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ORB-SLAM2

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

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.

1. Prerequisites

The library has been tested in Ubuntu 12.04, 14.04 and 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.

C++11 or C++0x Compiler

New thread and chrono functionalities of C++11 are used.

Pangolin

Pangolin is used for visualization and user interface. Download and install instructions can be found at: https://github.com/stevenlovegrove/Pangolin.

OpenCV

OpenCV is used to manipulate images and features. Download and install instructions can be found at: http://opencv.org. Required at least 2.4.3. Tested with OpenCV 2.4.11 and OpenCV 3.2.

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)

Modified versions of the DBoW2 library to perform place recognition and g2o library to perform non-linear optimizations are used. Both modified libraries (which are BSD) are included in the Thirdparty folder.

Evaluation Script

Requires matplotlib, numpy, math, copy libraries.

2. Building ORB-SLAM2 library and examples

Clone the repository:

git clone https://github.com/raulmur/ORB_SLAM2.git ORB_SLAM2

A script build.sh to build the Thirdparty libraries and ORB-SLAM2 is provided. Please make sure you have installed all required dependencies (see section 1). Execute:

cd ORB_SLAM2
chmod +x build.sh
./build.sh

This will create libORB_SLAM2.so at lib folder and the executable stereo_mpc in Examples folder.

3. Stereo Examples

MPC

Execute the following command. Change mpc.yamlto your yaml file. Change PATH_TO_DATASET_FOLDER to the uncompressed dataset folder. Change SEQUENCE_NUMBER to your sequence name. The sequence to run the SLAM for requires a times.txt in the same PATH_TO_DATASET_FOLDER/Examples/Stereo/SEQUENCE_NUMBER directory which contains the timestamps of the corresponding frames on each line.

./Examples/Stereo/stereo_mpc ./Vocabulary/ORBvoc.txt Examples/Stereo/mpc.yaml PATH_TO_DATASET_FOLDER/Examples/Stereo/SEQUENCE_NUMBER

Evaluate SLAM predictions

Change gt_poses_path and pred_poses_path to your pose files. Execute the following command.

python eval_slam.py

4. License

ORB-SLAM2 is released under a GPLv3 license. For a list of all code/library dependencies (and associated licenses), please see Dependencies.md.

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}
 }