Air pointer with improved user experience

• US 10,331,240 B2
• Filed: 09/26/2014
• Issued: 06/25/2019
• Est. Priority Date: 09/27/2013
• Status: Active Grant

• Current Assignee: Movea
• Original Assignee: Movea
• Inventors: Grenet, Pierre
• Primary Examiner(s): Edwards, Carolyn R
• Application Number:
  US15/025,474
• Publication Number:
  US 20160216775A1
• Time in Patent Office: 1,733 Days
• 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

The invention discloses a device and a method to control an object on a surface using motion sensor measurements and relative and absolute pointing algorithms. Relative pointing is preferred at low speeds and absolute pointing is preferred at high speeds. Transition between the results of the two algorithms has to be smoothed, using a weighted combination of the two results. The weights may be made dependent over time or over speed of the device. Maximum and minimum values of different parameters may have to be set to avoid jumps or changes in direction which may affect the user'"'"'s experience negatively.

The goal is that the user has consistent perceptions what he or she sees on the screen and the movements he or she imparts on the device, while still keeping the position of the object as close as possible to the position calculated by an absolute pointing algorithm.

Claims

1. A device for controlling a virtual object on a surface comprising:

inertial sensing capabilities configured for producing an output formed from a timed series of measurements in relation to the device, said measurements selected from a group comprising altitude, position, first and second derivatives thereof;

processing capabilities configured for producing, at least, during a time window determined by at least one of a starting and an ending triggering event, from the output of the sensing capabilities, at least a first inertial dataset and a second inertial dataset, each one corresponding to the time window and representative of at least one of orientation and position of the virtual object, first and second derivatives thereof;

said processing capabilities being further configured to

produce during said time window, from the at least the first and second datasets, a third dataset corresponding to the time window and representative of at least one of a position of a point of the virtual object and an orientation of said virtual object, said third dataset being calculated as a weighted combination of the at least first and second datasets, wherein a weight of the at least first and second datasets in said combination varies during the time window such that the weight of the first dataset transitions from a maximum at a beginning of the time window to a minimum at an end of the time window and the weight of the second dataset transitions from a minimum at the beginning of the time window to a maximum at the end of the time window.

2. A method for controlling with a device at least one of an orientation and position of a virtual object on a surface, first and second derivatives thereof, said method comprising:

a step of producing an output formed from a timed series of measurements using inertial sensing capabilities located in the device, said measurements selected from a group comprising attitude, position, first and second derivatives thereof;

a step of processing said output of the sensing capabilities to produce, during at least a time window determined by at least one of a starting and an ending triggering event, at least a first inertial dataset and a second inertial dataset, each one corresponding to the time window and representative of at least one of orientation and position of the virtual object, first and second derivatives thereof;

said method further comprising a step of;

producing, during said time window, from the at least first and second datasets, a third dataset corresponding to the time window and representative of at least one of a position of a point of the virtual object and an orientation of said virtual object, said third dataset being calculated as a weighted combination of the at least first and second datasets, wherein a weight of the at least first and second datasets in said combination varies during the time window such that the weight of the first dataset transitions from a maximum at a beginning of the time window to a minimum at an end of s the time window and the weight of the second dataset transitions from a minimum at the beginning of the time window to a maximum at the end of the time window.

3. The method of claim 2 wherein at least one of the starting and ending triggering event is determined based on one of a dynamicity parameter, a to change of application running on the system, respectively the starting and ending of operation of the device, a button press, and a position of the virtual object.

4. The method of claim 3, wherein the dynamicity parameter is calculated is from at least one of the angular velocities of the device, the transitional velocities of the device, the displacement velocity of the virtual object on the surface, and at least part of the first and second datasets.

5. The method of claim 2, wherein the rate of change of at least one of the weights of the at least first and second datasets is limited to a maximum.

6. The method of claim 2, wherein a ratio of a change in position d of a point of the virtual object, to a parameter representative of the dynamicity of the device, is limited to a set of values between a minimum and a maximum.

7. The method of claim 6, wherein the weight of at least one of the at least first and second datasets is one of minimized and maximized while keeping the ratio between the minimum and the maximum of the set of values.

8. The method of claim 2, wherein one of the first and second datasets is composed of the coordinates of a point of the virtual object in a frame of reference of the surface, said coordinates being determined in relation to previous coordinates of the point by adding to said previous coordinates a displacement determined from measurements representative of at least one of angular velocities and translational velocities of the device.

9. The method of claim 2, wherein one of the first and to second datasets is composed of the coordinates of a point of the virtual object in a frame of reference of the surface, said coordinates being determined from measurements representative of at least one of the attitude and the position of the device.

10. The method of claim 9, wherein said coordinates are calculated from the intersection with the surface of a vector representative of said attitude of the device.

11. The method of claim 9, wherein said coordinates are calculated by applying a gain coefficient to the orientation angles of the device with respect to reference orientation angles, and adding the result of said calculation to the coordinates of a reference point on the surface.

12. The method of claim 2, wherein:

a) the first dataset is composed of the coordinates of a point of the virtual object in a frame of reference of the surface, said coordinates being determined in relation to previous coordinates of the point by adding to said previous coordinates a displacement determined from measurements representative of at least one of angular velocities and translational velocities of the device; and

b) the second dataset is composed of the coordinates of a point of the virtual object in a frame of reference of the surface;

said coordinates being determined from measurements representative of at least one of the attitude and the position of the device; and

c) the third dataset is a combination of the first dataset with weight w and the second dataset with weight (1−

w).

13. The method of claim 12, wherein a ratio of the change in position d based on the third dataset, to the change in position d_rel based on the first dataset, is limited to a set of values between a minimum and a maximum.

14. The method of claim 13, wherein the minimum and maximum depend on a dynamicity parameter, said dynamicity parameter based on at least one of the angular velocities of the device, the translational velocities of the device, and the displacement velocity of the virtual object.

15. The method of claim 13, wherein the weight w of the first dataset is chosen as the smallest value for which the ratio stays between the minimum and the maximum of the set of values.

16. The method of claim 12, wherein the third dataset is recalculated from time to time by reducing the weight w of the first dataset to a preset value over a given period of time.

17. The method of claim 16, wherein the third dataset is recalculated, one of, when a difference between a first value derived from at least part of the first dataset and a second value derived from at least part of the second dataset exceeds a preset threshold, when the virtual object is one of invisible or blurred, and at a given preset frequency.

18. A non-transitory storage medium having a computer program for executing a method for controlling with a device in space at least one of an orientation and position of a virtual object on a surface, first and second derivatives thereof, said program comprising:

a first module for producing an output formed from a timed series of measurements using inertial sensing capabilities located in the device, said measurements from a group comprising attitude, position, first and second derivatives thereof;

a second module for processing said output of the sensing capabilities to produce, during at least a time window determined by at least one of a starting and an ending triggering events, at least a first inertial dataset and a second inertial dataset, each one corresponding to the time window and representative of at least one of orientation and position of the virtual object, first and second derivatives thereof;

said computer program further comprising;

a third module for producing, during said time window, from the at least first and second datasets, a third dataset corresponding to the time window and representative of at least one of a position of a point of the virtual object and an orientation of said virtual object, said third dataset being calculated as a weighted combination of the at least first and second datasets, wherein a weight of said first and second datasets in said combination varies during the time window such that the weight of the first dataset transitions from a maximum at a beginning of the time window to a minimum at an end of s the time window and the weight of the second dataset transitions from a minimum at the beginning of the time window to a maximum at the end of the time window.