Use numerically robust norm

[baggepinnen]

This PR is mostly a FYI regarding JuliaArrays/StaticArrays.jl#913
Depending on whether or not that issue is closed, you may want to switch to explicitly calling generic_norm2 on static vectors to circumvent the accuracy issue. The issue arises when combining static arrays with ForwardDiff, in our case it occured in exp(::SkewSymmetric) using my implementation in Robotlib
https://github.com/baggepinnen/Robotlib.jl/blob/master/src/utils.jl#L94

I did some benchmarking, and generic_norm2 is faster than norm for all standard arrays up to at least length 9. For static arrays, it appears to do okay as well, while avoiding the accuracy issue.

julia> a
3-element SVector{3, Int64} with indices SOneTo(3):
 1
 2
 3

julia> @btime norm($(Ref(a))[]) # standard norm of static array
  5.135 ns (0 allocations: 0 bytes)
3.74166

julia> @btime norm($(Ref(Vector(a)))[]) # standard norm of  array
  8.545 ns (0 allocations: 0 bytes)
3.74166

julia> @btime LinearAlgebra.generic_norm2($(Ref((a)))[]) # generic_norm2 of static array
  4.490 ns (0 allocations: 0 bytes)
3.74166

julia> @btime LinearAlgebra.generic_norm2($(Ref(Vector(a)))[]) # generic_norm2 of array
  7.671 ns (0 allocations: 0 bytes)
3.74166