Better motion control using accelerometer/gyroscope sensor fusion


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This is my Droidcon Tunis 2012 presentation about sensor fusion between gyroscope and accelerometer sensors.

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Better motion control using accelerometer/gyroscope sensor fusion

  1. 1. Better motion control usingaccelerometer/gyroscope sensor fusion Gabor Paller Sfonge Ltd.
  2. 2. Where were we?● Droidcon 2011, London: Motion recognition on Android devices ● presentation-about-motion.html● Processing only the accelerometer for motion recognition
  3. 3. AccelerationAcceleration caused by Acceleration caused by the changethe change of direction of velocity v1 dV dV v2 v2 v1 ΔV a= Δt
  4. 4. Extract motion information from accelerometer data● Accelerometer data is a vector, having 3 axes (x,y,z)● This vector has the following components: ● Gravity acceleration – Pointing toward the center of the Earth – Value of about 10 m/s2 – Thats what we measure when the accelerometer is used to calculate tilt ● Any other acceleration the device is subject to – Added to the gravity acceleration – “Disturbs” tilt measurement in gaming (swift movements cause acceleration) – hence the reason for gyroscopes – Can be used for movement detection
  5. 5. Measured acceleration
  6. 6. Absolute value● x, y, z: acceleration vector components● g – value of the gravity acceleration (can be approximated as 10) a= √ x +y +z −g 2 2 2
  7. 7. Snap – one way accelerating Movement starts: Movement ends: decelerating
  8. 8. Droidcon 2011 flashback● Conclusions: ● Power consumption is a problem ● Some neat functionality can be implemented by doing pattern recognition on the acceleration vectors absolute value ● In general case the gravity and motion acceleration components cannot be separated ● You can try to use an additional sensor like the gyro to help the separation
  9. 9. Gyroscope● Very new phenomenon as gyroscopes suitable for consumer electronic devices appeared very recently● First appearance: Wii Motion Plus accessory, 2009 June● First Android smart phone: Nexus S (end of 2010)● Pros: ● Not sensitive to gravity● Cons: ● Currently supported only by high-end Android phones ● Drift problems (more about that later)
  10. 10. Compass● Measures the device orientation wrt. the magnetic vector of the Earth ● This vector points toward the magnetic center of the Earth – It has a component that points to the magnetic North pole – thats what we use for orientation – Beware of the z component! (also called magnetic inclination). If the device is not held horizontally, the downward vector element influences the measurement● Pros: ● Can be used to deduce gravity, not sensitive to motion acceleration ● Widely available in Android devices● Cons: ● Requires calibration ● Sensitive to metal objects, magnetic fields (e.g. electric motors)
  11. 11. This time it is gyroscope only
  12. 12. Gyroscope
  13. 13. Gyroscope measurement data● Measures rotation around 3 axes● More exactly: measures rotation speed (angular velocity) around the axes Δφ v x= Δt
  14. 14. Getting the rotation angle● Get the angle difference Δ φ=v x Δ t● Get the absolute angle φ =φ+Δ φ
  15. 15. Drift
  16. 16. Noise
  17. 17. Gyro as support sensor● Because of accumulating error, gyro alone can be rarely used● But ● The accelerometer has no accumulated error but has the gravity component problem ● The gyro has accumulated error but is not sensitive to gravity● Sensor fusion: the use of multiple sensors so that they compensate each others weaknesses
  18. 18. Accelerometer-gyro fusion● The easy way ● Use the virtual sensors that calculate gravity and linear acceleration from multiple sensors● The hard way ● Process raw accelerometer and gyroscope data to yield the motion information you need
  19. 19. Virtual sensors Gravity and motion acceleration deduced from the accelerometer and the gyroscope Roll/pitch/yaw from the compass Drift-compensated gyroscope
  20. 20. Drift-compensated gyroscope
  21. 21. The hard way● Why would you go the hard way? ● Sensor fusion co-processing provided by the phone is not precise enough or can have undesirable properties (like auto-calibration in Nexus S) ● Virtual sensors are not available (is there any such case with gyro-equipped phone?) ● You would like to understand how it works and what to expect from built-in sensor fusion ● Just for the fun of it :-)
  22. 22. What we want● Remember: accelerometer measures the sum of gravity and motion acceleration● Kills two use cases: ● If you need device tilt, the motion acceleration component corrupts the measurement ● If you want motion acceleration, it is impossible to subtract the gravity acceleration in a general case● Separate gravity and motion acceleration with the help of the gyroscope
  23. 23. Idea
  24. 24. Idea in words● Pick a reliable gravity vector measurement (make sure that theres no motion then)● If you detect motion (more about later), rotate the previous gravity vector using the gyroscope data and use it as gravity vector estimation● Subtract this gravity vector estimation from the measured acceleration – this yields the motion acceleration
  25. 25. Updating the gravity vector estimation● The gravity vector estimation has to be updated time to time as rotation angle errors accumulate● If we detect an acceleration measurement where there is no motion acceleration, we can take it as new reliable gravity vector estimation● Remember slide #7: if the absolute value of the accelerometer output is close to the Earths gravity, we can assume that theres no motion → the gravity vector estimation can be updated with the current accelerometer output
  26. 26. Implementation● Example program: application-accelerometergyroscope- processing-android
  27. 27. Now what?3D linear acceleration signal of a well-known motion
  28. 28. Recognizing motion● 3D linear acceleration signals are not so intuitive● Motion recognition: ● Record acceleration pattern of reference motion and compare with these references ● Convert from acceleration domain to something more intuitive like velocity – Accelerometer/gyroscope bias will become linearly growing drift after you integrate the acceleration signal!
  29. 29. Walking with swinging hand
  30. 30. Walking with steady hand
  31. 31. Cutting corners
  32. 32. Conclusions● Each sensor has strengths and weaknesses● Combine them and they compensate each other● Some sensor fusion is already built-in● If not → dont worry, come up with your own, its fun!● Motion recognition based on 3D linear acceleration signal is much more exact than doing the same from 1D signal
  33. 33. Questions?