5 ms·
That’s true when averaging together iid samples, but you could probably do something smarter if you know about the likely errors and the sensors’ covariance. Fo
by mattkrause 8y ago
That’s true when averaging together iid samples, but you could probably do something smarter if you know about the likely errors and the sensors’ covariance. For example, if three of the four sensors agree, you should probably completely disregard the fourth, rather than averaging it in.
- robotresearcher 8y ago> you could probably do something smarter Assuming measurements are independent and errors are Gaussian, the Kalman filter is this smarter thing. Variants exist for nonlinear systems. https://en.wikipedia.org/wiki/Kalman_filter https://en.wikipedia.org/wiki/Kalman_filter