qihaoliu/orbslam2-docker repository overview

⁠ORB_SLAM2 Docker Image

Authors: Qihao LIU⁠ (Engineer 2A at CentraleSupelec in France)

30 May 2020: ORB_SLAM2 based on Ubuntu 16.04

• Provide ORB_SLAM2⁠ operating environment and source code⁠;
• All routines have been compiled;
• Provides VNC visualization interface in Desktop or ubuntu-vnc-desktop⁠ to access a Ubuntu 16.04 desktop environment.

⁠Quick Start

⁠Get image

⁠Pull the image from DockerHub:

docker pull qihaoliu/orbslam2-docker:v1.0

⁠Or use Dockerfile to build locally

git clone --branch v1.0 https://github.com/buaalqh/orbslam-docker.git
cd ./orbslam-docker
docker build -t qihaoliu/orbslam2-docker:v1.0 .

⁠Run the docker container

⁠Download Dataset

This ORB_SLAM2 routine provides a demonstration program for the KITTI odometry⁠ Dataset. Here, using sequence 04 in data_odometry_gray⁠.

⁠Create container

Execute the following command to instantiate the container:

docker run -it --rm -v YOUR_PATH_TO_KITTI/sequences/04:/root/Dataset/04 -p 5900:5900 qihaoliu/orbslam2-docker:v1.0

⁠Connect VNC Desktop port 5900 to host:

Then open VNC Viewer Desktop and use adress localhost:5900 to connect. And in this image, the access is free (no password) as default. (Note: docker toolbox in wins, the adress of localhost is a special adress assigned to your docker VM, ex: http://192.168.xx.xx:5900/)

⁠Pangolin routines

cd /slam/orbslam2/ORB_SLAM2
./Examples/Stereo/stereo_kitti Vocabulary/ORBvoc.txt Examples/Stereo/KITTI04-12.yaml /root/Dataset/04

⁠End

⁠ORB-SLAM2

Authors: Raul Mur-Artal⁠, Juan D. Tardos⁠, J. M. M. Montiel⁠ and Dorian Galvez-Lopez⁠ (DBoW2⁠)

13 Jan 2017: OpenCV 3 and Eigen 3.3 are now supported.

22 Dec 2016: Added AR demo (see section 7).

ORB-SLAM2 is a real-time SLAM library for Monocular, Stereo and RGB-D cameras that computes the camera trajectory and a sparse 3D reconstruction (in the stereo and RGB-D case with true scale). It is able to detect loops and relocalize the camera in real time. We provide examples to run the SLAM system in the KITTI dataset⁠ as stereo or monocular, in the TUM dataset⁠ as RGB-D or monocular, and in the EuRoC dataset⁠ as stereo or monocular. We also provide a ROS node to process live monocular, stereo or RGB-D streams. The library can be compiled without ROS. ORB-SLAM2 provides a GUI to change between a SLAM Mode and Localization Mode, see section 9 of this document.

⁠Related Publications:

[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.

[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.

[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-SLAM2 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-SLAM}: a Versatile and Accurate Monocular {SLAM} System},
  author={Mur-Artal, Ra\'ul, Montiel, J. M. M. and Tard\'os, Juan D.},
  journal={IEEE Transactions on Robotics},
  volume={31},
  number={5},
  pages={1147--1163},
  doi = {10.1109/TRO.2015.2463671},
  year={2015}
 }

if you use ORB-SLAM2 (Stereo or RGB-D) in an academic work, please cite:

@article{murORB2,
  title={{ORB-SLAM2}: an Open-Source {SLAM} System for Monocular, Stereo and {RGB-D} Cameras},
  author={Mur-Artal, Ra\'ul and Tard\'os, Juan D.},
  journal={IEEE Transactions on Robotics},
  volume={33},
  number={5},
  pages={1255--1262},
  doi = {10.1109/TRO.2017.2705103},
  year={2017}
 }