awesome-orb-slam3-3dvisioncraft-version: 本demo主要基于知识星球「3D视觉从入门到精通」特邀嘉宾魏宏宇博士对orb-slam3的解读所做的代码注释。

# ORB_SLAM3_vision3d

本仓库主要folked from  [ORB-SLAM3](https://github.com/UZ-SLAMLab/ORB_SLAM3), 主要对其进行代码注释，与bug修复。

### 主要参与的工作人员

本工程主要由公众号「3D视觉工坊」的嘉宾宏宇博士完成, 课件已经分享在[「3D视觉从入门到精通」知识星球](https://www.3dcver.com/page/2734639?navIndex=3)

作者介绍：

魏宏宇博士，东南大学博三在读，在IEEE Sensors Journal等期刊发表论文2篇，精通视觉惯性SLAM。「3D视觉从入门到精通」知识星球特邀嘉宾。主讲课程如下：

orb-slam3系列视频课程，学习地址->[前往](https://www.3dcver.com/detail/p_625d6caae4b09dda125cab85/6)

1、[ORB-SLAM3源码与原理彻底解析-第一讲-绪论](https://www.3dcver.com/detail/v_6241bfd9e4b0f7cb7c77e870/3)

2、[ORB-SLAM3系列之匹配算法（一）](https://www.3dcver.com/detail/l_62503788e4b04e8d902d54b2/4)

3、[ORB-SLAM3系列之匹配算法（二）](https://t.zsxq.com/3fYZJMf)

4、[ORB-SLAM3系列之特征匹配（三）](https://www.3dcver.com/detail/l_62a3dfe3e4b0812e17a59288/4)

5、[ORBSLAM3系列之地图点（一）](https://www.3dcver.com/detail/l_62bc5bf1e4b0c94264742d45/4)

6、[ORB-SLAM3系列代码讲解-地图点二](https://t.zsxq.com/04jeuBeAq)

7、[ORB-SLAM3系列代码讲解——关键帧（专题一）](https://www.3dcver.com/detail/l_62ed21aae4b050af23a57764/4)

### 说明

Two ROS interfaces Mono_inertial and Stereo_inertial were added to the ORB-SLAM3. Modified the loading vocabulary in a faster binary way.

增加了两个ROS接口，单目IMU和双目IMU，替换了词典为二进制格式，加载速度更快。

euroc_data download for test:

链接: https://pan.baidu.com/s/1YEt1WNl1cHdgHWdbtWQDOQ?pwd=45uk 提取码: 45uk

### Other things

由于我们在学习过程中，难免会借鉴参考其他优秀的博客与工程，如有不当之处，还请批评指正。

也希望更多的小伙伴能够加入我们的学习队伍，联系邮箱：vision3d@yeah.net

---------------------------------------------------------------------------------------------------------------------------------------------------

# ORB-SLAM3

### V0.2: Beta version, 21 Jul 2020
**Authors:** Carlos Campos, Richard Elvira, Juan J. Gómez Rodríguez, [José M. M. Montiel](http://webdiis.unizar.es/~josemari/), [Juan D. Tardos](http://webdiis.unizar.es/~jdtardos/).

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](http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets) using stereo or monocular, with or without IMU, and in the [TUM-VI dataset](https://vision.in.tum.de/data/datasets/visual-inertial-dataset) using fisheye stereo or monocular, with or without IMU. Videos of some example executions can be found at [ORB-SLAM3 channel](https://www.youtube.com/channel/UCXVt-kXG6T95Z4tVaYlU80Q).

This software is based on [ORB-SLAM2](https://github.com/raulmur/ORB_SLAM2) developed by [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)).

### 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**, Under review. **[PDF](https://arxiv.org/pdf/2007.11898.pdf)**.

[IMU-Initialization] Carlos Campos, J. M. M. Montiel and Juan D. Tardós, **Inertial-Only Optimization for Visual-Inertial Initialization**, *ICRA 2020*. **[PDF](https://arxiv.org/pdf/2003.05766.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](https://arxiv.org/pdf/1908.11585.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](https://arxiv.org/pdf/1610.05949.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://arxiv.org/pdf/1610.06475.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](https://arxiv.org/pdf/1502.00956.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](http://doriangalvez.com/php/dl.php?dlp=GalvezTRO12.pdf)**

# 1. License

ORB-SLAM3 is released under [GPLv3 license](https://github.com/UZ-SLAMLab/ORB_SLAM3/LICENSE). For a list of all code/library dependencies (and associated licenses), please see [Dependencies.md](https://github.com/UZ-SLAMLab/ORB_SLAM3/blob/master/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}
     }

# 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](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 3.0. Tested with OpenCV 3.2.0**.

## 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](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.

## 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

# 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. EuRoC Examples
[EuRoC dataset](http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets) 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
```

# 5. TUM-VI Examples
[TUM-VI dataset](https://vision.in.tum.de/data/datasets/visual-inertial-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
```

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