LSD-SLAM: Large-Scale Direct Monocular SLAM

This fork started from Thomas Whelan's fork which "relieves the user of the horrors of a ROS dependency and uses the much nicer lightweight Pangolin framework instead."

Here is Jakob's original description:

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.

This repo contains my experiments with LSD-SLAM, for performance, functionality and structure. As of March 2016, it diverges significantly from either Jakob or Thomas's branches in structure (I refactored as a way of learning the code), but not significantly in terms of functionality.

master is my working / stable-ish branch. aaron_dev is my really unstable branch. Other branches are for hardware-specific ports although in the long run I try to merge those functionalities into master and use CMake to turn hardware-specific elements on and off.

1. Quickstart / Minimal Setup

Requires OpenCV 2.4 (with nonfree if you want FABMAP), TCLAP, Boost, Eigen, Pangolin and g2o.

2. Installation

Install everything from apt repos if you can, otherwise there are githubs for Pangolin and g2o.

apt-get --yes install cmake git libeigen3-dev \
  libboost-filesystem1.55-dev libboost-thread1.55-dev \
  libboost-system1.55-dev libopencv-dev libtclap-dev \
  libglm-dev

You then need to manually build Pangolin and g2o using the standard CMake build procedure. For both I made "Release" and installed in /usr/local. For g2o I needed to install:

apt-get --yes install libgomp1 libsuitesparse-dev

Then:

git clone -b jetson https://github.com/amarburg/lsd_slam.git
mkdir build_jetson
cd build_jetson

I then needed to manually specify the path to the Boost libs which seems strange

BOOST_LIBRARYDIR=/usr/lib/arm-linux-gnueabihf/  cmake ..

On Mac

or on the Mac using Homebrew

brew install eigen boost ...

Then usual cmake building process.

Common problems

../lib/lsd_core/liblsdslam.so: undefined reference to `g2o::csparse_extension::cs_chol_workspace(cs_di_sparse const*, cs_di_symbolic const*, int*, double*)'
../lib/lsd_core/liblsdslam.so: undefined reference to `g2o::csparse_extension::cs_cholsolsymb(cs_di_sparse const*, double*, cs_di_symbolic const*, double*, int*)'
../lib/lsd_core/liblsdslam.so: undefined reference to `g2o::csparse_extension::writeCs2Octave(char const*, cs_di_sparse const*, bool)'

g2o should be built with the system libcsparse provided by the libsuitesparse-dev package. Ensure the CMake variable BUILD_CSPARSE=OFF, and that CSPARSE_INCLUDE_DIR and CSPARSE_LIBRARY point to system libraries, not the libraries included in the g2o source code.

../lib/lsd_core/liblsdslam.so: undefined reference to `pangolin::CreateGlutWindowAndBind(std::string, int, int, unsigned int)'

Thomas' Pangolin wrapper assumes Glut has been installed. I needed to

cmake -DFORCE_GLUT=ON ..

3. Running

Supports directories or sets of raw PNG images. For example, you can down any dataset from here in PNG format, and run like;

./LSD --calib datasets/LSD_machine/cameraCalibration.cfg datasets/LSD_machine/images/

4. 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

5. License

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