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raulmur logoORB_SLAM2

Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities

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Quick Overview

ORB_SLAM2 is an open-source SLAM (Simultaneous Localization and Mapping) system for monocular, stereo, and RGB-D cameras. It provides real-time camera tracking, map generation, and loop closing capabilities, making it suitable for various applications in robotics and augmented reality.

Pros

  • Robust and accurate localization and mapping in real-time
  • Supports multiple camera types (monocular, stereo, and RGB-D)
  • Includes loop closing and relocalization features
  • Well-documented and actively maintained

Cons

  • Computationally intensive, requiring a powerful CPU for real-time performance
  • Sensitive to rapid camera movements and motion blur
  • Limited to feature-rich environments
  • Steep learning curve for beginners

Code Examples

  1. Initializing the SLAM system:
// Create SLAM system. It initializes all system threads and gets ready to process frames.
ORB_SLAM2::System SLAM(argv[1], argv[2], ORB_SLAM2::System::RGBD, true);
  1. Processing a new frame:
// Pass the image to the SLAM system
SLAM.TrackRGBD(cv_ptrRGB->image, cv_ptrD->image, cv_ptrRGB->header.stamp.toSec());
  1. Saving the map:
// Stop all threads
SLAM.Shutdown();

// Save camera trajectory
SLAM.SaveTrajectoryTUM("CameraTrajectory.txt");
SLAM.SaveKeyFrameTrajectoryTUM("KeyFrameTrajectory.txt");

Getting Started

  1. Clone the repository:

    git clone https://github.com/raulmur/ORB_SLAM2.git ORB_SLAM2

  2. Build the project:

    cd ORB_SLAM2
chmod +x build.sh
./build.sh

  3. Download a vocabulary file and dataset, then run an example:

    ./Examples/RGB-D/rgbd_tum Vocabulary/ORBvoc.txt Examples/RGB-D/TUM1.yaml PATH_TO_SEQUENCE_FOLDER ASSOCIATIONS_FILE

Competitor Comparisons

UZ-SLAMLab logo

ORB_SLAM3

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ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM

Pros of ORB_SLAM3

  • Supports multi-map and multi-session capabilities
  • Includes inertial sensor integration for improved tracking
  • Offers visual-inertial odometry and SLAM modes

Cons of ORB_SLAM3

  • Higher computational requirements due to additional features
  • Potentially more complex setup and configuration process

Code Comparison

ORB_SLAM2:

void Tracking::Track()
{
    if(mState==NO_IMAGES_YET)
    {
        mState = NOT_INITIALIZED;
    }

ORB_SLAM3:

void Tracking::Track()
{
    if(mState==NO_IMAGES_YET)
    {
        mState = NOT_INITIALIZED;
    }

    if (bStepByStep && (mState==NOT_INITIALIZED || mState==OK))
        std::cin.get();

The ORB_SLAM3 code includes additional functionality for step-by-step execution, which is not present in ORB_SLAM2. This reflects the enhanced features and flexibility of ORB_SLAM3.

Both projects are based on the ORB-SLAM algorithm, but ORB_SLAM3 builds upon its predecessor with significant improvements and additional capabilities. While ORB_SLAM3 offers more advanced features, it may require more resources and setup time compared to the simpler ORB_SLAM2.

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  • Integrates visual-inertial odometry, providing more robust pose estimation in dynamic environments
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  • Offers loop closure and global optimization for improved accuracy

Cons of VINS-Fusion

  • Higher computational complexity due to sensor fusion and optimization
  • Requires additional sensors (IMU) for full functionality
  • Steeper learning curve for implementation and parameter tuning

Code Comparison

ORB_SLAM2 (Feature extraction):

void Frame::ExtractORB(int flag, const cv::Mat &im)
{
    if(flag==0)
        (*mpORBextractorLeft)(im,cv::Mat(),mvKeys,mDescriptors);
    else
        (*mpORBextractorRight)(im,cv::Mat(),mvKeysRight,mDescriptorsRight);
}

VINS-Fusion (IMU pre-integration):

void IMUProcessor::processIMU(double dt, const Vector3d &linear_acceleration, const Vector3d &angular_velocity)
{
    if (!first_imu)
    {
        first_imu = true;
        acc_0 = linear_acceleration;
        gyr_0 = angular_velocity;
    }
    // ... (additional pre-integration code)
}

Both projects are open-source SLAM systems, but VINS-Fusion focuses on multi-sensor fusion while ORB_SLAM2 is primarily vision-based. VINS-Fusion offers more robust performance in challenging environments, while ORB_SLAM2 is simpler to implement and requires fewer sensors.

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kalibr

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The Kalibr visual-inertial calibration toolbox

Pros of Kalibr

  • Specialized for multi-sensor calibration (cameras, IMUs)
  • Supports various camera models and sensor configurations
  • Provides detailed calibration reports and visualization tools

Cons of Kalibr

  • Narrower focus on calibration, not a full SLAM system
  • May require more setup and configuration for specific sensor setups
  • Less suitable for real-time applications compared to ORB_SLAM2

Code Comparison

ORB_SLAM2 (tracking.cc):

void Tracking::Track()
{
    if(mState==NO_IMAGES_YET)
    {
        mState = NOT_INITIALIZED;
    }

Kalibr (camera_calibration.py):

def calibrate(self):
    self.initializeIntrinsics()
    self.estimateTransforms()
    self.optimizeSystem()

ORB_SLAM2 focuses on real-time tracking and mapping, while Kalibr emphasizes precise calibration of multi-sensor systems. ORB_SLAM2 is more suitable for SLAM applications, whereas Kalibr excels in sensor calibration tasks. ORB_SLAM2 is implemented in C++ for performance, while Kalibr uses Python for flexibility and ease of use in calibration workflows.

MIT-SPARK logo

Kimera-VIO

1,527

Visual Inertial Odometry with SLAM capabilities and 3D Mesh generation.

Pros of Kimera-VIO

  • Integrates visual-inertial odometry (VIO) for improved accuracy in dynamic environments
  • Supports multi-sensor fusion, including IMU and wheel odometry
  • Provides 3D mesh reconstruction capabilities

Cons of Kimera-VIO

  • More complex setup and configuration due to additional sensor requirements
  • Potentially higher computational requirements for real-time performance
  • Less extensive community support and documentation compared to ORB_SLAM2

Code Comparison

ORB_SLAM2 (Feature extraction):

void Frame::ExtractORB(int flag, const cv::Mat &im)
{
    if(flag==0)
        (*mpORBextractorLeft)(im,cv::Mat(),mvKeys,mDescriptors);
    else
        (*mpORBextractorRight)(im,cv::Mat(),mvKeysRight,mDescriptorsRight);
}

Kimera-VIO (Feature tracking):

void VioBackEnd::featureTracking(const Frame& frame_lkf,
                                 const Frame& frame_k,
                                 const TrackingStatus& tracking_status) {
  CHECK(frame_lkf.isKeyframe());
  FeatureTracker::trackFeatures(frame_lkf, frame_k, tracking_status);
}

Both systems use feature extraction and tracking, but Kimera-VIO incorporates additional sensor data for improved localization and mapping.

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README

ORB-SLAM2

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

ORB-SLAM2 ORB-SLAM2 ORB-SLAM2

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. IEEE Transactions on Robotics, vol. 33, no. 5, pp. 1255-1262, 2017. 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={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 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

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_FOLDERto 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.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

  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.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

  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/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

  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 associations file executing:

python associate.py PATH_TO_SEQUENCE/rgb.txt PATH_TO_SEQUENCE/depth.txt > associations.txt
  1. 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

  1. 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
  1. 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.