This platform provide a real-time monocular SLAM method that computes the camera trajectory and a sparse 3D reconstruction by leveraging point (ORB) and line (LSD) features. We provide examples to run the system on the ICL NUIM dataset.
Structure-SLAM(PL) is released under a GPLv3 license. For a closed-source version of Structure-SLAM(PL) for commercial purposes, please contact me yanyan.li at tum.de
We have tested the library in Ubuntu 16.04, but it should be easy to compile in other platforms. A powerful computer (e.g. i7) will ensure real-time performance and provide more stable and accurate results.
We use the new thread and chrono functionalities of C++11.
We use Pangolin for visualization and user interface. Dowload and install instructions can be found at: https://github.com/stevenlovegrove/Pangolin.
We use OpenCV to manipulate images and features. Dowload and install instructions can be found at: http://opencv.org. Required at least 2.4.3. Tested with OpenCV 3.4.0.
Required by g2o (see below). Download and install instructions can be found at: http://eigen.tuxfamily.org. Required at least 3.1.0.
We use modified versions of the DBoW2 library to perform place recognition and g2o library to perform non-linear optimizations. Both modified libraries (which are BSD) are included in the Thirdparty folder.
We provide a script build.sh to build the Thirdparty libraries and Structure-SLAM. Please make sure you have installed all required dependencies (see section 2). Execute:
cd Structure-SLAM
chmod +x build.sh
./build.sh
- Download ICL NUIM dataset and uncompress it to PATH_TO_SEQUENCE_FOLDER
- Execute the following command.
./Examples/Structure-SLAM Vocabulary/ORBvoc.txt Examples/ICL.yaml PATH_TO_SEQUENCE_FOLDER
This platform is a part of Structure-SLAM, please cite it if you use the repo in an academic work.
@inproceedings{Li2020SSLAM,
author = {Li, Yanyan and Brasch, Nikolas and Wang, Yida and Navab, Nassir and Tombari, Federico},
title = {Structure-SLAM: Low-Drift Monocular SLAM in Indoor Environments},
year = {2020},
booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
}
We thank Raul Mur-Artal for his impressive work, ORB-SLAM2, which is a completed feature-based SLAM system.