I am working on my project, which is an autonomous vehicle. We have fixed 1 pair of odometry encoder on 2 wheels and 1 laser gyro on top of it. I am designing a Kalman filter to get filter the noise in measurement. However, my problem is that, I do not know how to work with 2 separate measurement noise.
In Kalman filter equation, the measurement prediction is that
y(k+1)=g(xk, uk,vk)
where vk is measurement noise covariance matrix. Well I am confused because in my case, I have 2 sensors (odometry and gyro) measuring 2 different thing, then how can I construct the convariance matrix for the Kalman filter?
As far as I can see you are mixing measurements together. The odometry and gyro readings have no relation to each other as far as the kalman filter is concerned. The filter provides a means to predict a next measurement of a single reading. You'd therefore have to use one filter on each sensor. On the question on how to calculate the covariance matrix I'd recommend reading more into Kalman filters in general, as it is likely tricky to implement the filter without having an understanding how it works.