rgbd_xtion_orb_slam

# 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)) **Current version:** 1.0.0 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, and in the [TUM dataset](http://vision.in.tum.de/data/datasets/rgbd-dataset) as RGB-D or monocular. We also provide a ROS node to process live monocular 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. #####Videos showing ORB-SLAM2: <a href="http://www.youtube.com/watch?feature=player_embedded&v=dF7_I2Lin54 " target="_blank"><img src="http://img.youtube.com/vi/dF7_I2Lin54/0.jpg" alt="Tsukuba Dataset" width="240" height="180" border="10" /></a> <a href="http://www.youtube.com/watch?feature=player_embedded&v=51NQvg5n-FE " target="_blank"><img src="http://img.youtube.com/vi/51NQvg5n-FE/0.jpg" alt="KITTI Dataset" width="240" height="180" border="10" /></a> <a href="http://www.youtube.com/watch?feature=player_embedded&v=LnbAI-o7YHk " target="_blank"><img src="http://img.youtube.com/vi/LnbAI-o7YHk/0.jpg" alt="TUM RGBD Dataset" width="240" height="180" border="10" /></a> **Notice for ORB-SLAM Monocular users:** The monocular capabilities of ORB-SLAM2 compared to [ORB-SLAM Monocular](https://github.com/raulmur/ORB_SLAM) are similar. However in ORB-SLAM2 we apply a full bundle adjustment after a loop closure, the extraction of ORB is slightly different (trying to improve the dispersion on the image) and the tracking is also slightly faster. The GUI of ORB-SLAM2 also provides you new capabilities as the *modes* mentioned above and a reset button. We recommend you to try this new software :) ###Related Publications: [1] 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. **[PDF](http://webdiis.unizar.es/~raulmur/MurMontielTardosTRO15.pdf)** [2] 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 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} } #2. Prerequisites We have tested the library in **Ubuntu 12.04** and **14.04**, 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**. ## Eigen3 Required by g2o (see below). Download and install instructions can be found at: http://eigen.tuxfamily.org. **Required at least 3.1.0**. ## BLAS and LAPACK [BLAS](http://www.netlib.org/blas) and [LAPACK](http://www.netlib.org/lapack) libraries are requiered by g2o (see below). On ubuntu: ``` sudo apt-get install libblas-dev sudo apt-get install liblapack-dev ``` ## 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 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 TUM/KITTI 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_SLAM.so** at *lib* folder and the executables **mono_tum**, **mono_kitti**, **rgbd_tum**, **stereo_kitti** 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 ``` ## 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 ``` #5. Stereo Example ## 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`to 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/Stereo/stereo_kitti Vocabulary/ORBvoc.txt Examples/Stereo/KITTIX.yaml PATH_TO_DATASET_FOLDER/dataset/sequences/SEQUENCE_NUMBER ``` #6. RGB-D Example ## TUM Dataset 1. Download a sequence from http://vision.in.tum.de/data/datasets/rgbd-dataset/download and uncompress it. 2. Associate RGB images and depth images using the python script [associate.py](http://vision.in.tum.de/data/datasets/rgbd-dataset/tools). We already provide associations for some of the sequences in *Examples/RGB-D/associations/*. You can generate your own associations file executing: ``` python associate.py PATH_TO_SEQUENCE/rgb.txt PATH_TO_SEQUENCE/depth.txt > associations.txt ``` 3. Execute th