zzzzzzs/orb-slam3-windows-shared-lib

ORB_SLAM3 for windows with detailed comments

ORB_SLAM3 for Windows with Shared lib 共享库运行

this repository is pull from https://github.com/electech6/ORB_SLAM3_detailed_comments which contain detailed chinese comment that benefit SLAM learners. This repository transplant ORB_SLAM 3 to windows platform, and with a little modification to make it better compatibility to OpenCV4 and newer version of g2o.

这个存储库是从 https://github.com/electech6/ORB_SLAM3_detailed_comments 中提取的，其中包含有利于 SLAM 学习者的详细中文评论。 本库将 ORB_SLAM 3 移植到 windows 平台，并稍作修改使其与 OpenCV4 和更新版本的 g2o 具有更好的兼容性。

How to Build 如何构建

build required libs 构建依赖

To build this repository, g2o, opencv4, Sophus, boost, Eigen3 and Pangolin is required. It is recommended to use Vcpkg to get those libs, however, you can also build these libs yourself. simply typing the command below, and the required libs will be installed. the modified version of DBoW2 is integrated in this repo, no separate installation required.

要构建此存储库，需要 g2o、opencv4、Sophus、boost、Eigen3 和 Pangolin。 建议使用 Vcpkg 来获取这些库。 只需在下面键入命令，即可安装所需的库。注意在**大陆地区vcpkg的连接并不稳定，您也可以选择手动安装这些库。修改版的DBoW2已经集成在本仓库中，不需要单独安装。

vcpkg install opencv4:x64-windows sophus:x64-windows g2o:x64-windows boost:x64-windows eigen3:x64-windows pangolin:x64-windows

build ORB_SLAM3

• Open cmake-gui and make a 'build' directory in ORB-SLAM3forWindows
• Click Configure and select Visual Studio(we tested on VS2022, other version should also work)
• Click Generate and open the resulting project in the build directory.
• ORB-SLAM3 project -> Properties -> C/C++ Tab -> Code Generation -> Runtime Library: Multi-threaded(/MT)(for time saving)
• Build ORB-SLAM3

构建ORB_SLAM3

• 打开 cmake-gui 并在 ORB-SLAM3forWindows 中创建一个“构建”目录
• 点击Configure，选择Visual Studio（我们在VS2022上测试过，其他版本也应该可以）
• 单击生成并在构建目录中打开生成的项目。
• ORB-SLAM3 project -> Properties -> C/C++ Tab -> Code Generation -> Runtime Library: Multi-threaded(/MT)(节省时间)
• 构建 ORB-SLAM3

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ORB-SLAM3 超详细注释

-by 计算机视觉life 公众号旗下开源学习小组：SLAM研习社

ORB-SLAM3 逐行源码讲解

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ORB-SLAM3

V1.0, December 22th, 2021

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

The Changelog describes the features of each version.

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).

<a href="https://youtu.be/HyLNq-98LRo" target="_blank"><img src="https://img.youtube.com/vi/HyLNq-98LRo/0.jpg" alt="ORB-SLAM3" width="240" height="180" border="10" />``</a>

Related Publications:

[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, IEEE Transactions on Robotics 37(6):1874-1890, Dec. 2021. 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

1. License

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_TRO,
  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={IEEE Transactions on Robotics},
  volume={37},
  number={6},
  pages={1874-1890},
  year={2021}
 }

2. Prerequisites

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.

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 3.0. Tested with OpenCV 3.2.0 and 4.4.0.

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.

Python

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

ROS (optional)

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.

3. Building ORB-SLAM3 library and examples

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.

4. Running ORB-SLAM3 with your camera

Directory Examples contains several demo programs and calibration files to run ORB-SLAM3 in all sensor configurations with Intel Realsense cameras T265 and D435i. The steps needed to use your own camera are:

1. Calibrate your camera following Calibration_Tutorial.pdf and write your calibration file your_camera.yaml
2. Modify one of the provided demos to suit your specific camera model, and build it
3. Connect the camera to your computer using USB3 or the appropriate interface
4. Run ORB-SLAM3. For example, for our D435i camera, we would execute:

./Examples/Stereo-Inertial/stereo_inertial_realsense_D435i Vocabulary/ORBvoc.txt ./Examples/Stereo-Inertial/RealSense_D435i.yaml

5. EuRoC Examples

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

Evaluation

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

6. TUM-VI 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

Evaluation

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

7. ROS Examples

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

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

Running Monocular Node

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

Running Monocular-Inertial Node

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]

Running Stereo Node

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

Running Stereo-Inertial Node

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]

Running RGB_D Node

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

8. Running time analysis

A flag in include\Config.h activates time measurements. It is necessary to uncomment the line #define REGISTER_TIMES to obtain the time stats of one execution which is shown at the terminal and stored in a text file(ExecTimeMean.txt).

9. Calibration

You can find a tutorial for visual-inertial calibration and a detailed description of the contents of valid configuration files at Calibration_Tutorial.pdf