Merge pull request #892 from JuliaRobotics/master

v0.13.3-rc2

Author: dehann

Project.toml

@@ -26,8 +26,8 @@ JSON2 = "2535ab7d-5cd8-5a07-80ac-9b1792aadce3"
 KernelDensityEstimate = "2472808a-b354-52ea-a80e-1658a3c6056d"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Manifolds = "1cead3c2-87b3-11e9-0ccd-23c62b72b94e"
-NearestNeighbors = "b8a86587-4115-5ab1-83bc-aa920d37bbce"
 NLsolve = "2774e3e8-f4cf-5e23-947b-6d7e65073b56"
+NearestNeighbors = "b8a86587-4115-5ab1-83bc-aa920d37bbce"
 Optim = "429524aa-4258-5aef-a3af-852621145aeb"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
@@ -66,13 +66,15 @@ JSON2 = "0.3, 0.4"
 KernelDensityEstimate = "0.5"
 Manifolds = "0.8"
 NLsolve = "4"
+NearestNeighbors = "0.4"
 Optim = "1"
 ProgressMeter = "1"
 Reexport = "1"
 Requires = "1"
 RoME = "0.20"
 Rotations = "1.1"
 StaticArrays = "1"
+StatsBase = "0.33"
 TensorCast = "0.4"
 TimeZones = "1.3.1, 1.4"
 TransformUtils = "0.2.14"

docs/src/concepts/solving_graphs.md

@@ -1,10 +1,11 @@
 # [Solving Graphs](@id solving_graphs)
+## Non-parametric Batch Solve
 
 When you have built the graph, you can call the solver to perform inference with the following:
 
 ```julia
 # Perform inference
-tree = solveTree!(fg)
+tree = solveTree!(fg) # or solveGraph!
 ```
 
 The returned Bayes (Junction) `tree` object is described in more detail on [a dedicated documentation page](https://juliarobotics.org/Caesar.jl/latest/principles/bayestreePrinciples/), while `smt` and `hist` return values most closely relate to development and debug outputs which can be ignored during general use.  Should an error occur during, the exception information is easily accessible in the `smt` object (as well as file logs which default to `/tmp/caesar/`).
@@ -13,6 +14,26 @@ The returned Bayes (Junction) `tree` object is described in more detail on [a de
 solveTree!
 ```
 
+## Automatic vs Manual Init
+
+Currently the main automatic initialization technique used by IncrementalInference.jl by delayed propagation of belief on the factor graph.  This can be globally or locally controlled via:
+```julia
+getSolverParams(fg).graphinit = false
+
+# or locally at each addFactor
+addFactor!(fg, [:x0;:x1], LinearRelative(Normal()); graphinit=false)
+```
+
+Use [`initVariable!`](@ref) if you'd like to force a particular numerical initialization of some or all the variables.
+```@docs
+initVariable!
+```
+
+All the variables can be initialized without solving with:
+```@docs
+initAll!
+```
+
 ## Using Incremental Updates (Clique Recycling I)
 
 One of the major features of the MM-iSAMv2 algorithm (implemented by [IncrementalInference.jl](http://www.github.com/JuliaRobotics/IncrementalInference.jl)) is reducing computational load by recycling and marginalizing different (usually older) parts of the factor graph.  In order to utilize the benefits of recycing, the previous Bayes (Junction) tree should also be provided as input (see fixed-lag examples for more details):

src/3rdParty/_PCL/services/ICP_Simple.jl

@@ -231,7 +231,7 @@ Notes
 - See here for a brief example on [Visualizing Point Clouds](@ref viz_pointcloud_makie).
 
 DevNotes
-- TODO switch rigid transfrom to `Caesar._PCL.apply` along with performance considerations, instead of curent `transform!`.
+- TODO switch rigid transfrom to `Caesar._PCL.apply` along with performance considerations, instead of current `transform!`.
 
 See also: [`PointCloud`](@ref)
 """

src/images/ScatterAlignPose2.jl

@@ -92,7 +92,9 @@ struct ScatterAlignPose2 <: IIF.AbstractManifoldMinimize
 end
 
 """
-    ScatterAlignPose3(; mkd1::ManifoldKernelDensity, mkd2::ManifoldKernelDensity, moreoptions...)
+    ScatterAlignPose3(; cloud1=mkd1::ManifoldKernelDensity, 
+                        cloud2=mkd2::ManifoldKernelDensity, 
+                        moreoptions...)
 
 Specialization of [`ScatterAlign`](@ref) for [`Pose3`](@ref).
 

test/pcl/testPointCloud2.jl

@@ -135,6 +135,8 @@ pc_ = Caesar._PCL.apply(M, rPc, pc)
 end
 
 
+if v"1.7" <= VERSION
+
 @testset "PandarXT test point cloud conversion test" begin
 ##
 
@@ -220,6 +222,11 @@ pc_3D = Caesar._PCL.apply(M, rPc, pc)
 ##
 end
 
+else
+  @error "Skip PointCloud testdata serialization owning to Julia version."
+  @test_broken false
+end
+
 
 
 @testset "Test PointCloud on Terrestrial Lidar and ICP_Simple alignment" begin