README.md 11 KB
ORB-SLAM3
V0.2: Beta version, 21 Jul 2020
Authors: [Carlos Campos], [Richard Elvira], [Juan J. Gómez], Juan D. Tardos, J. M. M. Montiel
ORB-SLAM3 is a real time SLAM library for Monocular, Stereo and RGB-D sensors with capabilities to handle Inertial sensors and Multiple-Maps. The camera abstraction allow to work with different kind of camera, the code include the implementation of Pinhole and Fisheye sensors. This software is able to combine the diffenrent configurations to build a 3D sparse map where the main sensor is located.
We provide examples to run the SLAM system in the EuRoC dataset as pinhole stereo or monocular with or without inertial measures, in the TUM-VI dataset as fisheye stereo or monocular with or without inertial.
This code is the next version of ORB-SLAM2 developed by Raul Mur-Artal, Juan D. Tardos, J. M. M. Montiel and Dorian Galvez-Lopez (DBoW2)
Related Publications:
[ORB-SLAM3] Carlos Campos, Richard Elvira, Juan J. Gómez, J. M. M. Montiel and Juan D. Tardós. ORB-SLAM3. Submited to IEEE Transactions on Robotics. PDF. *Same contribution to the article.
[Inertial-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.
[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, J. 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 Recognizer] 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
1. License
ORB-SLAM2 is released under a GPLv3 license. For a list of all code/library dependencies (and associated licenses), please see Dependencies.md.
For a closed-source version of ORB-SLAM3 for commercial purposes, please contact the authors: orbslam (at) unizar (dot) es.
If you use ORB-SLAM2 (Monocular) in an academic work, please cite:
@article{murTRO2015,
title={{ORB-SLAM3}:},
author={Campos, Carlos, Elvira, Richard, G\´omez, Juan J., Montiel, J. M. M. and Tard\'os, Juan D.},
journal={IEEE Transactions on Robotics},
volume={},
number={},
pages={},
doi = {},
year={}
}
2. Prerequisites
We have tested the library in Ubuntu 16.04 and 18.04, 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.
C++11 or C++0x Compiler
We use the new thread and chrono functionalities of C++11.
Pangolin
We use Pangolin for visualization and user interface. Dowload and install instructions can be found at: https://github.com/stevenlovegrove/Pangolin.
OpenCV
We use OpenCV to manipulate images and features. Dowload and install instructions can be found at: http://opencv.org. Required at leat 3.0. Tested with OpenCV 3.2.0.
Eigen3
Required by g2o (see below). Download and install instructions can be found at: http://eigen.tuxfamily.org. Required at least 3.1.0.
DBoW2 and g2o (Included in Thirdparty folder)
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.
3. Building ORB-SLAM3 library and examples
Clone the repository:
git clone https://github.com/UZ-SLAMLab/ORB_SLAM3.git ORB_SLAM3
We provide a script build.sh to build the Thirdparty libraries and ORB-SLAM3. Please make sure you have installed all required dependencies (see section 2). Execute:
cd ORB_SLAM3
chmod +x build.sh
./build.sh
This will create libORB_SLAM3.so at lib folder and the executables in Examples folder.
4. Monocular Examples
EuRoC Dataset(Pinhole)
Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets
Execute the following first command to execute as pure visual , or the second command to visual-inertial. Change PATH_TO_SEQUENCE_FOLDER and SEQUENCE according to the sequence you want to run.
./Examples/Monocular/mono_euroc Vocabulary/ORBvoc.txt Examples/Monocular/EuRoC.yaml PATH_TO_SEQUENCE_FOLDER/mav0/cam0/data Examples/Monocular/EuRoC_TimeStamps/SEQUENCE.txt./Examples/Monocular-Inertial/mono_inertial_euroc Vocabulary/ORBvoc.txt Examples/Monocular-Inertial/EuRoC.yaml PATH_TO_SEQUENCE_FOLDER/cam0/data Examples/Monocular-Inertial/EuRoC_TimeStamps/SEQUENCE.txt PATH_TO_SEQUENCE_FOLDER/mav0/imu0/data.csv
TUM-VI Dataset(Fisheye)
Download a sequence from https://vision.in.tum.de/data/datasets/visual-inertial-dataset and uncompress it.
Execute the following command to execute as monocular-inertial. Change PATH_TO_SEQUENCE_FOLDER and SEQUENCE according to the sequence you want to run.
./Examples/Monocular-Inertial/mono_inertial_tum Vocabulary/ORBvoc.txt Examples/Monocular-Inertial/TUM_512.yaml PATH_TO_SEQUENCE_FOLDER/mav0/cam0/data Examples/Monocular-Inertial/TUM_TimeStamps/SEQUENCE.txt Examples/Monocular-Inertial/TUM_IMU/SEQUENCE.txt
5. Stereo Examples
EuRoC Dataset(Pinhole)
Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets
Execute the following first command to execute as pure visual , or the second command to visual-inertial. Change PATH_TO_SEQUENCE_FOLDER and SEQUENCE according to the sequence you want to run.
./Examples/Stereo/stereo_euroc Vocabulary/ORBvoc.txt Examples/Stereo/EuRoC.yaml PATH_TO_SEQUENCE_FOLDER/mav0/cam0/data PATH_TO_SEQUENCE_FOLDER/mav0/cam1/data Examples/Stereo/EuRoC_TimeStamps/SEQUENCE.txt./Examples/Stereo-Inertial/stereo_inertial_euroc Vocabulary/ORBvoc.txt Examples/Stereo-Inertial/EuRoC.yaml PATH_TO_SEQUENCE_FOLDER/cam0/data PATH_TO_SEQUENCE_FOLDER/cam1/data Examples/Stereo-Inertial/EuRoC_TimeStamps/SEQUENCE.txt PATH_TO_SEQUENCE_FOLDER/mav0/imu0/data.csv
TUM-VI Dataset(Fisheye)
Download a sequence from https://vision.in.tum.de/data/datasets/visual-inertial-dataset and uncompress it.
Execute the following command to execute as monocular-inertial. Change PATH_TO_SEQUENCE_FOLDER and SEQUENCE according to the sequence you want to run.
./Examples/Stereo-Inertial/stereo_inertial_tum Vocabulary/ORBvoc.txt Examples/Stereo-Inertial/TUM_512.yaml PATH_TO_SEQUENCE_FOLDER/mav0/cam0/data PATH_TO_SEQUENCE_FOLDER/mav0/cam1/data Examples/Stereo-Inertial/TUM_TimeStamps/SEQUENCE.txt Examples/Stereo-Inertial/TUM_IMU/SEQUENCE.txt
6. Multiple-Map Examples
There is an executable to process a set of dataset sequentially, each dataset starts from scratch a new map.
EuRoC Monocular
Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets
Execute the following command to execute for N dataset. Change PATH_TO_SEQUENCE_FOLDER_i and SEQUENCE_i according to the sequence you want to run.
EuRoC Monocular-Inertial
Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets
Execute the following command to execute for N dataset. Change PATH_TO_SEQUENCE_FOLDER_i and SEQUENCE_i according to the sequence you want to run.
./Examples/Monocular-Inertial/mono_inertial_euroc_2 Vocabulary/ORBvoc.txt Examples/Monocular-Inertial/EuRoC.yaml PATH_TO_SEQUENCE_FOLDER_1/mav0/cam0/data Examples/Monocular-Inertial/EuRoC_TimeStamps/SEQUENCE_1.txt PATH_TO_SEQUENCE_FOLDER_1/mav0/imu0/data.csv PATH_TO_SEQUENCE_FOLDER_2/mav0/cam0/data Examples/Monocular-Inertial/EuRoC_TimeStamps/SEQUENCE_2.txt PATH_TO_SEQUENCE_FOLDER_2/mav0/imu0/data.csv ... PATH_TO_SEQUENCE_FOLDER_N/mav0/cam0/data Examples/Monocular-Inertial/EuRoC_TimeStamps/SEQUENCE_N.txt PATH_TO_SEQUENCE_FOLDER_N/mav0/imu0/data.csv
Examples to process the whole Machine Hall. Change PATH_TO_SEQUENCE_FOLDER to the path of the sequences:
./Examples/Monocular-Inertial/mono_inertial_euroc_2 Vocabulary/ORBvoc.txt Examples/Monocular-Inertial/EuRoC.yaml PATH_TO_SEQUENCE_FOLDER/MH01/mav0/cam0/data Examples/Monocular-Inertial/EuRoC_TimeStamps/MH01.txt PATH_TO_SEQUENCE_FOLDER/MH01/mav0/imu0/data.csv PATH_TO_SEQUENCE_FOLDER/MH02/mav0/cam0/data Examples/Monocular-Inertial/EuRoC_TimeStamps/MH02.txt PATH_TO_SEQUENCE_FOLDER/MH02/mav0/imu0/data.csv
PATH_TO_SEQUENCE_FOLDER/MH03/mav0/cam0/data Examples/Monocular-Inertial/EuRoC_TimeStamps/MH03.txt PATH_TO_SEQUENCE_FOLDER/MH03/mav0/imu0/data.csv PATH_TO_SEQUENCE_FOLDER/MH04/mav0/cam0/data Examples/Monocular-Inertial/EuRoC_TimeStamps/MH04.txt PATH_TO_SEQUENCE_FOLDER/MH04/mav0/imu0/data.csv PATH_TO_SEQUENCE_FOLDER/MH05/mav0/cam0/data Examples/Monocular-Inertial/EuRoC_TimeStamps/MH05.txt PATH_TO_SEQUENCE_FOLDER/MH05/mav0/imu0/data.csv
EuRoC Stereo
Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets
Execute the following command to execute for N dataset. Change PATH_TO_SEQUENCE_FOLDER_i and SEQUENCE_i according to the sequence you want to run.
EuRoC Stereo-Inertial
Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets
Execute the following command to execute for N dataset. Change PATH_TO_SEQUENCE_FOLDER_i and SEQUENCE_i according to the sequence you want to run.