Indoor tracking (IMU + tags)

Question

this is another question about indoor tracking using inertial (smartphone + aceel + gyro) Firstly, I would like to say that I have read almost every post on stackoverflow talking about this subject. And I know that to track a position We will have to integrate TWICE the accel and that is very useless in a real life application because of all the drift errors...

But it turned out that I don't need to build a plane or whatever And i don't need to develop an application that have to WORK to be sold or something. I just want to realize a simple Android App that use "theoretical" concept of an Indoor tracking-

• What's the possibilities?
• What do we need?

Basically my phone is resting on a desk screen facing UP at a known position (0,0) if a push my phone to 2 or 3 meters and then I rotate it and push it again for 2 or 3 meters I the to see after how many meters it becomes to inaccurate an so use a tag tu recalibrate the measurements <--- That's my main question

what do I need ? - the angle ? (ok integrating the the gyro) (i don't wanna use the compass) - the accel? (i have) - the velocity ? (integrating the accel) - and the position (double accel integration)

The thing that I would like to know is How can I put this number together? Is it the right way to do it? Is there another solution (to resolve my problem, not to track someone really accurately)?

I also looked at the theory of the DCM (If I understood correctly, it will give me the orientation of the phone in 6 axes right? But what's the difference about getting the angle from the Accel or the gyro (pitch, roll etc..) ?

Thank you

Answer 1

Your smartphone probably has a 3-axis gyro, a 3-axis magnetometer and a 3-axis accelerometer. This is enough for a good estimation of attitude. Each has its advantages and disadvantages:

The accelerometers can measure the gravity force, it gives you the attitude of your phone, but in a horizontal position, you can't know where it's pointing. And it's very sensitive to inertial noise.

The gyroscopes are fastest and the most accurate, but its problem is the drift.

The magnetometers don't have drift and they aren't sensitive to inertial forces, but are too slow.

The combination of the three give you all advantages and no disadvantages. You must read the gyro measure faster as you can (this minimizes the drift) and then use the slow and not as accurate measure of magnetometer and accelerometer to correct them.

I leave you some links that may interest you:

• A Guide to using IMU: http://www.starlino.com/imu_guide.html
• DCM tutorial: http://www.starlino.com/dcm_tutorial.html

I hope I've been helpful and sorry for my bad English.

Answer 2

With the sensors you have, not considering computational power at this point yet, I know of only one method of position / displacement estimation. This would either involve just optical flow with the onboard camera, or the above with addidional info from fused data from accels / gyros (eg. with a Kalman-Filter) to improve accuracy. I guess OpenCV has all you need (including support for Android), so I'd start there.

Start by implementing an attitude-estimator with just accels and gyros. This will drift in yaw-axis (ie. the axis perpendicular to the ground, or rather parallel to gravity vector). This can be done with a Kalman-Filter or other algorithms. This won't be any good for position estimation, as the estimated position will drift tenths of meters away in just a couple of seconds.

Then try implementing optical flow with your camera, which is computationally expensive. Actually this alone could be a solution, but with less accuracy than with additional data from an IMU.

Good luck.

EDIT : I recently found this - it may be helpful to you. If there is not a lot of noise (due to vibration), this would work (I'm on a quadrotor UAV and it unfortunately doesn't work for me).