a current orientation of the device; and

a sensor fusion block configured to generate a quaternion representing a predicted future orientation of the device by combining the predicted future motion sensor sample with a preceding orientation quaternion.

15. The system of claim 14, wherein the data prediction block is configured to output a plurality of predicted future motion sensor samples, wherein each motion sensor sample represents a successive future time and wherein the sensor fusion block is configured to generate a plurality of quaternions representing predicted future orientations of the device each derived by combining one of the plurality of motion sensor samples with a preceding orientation quaternion.

16. The system of claim 14, wherein the data prediction block is configured to predict data from at least one of the group consisting of a gyroscope, an accelerometer and a magnetometer.

17. The system of claim 14, wherein the data prediction block is configured to output the predicted future motion sensor sample by deriving a linear function from the plurality of motion sensor samples.

18. The system of claim 14, wherein the data prediction block is configured to output the predicted future motion sensor sample by deriving a nonlinear function from the plurality of motion sensor samples.

19. The system of claim 14, wherein the data prediction block comprises a frequency domain representation of a differential equation corresponding to typical motion of the device receiving as inputs the plurality of motion sensor samples.

20. The system of claim 14, wherein the data prediction block comprises an artificial neural network representing typical motion of the device receiving as inputs the plurality of motion sensor samples.

21. The system of claim 14, wherein the sensor fusion block is configured to generate the quaternion representing a predicted future orientation of the device by integrating the predicted future motion sensor sample with the current orientation quaternion.

22. The system of claim 14, further comprising an image generator configured to render a graphical representation of a virtual environment using the predicted future orientation quaternion.

23. The system of claim 22, wherein the device is configured to track the motion of the user'"'"'s head.

24. The system of claim 23, further comprising a display configured to output the rendered graphical representation.