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slam_gmapping: gmapping
Package LinksDependencies (8)
Used by (10)

Package Summary

Documented

This package contains GMapping, from OpenSlam, and a ROS wrapper. The gmapping package provides laser-based SLAM (Simultaneous Localization and Mapping), as a ROS node called slam_gmapping. Using slam_gmapping, you can create a 2-D occupancy grid map (like a building floorplan) from laser and pose data collected by a mobile robot. This package uses r39 from GMapping SVN repsitory at openslam.org, with minor patches applied to support newer versions of GCC and OSX.

slam_gmapping: gmapping
Package LinksDependencies (8)
Used by (13)

Package Summary

Documented

This package contains GMapping, from OpenSlam, and a ROS wrapper. The gmapping package provides laser-based SLAM (Simultaneous Localization and Mapping), as a ROS node called slam_gmapping. Using slam_gmapping, you can create a 2-D occupancy grid map (like a building floorplan) from laser and pose data collected by a mobile robot. This package uses r39 from GMapping SVN repsitory at openslam.org, with minor patches applied to support newer versions of GCC and OSX.

slam_gmapping: gmapping
Package LinksDependencies (8)
Used by (13)

Package Summary

Documented

This package contains GMapping, from OpenSlam, and a ROS wrapper. The gmapping package provides laser-based SLAM (Simultaneous Localization and Mapping), as a ROS node called slam_gmapping. Using slam_gmapping, you can create a 2-D occupancy grid map (like a building floorplan) from laser and pose data collected by a mobile robot. This package uses r39 from GMapping SVN repsitory at openslam.org, with minor patches applied to support newer versions of GCC and OSX.

slam_gmapping: gmapping | openslam_gmapping
Package LinksDependencies (6)
Used by (12)

Package Summary

Released Continuous integration Documented

This package contains a ROS wrapper for OpenSlam's Gmapping. The gmapping package provides laser-based SLAM (Simultaneous Localization and Mapping), as a ROS node called slam_gmapping. Using slam_gmapping, you can create a 2-D occupancy grid map (like a building floorplan) from laser and pose data collected by a mobile robot.

slam_gmapping: gmapping | openslam_gmapping
Package LinksDependencies (7)
Used by (19)
Jenkins jobs (9)

Package Summary

Released Continuous integration Documented

This package contains a ROS wrapper for OpenSlam's Gmapping. The gmapping package provides laser-based SLAM (Simultaneous Localization and Mapping), as a ROS node called slam_gmapping. Using slam_gmapping, you can create a 2-D occupancy grid map (like a building floorplan) from laser and pose data collected by a mobile robot.

slam_gmapping: gmapping | openslam_gmapping
Package LinksDependencies (7)
Used by (2)
Jenkins jobs (12)

Package Summary

Released Continuous integration Documented

This package contains a ROS wrapper for OpenSlam's Gmapping. The gmapping package provides laser-based SLAM (Simultaneous Localization and Mapping), as a ROS node called slam_gmapping. Using slam_gmapping, you can create a 2-D occupancy grid map (like a building floorplan) from laser and pose data collected by a mobile robot.

slam_gmapping: gmapping | openslam_gmapping
Package LinksDependencies (7)
Used by (5)
Jenkins jobs (13)

Package Summary

Released Continuous integration Documented

This package contains a ROS wrapper for OpenSlam's Gmapping. The gmapping package provides laser-based SLAM (Simultaneous Localization and Mapping), as a ROS node called slam_gmapping. Using slam_gmapping, you can create a 2-D occupancy grid map (like a building floorplan) from laser and pose data collected by a mobile robot.

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Contents

  1. External Documentation
  2. Hardware Requirements
  3. Example
  4. Nodes
    1. slam_gmapping
      1. ROS API
      2. tf API

External Documentation

This is mostly a third party package; the underlying GMapping library is externally documented. Look there for details on many of the parameters listed below.

Hardware Requirements

To use slam_gmapping, you need a mobile robot that provides odometry data and is equipped with a horizontally-mounted, fixed, laser range-finder. The slam_gmapping node will attempt to transform each incoming scan into the odom (odometry) tf frame. See the "tf API" for more on required transforms.

Example

To make a map from a robot with a laser publishing scans on the base_scan topic: 

rosrun gmapping slam_gmapping scan:=base_scan

Nodes

slam_gmapping

The slam_gmapping node takes in sensor_msgs/LaserScan messages and builds a map (nav_msgs/OccupancyGrid. The map can be retrieved via a ROS Topic or Service.

ROS API

Subscribed topics

tf (tf/tfMessage)

  • Transforms necessary to relate frames for laser, base, and odometry (see below)

scan (sensor_msgs/LaserScan)

  • Laser scans to create the map from

Published topics

map_metadata (nav_msgs/MapMetaData)

  • Get the map data from this topic, which is latched, and updated periodically.

map (nav_msgs/OccupancyGrid)

  • Get the map data from this topic, which is latched, and updated periodically

Services

dynamic_map (nav_msgs/GetMap)

  • Call this service to get the map data

Parameters used

~inverted_laser (default: false)

  • Is the laser right side up (scans are ordered CCW), or upside down (scans are ordered CW)?

~throttle_scans (default: 1)

  • Process 1 out of every this many scans (set it to a higher number to skip more scans)

~base_frame (default: "base_link")

  • The frame attached to the mobile base.

~map_frame (default: "map")

  • The frame attached to the map.

~odom_frame (default: "odom")

  • The frame attached to the odometry system.

~map_update_interval (default: 5.0)

  • How long (in seconds) between updates to the map. Lowering this number updates the occupancy grid more often, at the expense of greater computational load.

~maxUrange (default: 80.0)

  • The maximum usable range of the laser. A beam is cropped to this value.

~sigma (default: 0.05)

  • The sigma used by the greedy endpoint matching

~kernelSize (default: 1)

  • The kernel in which to look for a correspondence

~lstep (default: 0.05)

  • The optimization step in translation

~astep (default: 0.05)

  • The optimization step in rotation

~iterations (default: 5)

  • The number of iterations of the scanmatcher

~lsigma (default: 0.075)

  • The sigma of a beam used for likelihood computation

~ogain (default: 3.0)

  • Obstacle gain (?)

~lskip (default: 0)

  • Number of beams to skip in each scan.

~srr (default: 0.1)

  • Odometry error in translation as a function of translation (rho/rho)

~srt (default: 0.2)

  • Odometry error in translation as a function of rotation (rho/theta)

~str (default: 0.1)

  • Odometry error in rotation as a function of translation (theta/rho)

~stt (default: 0.2)

  • Odometry error in rotation as a function of rotation (theta/theta)

`~linearUpdate (default: 1.0)

  • Process a scan each time the robot translates this far

~angularUpdate (default: 0.5)

  • Process a scan each time the robot rotates this far

~resampleThreshold (default: 0.5)

  • The neff based resampling threshold (?)

~particles (default: 30)

  • Number of particles in the filter

~xmin (default: -100.0)

  • Initial map size

~ymin (default: -100.0)

  • Initial map size

~xmax (default: 100.0)

  • Initial map size

~ymax (default: 100.0)

  • Initial map size

~delta (default: 0.05)

  • Processing parameters (resolution of the map)

~llsamplerange (default: 0.01)

  • Translational sampling range for the likelihood

~llsamplestep (default: 0.01)

  • Translational sampling range for the likelihood

~lasamplerange (default: 0.005)

  • Angular sampling range for the likelihood

~lasamplestep (default: 0.005)

  • Angular sampling step for the likelihood

tf API

Required transforms

<the frame attached to incoming scans> → base_link

  • usually a fixed value, broadcast periodically by mechanism_control, or a tf/transform_sender

base_link → odom

  • usually provided by the odometry system (e.g., the driver for the mobile base)

Provided transforms

map → odom

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