Only released in EOL distros:   fuerte groovy hydro

Overview

One of the main problems with point-cloud registration solutions is that their efficiency often depends on the application. This package provides a convenient way to tune, test and deploy several registration solutions using the same node. It provides a real-time tracker and mapper in 2D and 3D, based on the libpointmachter and libnabo libraries. This allows a complete configuration of the registration chain through YAML files, see the chain configuration page for a list of modules and their parameters. You can think of this package as the front end of a SLAM system, including everything but loop-closure detection and relaxation.

For installation instructions, see the ethzasl_icp_mapping stack page. A preliminary version of this package was deployed in the ROS kinect contest.

Citing

Two papers talk about our open-source ICP library. The first one introduces the library and proposes a sound evaluation methodology using it. It was published in Autonomous Robots and can be found here (full text):

@ARTICLE{pomerleau13comparing,
   Author = {F. Pomerleau and F. Colas and R. Siegwart and S. Magnenat},
   Title = {Comparing ICP variants on real-world data sets},
   Journal = {Autonomous Robots},
   Volume = {34},
   Number = {3},
   Month = {April},
   Year = {2013},
   Pages = {133--148}
}

In addition, a paper using a preliminary version of this package to evaluate a Kinect tracker was presented at IROS 2011 and can be found here (fulltext):

@INPROCEEDINGS{pomerleau11tracking,
    author = {François Pomerleau and Stéphane Magnenat and Francis Colas and Ming Liu and Roland Siegwart},
    title = {Tracking a Depth Camera: Parameter Exploration for Fast ICP},
    booktitle = {Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
    publisher = {IEEE Press},
    pages = {3824--3829},
    year = {2011}
}

Quick Start

Since the proposed ICP chain is highly modular, it has many parameters. You can play with it directly if you have an openni-compatible sensor:

roscd ethzasl_icp_mapper 
roslaunch launch/openni/tracker_viewer.launch

You can then have a look into the launch/openni/ files to see how the openni tracker is configured. This file roughly corresponds to the following chain:

Starting from this chain, you can develop your own chain.

Nodes

mapper

This node performs continuous registration of point clouds and publishes the resulting pose using tf and through nav_msgs/Odometry messages. The type of topic messages must be sensor_msgs/LaserScan (2D) or sensor_msgs/PointCloud2 (2D or 3D). This effectively implements a real-time tracker in 2D or 3D.

Subscribed Topics

/scan (sensor_msgs/LaserScan)

• If parameter ~subscribe_scan is true, the topic on which laser scans (2D) are received.

/cloud_in (sensor_msgs/PointCloud2)

• If parameter ~subscribe_cloud is true, the topic on which point clouds (2D or 3D) are received.

Published Topics

/point_map (sensor_msgs/PointCloud2)

• Resulting map (2D or 3D depending on incoming data)

/icp_odom (nav_msgs/Odometry)

• sensor to map frame odometry message

/icp_error_odom (nav_msgs/Odometry)

• odom to map frame odometry message

Services

dynamic_point_map (map_msgs/GetPointMap)

• Return the built map

~save_map (map_msgs/SaveMap)

• Save the built map to the disk

~load_map (ethzasl_icp_mapper/LoadMap)

• Load a map from the disk, reset odom-to-map transform to identity

~reset (std_srvs/Empty)

• Clear the map and reset odom-to-map transform to identity

~correct_pose (ethzasl_icp_mapper/CorrectPose)

• Set the odom-to-map transform

~set_mode (ethzasl_icp_mapper/SetMode)

• Set the localizing/mapping mode

~get_mode (ethzasl_icp_mapper/GetMode)

• Get the current localizing/mapping mode

Required tf Transforms

sensor frame → /odom

• sensor to odom frame transform, input from odometry

Provided tf Transforms

/odom → /map

• odom to map frame transform, result of ICP

Parameters

~icpConfig (string, no default)

• Name of the YAML file configuring the modular ICP chain, see Configuration section.

~useROSLogger (bool, default: false)

• If true, use ROS console for logging, otherwise use logger defined in config file.

~cloudTopic (string, default: "/camera/rgb/points")

• The node listens to the topic whose name is defined by this parameter and expects a sensor_msgs/PointCloud2.

~deltaPoseTopic (string, default: "/openni_delta_pose")

• Topic on which to send delta pose.

~fixedFrame (string, default: "/world")

• Frame name used as fixed frame in the tf published by the node.

~sensorFrame (string, default: "/openni_rgb_optical_frame")

• Frame name used as moving frame in the tf published by the node.

~startupDropCount (unsigned, default: 0)

• Number of clouds to drop on startup, useful if sensor is producing garbage in a startup phase.

Node: matcher_service

This node provides a service to match two point clouds.

Services offered

match_clouds (ethzasl_icp_mapper/MatchClouds.srv - custom messages in the package)

• This service takes two sensor_msgs/PointCloud2 messages as inputs (the first is the reference and the second the reading) and provides a geometry_msgs/Transform message as output. The resulting transformation can be used to move the reading point cloud.

Parameters

As with the mapper node, the chain is configured through a YAML file.

~config (string, no default)

• Name of the YAML file configuring the modular ICP chain, see ICP chain configuration file.

~useROSLogger (bool, default: false)

• If true, use ROS console for logging, otherwise use logger defined in config file.

Node: occupancy_grid_builder

This node builds a 2D nav_msgs/OccupancyMap from sensor_msgs/LaserScan. It is yet to be fully documented.

Limitations

This library solely performs point-cloud registration, it does not incorporate data fusion based on IMU or odometry. This can be done through other nodes such as robot_pose_ekf.

Known bugs

The version released in ROS does not work in 2D, please use the latest git master version for this use case.

Report a Bug

Please report bugs and request features using the github page.