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navigation: amcl | base_local_planner | carrot_planner | clear_costmap_recovery | costmap_2d | dwa_local_planner | fake_localization | map_server | move_base | move_base_msgs | move_slow_and_clear | nav_core | navfn | robot_pose_ekf | rotate_recovery | voxel_grid

Package Summary

amcl is a probabilistic localization system for a robot moving in 2D. It implements the adaptive (or KLD-sampling) Monte Carlo localization approach (as described by Dieter Fox), which uses a particle filter to track the pose of a robot against a known map.

This node is derived, with thanks, from Andrew Howard's excellent 'amcl' Player driver.

navigation: amcl | base_local_planner | carrot_planner | clear_costmap_recovery | costmap_2d | dwa_local_planner | fake_localization | map_server | move_base | move_base_msgs | move_slow_and_clear | nav_core | navfn | robot_pose_ekf | rotate_recovery | voxel_grid

Package Summary

amcl is a probabilistic localization system for a robot moving in 2D. It implements the adaptive (or KLD-sampling) Monte Carlo localization approach (as described by Dieter Fox), which uses a particle filter to track the pose of a robot against a known map.

This node is derived, with thanks, from Andrew Howard's excellent 'amcl' Player driver.

navigation: amcl | base_local_planner | carrot_planner | clear_costmap_recovery | costmap_2d | dwa_local_planner | fake_localization | map_server | move_base | move_base_msgs | move_slow_and_clear | nav_core | navfn | robot_pose_ekf | rotate_recovery | voxel_grid

Package Summary

amcl is a probabilistic localization system for a robot moving in 2D. It implements the adaptive (or KLD-sampling) Monte Carlo localization approach (as described by Dieter Fox), which uses a particle filter to track the pose of a robot against a known map.

This node is derived, with thanks, from Andrew Howard's excellent 'amcl' Player driver.

navigation: amcl | base_local_planner | carrot_planner | clear_costmap_recovery | costmap_2d | dwa_local_planner | fake_localization | map_server | move_base | move_base_msgs | move_slow_and_clear | nav_core | navfn | robot_pose_ekf | rotate_recovery | voxel_grid

Package Summary

amcl is a probabilistic localization system for a robot moving in 2D. It implements the adaptive (or KLD-sampling) Monte Carlo localization approach (as described by Dieter Fox), which uses a particle filter to track the pose of a robot against a known map.

This node is derived, with thanks, from Andrew Howard's excellent 'amcl' Player driver.

navigation: amcl | base_local_planner | carrot_planner | clear_costmap_recovery | costmap_2d | dwa_local_planner | fake_localization | global_planner | map_server | move_base | move_base_msgs | move_slow_and_clear | nav_core | navfn | robot_pose_ekf | rotate_recovery | voxel_grid

Package Summary

amcl is a probabilistic localization system for a robot moving in 2D. It implements the adaptive (or KLD-sampling) Monte Carlo localization approach (as described by Dieter Fox), which uses a particle filter to track the pose of a robot against a known map.

This node is derived, with thanks, from Andrew Howard's excellent 'amcl' Player driver.

  • Maintainer status: maintained
  • Maintainer: David V. Lu!! <davidvlu AT gmail DOT com>, Michael Ferguson <mferguson AT fetchrobotics DOT com>
  • Author: Brian P. Gerkey, contradict@gmail.com
  • License: LGPL
  • Source: git https://github.com/ros-planning/navigation.git (branch: hydro-devel)
navigation: amcl | base_local_planner | carrot_planner | clear_costmap_recovery | costmap_2d | dwa_local_planner | fake_localization | global_planner | map_server | move_base | move_base_msgs | move_slow_and_clear | nav_core | navfn | robot_pose_ekf | rotate_recovery | voxel_grid

Package Summary

amcl is a probabilistic localization system for a robot moving in 2D. It implements the adaptive (or KLD-sampling) Monte Carlo localization approach (as described by Dieter Fox), which uses a particle filter to track the pose of a robot against a known map.

This node is derived, with thanks, from Andrew Howard's excellent 'amcl' Player driver.

  • Maintainer status: maintained
  • Maintainer: David V. Lu!! <davidvlu AT gmail DOT com>, Michael Ferguson <mfergs7 AT gmail DOT com>, Aaron Hoy <ahoy AT fetchrobotics DOT com>
  • Author: Brian P. Gerkey, contradict@gmail.com
  • License: LGPL
  • Source: git https://github.com/ros-planning/navigation.git (branch: indigo-devel)
navigation: amcl | base_local_planner | carrot_planner | clear_costmap_recovery | costmap_2d | dwa_local_planner | fake_localization | global_planner | map_server | move_base | move_base_msgs | move_slow_and_clear | nav_core | navfn | robot_pose_ekf | rotate_recovery | voxel_grid

Package Summary

amcl is a probabilistic localization system for a robot moving in 2D. It implements the adaptive (or KLD-sampling) Monte Carlo localization approach (as described by Dieter Fox), which uses a particle filter to track the pose of a robot against a known map.

This node is derived, with thanks, from Andrew Howard's excellent 'amcl' Player driver.

  • Maintainer status: maintained
  • Maintainer: David V. Lu!! <davidvlu AT gmail DOT com>, Michael Ferguson <mferguson AT fetchrobotics DOT com>
  • Author: Brian P. Gerkey, contradict@gmail.com
  • License: LGPL
  • Source: git https://github.com/ros-planning/navigation.git (branch: jade-devel)
navigation: amcl | base_local_planner | carrot_planner | clear_costmap_recovery | costmap_2d | dwa_local_planner | fake_localization | global_planner | map_server | move_base | move_base_msgs | move_slow_and_clear | nav_core | navfn | robot_pose_ekf | rotate_recovery | voxel_grid

Package Summary

amcl is a probabilistic localization system for a robot moving in 2D. It implements the adaptive (or KLD-sampling) Monte Carlo localization approach (as described by Dieter Fox), which uses a particle filter to track the pose of a robot against a known map.

This node is derived, with thanks, from Andrew Howard's excellent 'amcl' Player driver.

  • Maintainer status: maintained
  • Maintainer: David V. Lu!! <davidvlu AT gmail DOT com>, Michael Ferguson <mfergs7 AT gmail DOT com>, Aaron Hoy <ahoy AT fetchrobotics DOT com>
  • Author: Brian P. Gerkey, contradict@gmail.com
  • License: LGPL
  • Source: git https://github.com/ros-planning/navigation.git (branch: kinetic-devel)
navigation: amcl | base_local_planner | carrot_planner | clear_costmap_recovery | costmap_2d | dwa_local_planner | fake_localization | global_planner | map_server | move_base | move_base_msgs | move_slow_and_clear | nav_core | navfn | robot_pose_ekf | rotate_recovery | voxel_grid

Package Summary

amcl is a probabilistic localization system for a robot moving in 2D. It implements the adaptive (or KLD-sampling) Monte Carlo localization approach (as described by Dieter Fox), which uses a particle filter to track the pose of a robot against a known map.

This node is derived, with thanks, from Andrew Howard's excellent 'amcl' Player driver.

  • Maintainer status: maintained
  • Maintainer: David V. Lu!! <davidvlu AT gmail DOT com>, Michael Ferguson <mfergs7 AT gmail DOT com>, Aaron Hoy <ahoy AT fetchrobotics DOT com>
  • Author: Brian P. Gerkey, contradict@gmail.com
  • License: LGPL
  • Source: git https://github.com/ros-planning/navigation.git (branch: lunar)
navigation: amcl | base_local_planner | carrot_planner | clear_costmap_recovery | costmap_2d | dwa_local_planner | fake_localization | global_planner | map_server | move_base | move_base_msgs | move_slow_and_clear | nav_core | navfn | rotate_recovery | voxel_grid

Package Summary

amcl is a probabilistic localization system for a robot moving in 2D. It implements the adaptive (or KLD-sampling) Monte Carlo localization approach (as described by Dieter Fox), which uses a particle filter to track the pose of a robot against a known map.

This node is derived, with thanks, from Andrew Howard's excellent 'amcl' Player driver.

  • Maintainer status: maintained
  • Maintainer: David V. Lu!! <davidvlu AT gmail DOT com>, Michael Ferguson <mfergs7 AT gmail DOT com>, Aaron Hoy <ahoy AT fetchrobotics DOT com>
  • Author: Brian P. Gerkey, contradict@gmail.com
  • License: LGPL
  • Source: git https://github.com/ros-planning/navigation.git (branch: melodic-devel)

Algorithms

Many of the algorithms and their parameters are well-described in the book Probabilistic Robotics, by Thrun, Burgard, and Fox. The user is advised to check there for more detail. In particular, we use the following algorithms from that book: sample_motion_model_odometry, beam_range_finder_model, likelihood_field_range_finder_model, Augmented_MCL, and KLD_Sampling_MCL.

As currently implemented, this node works only with laser scans and laser maps. It could be extended to work with other sensor data.

Example

To localize using laser data on the base_scan topic:

amcl scan:=base_scan

Nodes

amcl

amcl takes in a laser-based map, laser scans, and transform messages, and outputs pose estimates. On startup, amcl initializes its particle filter according to the parameters provided. Note that, because of the defaults, if no parameters are set, the initial filter state will be a moderately sized particle cloud centered about (0,0,0).

Subscribed Topics

scan (sensor_msgs/LaserScan) tf (tf/tfMessage) initialpose (geometry_msgs/PoseWithCovarianceStamped) map (nav_msgs/OccupancyGrid)

Published Topics

amcl_pose (geometry_msgs/PoseWithCovarianceStamped) particlecloud (geometry_msgs/PoseArray) tf (tf/tfMessage)

Services

global_localization (std_srvs/Empty) request_nomotion_update (std_srvs/Empty) set_map (nav_msgs/SetMap)

Services Called

static_map (nav_msgs/GetMap)

Parameters

There are three categories of ROS Parameters that can be used to configure the amcl node: overall filter, laser model, and odometery model.

Overall filter parameters

~min_particles (int, default: 100)

~max_particles (int, default: 5000) ~kld_err (double, default: 0.01) ~kld_z (double, default: 0.99) ~update_min_d (double, default: 0.2 meters) ~update_min_a (double, default: π/6.0 radians) ~resample_interval (int, default: 2) ~transform_tolerance (double, default: 0.1 seconds) ~recovery_alpha_slow (double, default: 0.0 (disabled)) ~recovery_alpha_fast (double, default: 0.0 (disabled)) ~initial_pose_x (double, default: 0.0 meters) ~initial_pose_y (double, default: 0.0 meters) ~initial_pose_a (double, default: 0.0 radians) ~initial_cov_xx (double, default: 0.5*0.5 meters) ~initial_cov_yy (double, default: 0.5*0.5 meters) ~initial_cov_aa (double, default: (π/12)*(π/12) radian) ~gui_publish_rate (double, default: -1.0 Hz) ~save_pose_rate (double, default: 0.5 Hz) ~use_map_topic (bool, default: false) ~first_map_only (bool, default: false)

Laser model parameters

Note that whichever mixture weights are in use should sum to 1. The beam model uses all 4: z_hit, z_short, z_max, and z_rand. The likelihood_field model uses only 2: z_hit and z_rand.

~laser_min_range (double, default: -1.0)

~laser_max_range (double, default: -1.0) ~laser_max_beams (int, default: 30) ~laser_z_hit (double, default: 0.95) ~laser_z_short (double, default: 0.1) ~laser_z_max (double, default: 0.05) ~laser_z_rand (double, default: 0.05) ~laser_sigma_hit (double, default: 0.2 meters) ~laser_lambda_short (double, default: 0.1) ~laser_likelihood_max_dist (double, default: 2.0 meters) ~laser_model_type (string, default: "likelihood_field")

Odometry model parameters

If ~odom_model_type is "diff" then we use the sample_motion_model_odometry algorithm from Probabilistic Robotics, p136; this model uses the noise parameters odom_alpha1 through odom_alpha4, as defined in the book.

If ~odom_model_type is "omni" then we use a custom model for an omni-directional base, which uses odom_alpha1 through odom_alpha5. The meaning of the first four parameters is similar to that for the "diff" model. The fifth parameter capture the tendency of the robot to translate (without rotating) perpendicular to the observed direction of travel.

A bug was found and fixed. But fixing the old models would have changed or broken the localisation of already tuned robot systems, so the new fixed odometry models were added as new types "diff-corrected" and "omni-corrected". The default settings of the odom_alpha parameters only fit the old models, for the new model these values probably need to be a lot smaller, see http://answers.ros.org/question/227811/tuning-amcls-diff-corrected-and-omni-corrected-odom-models/.

Also, another bug was found but only fixed after Navigation 1.16, while the current release for Kinetic is Navigation 1.14.1. This bug only affects robot with type "omni" and "omni-corrected", where odom_alpha1 and odom_alpha4 are actually reversed. I.e. odom_alpha1 is for the translation odometry noise from robot translation-al motion, and odom_alpha4 represents the odometry rotation noise from robot's rotation motion.

~odom_model_type (string, default: "diff")

~odom_alpha1 (double, default: 0.2) ~odom_alpha2 (double, default: 0.2) ~odom_alpha3 (double, default: 0.2) ~odom_alpha4 (double, default: 0.2) ~odom_alpha5 (double, default: 0.2) ~odom_frame_id (string, default: "odom") ~base_frame_id (string, default: "base_link") ~global_frame_id (string, default: "map") ~tf_broadcast (bool, default: true)

Transforms

amcl transforms incoming laser scans to the odometry frame (~odom_frame_id). So there must exist a path through the tf tree from the frame in which the laser scans are published to the odometry frame.

An implementation detail: on receipt of the first laser scan, amcl looks up the transform between the laser's frame and the base frame (~base_frame_id), and latches it forever. So amcl cannot handle a laser that moves with respect to the base.

The drawing below shows the difference between localization using odometry and amcl. During operation amcl estimates the transformation of the base frame (~base_frame_id) in respect to the global frame (~global_frame_id) but it only publishes the transform between the global frame and the odometry frame (~odom_frame_id). Essentially, this transform accounts for the drift that occurs using Dead Reckoning. The published transforms are future dated.

amcl_localization.png


2019-12-07 12:25