ORB-SLAM2

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

Linux	Windows

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 as stereo or monocular, in the TUM dataset as RGB-D or monocular, and in the EuRoC dataset 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.

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.

[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. ArXiv preprint arXiv: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

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: 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={arXiv preprint arXiv:1610.06475},
  year={2016}
 }

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

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 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 library to perform place recognition and 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. 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

3.1 Building on Windows

We use vcpkg package manager to install the dependencies. The script bootstrap_windows.bat assumes that vcpkg is installed from https://github.com/paul-michalik/vcpkg in \Software\vcpkg\vcpkg. Please follow the instructions in Readme to install vcpkg and modify bootstrap_windows.bat if other installation folder is used.

There is a script that automates building of orb-slam2 and its dependencies for Windows. We provide a script scripts/windows/build.bat to build the Thirdparty libraries and ORB-SLAM2. Please make sure you have installed all required dependencies (see section 2). Execute:

cd ORB_SLAM2
scripts/windows/build.bat

scripts/windows/build.bat takes following (optional) parameters:

Platform: x86 or x64
Toolset: v140, v141
Configuration: Debug, Release

Defaults correspond to:

scripts/windows/build.bat x86 v141 Debug

scripts/windows/build.bat invokes scripts/windows/bootstrap.bat which installs all required dependencies via vcpkg. Please install vcpkg according to

3.2 Building on Ubuntu Linux

We use apt-get package manager to install the dependencies. The script scripts/linux/bootstrap.sh will download and install all packages required to build the project.

We provide a script scripts/linux/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
scripts/linux/build.sh

scripts/linux/build.sh takes following (optional) parameters:

Platform
Toolset
Configuration

Defaults correspond to:

scripts/linux/build.sh `uname -m` gcc.`gcc -dumpversion` Debug

4. Monocular Examples

TUM Dataset

Download a sequence from http://vision.in.tum.de/data/datasets/rgbd-dataset/download and uncompress it.
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
```

KITTI Dataset

Download the dataset (grayscale images) from http://www.cvlibs.net/datasets/kitti/eval_odometry.php
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
```

EuRoC Dataset

Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets
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

./Examples/Monocular/semantic_monocular_video VideoFile Vocabulary/ORBvoc.txt Examples/Monocular/CameraSetting.yaml [JsonFile]

./Examples/Monocular/semantic_monocular Vocabulary/ORBvoc.txt Examples/Monocular/KITTIX.yaml PATH_TO_DATASET_FOLDER/dataset/sequences/SEQUENCE_NUMBER [JsonFile]

5. Stereo Examples

KITTI Dataset

Download the dataset (grayscale images) from http://www.cvlibs.net/datasets/kitti/eval_odometry.php
Execute the following command. Change KITTIX.yamlto 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
```

EuRoC Dataset

Download a sequence (ASL format) from http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets

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/Stereo/stereo_euroc Vocabulary/ORBvoc.txt Examples/Stereo/EuRoC.yaml PATH_TO_SEQUENCE/mav0/cam0/data PATH_TO_SEQUENCE/mav0/cam1/data Examples/Stereo/EuRoC_TimeStamps/SEQUENCE.txt

./Examples/Stereo/stereo_euroc Vocabulary/ORBvoc.txt Examples/Stereo/EuRoC.yaml PATH_TO_SEQUENCE/cam0/data PATH_TO_SEQUENCE/cam1/data Examples/Stereo/EuRoC_TimeStamps/SEQUENCE.txt

6. RGB-D Example

TUM Dataset

Download a sequence from http://vision.in.tum.de/data/datasets/rgbd-dataset/download and uncompress it.
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 associations file executing:
```
python associate.py PATH_TO_SEQUENCE/rgb.txt PATH_TO_SEQUENCE/depth.txt > associations.txt
```
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

Building the nodes for mono, monoAR, stereo and RGB-D

Add the path including Examples/ROS/ORB_SLAM2 to the ROS_PACKAGE_PATH environment 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
```
Execute build_ros.sh script:
```
chmod +x build_ros.sh
./build_ros.sh
```

Running Monocular Node

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

Running Monocular Augmented Reality Demo

This is a demo of augmented reality where you can use an interface to insert virtual cubes in planar regions of the scene. The node reads images from topic /camera/image_raw.

  rosrun ORB_SLAM2 MonoAR PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE

Running Stereo Node

For a stereo input from topic /camera/left/image_raw and /camera/right/image_raw run node ORB_SLAM2/Stereo. You will need to provide the vocabulary file and a settings file. If you provide rectification matrices (see Examples/Stereo/EuRoC.yaml example), the node will recitify the images online, otherwise images must be pre-rectified.

  rosrun ORB_SLAM2 Stereo PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE ONLINE_RECTIFICATION

Example: Download a rosbag (e.g. V1_01_easy.bag) from the EuRoC dataset (http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets). Open 3 tabs on the terminal and run the following command at each tab:

  roscore

  rosrun ORB_SLAM2 Stereo Vocabulary/ORBvoc.txt Examples/Stereo/EuRoC.yaml true

  rosbag play --pause V1_01_easy.bag /cam0/image_raw:=/camera/left/image_raw /cam1/image_raw:=/camera/right/image_raw

Once ORB-SLAM2 has loaded the vocabulary, press space in the rosbag tab. Enjoy!. Note: a powerful computer is required to run the most exigent sequences of this dataset.

Running RGB_D Node

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 monocular, stereo and RGB-D cameras. We use the calibration model of OpenCV. See the examples to learn how to create a program that makes use of the ORB-SLAM2 library and how to pass images to the SLAM system. Stereo input must be synchronized and rectified. RGB-D input must be synchronized and depth registered.

9. SLAM and Localization Modes

You can change between the SLAM and Localization mode using the GUI of the map viewer.

SLAM Mode

This is the default mode. The system runs in parallal three threads: Tracking, Local Mapping and Loop Closing. The system localizes the camera, builds new map and tries to close loops.

Localization Mode

This mode can be used when you have a good map of your working area. In this mode the Local Mapping and Loop Closing are deactivated. The system localizes the camera in the map (which is no longer updated), using relocalization if needed.

10. Tools

10.1 Vocabulary Converter

ORB-SLAM2 supports different formats of vocabulary like text, binary and xml. We can convert vocabulary from one format to another using vocconv tool

Usage

vocconv <input-file> <output-file>

Format of the file is detected using file extention. Valid file extentions are .txt, .bin, .xml. ORB-SALM2 also use file extention to detect vocabulary format.

Examples

vocconv Vocabulary/ORBvoc.txt Vocabulary/ORBvoc.bin
vocconv Vocabulary/ORBvoc.txt Vocabulary/ORBvoc.xml
vocconv Vocabulary/ORBvoc.bin Vocabulary/ORBvoc.txt

Note

Although different input formats are supported it is recommended to use binary format, as it saves lot of loading time. ORB-SLAM2 loads binary vocabulary much faster compared to xml and text vocabulary

10.2 eval_vo

eval_vo is a evaluation tool from KITTI which calculates and plots trajectory errors using ground truth as reference

Dependencies

eval_vo uses gnuplot and texlive-extra-utils packages. scripts/linux/bootstrap.sh will install these packages

Usage

Generate trajectory in KITTI pose format
Paste trajectory under tools/eval_vo/results/<result_sha>/data/ directory
Paste ground truth under tools/eval_vo/data/odometry/poses/ directory
Input trajectory filename and ground truth filename should match. Filenames from 00.txt to 21.txt are supported
Number of lines in ground truth file and input trajectory should match
Run ./tools/eval_vo/run.sh <result_sha>

Example

Lets assume, we have KeyFrameTrajectoryKITTI.txt generated for 00 sequence, then we should follow below steps.

Rename KeyFrameTrajectoryKITTI.txt to 00.txt
Paste 00.txt under tools/eval_vo/results/00/data/00.txt
Ground truth for 00 sequence is already present at tools/eval_vo/data/odometry/poses/00.txt, if you have different ground truth file you can overwrite this.
Run ./tools/eval_vo/run.sh 00
Results will be generated under tools/eval_vo/results/00/

paul-michalik / ORB_SLAM2

readme

ORB-SLAM2

Related Publications:

1. License

2. Prerequisites

C++11 or C++0x Compiler

Pangolin

OpenCV

Eigen3

DBoW2 and g2o (Included in Thirdparty folder)

ROS (optional)

3. Building ORB-SLAM2 library and examples

3.1 Building on Windows

3.2 Building on Ubuntu Linux

4. Monocular Examples

TUM Dataset

KITTI Dataset

EuRoC Dataset

5. Stereo Examples

KITTI Dataset

EuRoC Dataset

6. RGB-D Example

TUM Dataset

7. ROS Examples

Building the nodes for mono, monoAR, stereo and RGB-D

Running Monocular Node

Running Monocular Augmented Reality Demo

Running Stereo Node

Running RGB_D Node

8. Processing your own sequences

9. SLAM and Localization Modes

SLAM Mode

Localization Mode

10. Tools

10.1 Vocabulary Converter

Usage

Examples

Note

10.2 eval_vo

Dependencies

Usage

Example