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Adaptive Monte Carlo Localization (AMCL) in 3D.
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Build Status License


This is a package is a "Adaptive Monte-Carlo Localization in 3D".

It is a particle filter that estimates the localization of a robot moving in a 3D environment without using GPS.

It takes information from an odometry source, point-clouds from an onboard sensor (e.g. laser) and distance measurements from radio-range sensors.


Apache 2.0

Author: Paloma Carrasco Fernández (, Francisco Cuesta Rodríguez (, Francisco J.Perez-Grau (

Affiliation: FADA-CATEC

Maintainer: Paloma Carrasco Fernández (, Francisco Cuesta Rodríguez (

The amcl3d package has been tested under [ROS] Kinetic and Ubuntu 16.04.


If you want more information about the algorithm or use this work in your project, please check and cite the following publication:

Detailed Description

To know in more detail the behavior of the package:


Building from Source


To build from source, clone the latest version from this repository into your catkin workspace and compile the package using

 cd catkin_workspace/src
 git clone
 cd ../
 catkin build


Run the test with

 roslaunch ouster_ros os1.launch os1_hostname:= replay:=true
 roslaunch amcl3d amcl3d_test.launch

Launch files

  • amcl3d.launch: it contains the start of amcl3d node with a standard configuration of parameters.

        roslaunch amcl3d amcl3d.launch
  • amcl3d_test.launch: this roslaunch allows you to start the RViz with the aforementioned configuration, the amcl3d node, the test-amcl3d node, the bag player and creates a transformation to relate the point-cloud frame of test-amcl3d node with the robot frame of amcl3d node.

        roslaunch amcl3d amcl3d_test.launch

Bugs & Feature Requests

Please report bugs and request features using the Issue Tracker.



Supported by ROSIN - ROS-Industrial Focused Technical Projects (FTP).
More information:

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