Skip to content

IshitaTakeshi/lsd_slam_noros

 
 

Repository files navigation

LSD-SLAM: Large-Scale Direct Monocular SLAM

LSD-SLAM is a novel approach to real-time monocular SLAM. It is fully direct (i.e. does not use keypoints / features) and creates large-scale, semi-dense maps in real-time on a laptop. For more information see http://vision.in.tum.de/lsdslam where you can also find the corresponding publications and Youtube videos, as well as some example-input datasets, and the generated output as rosbag or .ply point cloud.

How to run

We highly recommend to use docker to build the source, because it is very difficult to reproduce the execution environment on general Linux distributions such as Ubuntu etc.

Clone the repository

$git clone https://github.com/IshitaTakeshi/lsd_slam_noros.git
$cd lsd_slam_noros

Building a docker image

Run the following command in a directory where Dockerfile is placed.

$docker build -t lsdslam_noros:latest .

Launch a container

This part is usually environment dependent because we need to share a window with the host.

If you are on Mac, make sure the latest XQuartz is installed, and run $./scripts/launch-container-mac.sh.
On Linux, run $./scripts/launch-container-linux.sh.

Download the dataset

Download the TUM dataset sequence by running

$export SEQUENCE=30  # sequence number of the TUM-mono dataset 
$./scripts/download_tum_mono.sh

Run LSD-SLAM

The debug window should appear by executing $./bin/main_on_images data/sequence_$SEQUENCE.

If segmentation fault or Gtk-WARNING happens, executing the command above multiple times may solve it.

Related Papers

  • LSD-SLAM: Large-Scale Direct Monocular SLAM, J. Engel, T. Schöps, D. Cremers, ECCV '14

  • Semi-Dense Visual Odometry for a Monocular Camera, J. Engel, J. Sturm, D. Cremers, ICCV '13

License

LSD-SLAM is licensed under the GNU General Public License Version 3 (GPLv3), see http://www.gnu.org/licenses/gpl.html.

For commercial purposes, the original lsd slam authors also offer a professional version under different licencing terms.

About

ROS-independent implementation of LSD-SLAM

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • C++ 89.0%
  • CMake 10.3%
  • Other 0.7%