Richer models that fit better are often a trap, in particular for beginners. Use the simplest model you can get away with. Unless you know what you are doing, most people are better of with a simpler model as it will be more robust to observability issues. If you dont know what those are, use a simpler model.

Uncertainty propagation is very difficult to use for vision, and largely just modelling errors in vision as the error distributions, e.g. for anything observed or reconstructed from images are either subpixel accurate, or too non linear.