I couldn't read due to the much caps locks. After reading the comments, I thought I might add a few things. As others have pointed out, outdoors you would use

I couldn't read due to the much caps locks. After reading the comments, I thought I might add a few things. As others have pointed out, outdoors you would use mostly GPS, and maybe a camera pointing to the ground to estimate the position. Accelerometer are more or less worthless when you want to estimate a translational position of a free flying rigid body. However, orientation can bes estimated very well with the sensors you find in a smartphone. At least outdoors. The acceleration gives you the orientation of the earth gravity vector, assuming that translational acceleration can be neglected. This information corresponds to pitch and roll in an airplane. The iPad, and most smartphones, also have a magnetometer (digital compass) which gives you the direction of the earth magnetic field and which works reasonably well outdoors. You can combine these two 3D vectors with an iterative algorithm, e.g. QUEST [3] or Madgwicks AHRS [4] to obtain an estimation of your orientation in quaternions. No gyroscope required so far. Usually, the gyroscope is only used to give a more accurate angular velocity and acceleration signal, speed up the orientation estimation and serve as a short term replacement in case of a larger disturbance in acceleration or the magnetic field. A Kalman filter is the most common way to do this. If you want to play around with these values on your phone I recommend the Sensor Kinetics [5] app for Android, or if you have an iOS device, I believe XSens has an app, too. There is usually need to implement the above method yourself. Most IMU's you can buy of the shelf provide these higher level estimations already. [1] http://en.wikipedia.org/wiki/MEMS_gyroscope#MEMS_gyroscope http://en.wikipedia.org/wiki/MEMS_gyroscope#MEMS_gyroscope [2] http://www.apple.com/ipad/specs/ http://www.apple.com/ipad/specs/ [3] e.g. EXTENDED QUEST ATTITUDE DETERMINATION FILTERING Mark L. Psiaki 1999 http://www.google.ch/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&ved=0CCkQFjAB&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.151.7265%26rep%3Drep1%26type%3Dpdf&ei=XDVoUJTPI-P_4QSkqoGwAg&usg=AFQjCNGouU0km_Q-BeIthUrTnqmaES2ckQ&sig2=LJC_j6hylww0fp3y_pAT-g http://www.google.ch/url?sa=t&rct=j&q=&esrc=s&#3... [4] Includes working (tested it myself) Matlab, C and C# code http://www.x-io.co.uk/node/8 http://www.x-io.co.uk/node/8 [5] https://play.google.com/store/apps/details?id=com.innoventions.sensorkinetics&hl=en https://play.google.com/store/apps/details?id=com.innoventio...