Last week I was using a particle filter to model the bees, but this morning I tried out (with very simple simulated dataset) using a multioutput Gaussian process. I think these results look better*:
The dots are samples to show uncertainty, set to 1/3 of the true standard deviation for clarity.
The straight lines are from the ‘photographs’ taken from two cameras. The green helix is the ‘true’ path of the simulated bee.
I can’t believe how quick it is to use stochastic variational inference! The slowest bit was having to write some code to do batch-compatible cross-products in tensor flow. Notebook here.
I’ve already run the particle smoother on some learning flights, but will try this out on them instead…