ORB_SLAM2.tar.gz

# ORB-SLAM2 **Authors:** [Raul Mur-Artal](http://webdiis.unizar.es/~raulmur/), [Juan D. Tardos](http://webdiis.unizar.es/~jdtardos/), [J. M. M. Montiel](http://webdiis.unizar.es/~josemari/) and [Dorian Galvez-Lopez](http://doriangalvez.com/) ([DBoW2](https://github.com/dorian3d/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](http://www.cvlibs.net/datasets/kitti/eval_odometry.php) as stereo or monocular, in the [TUM dataset](http://vision.in.tum.de/data/datasets/rgbd-dataset) as RGB-D or monocular, and in the [EuRoC dataset](http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets) 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. <a href="https://www.youtube.com/embed/ufvPS5wJAx0" target="_blank"><img src="http://img.youtube.com/vi/ufvPS5wJAx0/0.jpg" alt="ORB-SLAM2" width="240" height="180" border="10" /></a> <a href="https://www.youtube.com/embed/T-9PYCKhDLM" target="_blank"><img src="http://img.youtube.com/vi/T-9PYCKhDLM/0.jpg" alt="ORB-SLAM2" width="240" height="180" border="10" /></a> <a href="https://www.youtube.com/embed/kPwy8yA4CKM" target="_blank"><img src="http://img.youtube.com/vi/kPwy8yA4CKM/0.jpg" alt="ORB-SLAM2" width="240" height="180" border="10" /></a> ### 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](http://webdiis.unizar.es/~raulmur/MurMontielTardosTRO15.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](https://128.84.21.199/pdf/1610.06475.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](http://doriangalvez.com/php/dl.php?dlp=GalvezTRO12.pdf)** # 1. License ORB-SLAM2 is released under a [GPLv3 license](https://github.com/raulmur/ORB_SLAM2/blob/master/License-gpl.txt). For a list of all code/library dependencies (and associated licenses), please see [Dependencies.md](https://github.com/raulmur/ORB_SLAM2/blob/master/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} } # 2. Prerequisites We have tested the library 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 We use the new thread and chrono functionalities of C++11. ## Pangolin We use [Pangolin](https://github.com/stevenlovegrove/Pangolin) for visualization and user interface. Dowload and install instructions can be found at: https://github.com/stevenlovegrove/Pangolin. ## OpenCV We use [OpenCV](http://opencv.org) to manipulate images and features. Dowload and install instructions can be found at: http://opencv.org. **Required at leat 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) We use modified versions of the [DBoW2](https://github.com/dorian3d/DBoW2) library to perform place recognition and [g2o](https://github.com/RainerKuemmerle/g2o) library to perform non-linear optimizations. Both modified libraries (which are BSD) are included in the *Thirdparty* folder. ## ROS (optional) We provide some examples to process the live input of a monocular, stereo or RGB-D camera using [ROS](ros.org). Building these examples is optional. In case you want to use ROS, a version Hydro or newer is needed. # 3. Building ORB-SLAM2 library and examples Clone the repository: ``` git clone https://github.com/raulmur/ORB_SLAM2.git ORB_SLAM2 ``` We provide a script `build.sh` to build the *Thirdparty* libraries and *ORB-SLAM2*. Please make sure you have installed all required dependencies (see section 2). Execute: ``` cd ORB_SLAM2 chmod +x build.sh ./build.sh ``` This will create **libORB_SLAM2.so** at *lib* folder and the executables **mono_tum**, **mono_kitti**, **rgbd_tum**, **stereo_kitti**, **mono_euroc** and **stereo_euroc** in *Examples* folder. # 4. Monocular Examples ## TUM Dataset 1. Download a sequence from http://vision.in.tum.de/data/datasets/rgbd-dataset/download and uncompress it. 2. Execute the following command. Change `TUMX.yaml` to TUM1.yaml,TUM2.yaml or TUM3.yaml for freiburg1, freiburg2 and freiburg3 sequences respectively. Change `PATH_TO_SEQUENCE_FOLDER`to the uncompressed sequence folder. ``` ./Examples/Monocular/mono_tum Vocabulary/ORBvoc.txt Examples/Monocular/TUMX.yaml PATH_TO_SEQUENCE_FOLDER ``` 例子./Examples/Monocular/mono_tum Vocabulary/ORBvoc.txt Examples/Monocular/TUM1.yaml Data/rgbd_dataset_freiburg1_xyz ## KITTI Dataset 1. Download the dataset (grayscale images) from http://www.cvlibs.net/datasets/kitti/eval_odometry.php 2. Execute the following command. Change `KITTIX.yaml`by KITTI00-02.yaml, KITTI03.yaml or KITTI04-12.yaml for sequence 0 to 2, 3, and 4 to 12 respectively. Change `PATH_TO_DATASET_FOLDER` to the uncompressed dataset folder. Change `SEQUENCE_NUMBER` to 00, 01, 02,.., 11. ``` ./Examples/Monocular/mono_kitti Vocabulary/ORBvoc.txt Examples/Monocular/KITTIX.yaml PATH_TO_DATASET_FOLDER/dataset/sequences/SEQUENCE_NUMBER ``` ## EuRoC Dataset 1. Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets 2. Execute the following first command for V1 and V2 sequences, or the second command for MH sequences. 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/mono_euroc Vocabulary/ORBvoc.txt Examples/Monocular/EuRoC.yaml PATH_TO_SEQUENCE/cam0/data Examples/Monocular/EuRoC_TimeStamps/SEQUENCE.txt ``` # 5. Stereo Examples ## KITTI Dataset 1. Download the dataset (grayscale images) from http://www.cvlibs.net/datasets/kitti/ev