# 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
```
## 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/eval_odometry.php
2. Execute the following command. Change `KITTIX.yaml`to KITTI00-02.yaml, KITTI03.yaml or KITTI04-12
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毕设&课设&项目&实训-基于双目视觉惯性的四轮差速自主导航机器人.zip (331个子文件)
mmap_address.bin 8B
zoulang_car_rtime_octomap_sp8.bt 455KB
524_car_rtime_octomap_sp8.bt 247KB
map.c 2KB
os_specific.c 2KB
ORBmatcher.cc 48KB
Tracking.cc 47KB
ORBextractor.cc 43KB
Optimizer.cc 40KB
PnPsolver.cc 28KB
Initializer.cc 26KB
LoopClosing.cc 25KB
LocalMapping.cc 23KB
Frame.cc 20KB
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ros_stereo.cc 17KB
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MapPoint.cc 11KB
KeyFrameDatabase.cc 10KB
ros_rgbd.cc 9KB
MapDrawer.cc 8KB
stereo_euroc.cc 7KB
Viewer.cc 6KB
FrameDrawer.cc 6KB
rgbd_tum.cc 5KB
stereo_kitti.cc 5KB
ros_mono_ar.cc 5KB
mono_kitti.cc 4KB
mono_euroc.cc 4KB
mono_tum.cc 4KB
Converter.cc 4KB
Map.cc 3KB
ros_mono.cc 2KB
FindBLAS.cmake 13KB
FindLAPACK.cmake 10KB
FindEigen3.cmake 3KB
FindEigen3.cmake 3KB
surfel_map.cpp 76KB
fusion_functions.cpp 66KB
main_navigation.cpp 54KB
surfel_map.cpp 51KB
fusion_functions.cpp 40KB
optimizable_graph.cpp 27KB
sparse_optimizer.cpp 20KB
TrackingPthread.cpp 19KB
map_process.cpp 18KB
types_six_dof_expmap.cpp 10KB
estimate_propagator.cpp 10KB
hyper_dijkstra.cpp 9KB
hyper_graph_action.cpp 9KB
nav.cpp 8KB
optimization_algorithm_dogleg.cpp 8KB
map_image.cpp 8KB
ScoringObject.cpp 8KB
marginal_covariance_cholesky.cpp 7KB
CameraPoseVisualization.cpp 7KB
modbus.cpp 7KB
optimization_algorithm_levenberg.cpp 7KB
factory.cpp 6KB
types_seven_dof_expmap.cpp 6KB
mapreadandwrite.cpp 6KB
cache.cpp 5KB
Timestamp.cpp 5KB
A_star.cpp 5KB
map_deal.cpp 5KB
string_tools.cpp 5KB
robust_kernel_impl.cpp 5KB
optimization_algorithm_factory.cpp 5KB
parameter_container.cpp 4KB
FORB.cpp 4KB
hyper_graph.cpp 4KB
timeutil.cpp 4KB
paras.cpp 4KB
matrix_structure.cpp 3KB
optimization_algorithm_with_hessian.cpp 3KB
optimization_algorithm_gauss_newton.cpp 3KB
property.cpp 3KB
robust_kernel_factory.cpp 3KB
jacobian_workspace.cpp 3KB
batch_stats.cpp 3KB
ros_node.cpp 3KB
sparse_block_matrix_test.cpp 3KB
Random.cpp 3KB
main_map_server.cpp 3KB
BowVector.cpp 3KB
solver.cpp 3KB
teleop_joy.cpp 2KB
optimization_algorithm.cpp 2KB
pcdto_octomap_pub.cpp 2KB
types_sba.cpp 2KB
FeatureVector.cpp 2KB
robust_kernel.cpp 2KB
parameter.cpp 2KB
ros_node.cpp 1KB
main_Tracking.cpp 410B
.gitignore 552B
ORBvoc.txt.tar.gz 40.56MB
TemplatedVocabulary.h 41KB
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