Authors: Ioannis Alamanos, Costas Tzafestas.
ORB-LINE-SLAM is a Visual SLAM library for Stereo Cameras which is able to function either by combining point and line features or by using line features exclusively. We provide examples to run ORB-LINE-SLAM in the EuRoC dataset and in the UMA dataset. The system is also able to run in real-time using ROS.
This software is based on ORB-SLAM3 developed by Carlos Campos, Richard Elvira, Juan J. Gómez Rodríguez, José M. M. Montiel, Juan D. Tardos.
[ORB-SLAM3] Carlos Campos, Richard Elvira, Juan J. Gómez Rodríguez, José M. M. Montiel and Juan D. Tardós, ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM, IEEE Transactions on Robotics, 2021. PDF.
[IMU-Initialization] Carlos Campos, J. M. M. Montiel and Juan D. Tardós, Inertial-Only Optimization for Visual-Inertial Initialization, ICRA 2020. PDF
[ORBSLAM-Atlas] Richard Elvira, J. M. M. Montiel and Juan D. Tardós, ORBSLAM-Atlas: a robust and accurate multi-map system, IROS 2019. PDF.
[ORBSLAM-VI] Raúl Mur-Artal, and Juan D. Tardós, Visual-inertial monocular SLAM with map reuse, IEEE Robotics and Automation Letters, vol. 2 no. 2, pp. 796-803, 2017. PDF.
[Stereo and RGB-D] Raúl Mur-Artal and Juan D. Tardós. ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras. IEEE Transactions on Robotics, vol. 33, no. 5, pp. 1255-1262, 2017. PDF.
[Monocular] Raúl Mur-Artal, José M. M. Montiel and Juan D. Tardós. ORB-SLAM: A Versatile and Accurate Monocular SLAM System. IEEE Transactions on Robotics, vol. 31, no. 5, pp. 1147-1163, 2015. (2015 IEEE Transactions on Robotics Best Paper Award). PDF.
[DBoW2 Place Recognition] Dorian Gálvez-López and Juan D. Tardós. Bags of Binary Words for Fast Place Recognition in Image Sequences. IEEE Transactions on Robotics, vol. 28, no. 5, pp. 1188-1197, 2012. PDF
ORB-LINE-SLAM is released under GPLv3 license. For a list of all code/library dependencies (and associated licenses), please see Dependencies.md.
We have tested the library in Ubuntu 18.04, but it should be easy to compile in other platforms. A powerful computer will ensure real-time performance and provide more stable and accurate results. In order to reduce the computational time, the user can decrease the value of the lsd_scale parameter in the yaml file. Moreover, through the same yaml file which can be found in PATH/Examples/Stereo-Line, the user can choose between different SLAM modes (only-line SLAM and point-line SLAM) and Line Detectors (LSD and EDlines).
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 3.0. Tested with OpenCV 3.2.0.
Required by g2o (see below). Download and install instructions can be found at: http://eigen.tuxfamily.org. Required at least 3.1.0. Tested with Eigen 3.3.8.
Required to calculate the alignment of the trajectory with the ground truth. Required Numpy module.
- (win) http://www.python.org/downloads/windows
- (deb)
sudo apt install libpython2.7-dev - (mac) preinstalled with osx
We provide some examples to process live input using ROS. Building these examples is optional. These have been tested with ROS Melodic under Ubuntu 18.04.
Clone the repository:
git clone https://github.com/Giannis-Alamanos/ORB-LINE-SLAM.git ORB-LINE-SLAM
We provide a script build.sh to build the Thirdparty libraries and ORB-LINE-SLAM. Please make sure you have installed all required dependencies (see section 2). Execute:
cd ORB-LINE-SLAM
chmod +x build.sh
./build.sh
- Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets
- Execute the following command in the terminal:
./Examples/Stereo-Line/stereo_line_euroc ./Vocabulary/ORBvoc.txt ./Vocabulary/LSDvoc.txt ./Examples/Stereo-Line/EuRoC.yaml SEQUENCE ./Examples/Stereo-Line/EuRoC_TimeStamps/SEQUENCE.txt
- Download a sequence from http://mapir.isa.uma.es/mapirwebsite/index.php/mapir-downloads/291-uma-visual-inertial-dataset.html
- Execute the following command in the terminal:
./Examples/Stereo-Line/stereo_line_UMA ./Vocabulary/ORBvoc.txt ./Vocabulary/LSDvoc.txt ./Examples/Stereo-Line/UMA_ueye.yaml SEQUENCE ./Examples/Stereo-Line/UMA_TimeStamps/SEQUENCE.txt
To run sequences from UMA Dataset other than hall1-rev-eng and corridor-eng, the user has to create a file containing the Timestamps.
Tested with ROS Melodic and ubuntu 18.04.
- Open .bashrc file:
gedit ~/.bashrc
and add at the end the following line. Replace PATH by the folder where you cloned ORB-LINE-SLAM:
export ROS_PACKAGE_PATH=${ROS_PACKAGE_PATH}:PATH/ORB-LINE-SLAM/Examples/ROS
- Execute
build_ros.shscript:
chmod +x build_ros.sh
./build_ros.sh
For a stereo input from topic /camera/left/image_raw and /camera/right/image_raw run node ORB_SLAM3/Stereo. You will need to provide the vocabulary files and a settings file. If you provide rectification matrices (see Examples/Stereo-Line/EuRoC.yaml example), the node will recitify the images online, otherwise images must be pre-rectified.
rosrun ORB_SLAM3 Stereo PATH_TO_VOCABULARY1 PATH_TO_VOCABULARY2 PATH_TO_SETTINGS_FILE ONLINE_RECTIFICATION
Example: Download a rosbag (e.g. V1_01_easy.bag) from the EuRoC dataset (http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets). Open 3 tabs on the terminal and run the following command at each tab:
roscore
rosrun ORB_SLAM3 Stereo ./Vocabulary/ORBvoc.txt ./Vocabulary/LSDvoc.txt ./Examples/Stereo-Line/EuRoC.yaml true
rosbag play --pause V1_01_easy.bag /cam0/image_raw:=/camera/left/image_raw /cam1/image_raw:=/camera/right/image_raw
Once ORB-LINE-SLAM has loaded the vocabulary, press space in the rosbag tab. Enjoy!. Note: a powerful computer is required to run exigent sequences in real-time.