juliarobotics / incrementalinference.jl Goto Github PK

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Clique recycling non-Gaussian (multi-modal) factor graph solver; also see Caesar.jl.

License: MIT License

Julia 100.00%

factor-graphs bayes bayes-tree inference slam robotics optimization sum-product belief-propagation isam

incrementalinference.jl's Issues

multithreaded-related issues when adding Point3Point3 to factor graph

I am getting the following error when adding a factor under the default thread model (multithreaded):

julia> addFactor!(fg,[:p1_1,:p2_1],sf)

Error thrown in threaded loop on thread 2: Base.MethodError(f=typeof(Sonar.ominus)(), args=(Array{Float64, 1}[], Array{Float64, 1}[-8.6476e-11, -9.00376e-11, -7.83454e-11]), world=0x0000000000005bcd)
Error thrown in threaded loop on thread 3: Base.MethodError(f=typeof(Sonar.ominus)(), args=(Array{Float64, 1}[], Array{Float64, 1}[-6.63141e-12, 1.78934e-10, -2.86034e-11]), world=0x0000000000005bcd)
Error thrown in threaded loop on thread 0: Base.MethodError(f=typeof(Sonar.ominus)(), args=(Array{Float64, 1}[], Array{Float64, 1}[-8.6476e-11, -9.00376e-11, -7.83454e-11]), world=0x0000000000005bcd)
Error thrown in threaded loop on thread 1: Base.MethodError(f=typeof(Sonar.ominus)(), args=(Array{Float64, 1}[], Array{Float64, 1}[-6.63141e-12, 1.78934e-10, -2.86034e-11]), world=0x0000000000005bcd)[drct]
Error thrown in threaded loop on thread 1: Base.MethodError(f=typeof(Sonar.ominus)(), args=(Array{Float64, 1}[], Array{Float64, 1}[-1.757e-11, -6.54201e-11, 1.89608e-10]), world=0x0000000000005bcd)
Error thrown in threaded loop on thread 2: Base.MethodError(f=typeof(Sonar.ominus)(), args=(Array{Float64, 1}[], Array{Float64, 1}[-1.757e-11, -6.54201e-11, 1.89608e-10]), world=0x0000000000005bcd)
Error thrown in threaded loop on thread 3: Base.MethodError(f=typeof(Sonar.ominus)(), args=(Array{Float64, 1}[], Array{Float64, 1}[-1.757e-11, -6.54201e-11, 1.89608e-10]), world=0x0000000000005bcd)
Error thrown in threaded loop on thread 0: Base.MethodError(f=typeof(Sonar.ominus)(), args=(Array{Float64, 1}[], Array{Float64, 1}[-1.757e-11, -6.54201e-11, 1.89608e-10]), world=0x0000000000005bcd)
Error thrown in threaded loop on thread 3: Base.MethodError(f=Sonar.Point3Point3(Zij=Distributions.MvNormal{Float64, PDMats.PDMat{Float64, Array{Float64, 2}}, Array{Float64, 1}}(μ=Array{Float64, 1}[0, 0, 0], Σ=PDMats.PDMat{Float64, Array{Float64, 2}}(dim=3, mat=Array{Float64, 2}[1, 0, 0, 0, 1, 0, 0, 0, 1], chol=Base.LinAlg.Cholesky{Float64, Array{Float64, 2}}(factors=Array{Float64, 2}[1, 0, 0, 0, 1, 0, 0, 0, 1], uplo=Char(0x00000055))))), args=(Array{Float64, 1}[-6.77912e-11, 5.10863e-11, -5.84564e-11], IncrementalInference.FactorMetadata(factoruserdata=#<null>, variableuserdata=Array{Any, 1}[
  RoME.Point3(dims=3, labels=Array{String, 1}[]),
  RoME.Point3(dims=3, labels=Array{String, 1}[])], variablesmalldata=#<null>, solvefor=:p2_1, variablelist=Array{Symbol, 1}[
  :p1_1,
  :p2_1], dbg=false), 39, (Array{Float64, 2}[-1.60718, -0.827202, -0.560053, -1.0462, 1.18009, 1.21969, -1.81406, 0.362306, 0.128432, 0.728457, -0.165647, 0.719411, 2.05103, -1.85439, 0.261555, -0.301361, 0.602443, -1.38964, -1.0192, 1.11935, -0.387305, -0.24777, -0.894487, 0.727994, 0.392731, 1.50659, 0.629323, 0.850487, 0.373695, -0.487173, -0.933595, -0.940111, 0.584677, 1.25121, -1.79677, -1.14011, -0.103531, -0.742929, -1.12193, 0.860561, 0.605493, -2.10195, 0.00396295, 1.94143, 0.300195, 1.93993, -0.28632, -0.363934, 0.678636, -2.20704, 0.356065, 0.771001, -0.368969, 0.154438, 0.887804, -0.611348, 0.771007, 0.874874, -0.101308, -0.334538, -0.132682, 2.46788, 0.213221, -0.734656, -0.986243, -0.776817, -1.24047, -1.2143, 1.35807, 0.313686, 0.862886, 0.483994, 1.33577, -0.484943, -0.149989, -1.16246, -0.322664, -1.91383, -0.0700782, -0.589759, 0.494971, -0.668845, -0.255701, 1.5658, -0.942667, 0.954469, 0.0246011, -1.24151, -1.25566, 1.12589, -1.51048, 1.54752, -0.0774541, -0.381969, -0.697515, -0.208141, -0.631817, 1.53478, 0.0639398, 0.474297, -1.10951, 1.98979, -0.636412, -0.785697, 0.778927, -2.33789, 0.461508, 0.522421, 0.68605, -0.426236, -0.843736, 2.13346, -0.686019, -1.00737, -0.898299, 0.0109888, 1.12862, 2.30876, 0.898455, -0.0431169, 1.37305, 0.597929, 0.503052, 0.407354, 0.157966, -1.37054, -1.97835, 0.188369, 0.484641, -1.58252, 0.59619, 0.261537, -0.00671637, -1.12001, 0.404141, 0.789859, -0.0560027, -0.568049, 0.35966, 0.667681, 1.02214, 1.03014, -0.0972912, -0.400676, -0.962565, -1.01264, -0.335247, -0.0050443, 0.609426, 0.599653],), Array{Float64, 2}[-8.6476e-11, -9.00376e-11, -7.83454e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6.63141e-12, 1.78934e-10, -2.86034e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -8.6476e-11, -9.00376e-11, -7.83454e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6.63141e-12, 1.78934e-10, -2.86034e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], Array{Float64, 2}[-6.77912e-11, 5.10863e-11, -5.84564e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6.77912e-11, 5.10863e-11, -5.84564e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6.77912e-11, 5.10863e-11, -5.84564e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6.77912e-11, 5.10863e-11, -5.84564e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), world=0x0000000000005bcd)
Error thrown in threaded loop on thread 2: Base.MethodError(f=Sonar.Point3Point3(Zij=Distributions.MvNormal{Float64, PDMats.PDMat{Float64, Array{Float64, 2}}, Array{Float64, 1}}(μ=Array{Float64, 1}[0, 0, 0], Σ=PDMats.PDMat{Float64, Array{Float64, 2}}(dim=3, mat=Array{Float64, 2}[1, 0, 0, 0, 1, 0, 0, 0, 1], chol=Base.LinAlg.Cholesky{Float64, Array{Float64, 2}}(factors=Array{Float64, 2}[1, 0, 0, 0, 1, 0, 0, 0, 1], uplo=Char(0x00000055))))), args=(Array{Float64, 1}[-6.77912e-11, 5.10863e-11, -5.84564e-11], IncrementalInference.FactorMetadata(factoruserdata=#<null>, variableuserdata=Array{Any, 1}[
  RoME.Point3(dims=3, labels=Array{String, 1}[]),
  RoME.Point3(dims=3, labels=Array{String, 1}[])], variablesmalldata=#<null>, solvefor=:p2_1, variablelist=Array{Symbol, 1}[
  :p1_1,
  :p2_1], dbg=false), 27, (Array{Float64, 2}[-1.60718, -0.827202, -0.560053, -1.0462, 1.18009, 1.21969, -1.81406, 0.362306, 0.128432, 0.728457, -0.165647, 0.719411, 2.05103, -1.85439, 0.261555, -0.301361, 0.602443, -1.38964, -1.0192, 1.11935, -0.387305, -0.24777, -0.894487, 0.727994, 0.392731, 1.50659, 0.629323, 0.850487, 0.373695, -0.487173, -0.933595, -0.940111, 0.584677, 1.25121, -1.79677, -1.14011, -0.103531, -0.742929, -1.12193, 0.860561, 0.605493, -2.10195, 0.00396295, 1.94143, 0.300195, 1.93993, -0.28632, -0.363934, 0.678636, -2.20704, 0.356065, 0.771001, -0.368969, 0.154438, 0.887804, -0.611348, 0.771007, 0.874874, -0.101308, -0.334538, -0.132682, 2.46788, 0.213221, -0.734656, -0.986243, -0.776817, -1.24047, -1.2143, 1.35807, 0.313686, 0.862886, 0.483994, 1.33577, -0.484943, -0.149989, -1.16246, -0.322664, -1.91383, -0.0700782, -0.589759, 0.494971, -0.668845, -0.255701, 1.5658, -0.942667, 0.954469, 0.0246011, -1.24151, -1.25566, 1.12589, -1.51048, 1.54752, -0.0774541, -0.381969, -0.697515, -0.208141, -0.631817, 1.53478, 0.0639398, 0.474297, -1.10951, 1.98979, -0.636412, -0.785697, 0.778927, -2.33789, 0.461508, 0.522421, 0.68605, -0.426236, -0.843736, 2.13346, -0.686019, -1.00737, -0.898299, 0.0109888, 1.12862, 2.30876, 0.898455, -0.0431169, 1.37305, 0.597929, 0.503052, 0.407354, 0.157966, -1.37054, -1.97835, 0.188369, 0.484641, -1.58252, 0.59619, 0.261537, -0.00671637, -1.12001, 0.404141, 0.789859, -0.0560027, -0.568049, 0.35966, 0.667681, 1.02214, 1.03014, -0.0972912, -0.400676, -0.962565, -1.01264, -0.335247, -0.0050443, 0.609426, 0.599653],), Array{Float64, 2}[-8.6476e-11, -9.00376e-11, -7.83454e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6.63141e-12, 1.78934e-10, -2.86034e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -8.6476e-11, -9.00376e-11, -7.83454e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6.63141e-12, 1.78934e-10, -2.86034e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], Array{Float64, 2}[-6.77912e-11, 5.10863e-11, -5.84564e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6.77912e-11, 5.10863e-11, -5.84564e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6.77912e-11, 5.10863e-11, -5.84564e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6.77912e-11, 5.10863e-11, -5.84564e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), world=0x0000000000005bcd)
Error thrown in threaded loop on thread 0: Base.MethodError(f=Sonar.Point3Point3(Zij=Distributions.MvNormal{Float64, PDMats.PDMat{Float64, Array{Float64, 2}}, Array{Float64, 1}}(μ=Array{Float64, 1}[0, 0, 0], Σ=PDMats.PDMat{Float64, Array{Float64, 2}}(dim=3, mat=Array{Float64, 2}[1, 0, 0, 0, 1, 0, 0, 0, 1], chol=Base.LinAlg.Cholesky{Float64, Array{Float64, 2}}(factors=Array{Float64, 2}[1, 0, 0, 0, 1, 0, 0, 0, 1], uplo=Char(0x00000055))))), args=(Array{Float64, 1}[-6.77912e-11, 5.10863e-11, -5.84564e-11], IncrementalInference.FactorMetadata(factoruserdata=#<null>, variableuserdata=Array{Any, 1}[
  RoME.Point3(dims=3, labels=Array{String, 1}[]),
  RoME.Point3(dims=3, labels=Array{String, 1}[])], variablesmalldata=#<null>, solvefor=:p2_1, variablelist=Array{Symbol, 1}[
  :p1_1,
  :p2_1], dbg=false), 1, (Array{Float64, 2}[-1.60718, -0.827202, -0.560053, -1.0462, 1.18009, 1.21969, -1.81406, 0.362306, 0.128432, 0.728457, -0.165647, 0.719411, 2.05103, -1.85439, 0.261555, -0.301361, 0.602443, -1.38964, -1.0192, 1.11935, -0.387305, -0.24777, -0.894487, 0.727994, 0.392731, 1.50659, 0.629323, 0.850487, 0.373695, -0.487173, -0.933595, -0.940111, 0.584677,1.25121, -1.79677, -1.14011, -0.103531, -0.742929, -1.12193, 0.860561, 0.605493, -2.10195, 0.00396295, 1.94143, 0.300195, 1.93993, -0.28632, -0.363934, 0.678636, -2.20704, 0.356065, 0.771001, -0.368969, 0.154438, 0.887804, -0.611348, 0.771007, 0.874874, -0.101308, -0.334538, -0.132682, 2.46788, 0.213221, -0.734656, -0.986243, -0.776817, -1.24047, -1.2143, 1.35807, 0.313686, 0.862886, 0.483994, 1.33577, -0.484943, -0.149989, -1.16246, -0.322664, -1.91383, -0.0700782, -0.589759, 0.494971, -0.668845, -0.255701, 1.5658, -0.942667, 0.954469, 0.0246011, -1.24151,-1.25566, 1.12589, -1.51048, 1.54752, -0.0774541, -0.381969, -0.697515, -0.208141, -0.631817, 1.53478, 0.0639398, 0.474297, -1.10951, 1.98979, -0.636412, -0.785697, 0.778927, -2.33789, 0.461508, 0.522421, 0.68605, -0.426236, -0.843736, 2.13346, -0.686019, -1.00737, -0.898299, 0.0109888, 1.12862, 2.30876, 0.898455, -0.0431169, 1.37305, 0.597929, 0.503052, 0.407354, 0.157966, -1.37054, -1.97835, 0.188369, 0.484641, -1.58252, 0.59619, 0.261537, -0.00671637, -1.12001, 0.404141, 0.789859, -0.0560027, -0.568049, 0.35966, 0.667681, 1.02214, 1.03014, -0.0972912, -0.400676, -0.962565, -1.01264, -0.335247, -0.0050443, 0.609426, 0.599653],), Array{Float64, 2}[-8.6476e-11, -9.00376e-11, -7.83454e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6.63141e-12, 1.78934e-10, -2.86034e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, -8.6476e-11, -9.00376e-11, -7.83454e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6.63141e-12, 1.78934e-10, -2.86034e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], Array{Float64, 2}[-6.77912e-11, 5.10863e-11, -5.84564e-11, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6.77912e-11, 5.10863e-11, -5.84564e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6.77912e-11, 5.10863e-11, -5.84564e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,-6.77912e-11, 5.10863e-11, -5.84564e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), world=0x0000000000005bcd)
Error thrown in threaded loop on thread 1: Base.MethodError(f=Sonar.Point3Point3(Zij=Distributions.MvNormal{Float64, PDMats.PDMat{Float64, Array{Float64, 2}}, Array{Float64, 1}}(μ=Array{Float64, 1}[0, 0, 0], Σ=PDMats.PDMat{Float64, Array{Float64, 2}}(dim=3, mat=Array{Float64, 2}[1, 0, 0, 0, 1, 0, 0, 0, 1], chol=Base.LinAlg.Cholesky{Float64, Array{Float64, 2}}(factors=Array{Float64, 2}[1, 0, 0, 0, 1, 0, 0, 0, 1], uplo=Char(0x00000055))))), args=(Array{Float64, 1}[-6.77912e-11, 5.10863e-11, -5.84564e-11], IncrementalInference.FactorMetadata(factoruserdata=#<null>, variableuserdata=Array{Any, 1}[
  RoME.Point3(dims=3, labels=Array{String, 1}[]),
  RoME.Point3(dims=3, labels=Array{String, 1}[])], variablesmalldata=#<null>, solvefor=:p2_1, variablelist=Array{Symbol, 1}[
  :p1_1,
  :p2_1], dbg=false), 14, (Array{Float64, 2}[-1.60718, -0.827202, -0.560053, -1.0462, 1.18009, 1.21969, -1.81406, 0.362306, 0.128432, 0.728457, -0.165647, 0.719411, 2.05103, -1.85439, 0.261555, -0.301361, 0.602443, -1.38964, -1.0192, 1.11935, -0.387305, -0.24777, -0.894487, 0.727994, 0.392731, 1.50659, 0.629323, 0.850487, 0.373695, -0.487173, -0.933595, -0.940111, 0.584677, 1.25121, -1.79677, -1.14011, -0.103531, -0.742929, -1.12193, 0.860561, 0.605493, -2.10195, 0.00396295, 1.94143, 0.300195, 1.93993, -0.28632, -0.363934, 0.678636, -2.20704, 0.356065, 0.771001, -0.368969, 0.154438, 0.887804, -0.611348, 0.771007, 0.874874, -0.101308, -0.334538, -0.132682, 2.46788, 0.213221, -0.734656, -0.986243, -0.776817, -1.24047, -1.2143, 1.35807, 0.313686, 0.862886, 0.483994, 1.33577, -0.484943, -0.149989, -1.16246, -0.322664, -1.91383, -0.0700782, -0.589759, 0.494971, -0.668845, -0.255701, 1.5658, -0.942667, 0.954469, 0.0246011, -1.24151, -1.25566, 1.12589, -1.51048, 1.54752, -0.0774541, -0.381969, -0.697515, -0.208141, -0.631817, 1.53478, 0.0639398, 0.474297, -1.10951, 1.98979, -0.636412, -0.785697, 0.778927, -2.33789, 0.461508, 0.522421, 0.68605, -0.426236, -0.843736, 2.13346, -0.686019, -1.00737, -0.898299, 0.0109888, 1.12862, 2.30876, 0.898455, -0.0431169, 1.37305, 0.597929, 0.503052, 0.407354, 0.157966, -1.37054, -1.97835, 0.188369, 0.484641, -1.58252, 0.59619, 0.261537, -0.00671637, -1.12001, 0.404141, 0.789859, -0.0560027, -0.568049, 0.35966, 0.667681, 1.02214, 1.03014, -0.0972912, -0.400676, -0.962565, -1.01264, -0.335247, -0.0050443, 0.609426, 0.599653],), Array{Float64, 2}[-8.6476e-11, -9.00376e-11, -7.83454e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6.63141e-12, 1.78934e-10, -2.86034e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -8.6476e-11, -9.00376e-11, -7.83454e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6.63141e-12, 1.78934e-10, -2.86034e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], Array{Float64, 2}[-6.77912e-11, 5.10863e-11, -5.84564e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6.77912e-11, 5.10863e-11, -5.84564e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6.77912e-11, 5.10863e-11, -5.84564e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6.77912e-11, 5.10863e-11, -5.84564e-11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]), world=0x0000000000005bcd)[2x0p,d3,N50],vertex [389] "p1_1p2_1f1"

works fine when in singlethreaded:

julia> addFactor!(fg,[:p1_1,:p2_1],sf,threadmodel=IIF.SingleThreaded)
vertex [390] "p1_1p2_1f2"

filepath functionality for bayes tree too

same as writegraphpdf for bayes tree or keyword argument to give wipeBuildNewTree a filepath?

re #118

loading a dfg is susceptible to changes in yaml config files

Some factors may have user defined configuration files, that although saved in the jld's, change over time and are no longer compatible with the new computational types. Usual options include saving the IIF/RoME/Caesar git SHA and tell the user go there. What else can we do?

cc @mc2922

Prior seems ignored in sands data sets

Solving, DB all seem to run fine, but x1 should much more tightly constrained by prior. This used to work, likely an issue with DataLayerAPI omission.

Fix testnullhypothesis unittest

Use ContinuousScalar and autoinit=true. New API is mostly backwards compatible with previous interface strategy.

Improve AbstractRelativeMinimize testing

allow parallel factors between nodes

Issue with ls(fg, sym, api=dlapi)

not discovering all the nodes as you would with ls(fg, sym, api=localapi)

Too many ports being used in DB version

Looks like many solver processes and visualization processes end up getting EADDRNOTAVAIL errors. Very sporadic.

ERROR: LoadError: connect: address not available (EADDRNOTAVAIL)
 in connect! at socket.jl:648
 in connect at socket.jl:671
 in open_stream at /home/dehann/.julia/v0.4/Requests/src/streaming.jl:186
 in do_stream_request at /home/dehann/.julia/v0.4/Requests/src/Requests.jl:350
 in do_request at /home/dehann/.julia/v0.4/Requests/src/Requests.jl:290
 in get at /home/dehann/.julia/v0.4/Requests/src/Requests.jl:413
 in get at /home/dehann/.julia/v0.4/Requests/src/Requests.jl:412
 in request at /home/dehann/.julia/v0.4/Neo4j/src/Neo4j.jl:174
 in get_vertex at /home/dehann/.julia/v0.4/CloudGraphs/src/CloudGraphs.jl:260
 in getExVertFromCloud at /home/dehann/.julia/v0.4/IncrementalInference/src/CloudGraphIntegration.jl:16
 in getExVertFromCloud at /home/dehann/.julia/v0.4/IncrementalInference/src/CloudGraphIntegration.jl:21
 [inlined code] from /home/dehann/.julia/v0.4/Caesar/src/DBCollectionsViewerService.jl:65
 in anonymous at no file:0
 in include at ./boot.jl:261
 in include_from_node1 at ./loading.jl:320
 in process_options at ./client.jl:280
 in _start at ./client.jl:378
while loading /home/dehann/.julia/v0.4/Caesar/src/DBCollectionsViewerService.jl, in expression starting on line 41

and

ERROR: LoadError: connect: address not available (EADDRNOTAVAIL)
 in connect! at socket.jl:648
 in connect at socket.jl:671
 in open_stream at /home/dehann/.julia/v0.4/Requests/src/streaming.jl:186
 in do_stream_request at /home/dehann/.julia/v0.4/Requests/src/Requests.jl:350
 in do_request at /home/dehann/.julia/v0.4/Requests/src/Requests.jl:290
 in get at /home/dehann/.julia/v0.4/Requests/src/Requests.jl:413
 in get at /home/dehann/.julia/v0.4/Requests/src/Requests.jl:412
 in request at /home/dehann/.julia/v0.4/Neo4j/src/Neo4j.jl:174
 in get_vertex at /home/dehann/.julia/v0.4/CloudGraphs/src/CloudGraphs.jl:260
 in getExVertFromCloud at /home/dehann/.julia/v0.4/IncrementalInference/src/CloudGraphIntegration.jl:16

Ability to Pop last / undo from factor graph

Some kind of History object?

PrintMax to print factorgraph

Pass userdata::FactorMetadata to `getSample`

Some situations require the measurement sampling process to know which variable is being solved for.

iterate(::Nothing) induced by new NLsolve v3.0.0

adopting issue defined in JuliaLang/METADATA.jl#19282

Local testing breaks in a newly created test file here (error below):

IncrementalInference.jl/test/testNLsolve.jl

Line 60 in 5065fdf

nlsolve( ccw, [0.0] )

cc @pkofod -- thanks for taking a look.

I'm little at a loss as to what is happening. I am using NLsolve in a functional way: Not just passing one function to nlsolve but actually programmatically building the required function and passing that ccw = CommonConvWrapper wrapper function to nlsolve(ccw, x0). ccw then internally does a few things and calls a function defined by the user with a much larger parameter list, that is in ccw.usrfnc!. This all works fine in NLsolve v2.1.0.

MethodError: no method matching iterate(::Nothing)
Closest candidates are:
  iterate(!Matched::Core.SimpleVector) at essentials.jl:589
  iterate(!Matched::Core.SimpleVector, !Matched::Any) at essentials.jl:589
  iterate(!Matched::ExponentialBackOff) at error.jl:171
  ...
copyto!(::Array{Float64,1}, ::Nothing) at abstractarray.jl:646
(::getfield(NLsolve, Symbol("#f!#1")){CommonConvWrapper{OneDimensionTest{Normal{Float64}}}})(::Array{Float64,1}, ::Array{Float64,1}) at helpers.jl:6
(::getfield(NLSolversBase, Symbol("#fj!#19")){getfield(NLsolve, Symbol("#f!#1")){CommonConvWrapper{OneDimensionTest{Normal{Float64}}}},DiffEqDiffTools.JacobianCache{Array{Float64,1},Array{Float64,1},Array{Float64,1},Val{:central},Float64,Val{true}}})(::Array{Float64,1}, ::Array{Float64,2}, ::Array{Float64,1}) at oncedifferentiable.jl:93
value_jacobian!!(::OnceDifferentiable{Array{Float64,1},Array{Float64,2},Array{Float64,1}}, ::Array{Float64,1}, ::Array{Float64,2}, ::Array{Float64,1}) at interface.jl:130
value_jacobian!! at interface.jl:128 [inlined]
trust_region_(::OnceDifferentiable{Array{Float64,1},Array{Float64,2},Array{Float64,1}}, ::Array{Float64,1}, ::Float64, ::Float64, ::Int64, ::Bool, ::Bool, ::Bool, ::Float64, ::Bool, ::NLsolve.NewtonTrustRegionCache{Array{Float64,1}}) at trust_region.jl:119
trust_region at trust_region.jl:229 [inlined]
trust_region at trust_region.jl:229 [inlined]
#nlsolve#14(::Symbol, ::Float64, ::Float64, ::Int64, ::Bool, ::Bool, ::Bool, ::Static, ::Float64, ::Bool, ::Int64, ::Float64, ::typeof(nlsolve), ::OnceDifferentiable{Array{Float64,1},Array{Float64,2},Array{Float64,1}}, ::Array{Float64,1}) at nlsolve.jl:23
#nlsolve at nlsolve.jl:0 [inlined]
#nlsolve#15 at nlsolve.jl:60 [inlined]
nlsolve(::Function, ::Array{Float64,1}) at nlsolve.jl:50
top-level scope at none:0
include_string(::Module, ::String, ::String) at loading.jl:1005
include_string(::Module, ::String, ::String, ::Int64) at eval.jl:30
(::getfield(Atom, Symbol("##114#119")){String,Int64,String})() at eval.jl:94
withpath(::getfield(Atom, Symbol("##114#119")){String,Int64,String}, ::String) at utils.jl:30
withpath at eval.jl:46 [inlined]
#113 at eval.jl:93 [inlined]
with_logstate(::getfield(Atom, Symbol("##113#118")){String,Int64,String}, ::Base.CoreLogging.LogState) at logging.jl:397
with_logger at logging.jl:493 [inlined]
#112 at eval.jl:92 [inlined]
hideprompt(::getfield(Atom, Symbol("##112#117")){String,Int64,String}) at repl.jl:85
macro expansion at eval.jl:91 [inlined]
(::getfield(Atom, Symbol("##111#116")))(::Dict{String,Any}) at eval.jl:86
handlemsg(::Dict{String,Any}, ::Dict{String,Any}) at comm.jl:164
(::getfield(Atom, Symbol("##19#21")){Array{Any,1}})() at task.jl:259

Option to suppress all printouts

In case user wants to see only their output -- won't be the default.

cc @mc2922

Deserialization of nodes results in loss of softtype and certainhypo

Additional requirements

Need to add these to the required installs:
sudo apt-get install libfontconfig1
sudo apt-get install gettext
sudo apt-get install libcairo2
sudo apt-get install libpango1.0-0

Separate config and data storage in FactorGraph object design

Currently FactorGraph contains both.

need show last MCMC product on variable function

num_edges(fg.g) = 0 when loading from jld2

Possibly losing information or not reconstructing the FactorGraph object when loading from jld2 file.

Repeated landmark constraints added from Python Turtlebot example

repeated constraints with potentially different values added to the graph.

Problem on infer if num particles different size

create graph with N=100, then try run inferOverTree!(...,N=200) and

ERROR: BoundsError: attempt to access 3x100 Array{Float64,2}:
 29.0817    29.4922    29.6258    29.6568   29.4491    …  29.1488    29.3001    29.2623    30.166     31.2157 
  2.24395    2.10491    2.56677    1.61801   1.33273       2.59031    0.557518   3.43693    1.11443    2.60181
  0.158318   0.161798   0.157094   0.15671   0.160954      0.158168   0.157333   0.157259   0.152206   0.16651
  at index [Colon(),101]
 in throw_boundserror at abstractarray.jl:156
 in solveSetSeps at /home/dehann/.julia/v0.4/IncrementalInference/src/TreePotentials02.jl:181
 in evalPotential at /home/dehann/.julia/v0.4/IncrementalInference/src/TreePotentials02.jl:243
 in evalFactor2 at /home/dehann/.julia/v0.4/IncrementalInference/src/TreePotentials01.jl:149
 in findRelatedFromPotential at /home/dehann/.julia/v0.4/IncrementalInference/src/TreePotentials01.jl:154
 in packFromLocalPotentials! at /home/dehann/.julia/v0.4/IncrementalInference/src/SolveTree01.jl:120
 in cliqGibbs at /home/dehann/.julia/v0.4/IncrementalInference/src/SolveTree01.jl:140
 in fmcmc! at /home/dehann/.julia/v0.4/IncrementalInference/src/SolveTree01.jl:179
 in upGibbsCliqueDensity at /home/dehann/.julia/v0.4/IncrementalInference/src/SolveTree01.jl:279
 in upMsgPassingRecursive at /home/dehann/.julia/v0.4/IncrementalInference/src/SolveTree01.jl:458
 in inferOverTreeR! at /home/dehann/.julia/v0.4/IncrementalInference/src/SolveTree01.jl:734

Fix lost marginal issue

It looks like there is some conflict in package versions

I tried adding the newest RoMEPlotting stuff, but there seems to be some conflicts with required versions. Here is some of them:

(v1.0) pkg> dev RoMEPlotting
  Updating git-repo `https://github.com/JuliaRobotics/RoMEPlotting.jl.git`
[ Info: Path `/home/johan/.julia/dev/RoMEPlotting` exists and looks like the correct package, using existing path instead of cloning
 Resolving package versions...
ERROR: Unsatisfiable requirements detected for package IncrementalInference [904591bb]:
 IncrementalInference [904591bb] log:
 ├─possible versions are: [0.0.1-0.0.2, 0.1.0, 0.2.0-0.2.11, 0.3.0-0.3.9, 0.4.0-0.4.5] or uninstalled
 ├─restricted to versions 0.4.4-* by RoMEPlotting [238d586b], leaving only versions 0.4.4-0.4.5
 │ └─RoMEPlotting [238d586b] log:
 │   ├─possible versions are: 0.0.1 or uninstalled
 │   └─RoMEPlotting [238d586b] is fixed to version 0.0.1+
 └─restricted to versions 0.4.3 by an explicit requirement — no versions left

(v1.0) pkg> add IncrementalInference
 Resolving package versions...
ERROR: Unsatisfiable requirements detected for package NLsolve [2774e3e8]:
 NLsolve [2774e3e8] log:
 ├─possible versions are: [0.1.0-0.1.4, 0.2.0, 0.3.0-0.3.3, 0.4.0, 0.5.0, 0.6.0-0.6.1, 0.7.0-0.7.3, 0.8.0, 0.9.0-0.9.1, 0.10.0-0.10.1, 0.11.0, 0.12.0-0.12.1, 0.13.0, 0.14.0-0.14.1, 1.0.0-1.0.1, 1.1.0-1.1.1, 2.0.0-2.0.1, 2.1.0, 3.0.0-3.0.1] or uninstalled
 ├─restricted by julia compatibility requirements to versions: 3.0.0-3.0.1 or uninstalled
 ├─restricted by compatibility requirements with Caesar [62eebf14] to versions: [2.0.0-2.0.1, 2.1.0, 3.0.0-3.0.1], leaving only versions: 3.0.0-3.0.1
 │ └─Caesar [62eebf14] log:
 │   ├─possible versions are: [0.0.1-0.0.5, 0.1.0-0.1.1, 0.2.0-0.2.3] or uninstalled
 │   └─restricted to versions 0.2.1 by an explicit requirement, leaving only versions 0.2.1
 └─restricted by compatibility requirements with IncrementalInference [904591bb] to versions: 2.1.0 — no versions left
   └─IncrementalInference [904591bb] log:
     ├─possible versions are: [0.0.1-0.0.2, 0.1.0, 0.2.0-0.2.11, 0.3.0-0.3.9, 0.4.0-0.4.5] or uninstalled
     └─restricted to versions 0.4.5 by an explicit requirement, leaving only versions 0.4.5

SolveTree01.jl:298 convert(::Type{Array{IncrementalInference.CliqGibbsMC,1}}, ::Type{Union{}})

MethodError: convert has no method matching convert(::Type{Array{IncrementalInference.CliqGibbsMC,1}}, ::Type{Union{}})
This may have arisen from a call to the constructor Array{IncrementalInference.CliqGibbsMC,1}(...),
since type constructors fall back to convert methods.
Closest candidates are:
call{T}(::Type{T}, ::Any)
convert{T}(::Type{Array{T,1}}, !Matched::Range{T})
convert{T,S,N}(::Type{Array{T,N}}, !Matched::SubArray{S,N,P<:AbstractArray{T,N},I<:Tuple{Vararg{Union{AbstractArray{T,1},Colon,Int64}}},LD})
...
in call at /home/dehann/.julia/v0.4/IncrementalInference/src/SolveTree01.jl:26
in upGibbsCliqueDensity at /home/dehann/.julia/v0.4/IncrementalInference/src/SolveTree01.jl:298
in anonymous at multi.jl:920
in run_work_thunk at multi.jl:661
in run_work_thunk at multi.jl:670
in anonymous at task.jl:58
in remotecall_fetch at multi.jl:747
in call_on_owner at multi.jl:793
in asyncProcessPostStacks! at /home/dehann/.julia/v0.4/IncrementalInference/src/SolveTree01.jl:625

`root()` has conflicting imports

module TestTest
  using KernelDensityEstimate, HDF5
  import KernelDensityEstimate: root
  import HDF5: root
end
WARNING: ignoring conflicting import of HDF5.root into TestTest

# reverse produces
module TestTest2
  using KernelDensityEstimate, HDF5
  import HDF5: root
  import KernelDensityEstimate: root
end
WARNING: ignoring conflicting import of KernelDensityEstimate.root into TestTest2

Importing these individually seems to work, but joined uncovers the conflict, although HDF5.root and KernelDensityEstimate.root are clearly different. Maybe in lower down dependencies?

Not fully robust to changes for solve when N not specified

inferOverTree!(...) without setting N=? results in error.

Increment numposes for MM landmark

attributes["numposes"] = 0 if MM created landmark

Weird results on bearing range factor (aka Funky21)

Adding a range+bearing factor on :x21 results in a strange solution, in which :x25 moves away from its true value and there is a significant increase in uncertainty.

drawPoseLandms:

Tree:

The behavior is reproducible.

Remove deprecated code in `addNode!`

Remove deprecated code from FactorGraph01:addNode!, specifically code related to stdev here:

setDefaultNodeData!(vert, zeros(dims,N), zeros(0,0), dodims, N, dims, initialized=!autoinit, softtype=st)

autoinit vs setValKDE

This initialization procedure with setValKDE appears to take into account tight GPS priors correctly, whereas doautoinit appears to initialize around (0,0) only.

for sm in [Symbol("x$i") for i in 1:5]
  IIF.doautoinit!(fg,sm)
  setValKDE!(getVert(fg,sm),kde!(getSample(getfnctype(getVert(fg, Symbol("$(sm)f1"),nt=:fnc)),N)[1]))
end

See: PropFac/examples/testBackwards.jl

`evalFactor2` not obeying N marginal particle counts

Defaults to 200 in some instances, particularly JuliaRobotics/RoME.jl/test/testDidsonFunctions.jl before the line L1pts = evalFactor2(fg, f2, fg.IDs[:l1])

Down values not always inserted

Seemingly after partials constraint upgrade. Left with some variables having large variance. Likely a missed assignment somewhere.

Likely also associated with multisession, since that is when the problem appeared.

Can you solve multiple orphaned graphs?

For example, a sequence of poses joined by factors and a separate landmark with a prior.

rmproc() breaks multiprocessor

addprocs(1)
...
rmprocs([2])
...
addprocs(...)
Pkg.test("IncrementalInference")

Then tests fail (Travis-CI).

Do multi-process inference over tree
child.attributes["label"] = "x5,x2,l1,x4,: "
child.attributes["label"] = "x3,: x2,x4,l1,"
child.attributes["label"] = "x1,: x2,"
child.attributes["label"] = "x7,x6,: x5,"
Start Clique x7,x6,: x5, =============================
====================== Clique x7,x6,: x5, =============================
Start Clique x1,: x2, =============================
====================== Clique x1,: x2, =============================
Start Clique x3,: x2,x4,l1, =============================
====================== Clique x3,: x2,x4,l1, =============================
Start Clique x5,x2,l1,x4,:  =============================
asyncProcessPostStacks -- 1, cliq=x5,x2,l1,x4,: , start on child x7,x6,: x5, haskey=false
End Clique x1,: x2, =============================
End Clique x3,: x2,x4,l1, =============================
	From worker 3:	up w 0 msgs------------- functional mcmc ----------------x1,: x2,
	From worker 3:	#1	 -- 
	From worker 3:	fmcmc! -- finished on x1,: x2,
	From worker 3:	------------- functional mcmc ----------------x1,: x2,
	From worker 4:	up w 0 msgs------------- functional mcmc ----------------x3,: x2,x4,l1,
	From worker 4:	#1	 -- 
	From worker 4:	fmcmc! -- finished on x3,: x2,x4,l1,
	From worker 4:	------------- functional mcmc ----------------x3,: x2,x4,l1,
	From worker 3:	#1	 -- x1 [2x,d1,N200], 
	From worker 3:	fmcmc! -- finished on x1,: x2,
	From worker 3:	------------- functional mcmc ----------------x1,: x2,
	From worker 3:	#1	 -- x2 [direct] 
	From worker 3:	fmcmc! -- finished on x1,: x2,
	From worker 4:	#1	 -- x3 [4x,d1,N200], 
	From worker 4:	fmcmc! -- finished on x3,: x2,x4,l1,
	From worker 4:	------------- functional mcmc ----------------x3,: x2,x4,l1,
	From worker 4:	#1	 -- x2 [direct] x4 [direct] l1 [direct] 
	From worker 4:	fmcmc! -- finished on x3,: x2,x4,l1,

No output has been received in the last 10m0s, this potentially indicates a stalled build or something wrong with the build itself.

Check the details on how to adjust your build configuration on: https://docs.travis-ci.com/user/common-build-problems/#Build-times-out-because-no-output-was-received

The build has been terminated

Running it locally and interrupting at this break point gives

	From worker 4:	------------- functional mcmc ----------------x3,: x2,x4,l1,
	From worker 4:	#1	 -- x2 [direct] x4 [direct] l1 [direct] 
	From worker 4:	fmcmc! -- finished on x3,: x2,x4,l1,
^C
signal (2): Interrupt
while loading /home/dehann/.julia/v0.5/IncrementalInference/test/fourdoortest.jl, in expression starting on line 71
unknown function (ip: 0x7f164d3c1568)
uv__epoll_wait at /home/centos/buildbot/slave/package_tarball64/build/deps/srccache/libuv-8d5131b6c1595920dd30644cd1435b4f344b46c8/src/unix/linux-syscalls.c:321
uv__io_poll at /home/centos/buildbot/slave/package_tarball64/build/deps/srccache/libuv-8d5131b6c1595920dd30644cd1435b4f344b46c8/src/unix/linux-core.c:267
uv_run at /home/centos/buildbot/slave/package_tarball64/build/deps/srccache/libuv-8d5131b6c1595920dd30644cd1435b4f344b46c8/src/unix/core.c:354
process_events at ./libuv.jl:82
wait at ./event.jl:147
wait at ./event.jl:27
wait at ./event.jl:319
sleep at ./event.jl:360
asyncProcessPostStacks! at /home/dehann/.julia/v0.5/IncrementalInference/src/SolveTree01.jl:530
unknown function (ip: 0x7f142df0a3f0)
jl_call_method_internal at /home/centos/buildbot/slave/package_tarball64/build/src/julia_internal.h:189 [inlined]
jl_apply_generic at /home/centos/buildbot/slave/package_tarball64/build/src/gf.c:1942
#37 at ./task.jl:360
unknown function (ip: 0x7f142df08aaf)
jl_call_method_internal at /home/centos/buildbot/slave/package_tarball64/build/src/julia_internal.h:189 [inlined]
jl_apply_generic at /home/centos/buildbot/slave/package_tarball64/build/src/gf.c:1942
jl_apply at /home/centos/buildbot/slave/package_tarball64/build/src/julia.h:1392 [inlined]
start_task at /home/centos/buildbot/slave/package_tarball64/build/src/task.c:253
unknown function (ip: 0xffffffffffffffff)
unknown function (ip: 0xffffffffffffffff)
Allocations: 40912850 (Pool: 40895095; Big: 17755); GC: 82

signal (11): Segmentation fault
while loading /home/dehann/.julia/v0.5/IncrementalInference/test/fourdoortest.jl, in expression starting on line 71
=====================================================================[ ERROR: IncrementalInference ]======================================================================

InterruptException:

==========================================================================================================================================================================
ERROR: IncrementalInference had test errors
 in #test#61(::Bool, ::Function, ::Array{AbstractString,1}) at ./pkg/entry.jl:740
 in (::Base.Pkg.Entry.#kw##test)(::Array{Any,1}, ::Base.Pkg.Entry.#test, ::Array{AbstractString,1}) at ./<missing>:0
 in (::Base.Pkg.Dir.##2#3{Array{Any,1},Base.Pkg.Entry.#test,Tuple{Array{AbstractString,1}}})() at ./pkg/dir.jl:31
 in cd(::Base.Pkg.Dir.##2#3{Array{Any,1},Base.Pkg.Entry.#test,Tuple{Array{AbstractString,1}}}, ::String) at ./file.jl:59
 in #cd#1(::Array{Any,1}, ::Function, ::Function, ::Array{AbstractString,1}, ::Vararg{Array{AbstractString,1},N}) at ./pkg/dir.jl:31
 in (::Base.Pkg.Dir.#kw##cd)(::Array{Any,1}, ::Base.Pkg.Dir.#cd, ::Function, ::Array{AbstractString,1}, ::Vararg{Array{AbstractString,1},N}) at ./<missing>:0
 in #test#3(::Bool, ::Function, ::String, ::Vararg{String,N}) at ./pkg/pkg.jl:258
 in test(::String, ::Vararg{String,N}) at ./pkg/pkg.jl:258

temporarily removed addprocs from runtests.jl

this suddenly seems to fail for some reason:

using IncrementalInference
...
addprocs(3)
...
using IncrementalInference

Iterated MCMC case missing

See testGenericWrapParam.jl for example. When root clique has two variables, a singleton and pairwise constraint, the iterated does one iteration and propagates belief.

[Feature] Allow for "Rao-Blackwellized" calibration

define a factor where there static calibration parameters (such as extrinsics) can be solved as a separate inference problem after the primary SLAM solution completes. These two solves could in theory iterate from one to the other. The benefit of this is that calibration parameters should not always just be part of the SLAM solve, but calibration is so very similar in spirit. For example:

fg = ...
addFactor!(fg, [:x1:x2], SpecialFactor(..., specialVar) )
...
batchSolve!(fg)

# somehow trigger the internal Rao-Blackwellized calibration problem
batchSolveRaoBVars!(fg, [SpecialVarList] )

# now repeat the original SLAM problem with new "calibrated" values
batchSolve!(fg)

cc @pvazteixeira , @nicrip

Ability to give drawGraph a file name

So we do not have to fight over tmp/fg.dot!

Automatic archiving of debug data for factors

Something like this:

fg = ...

dbgdest = Dict{Symbol, Any}()

tree = wipeBuildNewTree!(fg)
inferOverTree!(fg, tree, dbg=dbgdest)

dbgdest[:viatree][:cliq1][:up][...]
# or
dbgdest[:viafg][1]

This would also store contents contained in factor types that explicitly have the fieldname dbg.

different result from doautoinit and autoinit=true

autoinit keyword not working as intended?

Following code

addNode!(fg, sym, Point2)
pp = PriorPoint2(MvNormal(pos, cov) )
addFactor!(fg, [sym;], pp, autoinit=true)

will throws error: BoundsError: attempt to access 0×100 Array{Float64,2} at index [1, 1]
when inferOverTree! tries to initialize first point2 :x1 again (even though it should be initialized?)

Adding one line:

addNode!(fg, sym, Point2)
pp = PriorPoint2(MvNormal(pos, cov) )
addFactor!(fg, [sym;], pp, autoinit=true)
IncrementalInference.doautoinit!(fg,sym)

removes the error and initializes all the point2 as expected

Last marginal not being properly removed

Visible in DB version after inference has updated all local information on DB

Why is `ensureAllInitialized` slow?

ensureAllInitialized iterates over all nodes, and takes >1 second/node.

Allow for more flexible node names

Some node labels are longer than a single letter and number. For example maybe one can add node labels:

:x1
:s1
:p2
:offset3
:cal10_3
:pt100_1

Is deepcopy of `usrfnc` required?

FactorGraph01.jl has line:

setDefaultFactorNode!(fgl, newvert, Xi, deepcopy(usrfnc))

deepcopy seems unnecessary.

juliarobotics / incrementalinference.jl Goto Github PK

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