Hello, everyone.
Welcome to the Lampking Presentation.
My name is Llamar Sal.
I'm a robotics software developer at the command.
And last year we bring here Beluga.
Beluga is an MSEL.
Montecal localization framework for robotics.
So this year I'm pleased to bring here it's sibling.
It's about how to make much marketing to the work for the organization.
So what is Lampking?
Lampking is localization and mapping with machine for robotics.
And it is based on these four pylars.
So let's go straight on the pylars.
The architecture depends on the orchestration.
So first of all, reproduce all the environments.
Because it's based on your contents.
It's based on earthly.
At least a mix between Docker and make.
Also the another pylars is standard performance metrics.
So it contains EVO type memory.
Both are like tools for metrics.
So EVO gives you the absolute post error and the relative post error.
And the memory is really good for performance benchmarking.
And also we have to draw two that gives us like there was like play and recording.
And so on.
So it's it's really used to integrate it.
And other pylars.
The collaborative benchmark of the finishes.
So it contains also it was on the framework.
That it helps us to like to have a link between or a hook between the keyword.
And the phrase itself.
So it's it's really easy to write the sketches or that is bench.
So and finally for that fourth pylar.
Automatic representation.
We are using LATX.
So let's go.
So this is architecture.
So one of the shepherd part.
You can see mostly on the interaction between the robot framework.
With like gives us this easy way to to write.
That test benchmarks.
And the Lampking clerk is about more.
How we want to reproduce.
How do I want to plot.
How do I want to get the data and report it in LATX report.
So let's go to try in a use case.
So as I said before.
Let's start what we bring here Benuja.
So it's a Monte Carlo calculation framework.
So these are.
These are the configurations parameters that this bench contain.
But you can use whatever you want for your application.
So for example, number of particles and some model kinematic model.
It depends on your application.
Also you will have different topics that can use as an input for your application.
And lower post that is going to be what we contain.
So what happens in localization when you.
When you try to to get localization from a robot.
Sometimes you get like because it's a stochastically algorithm.
You can get that you are here.
Sometimes.
So you cannot run it once.
Just to know that it's work as expected.
You have to run multiple times.
That's why you need to do like.
This bench and to get different kind of test just to see how it works.
So how it works.
You have just.
Like fine.
That is this one.
And here you have.
The setup.
So we're going to run this the beginning of our test bench.
Then we're going to like run this at the end of our bench.
Or this bench.
And finally we're going to like.
This is going to be the test template itself.
So you can see that we can.
Like right really easily different.
Like by the voice.
And let's go try to the test template.
Sorry.
No.
Let's go to the by the voice.
So as you can see here we have.
We can do the test bench for two different laser models.
For the beam.
I'm from the likelihood field.
And we can also run this.
Using this specific laser models to different datasets.
So let's go try to the test bench.
So it is easy.
You are writing the different.
It's like syntax.
You are writing whatever you want.
You are.
You are telling. Okay.
Let's set this viable.
Let's use this dataset.
But so everything is dynamically.
So you can use different.
Data sets.
Different configuration parameters.
And you write the test case.
And.
Finally when you have all the tests.
You're running this multiple times.
For example, we want to run 10 times.
And finally in the.
Um, tear down what we are doing is getting the report itself.
So this is the report.
Feel free to catch.
And this is one of examples on every very value.
A release.
We release one of the report.
So it's nice.
And we are looking.
We are going to start refactoring this.
Um, report.
Because there are some of the packages.
That's been a long time.
So we're really open to.
To talk with you.
And feel free to download.
For.
Um, give stars.
And we are open to.
Like come.
Like to talk about.
And.
And let's do together.
The framework for local decision that we deserve.
Thank you.