ORB-SLAM2

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 system in the KITTI dataset as stereo or monocular, and in the TUM dataset as RGB-D or monocular. We also provide a ROS node to process live monocular or RGB-D streams. The system can be compiled without ROS. ORB-SLAM2 provides an interface to change between SLAM Mode and Localization Mode (only camera tracking, no map building).

Notice for ORB-SLAM Monocular users: The monocular capabilities of ORB-SLAM2 compared to ORB-SLAM Monocular, 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 slighly faster. The interface 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.

Link to pdf: http://webdiis.unizar.es/~raulmur/MurMontielTardosTRO15.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.

If you use ORB-SLAM2 in an academic work, please cite:

@article{murAcceptedTRO2015,
  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

2.0 C++11 or C++0x Compiler

We use the new thread and chrono functions of C++11.

2.1 Pangolin

We use Pangolin for visualization and user interface. Dowload and install instructions can be found at: https://github.com/stevenlovegrove/Pangolin.

##2.2 OpenCV We use OpenCV 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.

##2.3 Eigen3 Required by g2o (see below). Download and install instructions can be found at: http://eigen.tuxfamily.org. Required at least 3.1.0.

##2.4 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 both BSD) are included in the Thirdparty folder).

##2.5 ROS (optional) We provide some examples to process the live input of a monocular or RGB-D camera using ROS. 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. You will need to give first execute persmissions. At ORB_SLAM2 directory, execute:

chmod +x build.sh
./build.sh

This will create libORB_SLAM.so at lib folder and the exectuables mono_tum, mono_kitti, rgbd_tum, stereo_kitti in Examples folder.

#4. Monocular Examples

##4.1 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_FOLDERto the uncompressed sequence folder.

./Examples/Monocular/mono_tum Vocabulary/ORBvoc.txt Examples/Monocular/TUMX.yaml PATH_TO_SEQUENCE_FOLDER

##4.2 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.yamlby 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

##5.1 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.yamlby 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

##6.1 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. We already provide associations for some of the sequences in Examples/RGB-D/associations/. You can generate your own association file executing:

python associate.py PATH_TO_SEQUENCE/rgb.txt PATH_TO_SEQUENCE/depth.txt > associations.txt

3. 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_FOLDERto the uncompressed sequence folder. Change ASSOCIATIONS_FILE to the path to the corresponding associations file.

./Examples/RGB-D/rgbd_tum Vocabulary/ORBvoc.txt Examples/RGB-D/TUMX.yaml PATH_TO_SEQUENCE_FOLDER ASSOCIATIONS_FILE

#7. ROS Examples

1. First add the path including Examples/ROS/ORB_SLAM2 to the ROS_PACKAGE_PATH enviroment variable. Open .bashrc file and add at the end the following line. Replace PATH by the folder where you cloned ORB_SLAM2:

export ROS_PACKAGE_PATH=${ROS_PACKAGE_PATH}:PATH/ORB_SLAM2/Examples/ROS

2. Go to Examples/ROS/ORB_SLAM2 folder and execute:

mkdir build
cd build
cmake .. -DROS_BUILD_TYPE=Release
make -j

3. For a monocular input from topic /camera/image_raw run node ORB_SLAM2/Mono. You will need to provide the vocabulary file and a settings file. See the monocular examples above.

rosrun ORB_SLAM2 Mono PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE

4. For an RGB-D input from topics /camera/rgb/image_raw and /camera/depth_registered/image_raw, run node ORB_SLAM2/RGBD. You will need to provide the vocabulary file and a settings file. See the RGB-D example above.

rosrun ORB_SLAM2 RGBD PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE

#8. Processing your own sequences You will need to create a settings file with the calibration of your camera. See the settings file provided for the TUM and KITTI datasets for the monocular, stereo and RGB-D. We use the calibration model of OpenCV. See the examples to learn how to create a program that makes use of the ORB-SLAM library and how to pass images to the SLAM system. Stereo input must be synchronized and rectified. RGB-D input must be synchronized.

#9. Need Help?

If you have any trouble installing or using ORB-SLAM2, contact the authors.