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Android Sensor Fusion Tutorial

While working on my master thesis, I’ve made some experiences with sensors in Android devices and I thought  I’d share them with other Android developers stumbling over my blog. In my work I was developing a head tracking component for a prototype system. Since it had to adapt audio output to the orientation of the users head, it required to respond quickly and be accurate at the same time.

I used my Samsung Galaxy S2 and decided to use its gyroscope in conjunction with the accelerometer and the magnetic field sensor in order to measure the user’s head rotations both, quickly and accurately. To acheive this I implemented a complementary filter to get rid of the gyro drift and the signal noise of the accelerometer and magnetometer. The following tutorial describes in detail how it’s done.

There are already several tutorials on how to get sensor data from the Android API, so I’ll skip the details on android sensor basics and focus on the sensor fusion algorithm. The Android API Reference is also a very helpful entry point regarding the acquisition of sensor data. This tutorial is based on the Android API version 10 (platform 2.3.3), by the way.

This article is divided into two parts. The first part covers the theoretical background of a complementary filter for sensor signals as described by Shane Colton here. The second part describes the implementation in the Java programming laguage. Everybody who thinks the theory is boring and wants to start programing right away can skip directly to the second part. The first part is interesting for people who develop on other platforms than Android, iOS for example, and want to get better results out of the sensors of their devices.

Update (March 22, 2012):
I’ve created a small Android project which contains the whole runnable code from this tutorial. You can download it here:
SensorFusion1.zip

Update (April 4, 2012):
Added a small bugfix in the examples GUI code.

Update (July 9, 2012):
Added a bugfix regarding angle transitions between 179° <–> -179°. Special thanks to J.W. Alexandar Qiu who pointed it out and published the soultion!

Update (September 25, 2012):
Published the code under the MIT-License (license note added in code), which allows you to do with it pretty much everything you want. No need to ask me first ;)

Sensor Fusion via Complementary Filter

Before we start programming, I want to explain briefly how our sensor fusion approach works. The common way to get the attitude of an Android device is to use the SensorManager.getOrientation() method to get the three orientation angles. These two angles are based on the accelerometer and magenotmeter output. In simple terms, the acceletometer provides the gravitiy vector (the vector pointing towards the centre of the earth) and the magnetometer works as a compass. The Information from both sensors suffice to calculate the device’s orientation. However both sensor outputs are inacurate, expecially the output from the magnetic field sensor which includes a lot of noise.

The gyroscope in the device is far more accurate and has a very short response time. Its downside is the dreaded gyro drift. The gyro provides the angular rotation speeds for all three axes. To get the actual orientation those speed values need to be integrated over time.  This is done by multiplying the angular speeds with the time interval between the last and the current sensor output. This yields a rotation increment. The sum of all rotation increments yields the absolut orientation of the device. During this process small errors are introduced in each iteration. These small errors add up over time resulting in a constant slow rotation of the calculated orientation, the gyro drift.

To avoid both, gyro drift and noisy orientation, the gyroscope output is applied only for orientation changes in short time intervals, while the magnetometer/acceletometer data is used as support information over long periods of time. This is equivalent to low-pass filtering of the accelerometer and magnetic field sensor signals and high-pass filtering of the gyroscope signals. The overall sensor fusion and filtering looks like this:

So what exactly does high-pass and low-pass filtering of the sensor data mean? The sensors provide their data at (more or less) regular time intervals. Their values can be shown as signals in a graph with the time as the x-axis, similar to an audio signal. The low-pass filtering of the noisy accelerometer/magnetometer signal (accMagOrientation in the above figure) are orientation angles averaged over time within a constant time window.

Later in the implementation, this is accomplished by slowly introducing new values from the accelerometer/magnetometer to the absolute orientation:

// low-pass filtering: every time a new sensor value is available
// it is weighted with a factor and added to the absolute orientation
accMagOrientation = (1 - factor) * accMagOrientation + factor * newAccMagValue;

The high-pass filtering of the integrated gyroscope data is done by replacing the filtered high-frequency component from accMagOrientation with the corresponding gyroscope orientation values:

fusedOrientation =
    (1 - factor) * newGyroValue;    // high-frequency component
    + factor * newAccMagValue;      // low-frequency component

In fact, this is already our finished comlementary filter.

Assuming that the device is turned 90° in one direction and after a short time turned back to its initial position, the intermediate signals in the filtering process would look something like this:

Notice the gyro drift in the integrated gyroscope signal. It results from the small irregularities in the original angular speed. Those little deviations add up during the integration and cause an additional undesireable slow rotation of the gyroscope based orientation.

153 thoughts on “Android Sensor Fusion Tutorial”

  1. Yes I wanted to switch to quaternions while I wrote the code. But I had issues with accuracy because floating-point precession errors during all the quaternion multiplications caused errors that grew into oblivion. So I started to normalize the quaternions each pass. This again caused performance issues and I ran out of time (master thesis deadline). So I got stuck with all the joys that gimbal lock and rotation matrices bring. Trust me, I’m actually a big fan of quaternions ;)

  2. can u help me how can i use this to find distance using accelerometer distance covered by user

  3. I would discourage you from using this approach to determine linear movement. The accelerometer ist too noisy and produces too much errors to acurately calculate the total distance covered.

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