LaMa ROS - Alternative Localization and Mapping for ROS.

https://github.com/iris-ua/iris_lama_ros

Developed and maintained by Eurico Pedrosa, University of Aveiro (C) 2019. Ported to ROS2 by David Simoes, University of Aveiro (C) 2020.

Overview

ROS integration of LaMa, a Localization and Mapping package from the Intelligent Robotics and Systems (IRIS) Laboratory, University of Aveiro. It provides 2D Localization and SLAM. It works great on a TurtleBot2 with a Raspberry Pi 3 Model B+ and an Hokuyo (Rapid URG). It also works on a simulated TurtleBot3.

Environment

Use Docker if you want a containerized environment. A Dockerfile is supplied. If you are on an office network that blocks Google's DNS servers, configure your DNS. It should be as simple as editing /etc/docker/daemon.json with your office's DNS server.

To run the container, a suggestion is

wget https://partner-images.canonical.com/core/bionic/current/ubuntu-bionic-core-cloudimg-amd64-root.tar.gz
sudo docker build -t "ros2-dashing:Dockerfile" .
mkdir shared_folder/
sudo docker run -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix -v $(pwd)/shared_folder:/mnt/iris_lama_ros2 -it "ros2_dashing:Dockerfile" bash
./ros_entrypoint.sh
cd /mnt/iris_lama_ros2/

This Dockerfile includes TurtleBot3 packages for easy testing and deployment. Remove the clutter if unnecessary. Check the start.sh file for some guidelines.

Build

To build LaMa ROS2, clone it from GitHub and use colcon to build.

cd /mnt/iris_lama_ros2/
mkdir -p dev_ws/src
cd dev_ws/src
git clone https://github.com/iris-ua/iris_lama
git clone https://github.com/iris-ua/iris_lama_ros
cd iris_lama_ros
git checkout dashing-devel
cd ../..
colcon build
. install/setup.bash

The build was tested in the provided Dockerfile with Ubuntu 18.04 and ROS2 Dashing.

SLAM nodes

Edit the config/live.yaml file with whatever parameters you wish to set. To create a map using Online SLAM execute

ros2 launch iris_lama_ros2 slam2d_live_launch.py

and to create a map using Particle Filter SLAM execute

ros2 launch iris_lama_ros2 pf_slam2d_live_launch.py

Both nodes will publish to expected topics such as map and tf.

Offline Mapping (rosbag)

If you want to obtain a map from a rosbag and you want to save time (a lot), you can run rosbag2 alongside iris_lama_ros. Just edit the launch/slam2d_offline_launch.py and launch/pf_slam2d_offline_launch.py files with the correct rosbag path.

ros2 launch iris_lama_ros2 slam2d_offline_launch.py

or

ros2 launch iris_lama_ros2 pf_slam2d_offline_launch.py

Parameters

• ~global_frame_id: The frame attached to the map (default: "map").
• ~odom_frame_id: The frame attached to the odometry system (default: "odometry").
• ~base_frame_id: The frame attached to the mobile base (default: "base_link").
• ~scan_topic: Laser scan topic to subscribe (default: "/scan").
• ~initial_pos_x: Initial x position (default: 0 meters).
• ~initial_pos_y: Initial y position (default: 0 meters).
• ~initial_pos_a: Initial rotation (or angle) (default: 0 rad).
• ~d_thresh: Traveled distance to accumulate before updating (default: 0.01 meters).
• ~a_thresh: Angular motion to accumulate before updating (default: 0.25 rads).
• ~l2_max: Maximum distance to use in the dynamic Euclidean distance map (default: 0.5 meters).
• ~resolution: Resolution of the grid maps (default: 0.05 meters).
• ~patch_size: Length of a patch (default: 32 cells).
• ~strategy: Scan matching optimization strategy, GaussNewton ("gm") or Levenberg Marquard ("lm") (default: "gn").
• ~max_iterations: Maximum number of interations performed by the optimizer (default: 100)
• ~use_compression: Should the maps be compressed (default: false).
• ~compression_algorithm: Compression algorithm to use, lz4 or zstd (default: "lz4").
• ~cache_size: Size of the LRU used during online data compression (default: 100).
• ~mrange: Maximum laser scan range (default: 16 meters).
• ~map_publish_period: How long between updates to the map (default: 5 seconds).

Particle Filter SLAM only: * ~d_thresh: Traveled distance to accumulate before updating (default: 0.5 meters). * ~particles: Number of particles to use (default: 30). * ~seed: RNG seed value, use 0 for a random seed from device (default: 0) * ~threads: Number of working threads, -1 means disabled and 0 will expand to the available number of cores (default: -1). * ~sigma: Measurement variance (default: 0.05). * ~lgain: Gain value for smoothing the particles likelihood (default: 3.0). * ~srr: Odometry error in rotation as a function of rotation (default: 0.1). * ~str: Odometry error in rotation as a function of translation (default: 0.2). * ~stt: Odometry error in traslation as a function of translation (default: 0.1). * ~srt: Odometry error in translation as a funciton of rotation (default: 0.1).

Localization node

This node requires the existence of the /map service to load the map. To run the localization just execute

ros2 launch iris_lama_ros2 loc2d_launch.py

Please use rviz2 to set the initial pose. Global localization is not yet implemented.

Parameters

• ~global_frame_id: The frame attached to the map (default: "map").
• ~odom_frame_id: The frame attached to the odometry system (default: "odometry").
• ~base_frame_id: The frame attached to the mobile base (default: "base_link").
• ~scan_topic: Laser scan topic to subscribe (default: "/scan").
• ~initial_pos_x: Initial x position (default: 0 meters).
• ~initial_pos_y: Initial y position (default: 0 meters).
• ~initial_pos_a: Initial rotation (or angle) (default: 0 rad).
• ~d_thresh: Traveled distance to accumulate before updating (default: 0.01 meters).
• ~a_thresh: Angular motion to accumulate before updating (default: 0.2 rads).
• ~l2_max: Maximum distance to use in the dynamic Euclidean distance map (default: 0.5 meters).
• ~strategy: Scan matching optimization strategy, GaussNewton ("gm") or Levenberg Marquard ("lm") (default: "gn").
• ~patch_size: Length of a patch (default: 32 cells).