LaMa ROS - Alternative Localization and Mapping for ROS.
To build LaMa ROS, clone it from GitHub and use catkin tools
to build.
sudo apt install python-catkin-tools
After instruction catkin tools, you can clone githubs and build with catkin tools.
mkdir src
cd src
git clone https://github.com/sparolab/iris_lama
git clone https://github.com/sparolab/iris_lama_ros
cd ..
catkin config --extend /opt/ros/melodic
catkin build
https://github.com/iris-ua/iris_lama_ros
The main code is developed and maintained by Eurico Pedrosa, University of Aveiro. We modified the base code and launch files for 2d Navigation.
To create a map using Online SLAM execute
roslaunch iris_lama_ros sparo_2d_mapping_omor1.launch
The nodes will publish to expected topics such as /map
and /tf
.
If you want to obtain a map from a rosbag and you want to save time (a lot), you can let iris_lama_ros "play" the rosbag for you.
roslaunch iris_lama_ros sparo_2d_offline_mapping.launch rosbag:=/path/your/rosbag.bag
This node requires the existence of the /static_map
service to load the map.
To run the localization just execute
roslaunch iris_lama_ros sparo_2d_localization_omor1.launch
~global_frame_id
: The frame attached to the map (default: "map").~odom_frame_id
: The frame attached to the odometry system (default: "odom").~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).~beam_step
: Number of beams to step (or skip) in each scan (default: 1).~truncate
: Truncate the laser scan range from start to "middle" (default: 0.0 meters, 0.0 means no truncation).~truncate_ray
: Truncate the laser scan range (or ray) from "middle" to end (default: 0.0 meters, 0.0 means no truncation).~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).
This node requires the existence of the /static_map
service to load the map.
To run the localization just execute
rosrun iris_lama_ros loc2d_ros scan:=base_scan
Please use rviz
to set the initial pose. Global localization is not yet implemented.
/request_nomotion_update
: Called to trigger an update without moving the robot (no-motion update)/global_localization
: Called to trigger a global localization procedure.
~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").~mrange
: Maximum laser scan range. When 0, maximum range defaults to the sensor maximum range. (default: 0 meters).~beam_step
: Number of beams to step (or skip) in each scan (default: 1).~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).~use_map_topic
: True to subscribe to the/map
topic instead of requesting the map through the "static_map
" service (default:false
).~first_map_only
: True to use only the first map ever received (default:false
).~use_pose_on_new_map
: True to use the current algorithm pose when the map changes (default:false
).~force_update_on_initial_pose
: True to trigger a no-motion update when an initial pose is received (default:false
)~gloc_particles
: Number of particles used to find the best global localization (default: 3000)~gloc_thresh
: Value at which a global localization particle is considered viable. (default: 0.15 RMSE)~gloc_iters
: Maximum number of iterations executed by the global localization procedure (default: 20)
The base code is developed by IRIS Lab. 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).