In the few spare hours I’ve managed to carve out lately, I started working on a nice little project that I’m quite excited about: using cheap 2D lidars to calculate robot odometry.
The project is called YALIO, which stands for Yet Another Lidar ICP Odometry, and it’s available on GitHub at Myzhar/yalio-ros2.
To be honest, this is a project that has been on my mind for a long time: I could never find anything simple online that performs only odometry estimation using 2D lidar information.
There’s also a bit of personal history behind it. I worked with ICP algorithms back in 2005, right after getting my laurea degree, and I’ve always wanted to study how these algorithms evolved over the 21 years since then. After digging into the literature, I decided to go with Point-to-Line ICP (PL-ICP) because it seemed to be the most promising evolution of the original algorithm.
The idea is simple: low-cost 2D lidars like the LDRobot LD19 and LD06 are everywhere now, and the scan data they produce is good enough to estimate the motion of a robot by matching consecutive scans. YALIO does exactly that, using a PL-ICP scan matcher wrapped in a managed (lifecycle) ROS 2 component that loads into a shared container. The core algorithm lives in a pure C++ library with no ROS dependency, so it can be reused outside of ROS 2 as well.
If you’ve followed my recent ROS 2 Lifecycle Nodes tutorial, YALIO is also a real-world application of those concepts: the odometry node is a nav2_util::LifecycleNode composable component, optionally managed by the Nav2 lifecycle_manager.
⚠️ Keep in mind that the project is a work in progress. In the next few weeks, once it has been validated and tested in different conditions, I will add a new page to the ROS 2 Projects section to describe it in detail.
In the meantime, I’ve already added a new branch to the LD Lidar ROS 2 Driver repository, add_yalio_odometry, which uses YALIO as the odometry source in a new demo SLAM launch file, replacing the fake odometry node used in the original launch file. A real odometry estimate instead of a static placeholder makes the SLAM demo much more meaningful.
Stay tuned for updates, and feel free to try it out and open issues on GitHub if you find problems!
Happy robotics programming! 🤖
