Double Integration with the BNO055 for position data
How can you get &plusmn;&frac12; inch position accuracy?

You probably can't, at least not for very long.  Drift is a fundamental problem of inertial navigation.  Ultimately you have to double-integrate accelleration, and the errors of that blow up quadratically with time.  That's basic physics you can't get around.

Note that anything you do compute is only relative to the starting position.  Any error in the starting position directly adds to any error computed later.  Do you know your starting position to substantially better than &plusmn;&frac12; inch?

Since the error increases with the square of time, after some finite time the error is larger than your limit and any results are useless.  However, that also means the error is within acceptable range within some time limit.  If you can work within that time limit, then using inertial navigation might work for you.

For example, I once did a project that tracked the motion of a golfer's head during a swing.  It did this by inertial navigation using cheap MEMs sensors.  The result became useless after about two seconds.  Fortunately, that's all we needed, and we were looking for trends anyway and didn't need very accurate absolute position.  This same setup would have been completely useless, for example, to map the golfer's movements as he walked along the green.

In practice, most inertial navigation is used to fill in between absolute position fixes obtained other ways, like GPS.  The absolute fixes can be used to constrain the double integration errors, but still allow the intertial system to provide high-frequency details between absolute fixes.  Really clever algorithms can use the inertial information to average out the jitter noise of the absolute fixes, while having the absolute fixes limit the time-growth of errors from the inertial system.

Look up something called a Kallman filter.  It's good at interpolating between samples, with the occasional missing sample being reasonably well tolerated.