[mppi] Investigate speed up potential of preallocated noise. about navigation2 HOT 1 OPEN

We are not lazily evaluating it with xt::random::randn, if you look at the data structures involved, its immediately evaluated into the xtensor<float,2>, not an evaluation expression. But, happy for any contributions you might be able to provide to accelerate that process :-) Perhaps some different patterns here could be applied to other bottlenecks like trajectory rollouts.

Note that we have the regenerate_noises parameter that lets you select whether to regenerate noises each iteration or use a static set noise distribution for all iterations. After speaking with some folks about this, its not the most ideal for small samples, but as the number of trajectories being evaluated increases, it is increasingly less important that it is resampled each time. There are several companies that use MPPI in automotive applications which I spoke with and did not regenerate noises each iteration in their certified implementations, so I took my inspiration from them and "good enough for them, good enough for me".

But your vector 1k/3k being 10x faster seems really compelling, can you open a PR to introduce that and I can test it real quick on my side and merge it? I would mention that your numbers don't align with my experiences with computational time of these operations (they're a bit low) so I wonder in context if there's some cache breaks that result in some of that slow down. But hey, saving 0.7ms is a worthwhile activity!

from navigation2.