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content/notes/11-orientation sensors-2.md
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content/notes/11-orientation sensors-2.md
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---
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title: "11-orientation sensors-2"
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tags:
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- lecture
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- info305
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---
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> [!INFO] we need sensors to that computing can be ubiquitous. from this we can think of completey new interfaces
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> [!INFO] gyroscope
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> say a phone is rotating by 5 degree per seconds. usualy want to know absolute angle. this is the integral over time, we know what the angular rate is and the time.
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> problem: cannot take perfectly continuous integral. they do more than 100 measurements per second. gryoscope data changes faster than samplling frequency. this is called drift/bias, as the error accumulates.
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> when the sensor is stationary, it still measures a small value, which is inherent in accuracy. so the sensor thinks it is moving when it is not. the bell curve of inaccurate measurements is not centred.
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Advanced sensing
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- Current situation:
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- Many different sensors with different characteristics
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- Precise but slow, noisy but fast, drift biased,…
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- We cannot measure everything…
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- Can we combine sensors for model more complex states?
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- Can we filter sensor measurements to improve the data quality?
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Sensor Fusion
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- The idea:
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- Combine (fuse) sensor to achieve better results
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- Better Location (When signal ist lost, not enough satellites, ..)
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- Better Orientation (Less drift (gyroscope), less error prone (compass), ..)
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Sensor Fusion
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- Example for Orientation
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- Combine strength of several integrated sensors
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- Avoids gyro drift and noisy orientation
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- http://www.thousand-thoughts.com/2012/03/android-sensor-fusion-tutorial/
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- Gyroscope output is applied only for orientation changes in short time intervals (high-pass filtering)
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- Accelerometer/Magnetometer is used as support information over long periods of time for absolute orientation (low pass filtering)
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Dead Reckoning
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-
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@ -25,4 +25,5 @@ tags:
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- [x] [[08-proximity sensors]]
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- [x] [[09-location-sensors-3]]
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- [x] [[10-orientation-sensors]]
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- [ ] [[11-orientation sensors-2]]
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# Info
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