ROS Resources: Documentation | Support | Discussion Forum | Service Status | Q&A answers.ros.org

Announcing LaMa: An alternative localization and mapping package

Dear ROS users,

We would like to announce the release of the IRIS LaMa (Localization and Mapping) package.
It includes a framework for 3D volumetric grids (for mapping), a localization algorithm based on scan matching and two SLAM solution (an Online SLAM and a Particle Filter SLAM).

The main feature is efficiency. You can even run the Particle Filter SLAM in a Raspberry Pi.

We provide ROS integration with the iris_lama_ros package.

Fell free to try it and provide any feedback.

5 Likes

Very nice. I’ll definitely take the localization out for a spin

You should also give SLAM a chance :slight_smile:

Looking forward to test it!

Hi @eupedrosa,
Congratulations on the release!
I am wondering if you have found any differences comparing IRIS LaMa localization to amcl implementation that’s already in ROS. Same for mapping, with comparison to popular ones out there.
I am really curious to know.

1 Like

Yes, I did compared my solutions with popular ones found in ROS. In the README file you can find a few papers where I compare LaMa’s algorithms with solutions such as AMCL and GMapping. But here are my selling points:

  • LaMa Localization vs AMCL: In general both provide good accuracy but (by default) AMCL does not use all data to compensate for particle filter’s overhead and that can result in some errors. Scan Matching can be 5x times faster or more. I still use AMCL in applications where information is reduced and noisy.

  • LaMa SLAM vs GMapping: I think that GMapping is a wonderful piece of technology but very slow. I remember, back in early 2012, using GMapping online was difficult. LaMa PF SLAM is kinda like a fast GMapping or faster GMapping if you activate multi-threading. LaMa Online SLAM is the turbo version, it can generate the Intel map in 5seconds. Here is the result:

  • LaMa SLAM vs Others: I used the slam benchmark to compare with other SLAM solutions and we did good :slight_smile:. I believe that Cartographer also used the same benchmark.

  • LaMa Sparse-Dense Mapping (SDM) vs OctoMap: OctoMap is another top reference in robotics. I only developed SDM because OctoMap’s main focus is occupancy grids and I needed more flexibility. The inner structure of SDM is model agnostic and provides the same features for any type of grid map. Those features include Copy-on-Write and Online Data Compression.

3 Likes

Thanks! BTW that map looks awesome!