Windows version of ORB-SLAM3 with some slight updates. Please refer to original website for details: https://github.com/UZ-SLAMLab/ORB_SLAM3

Tested under Windows 10 Pro, Visual Studio 2019 Community. I started from Visual Studio 2015 but met many "fatal error C1060: compiler is out of heap space" when building. Those errors are solved by switch to VS2019.

For easy to use, I put all the resources except for datasets into a folder and organize them as a single Visual Studio project.

Usage: slam.exe testName testArguments

Available tests as the following...

Monocular examples:

mono_euroc path_to_vocabulary path_to_settings path_to_sequence_folder_1 path_to_times_file_1 (path_to_image_folder_2 path_to_times_file_2 ... path_to_image_folder_N path_to_times_file_N) (trajectory_file_name)
mono_kitti path_to_vocabulary path_to_settings path_to_sequence
mono_tum path_to_vocabulary path_to_settings path_to_sequence
mono_tum_vi path_to_vocabulary path_to_settings path_to_image_folder_1 path_to_times_file_1 (path_to_image_folder_2 path_to_times_file_2 ... path_to_image_folder_N path_to_times_file_N) (trajectory_file_name)

Monocular-Inertial examples:

mono_inertial_euroc path_to_vocabulary path_to_settings path_to_sequence_folder_1 path_to_times_file_1 (path_to_image_folder_2 path_to_times_file_2 ... path_to_image_folder_N path_to_times_file_N)
mono_inertial_tum_vi path_to_vocabulary path_to_settings path_to_image_folder_1 path_to_times_file_1 path_to_imu_data_1 (path_to_image_folder_2 path_to_times_file_2 path_to_imu_data_2 ... path_to_image_folder_N path_to_times_file_N path_to_imu_data_N) (trajectory_file_name)

RGB-D:

rgbd_tum path_to_vocabulary path_to_settings path_to_sequence path_to_association

Stereo:

stereo_euroc path_to_vocabulary path_to_settings path_to_sequence_folder_1 path_to_times_file_1 (path_to_image_folder_2 path_to_times_file_2 ... path_to_image_folder_N path_to_times_file_N) (trajectory_file_name)
stereo_kitti path_to_vocabulary path_to_settings path_to_sequence
stereo_tum_vi path_to_vocabulary path_to_settings path_to_image_folder1_1 path_to_image_folder2_1 path_to_times_file_1 (path_to_image_folder1_2 path_to_image_folder2_2 path_to_times_file_2 ... path_to_image_folder1_N path_to_image_folder2_N path_to_times_file_N) (trajectory_file_name)

Stereo-Inertial:

stereo_inertial_euroc path_to_vocabulary path_to_settings path_to_sequence_folder_1 path_to_times_file_1 (path_to_image_folder_2 path_to_times_file_2 ... path_to_image_folder_N path_to_times_file_N)
stereo_inertial_tum_vi path_to_vocabulary path_to_settings path_to_image_folder_1 path_to_image_folder_2 path_to_times_file path_to_imu_data (trajectory_file_name)

Please point "pathDatasetEuroc" to your datasets before run the scripts.

set pathDatasetEuroc=YOUR_DATASET_PATH

Available example scripts

euroc_examples.bat      : Run all EuRoC examples and save result and log files to ./log
euroc_examples_1.bat    : Run a single EuRoC test and display log information to Command Prompt
kitti_examples.bat      : Run all KITTI examples and save result and log files to ./log
kitti_examples_1.bat    : Run a single KITTI test and display log information to Command Prompt
tum_vi_examples.bat     : Run all TUM-VI examples and save result and log files to ./log
tum_vi_examples_1.bat   : Run a single TUM-VI test and display log information to Command Prompt

Available evaluation scripts

euroc_eval_examples.bat
euroc_eval_examples_1.bat
tum_vi_eval_examples.bat

1. Add a global.h to provide some controls over entire project.

2. Reading "ORBvoc.txt" in binary format to speed-up since it seems slowly on Windows. Please comment the macro "USE_BINARY_VOC" out (global.h) to reading it from a txt file.

3. Eigen version is 3.3.7

4. OpenCV version is 3.2.0 and its DLLs are put into target folder directly (./x64/Debug).

5. Several small cross-platform modifications (search for _WINDOWS).

6. Boost library related contents are removed temporarily.

======================================================================

The following copied from original website: https://github.com/UZ-SLAMLab/ORB_SLAM3

Authors: Carlos Campos, Richard Elvira, Juan J. Gómez Rodríguez, José M. M. Montiel, Juan D. Tardos.

ORB-SLAM3 is the first real-time SLAM library able to perform Visual, Visual-Inertial and Multi-Map SLAM with monocular, stereo and RGB-D cameras, using pin-hole and fisheye lens models. In all sensor configurations, ORB-SLAM3 is as robust as the best systems available in the literature, and significantly more accurate.

We provide examples to run ORB-SLAM3 in the EuRoC dataset using stereo or monocular, with or without IMU, and in the TUM-VI dataset using fisheye stereo or monocular, with or without IMU. Videos of some example executions can be found at ORB-SLAM3 channel.

This software is based on ORB-SLAM2 developed by Raul Mur-Artal, Juan D. Tardos, J. M. M. Montiel and Dorian Galvez-Lopez (DBoW2).

[ORB-SLAM3] Carlos Campos, Richard Elvira, Juan J. Gómez Rodríguez, José M. M. Montiel and Juan D. Tardós, ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM, Under review. PDF.

[IMU-Initialization] Carlos Campos, J. M. M. Montiel and Juan D. Tardós, Inertial-Only Optimization for Visual-Inertial Initialization, ICRA 2020. PDF

[ORBSLAM-Atlas] Richard Elvira, J. M. M. Montiel and Juan D. Tardós, ORBSLAM-Atlas: a robust and accurate multi-map system, IROS 2019. PDF.

[ORBSLAM-VI] Raúl Mur-Artal, and Juan D. Tardós, Visual-inertial monocular SLAM with map reuse, IEEE Robotics and Automation Letters, vol. 2 no. 2, pp. 796-803, 2017. 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.

[Monocular] Raúl Mur-Artal, José 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.

[DBoW2 Place Recognition] 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

ORB-SLAM3 is released under GPLv3 license. For a list of all code/library dependencies (and associated licenses), please see Dependencies.md.

For a closed-source version of ORB-SLAM3 for commercial purposes, please contact the authors: orbslam (at) unizar (dot) es.

If you use ORB-SLAM3 in an academic work, please cite:

@article{ORBSLAM3_2020,
  title={{ORB-SLAM3}: An Accurate Open-Source Library for Visual, Visual-Inertial 
           and Multi-Map {SLAM}},
  author={Campos, Carlos AND Elvira, Richard AND G\´omez, Juan J. AND Montiel, 
          Jos\'e M. M. AND Tard\'os, Juan D.},
  journal={arXiv preprint arXiv:2007.11898},
  year={2020}
 }

We have tested the library in Ubuntu 16.04 and 18.04, 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.

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

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

We use OpenCV to manipulate images and features. Dowload and install instructions can be found at: http://opencv.org. Required at leat 3.0. Tested with OpenCV 3.2.0.

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

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.

Required to calculate the alignment of the trajectory with the ground truth. Required Numpy module.

• (win) http://www.python.org/downloads/windows
• (deb) sudo apt install libpython2.7-dev
• (mac) preinstalled with osx

We provide some examples to process input of a monocular, monocular-inertial, stereo, stereo-inertial or RGB-D camera using ROS. Building these examples is optional. These have been tested with ROS Melodic under Ubuntu 18.04.

Clone the repository:

git clone https://github.com/UZ-SLAMLab/ORB_SLAM3.git ORB_SLAM3

We provide a script build.sh to build the Thirdparty libraries and ORB-SLAM3. Please make sure you have installed all required dependencies (see section 2). Execute:

cd ORB_SLAM3
chmod +x build.sh
./build.sh

This will create libORB_SLAM3.so at lib folder and the executables in Examples folder.

EuRoC dataset was recorded with two pinhole cameras and an inertial sensor. We provide an example script to launch EuRoC sequences in all the sensor configurations.

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

2. Open the script "euroc_examples.sh" in the root of the project. Change pathDatasetEuroc variable to point to the directory where the dataset has been uncompressed.

3. Execute the following script to process all the sequences with all sensor configurations:

./euroc_examples

EuRoC provides ground truth for each sequence in the IMU body reference. As pure visual executions report trajectories centered in the left camera, we provide in the "evaluation" folder the transformation of the ground truth to the left camera reference. Visual-inertial trajectories use the ground truth from the dataset.

Execute the following script to process sequences and compute the RMS ATE:

./euroc_eval_examples

TUM-VI dataset was recorded with two fisheye cameras and an inertial sensor.

1. Download a sequence from https://vision.in.tum.de/data/datasets/visual-inertial-dataset and uncompress it.

2. Open the script "tum_vi_examples.sh" in the root of the project. Change pathDatasetTUM_VI variable to point to the directory where the dataset has been uncompressed.

3. Execute the following script to process all the sequences with all sensor configurations:

./tum_vi_examples

In TUM-VI ground truth is only available in the room where all sequences start and end. As a result the error measures the drift at the end of the sequence.

Execute the following script to process sequences and compute the RMS ATE:

./tum_vi_eval_examples

Tested with ROS Melodic and ubuntu 18.04.

1. Add the path including Examples/ROS/ORB_SLAM3 to the ROS_PACKAGE_PATH environment variable. Open .bashrc file:

gedit ~/.bashrc

and add at the end the following line. Replace PATH by the folder where you cloned ORB_SLAM3:

export ROS_PACKAGE_PATH=${ROS_PACKAGE_PATH}:PATH/ORB_SLAM3/Examples/ROS

2. Execute build_ros.sh script:

chmod +x build_ros.sh
./build_ros.sh

For a monocular input from topic /camera/image_raw run node ORB_SLAM3/Mono. You will need to provide the vocabulary file and a settings file. See the monocular examples above.

rosrun ORB_SLAM3 Mono PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE

For a monocular input from topic /camera/image_raw and an inertial input from topic /imu, run node ORB_SLAM3/Mono_Inertial. Setting the optional third argument to true will apply CLAHE equalization to images (Mainly for TUM-VI dataset).

rosrun ORB_SLAM3 Mono PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE [EQUALIZATION]	

For a stereo input from topic /camera/left/image_raw and /camera/right/image_raw run node ORB_SLAM3/Stereo. You will need to provide the vocabulary file and a settings file. For Pinhole camera model, if you provide rectification matrices (see Examples/Stereo/EuRoC.yaml example), the node will recitify the images online, otherwise images must be pre-rectified. For FishEye camera model, rectification is not required since system works with original images:

rosrun ORB_SLAM3 Stereo PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE ONLINE_RECTIFICATION

For a stereo input from topics /camera/left/image_raw and /camera/right/image_raw, and an inertial input from topic /imu, run node ORB_SLAM3/Stereo_Inertial. You will need to provide the vocabulary file and a settings file, including rectification matrices if required in a similar way to Stereo case:

rosrun ORB_SLAM3 Stereo_Inertial PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE ONLINE_RECTIFICATION [EQUALIZATION]	

For an RGB-D input from topics /camera/rgb/image_raw and /camera/depth_registered/image_raw, run node ORB_SLAM3/RGBD. You will need to provide the vocabulary file and a settings file. See the RGB-D example above.

rosrun ORB_SLAM3 RGBD PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE

Running ROS example: Download a rosbag (e.g. V1_02_medium.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 for a Stereo-Inertial configuration:

roscore

rosrun ORB_SLAM3 Stereo_Inertial Vocabulary/ORBvoc.txt Examples/Stereo-Inertial/EuRoC.yaml true

rosbag play --pause V1_02_medium.bag /cam0/image_raw:=/camera/left/image_raw /cam1/image_raw:=/camera/right/image_raw /imu0:=/imu

Once ORB-SLAM3 has loaded the vocabulary, press space in the rosbag tab.

Remark: For rosbags from TUM-VI dataset, some play issue may appear due to chunk size. One possible solution is to rebag them with the default chunk size, for example:

rosrun rosbag fastrebag.py dataset-room1_512_16.bag dataset-room1_512_16_small_chunks.bag