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Merge pull request #1002 from JuliaRobotics/master
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release v0.16.2-rc1
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dehann authored Feb 17, 2024
2 parents 81f368b + bfa158a commit ec76b5a
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45 changes: 45 additions & 0 deletions .github/workflows/BuildDocs.yml
Original file line number Diff line number Diff line change
@@ -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: ""
39 changes: 6 additions & 33 deletions .github/workflows/ci.yml
Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@ jobs:
fail-fast: false
matrix:
version:
- '1.9'
- '1.10'
- 'nightly'
os:
- ubuntu-latest
Expand Down Expand Up @@ -62,7 +62,7 @@ jobs:
fail-fast: false
matrix:
version:
- '1.9'
- '1.10'
os:
- ubuntu-latest
arch:
Expand Down Expand Up @@ -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:
Expand All @@ -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: ""

1 change: 1 addition & 0 deletions .gitignore
Original file line number Diff line number Diff line change
Expand Up @@ -5,5 +5,6 @@ examples/lcmserver/lcmtypes/rome/
examples/lcmserver/rome/
examples/tracking/simpleradar/exports/*
docs/build
docs/Manifest.toml
results/*
build
5 changes: 3 additions & 2 deletions Project.toml
Original file line number Diff line number Diff line change
Expand Up @@ -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"
Expand Down Expand Up @@ -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"
Expand All @@ -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"
Expand Down
8 changes: 3 additions & 5 deletions docs/Project.toml
Original file line number Diff line number Diff line change
Expand Up @@ -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"
Expand Down Expand Up @@ -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"
13 changes: 10 additions & 3 deletions docs/make.jl
Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand All @@ -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],
Expand Down Expand Up @@ -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",
Expand Down
2 changes: 1 addition & 1 deletion docs/src/concepts/2d_plotting.md
Original file line number Diff line number Diff line change
Expand Up @@ -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]
Expand Down
2 changes: 1 addition & 1 deletion docs/src/concepts/arena_visualizations.md
Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
# [Visualization 3D](@ref visualization_3d)
# [Visualization 3D](@id visualization_3d)

## Introduction

Expand Down
8 changes: 3 additions & 5 deletions docs/src/concepts/available_varfacs.md
Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand All @@ -40,7 +39,6 @@ DynPose2
```@docs
Point3
Pose3
InertialPose3
```

!!! note
Expand Down Expand Up @@ -102,10 +100,10 @@ VelPose2VelPose2
DynPose2Pose2
Pose3Pose3
PriorPose3ZRP
PartialPriorRollPitchZ
PartialPose3XYYaw
Pose3Pose3XYYaw
```
<!-- PartialPose3XYYaw -->
<!-- PartialPriorRollPitchZ -->

# Extending Caesar with New Variables and Factors

Expand Down
10 changes: 2 additions & 8 deletions docs/src/concepts/building_graphs.md
Original file line number Diff line number Diff line change
@@ -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.

Expand Down Expand Up @@ -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

Expand Down
2 changes: 1 addition & 1 deletion docs/src/concepts/dataassociation.md
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@@ -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:

Expand Down
5 changes: 1 addition & 4 deletions docs/src/concepts/entry_data.md
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Expand Up @@ -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`.
2 changes: 1 addition & 1 deletion docs/src/concepts/flux_factors.md
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@@ -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).

6 changes: 3 additions & 3 deletions docs/src/concepts/interacting_fgs.md
Original file line number Diff line number Diff line change
Expand Up @@ -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:

Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -166,5 +166,5 @@ joinLogPath

```@docs
getFactorDim
getManifolds
getManifold
```
33 changes: 9 additions & 24 deletions docs/src/concepts/multilang.md
Original file line number Diff line number Diff line change
Expand Up @@ -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.

Expand Down
2 changes: 1 addition & 1 deletion docs/src/concepts/solving_graphs.md
Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down
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