Indoor Positioning System based on Gyroscope and Accelerometer
Solution 1:
Gyros and accelerometers are not enough.
You get position by integrating the linear acceleration twice but the error is horrible. It is useless in practice.
Here is an explanation by (Google Tech Talk) at 23:20. I highly recommend this video.
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 Calculating distance within a building.
Solution 2:
I think this is a great post answering your question. This kalman filter combine data from accelerometers and gyros. Hope it helps.
Solution 3:
For some other interesting reading on emerging indoor positioning technologies, check out this blog post from Qubulus. There are several startups and research projects trying to find a good replacement for GPS indoors. Attempts range from Dead Reckoning, to QR Codes, to light pulses, to radio fingerprinting. It looks like any viable solution will combine multiple technologies (similar to how most smartphones rely on A-GPS, where the satellite signal is assisted by cell tower multilateration).
Best of luck with your application!
Solution 4:
I think it is too late for answer this question, but now, there is a good solution called iBeacon technology.
You can scan iBeacon devices by your smartphone, and you can get the rssi from iBeacon. So, you can calculate your position by those rssi.
Solution 5:
To track indoor position starting from some reference point, only gyro and accelerometer is not enough. With accelerometer you can calculate speed, with gyro you can get direction of the mobile device, but to calculate indoor position you also need to have direction of movement, so for this case you need to use magnetic sensor. Such approach is called Dead Reckoning method and it's quite complex to combine all 3 sensors to get appropriate indoor position. Kalman filters allow you to smooth your measurement and filter some noise, but it's not the method to calculate indoor position. If you want to get indoor position you can try iBeacon approach and Trilateration method, there are some libraries (like this one) that already has this functionality, so you can try to investigate this method from such libs.