Stereo Visual Odometry by combining point and line segment features
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README.md

PL-StVO

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

Authors: Ruben Gomez-Ojeda and David Zuñiga-Noël and Javier Gonzalez-Jimenez

Related publication: Robust Stereo Visual Odometry through a Probabilistic Combination of Points and Line Segments

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

@InProceedings{Gomez2015,
  Title                    = {Robust Stereo Visual Odometry through a Probabilistic Combination of Points and Line Segments},
  Author                   = {Gomez-Ojeda, Ruben and Gonzalez-Jimenez, Javier},
  Booktitle                = {Robotics and Automation (ICRA), 2016 IEEE International Conference on},
  Year                     = {2016},
  Publisher                = {IEEE}
}

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

http://eigen.tuxfamily.org

Boost

Installation on Ubuntu 16.04:

sudo apt-get install libboost-dev

YAML

Installation on Ubuntu 16.04:

sudo apt install libyaml-cpp-dev

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

A CMakeLists.txt file is included to detect external dependencies and generate the project.

The project builds "imagesStVO", a customizable application where the user must introduce the inputs to the SVO algorithm, and then process the provided output.

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

VO Application

Usage: ./imagesStVO <dataset_name> [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:

./imagesStVO 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.