Multiple cheap IMUs vs one expensive IMUs In my project, there is a possibility of either buying one expensive IMU or 5 cheaper IMUs. They have to be cheaper due to budget limitations, and the idea is to use them in order to be able to do "voting" - i.e. vote out outlier measurements of one IMU if 4 other IMUs show a different reading - and also for redundancy. Question: does anybody know if 5 cheaper IMUs (on the order to 300 dollars each) can create a signal at least as smooth, via sensor fusion, as one expensive IMU (on the order of 1500 dollars)?
That really will depend on the variance (standard deviation) of the measurements you get from the five vs the one.

Suppose your expensive IMU gives a measurement distributed as:
$$
x_\mbox{expensive}  \sim  N\left(x_\mbox{truth}, \sigma^2_\mbox{expensive}\right)
$$
and suppose your cheaper ones give measurements distributed as:
$$
x_\mbox{cheap}^i  \sim  N\left(x_\mbox{truth}, \sigma^2_\mbox{cheap}\right)
$$
for $i=1\ldots 5$ and that you form the average of these cheaper measurements to get the "real" measurement:
$$
\hat{x} = \frac{1}{5} \sum_{i=1}^5 x_\mbox{cheap}^i
$$
then $\hat{x}$ is distributed as:
$$
\hat{x} \sim  N\left(x_\mbox{truth}, \frac{\sigma^2_\mbox{cheap}}{5}\right)
$$
So if 
$$
\frac{\sigma^2_\mbox{cheap}}{5} \le \sigma^2_\mbox{expensive}
$$
you should be OK with the cheaper sensors.

Of course, there may be other problems with your cheaper sensors: they may be biassed (so that the mean of their distributions will all be different from $x_\mbox{truth}$ ).

And I'm not quite sure what you mean by "voting"? Do you vote off the one furthest from the mean of the 5?  What if the mean changes for the remaining four?  I'll do a bit more digging to see if there's a viable algorithm there, but the direct mean of all five might be the easiest.