release v0.16.2-rc1

.github/workflows/BuildDocs.yml

@@ -0,0 +1,45 @@
+name: CI/CD Docs
+on:
+  pull_request:
+  push:
+    branches:
+      - master
+  workflow_dispatch:
+
+jobs:
+  docs:
+    name: 'Build Docs'
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        include:
+          - jlenv: 'docs/'
+            makejl: 'docs/make.jl'
+          # - jlenv: 'docs/pdf/'
+          #   makejl: 'docs/pdf/make.jl'
+    steps:
+      - uses: actions/checkout@v2
+      - uses: julia-actions/setup-julia@v1
+        with:
+          version: '1.10'
+          arch: x64
+      - name: 'Pkgs for Docs on ${{ github.head_ref }}'
+        run: |
+          export JULIA_PKG_SERVER=""
+          [ '${{ github.ref }}' == 'refs/heads/master' ] && export CJL_DOCS_BRANCH="master" || export CJL_DOCS_BRANCH="${{ github.head_ref }}"
+          export JULIA_PKG_PRECOMPILE_AUTO=0
+          julia -e 'println("Julia gets branch: ",ENV["CJL_DOCS_BRANCH"])'
+          julia --project=${{ matrix.jlenv }} --check-bounds=yes -e 'using Pkg; Pkg.instantiate(); Pkg.add(PackageSpec(name="Caesar", rev=ENV["CJL_DOCS_BRANCH"]))'
+          julia --project=${{ matrix.jlenv }} -e 'using Pkg; Pkg.add(PackageSpec(name="RoME", rev="master"))'
+          julia --project=${{ matrix.jlenv }} -e 'using Pkg; Pkg.add(PackageSpec(name="RoMEPlotting", rev="master"))'
+          julia --project=${{ matrix.jlenv }} -e 'using Pkg; Pkg.add(PackageSpec(name="KernelDensityEstimatePlotting", rev="master"))'
+          julia --project=${{ matrix.jlenv }} -e 'using Pkg; Pkg.add(PackageSpec(name="IncrementalInference", rev="master"))'
+      - name: 'Docs make.jl'
+        run: |
+          export JULIA_PKG_PRECOMPILE_AUTO=0
+          export DOCUMENTER_DEBUG="true"
+          julia --project=${{ matrix.jlenv }} --color=yes ${{ matrix.makejl }}
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}
+          JULIA_PKG_SERVER: ""

.github/workflows/ci.yml

@@ -16,7 +16,7 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.9'
+          - '1.10'
           - 'nightly'
         os:
           - ubuntu-latest
@@ -62,7 +62,7 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.9'
+          - '1.10'
         os:
           - ubuntu-latest
         arch:
@@ -109,7 +109,7 @@ jobs:
       - uses: actions/checkout@v2
       - uses: julia-actions/setup-julia@v1
         with:
-          version: ~1.10.0-0
+          version: '1.10'
           arch: x64
       - uses: actions/cache@v1
         env:
@@ -126,40 +126,13 @@ jobs:
           git config --global user.email [email protected]
       - name: Run tests on Upstream Dev
         run: |
+          export JULIA_PKG_PRECOMPILE_AUTO=0
           julia --project=@. --check-bounds=yes -e 'using Pkg; Pkg.add(PackageSpec(name="RoME",rev="master"));'
           julia --project=@. --check-bounds=yes -e 'using Pkg; Pkg.add(PackageSpec(name="IncrementalInference",rev="master"));'
           julia --project=@. --check-bounds=yes -e 'using Pkg; Pkg.add(PackageSpec(name="ApproxManifoldProducts",rev="master"));'
           julia --project=@. --check-bounds=yes -e 'using Pkg; Pkg.add(PackageSpec(name="DistributedFactorGraphs",rev="master"));'
+          unset JULIA_PKG_PRECOMPILE_AUTO
           julia --project=@. --check-bounds=yes -e 'using Pkg; Pkg.test("Caesar"; coverage=false)'
         shell: bash
 
-  docs:
-    name: 'Build Docs'
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        include:
-          - jlenv: 'docs/'
-            makejl: 'docs/make.jl'
-          # - jlenv: 'docs/pdf/'
-          #   makejl: 'docs/pdf/make.jl'
-    steps:
-      - uses: actions/checkout@v2
-      - uses: julia-actions/setup-julia@v1
-        with:
-          version: 1.9
-      - name: 'Docs on ${{ github.head_ref }}'
-        run: |
-          export JULIA_PKG_SERVER=""
-          [ '${{ github.ref }}' == 'refs/heads/master' ] && export CJL_DOCS_BRANCH="master" || export CJL_DOCS_BRANCH="${{ github.head_ref }}"
-          julia -e 'println("Julia gets branch: ",ENV["CJL_DOCS_BRANCH"])'
-          julia --project=${{ matrix.jlenv }} --check-bounds=yes -e 'using Pkg; Pkg.instantiate(); Pkg.add(PackageSpec(name="Caesar", rev=ENV["CJL_DOCS_BRANCH"]))'
-          julia --project=${{ matrix.jlenv }} -e 'using Pkg; Pkg.add(PackageSpec(name="RoME", rev="master"))'
-          julia --project=${{ matrix.jlenv }} -e 'using Pkg; Pkg.add(PackageSpec(name="RoMEPlotting", rev="master"))'
-          julia --project=${{ matrix.jlenv }} -e 'using Pkg; Pkg.add(PackageSpec(name="KernelDensityEstimatePlotting", rev="master"))'
-          julia --project=${{ matrix.jlenv }} -e 'using Pkg; Pkg.add(PackageSpec(name="IncrementalInference", rev="master"))'
-      - run: julia --project=${{ matrix.jlenv }} --color=yes ${{ matrix.makejl }}
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-          DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}
-          JULIA_PKG_SERVER: ""
+

.gitignore

@@ -5,5 +5,6 @@ examples/lcmserver/lcmtypes/rome/
 examples/lcmserver/rome/
 examples/tracking/simpleradar/exports/*
 docs/build
+docs/Manifest.toml
 results/*
 build

Project.toml

@@ -2,7 +2,7 @@ name = "Caesar"
 uuid = "62eebf14-49bc-5f46-9df9-f7b7ef379406"
 keywords = ["SLAM", "state-estimation", "MM-iSAM", "MM-iSAMv2", "inference", "robotics", "ROS"]
 desc = "Non-Gaussian simultaneous localization and mapping"
-version = "0.16.1"
+version = "0.16.2"
 
 [deps]
 ApproxManifoldProducts = "9bbbb610-88a1-53cd-9763-118ce10c1f89"
@@ -93,7 +93,7 @@ ImageCore = "0.8, 0.9, 0.10"
 ImageDraw = "0.2"
 ImageMagick = "1"
 IncrementalInference = "0.34, 0.35"
-Interpolations = "0.14"
+Interpolations = "0.14, 0.15"
 JLD2 = "0.3, 0.4"
 JSON = "0.20, 0.21"
 JSON2 = "0.3, 0.4"
@@ -111,6 +111,7 @@ Reexport = "1"
 RoME = "0.23, 0.24"
 Rotations = "1.1"
 StaticArrays = "1"
+Statistics = "1"
 StatsBase = "0.33, 0.34"
 TensorCast = "0.4"
 TimeZones = "1.3.1, 1.4"

docs/Project.toml

@@ -22,7 +22,6 @@ JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
 JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
 KernelDensityEstimate = "2472808a-b354-52ea-a80e-1658a3c6056d"
 KernelDensityEstimatePlotting = "c43967c8-f634-5d24-8eab-2867546b366b"
-LCMCore = "0ea44823-1ff1-5b9a-8293-5fd55a38e746"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 NLsolve = "2774e3e8-f4cf-5e23-947b-6d7e65073b56"
 ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
@@ -54,19 +53,18 @@ FunctionalStateMachine = "≥ 0.1"
 ImageCore = "≥ 0.7"
 ImageMagick = "≥ 0.7"
 Images = "≥ 0.24"
-IncrementalInference = "≥ 0.13"
+IncrementalInference = "≥ 0.35"
 JLD2 = "≥ 0.1"
 JSON = "≥ 0.18"
 KernelDensityEstimate = "≥ 0.5"
 KernelDensityEstimatePlotting = "≥ 0.1.4"
-LCMCore = "≥ 0.5"
 NLsolve = "≥ 3"
 ProgressMeter = "≥ 0.9"
 Reexport = "≥ 0.2"
-RoME = "≥ 0.7"
+RoME = "≥ 0.23"
 Rotations = "≥ 0.13"
 TransformUtils = "≥ 0.2.2"
 Unmarshal = "≥ 0.3"
 YAML = "≥ 0.3"
 ZMQ = "≥ 1.0"
-julia = "1.4"
+julia = "1.10"

docs/make.jl

@@ -3,10 +3,14 @@ using RoME
 using Colors
 using Images
 
+import Caesar._PCL as _PCL
+
 import IncrementalInference: fmcmc!, localProduct, prodmultiplefullpartials, prodmultipleonefullpartials, setfreeze!
 import IncrementalInference: cliqGibbs, packFromLocalPotentials!, treeProductDwn, updateFGBT!, upGibbsCliqueDensity
 import IncrementalInference: initfg, downGibbsCliqueDensity
 import IncrementalInference: solveGraphParametric, solveGraphParametric!
+import IncrementalInference: _solveCCWNumeric!
+import IncrementalInference: initParametricFrom!
 
 using KernelDensityEstimatePlotting
 # import KernelDensityEstimatePlotting: plotKDE
@@ -16,6 +20,7 @@ using RoMEPlotting
 using DistributedFactorGraphs
 import DistributedFactorGraphs: showFactor, showVariable
 import DistributedFactorGraphs: deleteVariable!
+import DistributedFactorGraphs: loadDFG, loadDFG!
 
 makedocs(
     modules = [Caesar, RoME, IncrementalInference, RoMEPlotting, KernelDensityEstimatePlotting, DistributedFactorGraphs],
@@ -96,10 +101,12 @@ makedocs(
         "Literature" => [
             "References" => "refs/literature.md"
         ],
-    ]
+    ],
+    # FIXME remove warnonly option once :missing_docs and :cross_references fixes are done
+    warnonly = Documenter.except(:autodocs_block, :docs_block, :doctest, :linkcheck, :eval_block, :example_block, :footnote, :linkcheck_remotes, :meta_block, :parse_error, :setup_block), # , :cross_references, :missing_docs
     # html_prettyurls = !("local" in ARGS),
-    )
-
+)
+# The possible Symbol values that can be passed to the function are: :autodocs_block, :cross_references, :docs_block, :doctest, :eval_block, :example_block, :footnote, :linkcheck_remotes, :linkcheck, :meta_block, :missing_docs, :parse_error, and :setup_block.
 
 deploydocs(
     repo   = "github.com/JuliaRobotics/Caesar.jl.git",

docs/src/concepts/2d_plotting.md

@@ -166,7 +166,7 @@ plotPose
 
 ### Debug With Local Graph Product Plot
 
-One useful function is to check that data in the factor graph makes sense.  While the full inference algorithm uses a Bayes (Junction) tree to assemble marginal belief estimates in an efficient manner, it is often useful for a straight forward graph based sanity check.  The [`plotLocalProduct`](@ref) projects through [`approxConv`](@ref) each of the factors connected to the target variable and plots the result.  This example looks at the loop-closure point around `:x0`, which is also pinned down by the only prior in the canonical Hexagonal factor graph.
+One useful function is to check that data in the factor graph makes sense.  While the full inference algorithm uses a Bayes (Junction) tree to assemble marginal belief estimates in an efficient manner, it is often useful for a straight forward graph based sanity check.  The [`plotLocalProduct`](@ref) projects through [`approxConvBelief`](@ref) each of the factors connected to the target variable and plots the result.  This example looks at the loop-closure point around `:x0`, which is also pinned down by the only prior in the canonical Hexagonal factor graph.
 ```julia
 @show ls(fg, :x0);
 # ls(fg, :x0) = [:x0f1, :x0x1f1, :x0l1f1]

docs/src/concepts/arena_visualizations.md

@@ -1,4 +1,4 @@
-# [Visualization 3D](@ref visualization_3d)
+# [Visualization 3D](@id visualization_3d)
 
 ## Introduction
 

docs/src/concepts/available_varfacs.md

@@ -21,8 +21,7 @@ The variables and factors in Caesar should be sufficient for a variety of roboti
 Default variables in IncrementalInference
 
 ```@docs
-ContinuousScalar
-ContinuousEuclid{N}
+Position{N}
 ```
 
 ### 2D Variables
@@ -40,7 +39,6 @@ DynPose2
 ```@docs
 Point3
 Pose3
-InertialPose3
 ```
 
 !!! note
@@ -102,10 +100,10 @@ VelPose2VelPose2
 DynPose2Pose2
 Pose3Pose3
 PriorPose3ZRP
-PartialPriorRollPitchZ
-PartialPose3XYYaw
 Pose3Pose3XYYaw
 ```
+<!-- PartialPose3XYYaw -->
+<!-- PartialPriorRollPitchZ -->
 
 # Extending Caesar with New Variables and Factors
 

docs/src/concepts/building_graphs.md

@@ -1,4 +1,4 @@
-# [Building Graphs](@ref building_graphs)
+# [Building Graphs](@id building_graphs)
 
 Irrespective of your application - real-time robotics, batch processing of survey data, or really complex multi-hypothesis modeling - you're going to need to add factors and variables to a graph. This section discusses how to do that in Caesar.
 
@@ -53,13 +53,7 @@ addVariable!
 deleteVariable!
 ```
 
-### Initializing Variables
-
-The MM-iSAMv2 algorithm uses one of two approaches to automatically initialize variables.  The `initManual!` function can be used if you wish to overwrite or pre-empt this initialization.
-
-```@docs
-initManual!
-```
+The MM-iSAMv2 algorithm uses one of two approaches to automatically initialize variables, or can be [initialized manually](@ref variable_init).
 
 ## Factors
 

docs/src/concepts/dataassociation.md

@@ -1,4 +1,4 @@
-# Data Association and Hypotheses
+# [Data Association and Hypotheses](@id data_multihypo)
 
 Ambiguous data and processing often produce complicated data association situations.  In SLAM, loop-closures are a major source of concern when developing autonomous subsystems or behaviors. To illustrate this point, consider the two scenarios depicted below:
 

docs/src/concepts/entry_data.md

@@ -140,7 +140,4 @@ addData!(dfg,:default_folder_store,:x0,:nnModel,
 
 ## Experimental Features
 
-Loading images is a relatively common task, hence a convenience function has been developed:
-```@docs
-Caesar.fetchDataImage
-```
+Loading images is a relatively common task, hence a convenience function has been developed, when `using ImageMagick` try `Caesar.fetchDataImage`.

docs/src/concepts/flux_factors.md

@@ -1,4 +1,4 @@
 # Incorporating Neural Network Factors
 
-IncrementalInference.jl and RoME.jl has native support for using Neural Networks (via [Flux.jl](https://fluxml.ai/Flux.jl/stable/)) as non-Gaussian factors.  Documentation is forthcoming, but meanwhile [see the following generic Flux.jl factor structure](https://github.com/JuliaRobotics/IncrementalInference.jl/tree/master/src/Flux).  Note also that a standard [`Mixture` approach already exists too](https://github.com/JuliaRobotics/RoME.jl/blob/master/src/factors/flux/MixtureFluxPose2Pose2.jl).
+IncrementalInference.jl and RoME.jl has native support for using Neural Networks (via [Flux.jl](https://fluxml.ai/Flux.jl/stable/)) as non-Gaussian factors.  Documentation is forthcoming, but meanwhile [see the following generic Flux.jl factor structure](https://github.com/JuliaRobotics/IncrementalInference.jl/blob/master/ext/IncrInfrFluxFactorsExt.jl).  Note also that a standard [`Mixture` approach already exists too](https://github.com/JuliaRobotics/RoME.jl/blob/master/ext/factors/MixtureFluxPose2Pose2.jl).
 

docs/src/concepts/interacting_fgs.md

@@ -32,7 +32,7 @@ loadDFG!
 
 A later part of the documentation will show [how to include a `Entry=>Data` blob store](https://juliarobotics.org/Caesar.jl/latest/concepts/entry_data/).
 
-## Querying the FactorGraph
+## [Querying the Graph](@id querying_graph)
 
 ### List Variables:
 
@@ -134,7 +134,7 @@ It is also possible to sample the above belief objects for more samples:
 pts = rand(X0, 200)
 ```
 
-## Building On-Manifold KDEs
+## [Building On-Manifold KDEs](@id build_manikde)
 
 These kernel density belief objects can be constructed from points as follows:
 ```julia
@@ -166,5 +166,5 @@ joinLogPath
 
 ```@docs
 getFactorDim
-getManifolds
+getManifold
 ```

docs/src/concepts/multilang.md

@@ -2,40 +2,25 @@
 
 The Caesar framework is not limited to direct Julia use. 
 
+## [NavAbility SDKs and APIs](@id navabilitysdk)
+
+The maintainers of Caesar.jl together with NavAbility.io are developing a standardized SDK / API for much easier multi-language / multi-access use of the solver features.  The [Documentation for the NavAbilitySDK's can be found here](https://navability.github.io/NavAbilitySDK.py/).
+
+Contact [email protected] for more information.
+
 ## Static, Shared Object `.so` Compilation
+
 See [Compiling Binaries](@ref compile_binaries).
 
 ## ROS Integration
+
 See [ROS Integration](@ref ros_direct).
 
 ## Python Direct
 
 For completeness, another design pattern is to wrap Julia packages for direct access from python, see [SciML/diffeqpy](https://github.com/SciML/diffeqpy) as example.
 
-## Caesar SDKs and APIs
-
-The maintainers of Caesar.jl together with NavAbility.io are developing a standardized SDK / API for much easier multi-language / multi-access use of the solver features.  Contact [email protected] for more information.
-
-!!! note
-    2021Q4, Coming Soon!  A new multilanguage SDK is under development and will replace and consolidate the previous methods listed below.
-
-### Previous Generation APIs
-
-The following Github projects provide access to features of Caesar in their language:
-
-* Julia Web interface:
-  * [GraffSDK.jl](https://github.com/GearsAD/GraffSDK.jl)
-
-* ZMQ Interface
-  * C/C++:
-    * [Graff Cpp](https://github.com/MarineRoboticsGroup/graff_cpp)
-    * [Caesar LCM](http://github.com/pvazteixeira/caesar-lcm)
-    * [Caesar ROS](http://github.com/pvazteixeira/caesar_ros)
-  * Python:
-    * [GraffSDK.py](https://github.com/nicrip/graff_py) (needs to be updated)
-    * [Synchrony_py](https://github.com/nicrip/SynchronySDK_py)
-
-## ZMQ Messaging Interface
+## [OUTDATED] ZMQ Messaging Interface
 
 Caesar.jl has a ZMQ messaging interface ([interested can see code here](https://github.com/JuliaRobotics/Caesar.jl/blob/master/src/zmq/ZmqCaesar.jl)) that allows users to interact with the solver code base in a variety of ways.  The messaging interface is not meant to replace static `.so` library file compilation but rather provide a more versatile and flexible development strategy.
 

docs/src/concepts/solving_graphs.md

@@ -14,7 +14,7 @@ The returned Bayes (Junction) `tree` object is described in more detail on [a de
 solveTree!
 ```
 
-## Automatic vs Manual Init
+## [Automatic vs Manual Init](@id variable_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