ORB-SLAM3

V0.2: Beta version, 21 Jul 2020

Authors: [Carlos Campos], [Richard Elvira], [Juan J. Gómez], José M. M. Montiel, Juan D. Tardos.

ORB-SLAM3 is the first real time SLAM library able to perform Visual, Visual-Inertial and Multi-Map SLAM with monocular, stereo and RGB-D cameras, using pin-hole and fisheye lens models. In all sensor configurations, ORB-SLAM3 is as robust as the best systems available in the literature, and significantly more accurate.

We provide examples to run ORB-SLAM3 system in the EuRoC dataset using stereo or monocular with or without IMU, and in the TUM-VI dataset using fisheye stereo or monocular with or without IMU.

This software is based on ORB-SLAM2 developed by Raul Mur-Artal, Juan D. Tardos, J. M. M. Montiel and Dorian Galvez-Lopez (DBoW2).

Related Publications:

Carlos Campos, Richard Elvira, Juan J. Gómez, J. M. M. Montiel and Juan D. Tardós, ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM, Under review. 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, 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 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

1. License

ORB-SLAM3 is released under 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-SLAM3 in an academic work, please cite:

@article{Campos2020,
  title={{ORB-SLAM3}: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map {SLAM}},
  author={Campos, Carlos AND Elvira, Richard AND G\´omez, Juan J. AND Montiel, J. M. M. AND Tard\'os, Juan D.},
  journal={arXiv preprint arXiv:xxxx.yyyy},
  year={2020}
 }

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)

1. Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets

2. 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)

1. Download a sequence from https://vision.in.tum.de/data/datasets/visual-inertial-dataset and uncompress it.

2. 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)

1. Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets

2. 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)

1. Download a sequence from https://vision.in.tum.de/data/datasets/visual-inertial-dataset and uncompress it.

2. 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

1. Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets

2. 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

1. Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets

2. 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

1. Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets

2. 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

1. Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets

2. 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.

TUM-VI Monocular-Inertial

TUM-VI Stereo-Inertial