That's a reasonable idea, but unfortunately wouldn't work in my case since the simulation relies on a lot of scientific libraries in Python and I need the inversion to happen on the microcontroller.
When you say "coordinate descent" do you mean gradient descent? I.e., updating a potential pose using the gradient of a loss term (e.g., (predicted sensor reading - actual sensor reading)**2)?
I bet that would work, but a tricky part would be calculating gradients. I'm not sure if the Python libraries I'm using support that. My understanding is that automatic differentiation through libraries might be easier in a language like Julia where dual numbers flow through everything via the multiple dispatch mechanism.
When you say "coordinate descent" do you mean gradient descent? I.e., updating a potential pose using the gradient of a loss term (e.g., (predicted sensor reading - actual sensor reading)**2)?
I bet that would work, but a tricky part would be calculating gradients. I'm not sure if the Python libraries I'm using support that. My understanding is that automatic differentiation through libraries might be easier in a language like Julia where dual numbers flow through everything via the multiple dispatch mechanism.