SegMap is a map representation based on 3D segments allowing for robot localization, environment reconstruction, and semantics extraction. The SegMap code is open-source (BSD License) and has been tested under Ubuntu 14.04 and ROS Indigo.
Note 20.4.2018: This repository has been renamed SegMap, to reflect the latest development presented in our paper "SegMap: 3D Segment Mapping using Data-Driven Descriptors". For clarity: in the instructions below, the name SegMatch will still be used to refer the the first iteration of the pipeline presented in the original SegMatch paper. The updated documentation and tutorials are coming soon!
Please consult our paper, video and wiki for the algorithm description and for instructions on running demonstrations. We recently uploaded a second video featuring SegMatch in a multi-robot configuration.
The following configuration was tested under Ubuntu 14.04 and ROS indigo. Please see the final note if you want to compile under ROS Kinetic.
First install the required system packages:
$ sudo apt-get install libopencv-dev python-wstool doxygen
SegMatch can be built using catkin_tools which can be installed from this link.
Then use wstool for installing catkin dependencies:
$ cd ~/catkin_ws/src
$ wstool init
$ wstool merge segmatch/dependencies.rosinstall
$ wstool update
Note: If you are using ROS Kinetic, at this point you should run the following command in your catkin workspace prior to building the packages:
$ catkin config --merge-devel
Finally build the laser_mapper package which will compile all SegMatch modules:
$ cd ~/catkin_ws
$ catkin build -DCMAKE_BUILD_TYPE=Release laser_mapper
Building dependencies will require some time according to which new packages need to be built (eg. Building the pcl_catkin
package can take up to two hours). Building pcl_catkin
might fail if you do not have sufficient RAM. It can help to add -j2
to catkin build in order to limit the parallel jobs and reduce memory usage.
Consult the wiki for instructions on running the demonstrations.
We would be very grateful if you would contribute to the code base by reporting bugs, leaving comments and proposing new features through issues and pull requests. Please see the dedicated wiki page on this topic and feel free to get in touch at rdube(at)ethz.ch. Thank you!
Thank you for citing our SegMatch paper if you use any of this code:
@inproceedings{segmatch2017,
title={SegMatch: Segment based place recognition in 3D point clouds},
author={Dub{\'e}, Renaud and Dugas, Daniel and Stumm, Elena and Nieto, Juan and Siegwart, Roland and Cadena, Cesar},
booktitle={International Conference on Robotics and Automation (ICRA)},
pages={5266--5272},
year={2017},
organization={IEEE}
}