ROS Direct

Since 2020, Caesar.jl has native support for ROS via the RobotOS.jl package.

Load the ROS Environment Variables

The first thing to ensure is that the ROS environment variables are loaded before launching Julia, see "1.5 Environment setup at ros.org", something similar to:

source /opt/ros/noetic/setup.bash

Setup a Catkin Workspace

Assuming you have bespoke msg types, we suggest using a catkin workspace of choice, for example:

mkdir -p ~/caesar_ws/src
cd ~/caesar_ws/src
git clone https://github.com/pvazteixeira/caesar_ros

Now build and configure your workspace

cd ~/caesar_ws
catkin_make
source devel/setup.sh

This last command is important, as you must have the workspace configuration in your environment when you run the julia process, so that you can import the service specifications.

RobotOS.jl with Correct Python

RobotOS.jl currently uses PyCall.jl to interface through the rospy system. After launching Julia, make sure that PyCall is using the correct Python binary on your local system.

# Assuming multiprocess will be used.
using Distributed

# Prepare python version
using Pkg
Distributed.@everywhere using Pkg

Distributed.@everywhere begin
ENV["PYTHON"] = "/usr/bin/python3"
Pkg.build("PyCall")
end

using PyCall
Distributed.@everywhere using PyCall

Load RobotOS.jl along with Caesar.jl

Caesar.jl has native by optional package tools relating to RobotOS.jl (leveraging Requires.jl):

using RobotOS

@rosimport sensor_msgs.msg: PointCloud2

rostypegen()

using Colors, Caesar
Distributed.@everywhere using Colors, Caesar

Colors.jl is added as a conditional requirement to get Caesar._PCL.PointCloud support (see PCL page here).

Note

Imports and type generation are necessary for RobotOS and Caesar to work properly.

Prepare Any Outer Objects

Usually a factor graph or detectors, or some more common objects are required. For the example lets just say a basic SLAMWrapper containing a regular fg=initfg():

robotslam = SLAMWrapperLocal()

Example Caesar.jl ROS Handler

Some function will also be required to consume the ROS traffic on any particular topic, where for the example we assume extraneous data will only be fg_:

function myHandler(msgdata, slam_::SLAMWrapperLocal)
# show some header information

# do stuff
#, etc.

nothing
end

Read or Write Bagfile Messages

Assuming that you are working from a bagfile, the following code makes it easy to consume the bagfile directly. Alternatively, see RobotOS.jl for wiring up publishers and subscribers for live data. Caesar.jl methods to consuming a bagfile are:

# find the bagfile
bagfile = joinpath(ENV["HOME"],"data/somedata.bag")

# open the file
bagSubscriber = RosbagSubscriber(bagfile)

# subscriber callbacks
bagSubscriber("/zed/left/image_rect_color", myHandler, robotslam)

Run the ROS Loop

Once everything is set up as you need, it's easy to loop over all the traffic in the bagfile (one message at a time):

maxloops = 1000
rosloops = 0
while loop!(bagSubscriber)
# plumbing to limit the number of messages
rosloops += 1
if maxloops < rosloops
@warn "reached --msgloops limit of \$rosloops"
break
end
# delay progress for whatever reason
blockProgress(robotslam) # required to prevent duplicate solves occuring at the same time
end

Write Msgs to a Bag

Support is also provided for writing messages to bag files with Caesar.RosbagWriter:

# Link with ROSbag infrastructure via rospy
using Pkg
ENV["PYTHON"] = "/usr/bin/python3"
Pkg.build("PyCall")
using PyCall
using RobotOS
@rosimport std_msgs.msg: String
rostypegen()
using Caesar

bagwr = Caesar.RosbagWriter("/tmp/test.bag")
s = std_msgs.msg.StringMsg("test")
bagwr.write_message("/ch1", s)
bagwr.close()

This has been tested and use with much more complicated types such as the Caesar._PCL.PCLPointCloud2.

More Tools for Real-Time

See tools such as

ST = manageSolveTree!(robotslam.dfg, robotslam.solveSettings, dbg=false)
RoME.manageSolveTree!Function
manageSolveTree!(dfg, mss; dbg, timinglog, limitfixeddown)


Asynchronous solver manager that can run concurrently while other Tasks are modifying a common distributed factor graph object.

Notes

• When adding Variables and Factors, use solvable=0 to disable the new fragments until ready for inference.
• e.g. addVariable!(fg, :x45, Pose2, solvable=0)
• These parts of the factor graph can simply be activated for solving setSolvable!(fg, :x45, 1)

for solving a factor graph while the middleware processes are modifying the graph, while documentation is being completed see the code here: https://github.com/JuliaRobotics/RoME.jl/blob/a662d45e22ae4db2b6ee20410b00b75361294545/src/Slam.jl#L175-L288

To stop or trigger a new solve in the SLAM manager you can just use either of these

RoME.stopManageSolveTree!Function
stopManageSolveTree!(slam)


Stops a manageSolveTree! session. Usually up to the user to do so as a SLAM process comes to completion.

Related

manageSolveTree!

RoME.triggerSolve!Function
triggerSolve!(slam)


Trigger a factor graph solveTree!(slam.dfg,...) after clearing the solvable buffer slam.?? (assuming the manageSolveTree! task is already running).

Notes

• Used in combination with manageSolveTree!
Note

Native code for consuming rosbags also includes methods:

RosbagSubscriber, loop!, getROSPyMsgTimestamp, nanosecond2datetime
Note

Additional notes about tricks that came up during development is kept in this wiki.

Note

See ongoing RobotOS.jl discussion on building a direct C++ interface and skipping PyCall.jl entirely: https://github.com/jdlangs/RobotOS.jl/issues/59