Notice that this repository is only an open-source version of PL-SLAM released with the aim of being useful for the community, however, it is far from being optimized and we are not including some features of PL-SLAM.

PL-SLAM

This code contains an algorithm to compute stereo visual SLAM by using both point and line segment features.

Authors: Ruben Gomez-Ojeda, David Zuñiga-Noël, Francisco Angel Moreno, Davide Scaramuzza, and Javier Gonzalez-Jimenez

Related publication: PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments

If you use PL-SLAM in your research work, please cite:

@article{gomez2017pl,
  title   = {{PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments}},
  author  = {Gomez-Ojeda, Ruben and Zuñiga-Noël, David and Moreno, Francisco-Angel and Scaramuzza, Davide and Gonzalez-Jimenez, Javier},
  journal = {arXiv preprint arXiv:1705.09479},
  year    = {2017}

}

The pdf file can be found at https://arxiv.org/abs/1705.09479.

PL-SLAM

Related publications:

Gomez-Ojeda, R., Briales, J., & Gonzalez-Jimenez, J. (2016, October). PL-SVO: Semi-direct monocular visual odometry by combining points and line segments. In Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on (pp. 4211-4216). IEEE.

Gomez-Ojeda, R., & Gonzalez-Jimenez, J. (2016, May). Robust stereo visual odometry through a probabilistic combination of points and line segments. In Robotics and Automation (ICRA), 2016 IEEE International Conference on (pp. 2521-2526). IEEE..

License:

The provided code is published under the General Public License Version 3 (GPL v3). More information can be found in the "LICENSE" also included in the repository.

Please do not hesitate to contact the authors if you have any further questions.

1. Prerequisites and dependencies

OpenCV 3.x.x

It can be easily found at http://opencv.org.

Eigen3 (tested with 3.2.92)

http://eigen.tuxfamily.org

Boost

Installation on Ubuntu:

sudo apt-get install libboost-dev

g2o - General Graph Optimization

It can be found at:

https://github.com/RainerKuemmerle/g2o.git

YAML (tested with 0.5.2)

Installation on Ubuntu:

sudo apt-get install libyaml-cpp-dev

stvo-pl

It can be found at: 

https://github.com/rubengooj/stvo-pl

MRPT

In case of using the provided representation class. Download and install instructions can be found at: http://www.mrpt.org/

Known Issues:

If working with the most recent versions of the MRPT library you might find some issues due to hard refactoring, for which we recommend to use this version instead (the last one we tested):

https://github.com/MRPT/mrpt/tree/0c3d605c3cbf5f2ffb8137089e43ebdae5a55de3

Line Descriptor

We have modified the line_descriptor module from the OpenCV/contrib library (both BSD) which is included in the 3rdparty folder.

2. Configuration and generation

Executing the file build.sh will configure and generate the line_descriptor and DBoW2 modules, uncompress the vocabulary files, and then will configure and generate the PL-SLAM library for which we generate: libplslam.so in the lib folder, and the application plslam_dataset that works with our dataset format (explained in the next section).

3. Usage

Datasets configuration

We employ an environment variable, ${DATASETS_DIR}, pointing the directory that contains our datasets. Each sequence from each dataset must contain in its root folder a file named dataset_params.yaml, that indicates at least the camera model and the subfolders with the left and right images. We provide dataset parameters files for several datasets and cameras with the format xxxx_params.yaml.

Configuration files

For running SLAM we can load the default parameters file or employ the config_xxxx.yaml files provided for every dataset.

SLAM Application

Usage: ./plslam_dataset [options] Options: -c Config file -o Offset (number of frames to skip in the dataset directory -n Number of frames to process the sequence -s Parameter to skip s-1 frames (default 1)

A full command would be:

./plslam_dataset kitti/00 -c ../config/config_kitti.yaml -o 100 -s 2 -n 1000

where we are processing the sequence 00 from the KITTI dataset (in our dataset folders) with the custom config file, with an offset -c allowing to skip the first 100 images, a parameter -s to consider only one every 2 images, and a parameter -n to only consider 1000 input pairs.