androidphysicsaccelerometeraccelerationcalibration

Android accelerometer accuracy (Inertial navigation)

I was looking into implementing an Inertial Navigation System for an Android phone, which I realise is hard given the accelerometer accuracy, and constant fluctuation of readings.

To start with, I set the phone on a flat surface and sampled 1000 accelerometer readings in the X and Y directions (parallel to the table, so no gravity acting in these directions). I then averaged these readings and used this value to calibrate the phone (subtracting this value from each subsequent reading).

I then tested the system by again placing it on the table and sampling 5000 accelerometer readings in the X and Y directions. I would expect, given the calibration, that these accelerations should add up to 0 (roughly) in each direction. However, this is not the case, and the total acceleration over 5000 iterations is nowhere near 0 (averaging around 10 on each axis).

I realise without seeing my code this might be difficult to answer but in a more general sense...

Is this simply an example of how inaccurate the accelerometer readings are on a mobile phone (HTC Desire S), or is it more likely that I've made some errors in my coding?

Solution

  • You get position by integrating the linear acceleration twice but the error is horrible. It is useless in practice.

    Here is an explanation why (Google Tech Talk) at 23:20. I highly recommend this video.

    It is not the accelerometer noise that causes the problem but the gyro white noise, see subsection 6.2.3 Propagation of Errors. (By the way, you will need the gyroscopes too.)

    As for indoor positioning, I have found these useful:

    RSSI-Based Indoor Localization and Tracking Using Sigma-Point Kalman Smoothers

    Pedestrian Tracking with Shoe-Mounted Inertial Sensors

    Enhancing the Performance of Pedometers Using a Single Accelerometer

    I have no idea how these methods would perform in real-life applications or how to turn them into a nice Android app.

    A similar question is this.

    UPDATE:

    Apparently there is a newer version than the above Oliver J. Woodman, "An introduction to inertial navigation", his PhD thesis:

    Pedestrian Localisation for Indoor Environments