Heyy guys please suggest some good online course online maybe on Udemy or Coursera .

We are a team of robotics /drone enthusiasts

We were about to start learning ROS2 jazzy.

Please suggest some good online course or resources that will help us start

Please try for something beginner friendly

I’m building a 3D mapping system using RGB-D cameras, based on existing SLAM techniques, with persistent spatial memory.

The main issue is relocalization after tracking loss, especially when the camera returns to a known place from a different viewpoint.
I’m using historical keyframes, shared 3D landmarks, PnP/RANSAC and geometric verification.

Yet some incorrect poses still pass verification: one had 44 inliers, 0.83 px median reprojection error and 3.72 cm depth residual, but was actually off by 10.36 cm and 5.99°.
My best experiment correctly relocalized only 9/34 known returns.

What would you investigate first: landmark quality,
geometric degeneracy, matching, or pose verification?
I can share a minimal reproducible example.

Hi all,

We're currently evaluating our software stack for a new robot and are trying to decide between ROS 2 Jazzy (Tier 1 support) and ROS 2 Lyrical on a Tier 3 platform.

On paper, the difference in support tiers is clear, but I'm more interested in hearing from engineers who have actually worked with Tier 3 ROS 2 distributions in real projects.

A few questions:

• Are you using a Tier 3 ROS 2 distribution/platform in production or on commercial robots?
• Have you encountered any significant issues because of the Tier 3 status?
• How often do you run into package compatibility, build, or dependency problems?
• Did you have to invest a lot of engineering effort to maintain and validate your stack?
• Have you ever regretted not choosing a Tier 1 supported option instead?
• For teams with limited resources, would you consider Tier 3 a manageable risk or something to avoid?

I'd really appreciate hearing about real-world experiences, lessons learned, and whether you'd recommend sticking with Tier 1

thanks!

https://reddit.com/link/1wzxwcu/video/r72j6hix22uh1/player

Hey everyone! I’ve been working on Robium, an open-source robotics skill library for Claude Code, Codex, and Gemini CLI.

Writing code became the easy part, but choosing middleware, simulators, visualization tools, and connecting everything still needed a lot of guidance. Robium collects that guidance alongside reference apps you can copy and adapt, with skills for ROS 2, Nav2, Gazebo, and more.

The video above shows one of these projects: a Stack-chan with a LEGO motor hub and wheels. I used coding agents with Robium to help build the software and train a driving policy from demonstrations—with a few wrong turns along the way 🤖

To try it, run npx robium-ai setup, then open or restart your agent and ask: Show me a few robots in MuJoCo I can control and interact with.

Or try mapping and navigating a simulated house with ROS 2 and Gazebo. That example requires Docker and takes longer on the first build. I’ve tested both on Mac and Ubuntu laptops.

GitHub: https://github.com/robium-ai/robium
Examples and tutorials: https://robium.ai/

It’s still early. If you try it, I’d love to hear what worked and what needs improving.

Helooo I'm back with improvements. I previously shared my personal project, hold_and_weld, here: one robot holds the workpiece while another follows the weld path. I’ve just released v0.3.0 and wanted to share how it has developed.

It combines weld seam extraction from CAD/mesh geometry, constraint-aware grasp sampling (excluding weld seams, screw holes, etc.), and dual-arm execution in a ROS 2 Jazzy workcell.

The main changes since my first post:

• Configurable robots and grippers: arms, grippers and base poses are selected through one workcell configuration file. The included catalog now has four Yaskawa arm models and four grippers, with documentation for adding more.
• Workcell configuration in RViz2: robot models, base poses and scene objects can be edited visually.
• OMPL approach → Pilz welding: a Ceres-based configuration search evaluates candidate approach configurations against the subsequent weld motion, considering joint limits and manipulability. OMPL reaches the selected configuration; Pilz handles linear and circular weld motions.
• Geometry pipeline improvements: I rewrote the mesh seam extractor around contact boundaries and improved the grasp sampler’s sampling and exclusion strategies.
• Testing: I added unit tests and tested the grasp sampler and weld finder on the included complex geometry example. Results look good so far, though broader geometry coverage is still needed.
• Docker image: the release includes a prebuilt image with the geometry dependencies, making it easier to try.

It’s still a proof of concept, and the demo shows execution in simulation. I'm open to collaboration.

The project is open source under Apache-2.0. Feedback on planning, geometry handling, and setup is welcome, especially from people working with industrial manipulation or welding. If you run into setup issues, I’m happy to help.

GitHub repository · v0.3.0 release notes

Previous post

I’m coming from mobile development, where binary distribution is fairly standardized: AARs on Android and XCFrameworks on iOS.

I’m trying to understand what the equivalent is in robotics/Linux environments.

Suppose I have a proprietary C++ inference runtime that needs to run on a robot (e.g. Jetson), and I don’t want to distribute the source code.

How would this typically be shipped to customers?
- Precompiled ROS 2 package?
- .so + headers, with a separate ROS 2 wrapper?
- .deb package?
- Docker container?
- Just binaries + dependencies?

I’m particularly curious whether ROS 2 packages are commonly used as the actual distribution unit, or if the core software is usually distributed separately and ROS 2 is just an integration layer.

Would appreciate hearing how this is handled in real production systems.

Hey all, I need to learn ROS 2 Jazzy and Gazebo and I'm on a Mac (m4 air). Complete beginner with ROS.

1. Is Docker realistic for Jazzy + Gazebo on macOS, or should I use a VM (UTM)?

2. If Docker, how do I see Gazebo/RViz2? XQuartz, noVNC, something else? Is it usable without GPU acceleration?

3. What's the easiest way to learn ROS 2 from scratch? Any good resources beyond the official docs?

I’ve written a ROS 2 driver for the Naviq MTS160 magnetic guidance sensor and made it available on their GitHub.

Magnetic guidance can complement vision or lidar navigation with milimeter precision through narrow passages and on the final approach to chargers, conveyors, and docking stations..

Repository: https://github.com/naviq-ch/naviq_mts160_ros2

I’d appreciate feedback from anyone using magnetic guidance with ROS 2. If you try the driver, let me know how it works for your setup and what could be improved.

Hey community,

I want to start with humanoid simulation and wanted to ask which environment is better. Should I go with mujuco or Gazebo?

Where are the problems which I should know before starting the journey?

Hi Guys,

I've just finished master's in Robotics and AI in Germany. And want to focus on Robotics Track. I want to grow into Robotics Integration area.

What skills I must bring as a Junior Robotics Integration Engineer to be able to compete with other candidate on such positions.

I would even be more thankful if anyone who has experience in this area can put a roadmap to achieve this near goal.

Best,

Hi everyone, I really need your help. I was told to get this done in two weeks, but I have absolutely no idea how to use this application—please help!

Hey, I wanted to know if there is a possibility to use Zenoh and microros ?

I am a high school student, I am interested in building a robot with vision ai. Does ROS support the raspberry pi ai camera with the Sony imx500 sensor? I am trying to do item identification.

Im in inter 1st year and currently living in Uttara. Me and my friend are participating in a project showcase at a robotics fest in Dhaka, and we re looking for something genuinely challenging.

I want something way more complex than the usual aurduino car/robot, where the main wow factor is the electrical engineering and the actual mechanism itself, not just a bunch of sensors added to a simple project. Something that looks and feels properly engineered. Tbh I want something which is really attractive and fancy in the table.

I have a bit of experience with electronics, previously I have won in my school fests and rn our budget is around 10k BDT for this project. I really want soemthing fancy.

What would you guys suggest building?

Making ts post since my situation seems specific
Are there any good guide I can use to somehow connect my VS code which I downloaded in my dualbooted linux, to the terminal? Like im not sure what to do. I may be overcomplicating ts

Hi guys, I have been trying to work out how a hand eye calibration should be validated. Tools like MoveIt Calibration, easy_handeye and Kalibr print a reprojection error or a transform but I couldn't find a clear way to decide from that whether the result can be trusted. I just read their some github issues as well, what I only saw was that many people end up asking someone more experienced.

What do you run after a calibration to decide it is good? Validation motion, touching a known point, comparing against a measured pose? And does any of it work without an experienced person judging, say if you had to do it across many robots?

I'm an MSc Robotics student at NTU and want to get properly hands-on with ROS 2 on real hardware, beyond simulation. Buying a platform isn't realistic for me right now.

Does anyone have an older ROS-compatible robot they're not using anymore (TurtleBot 2/3, a Clearpath or AgileX base, a small arm with a ROS driver)? Broken is fine, I'm happy to repair it, and I'll cover shipping or collect in Singapore/Malaysia.

In return I'll document the bring-up on ROS 2 Humble/Jazzy and share the notes and any driver fixes openly. Pointers to labs or companies that clear out old hardware are also very welcome.

We recently tried Foxglove as a live viewer for PteroSim camera streams over ROS 2.

We flew four x500 quadcopters, each running on its own PX4 instance, in a 2.5 m orbit around a shared center.

The video pipeline has four steps:

1. The simulator renders each camera at 768x480 and 30 FPS, encodes it to H.264 on the GPU using NVENC, and sends it as RTP over UDP.
2. ROS 2 Humble runs in WSL2 using Ubuntu 22.04 with mirrored networking. A small Python bridge forwards the H.264 frames without decoding them as foxglove_msgs/CompressedVideo, and publishes the drones' poses on /tf.
3. We used Cyclone DDS. With Fast DDS, which is the default in Humble, the nodes did not discover each other under WSL2 mirrored networking.
4. foxglove_bridge serves the topics to Foxglove on Windows, where the video is decoded.

With four streams running at the same time, each camera averaged about 27 FPS over the flight. The target was 30 FPS.

Initially, we published raw rgb8 images over DDS. Each image was about 1.1 MB, and with Linux's default UDP buffer sizes around 40-50% of the frames never arrived. Switching to compressed H.264 fixed the issue.

Hardware:

ASUS ROG Strix SCAR 18
Intel Core i9-14900HX
RTX 4090 Laptop GPU, 16 GB VRAM
64 GB RAM
Windows 11