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## ================================================================================================== | ||
## Hack to be removed or updated | ||
## FIXME ME ON FIRE use the ManifoldsBase.jl prescribed interface method instead: | ||
## https://juliamanifolds.github.io/Manifolds.jl/stable/examples/manifold.html#manifold-tutorial | ||
## ================================================================================================== | ||
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import ApproxManifoldProducts: coords, uncoords, getPointsManifold, _makeVectorManifold | ||
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export SE2E2_Manifold | ||
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# this is a hack and not fully implemented as per: | ||
struct _SE2E2 <: ManifoldsBase.Manifold{ManifoldsBase.ℝ} end | ||
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const SE2E2_Manifold = _SE2E2() | ||
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ManifoldsBase.manifold_dimension(::_SE2E2) = 5 | ||
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AMP.coords(::Type{<:typeof(SE2E2_Manifold)}, p::ProductRepr) = [p.parts[1][1], p.parts[1][2], atan(p.parts[2][2,1],p.parts[2][1,1]), p.parts[3][1], p.parts[3][2]] | ||
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function AMP.uncoords(::typeof(SE2E2_Manifold), p::AbstractVector{<:Real}) | ||
α = p[3] | ||
ProductRepr(([p[1], p[2]]), [cos(α) -sin(α); sin(α) cos(α)], ([p[4], p[5]])) | ||
end | ||
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function AMP.getPointsManifold(mkd::ManifoldKernelDensity{M}) where {M <: typeof(SE2E2_Manifold)} | ||
data_ = getPoints(mkd.belief) | ||
[uncoords(mkd.manifold, view(data_, :, i)) for i in 1:size(data_,2)] | ||
end | ||
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function Statistics.mean(::typeof(SE2E2_Manifold), pts::AbstractVector) | ||
se2_ = (d->ProductRepr(d.parts[1], d.parts[2])).(pts) | ||
mse2 = mean(SpecialEuclidean(2), se2_) | ||
e2_ = (d->ProductRepr(d.parts[3])).(pts) | ||
me2 = mean(Euclidean(2), e2_) | ||
ProductRepr(mse2.parts[1], mse2.parts[2], me2.parts[1]) | ||
end | ||
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AMP._makeVectorManifold(::M, prr::ProductRepr) where {M <: typeof(SE2E2_Manifold)} = coords(M, prr) | ||
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## ============================================================================= | ||
## Needs a home | ||
## ============================================================================= | ||
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export BearingRange_Manifold | ||
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struct _CircleEuclid <: ManifoldsBase.Manifold{ManifoldsBase.ℝ} end | ||
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ManifoldsBase.manifold_dimension(::_CircleEuclid) = 2 | ||
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const BearingRange_Manifold = _CircleEuclid() | ||
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AMP.coords(::Type{<:typeof(BearingRange_Manifold)}, p::ProductRepr) = [p.parts[1][1]; p.parts[2][1]] | ||
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function AMP.uncoords(::typeof(BearingRange_Manifold), p::AbstractVector{<:Real}) | ||
ProductRepr(([p[1];]), ([p[2];])) | ||
end | ||
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function AMP.getPointsManifold(mkd::ManifoldKernelDensity{M}) where {M <: typeof(BearingRange_Manifold)} | ||
data_ = getPoints(mkd.belief) | ||
[uncoords(mkd.manifold, view(data_, :, i)) for i in 1:size(data_,2)] | ||
end | ||
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function Statistics.mean(::typeof(BearingRange_Manifold), pts::AbstractVector) | ||
TensorCast.@cast bearing[i] := pts[i][1] | ||
TensorCast.@cast range_[i] := pts[i][2] | ||
mc = mean(Circle(), bearing) | ||
mr = mean(range_) | ||
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return [mc; mr] | ||
end | ||
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AMP._makeVectorManifold(::M, prr::ProductRepr) where {M <: typeof(BearingRange_Manifold)} = coords(M, prr) | ||
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## ================================================================================================== |
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