# ORB_SLAM3_Fixed

**Repository Path**: ink315/ORB_SLAM3_Fixed

## Basic Information

- **Project Name**: ORB_SLAM3_Fixed
- **Description**: ORB_SLAM3_Fixed
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2020-12-16
- **Last Updated**: 2023-05-31

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# ORB-SLAM3
The raw address:
https://github.com/UZ-SLAMLab/ORB_SLAM3

详见：
>EVO Evaluation of SLAM 4 --- ORB-SLAM3 编译和利用数据集运行
>https://blog.csdn.net/shanpenghui/article/details/109354918


>EVO Evaluation of SLAM 5 --- ORB-SLAM3 精度和性能效果评估
>https://blog.csdn.net/shanpenghui/article/details/109361766


Before running, you should install Pangolin
```
https://github.com/stevenlovegrove/Pangolin.git
```

```
sudo apt install libglew-dev
cd Pangolin
mkdir build
cd build
cmake ..
make -j 4
sudo make install
```

google log:

```
git clone https://github.com/google/glog
cd glog
cmake -H. -Bbuild -G "Unix Makefiles"
cmake --build build
cmake --build build --target test
cd build
sudo make install
```

### Build ORB-SLAM3:
Work in shells path
```
cd shells
./build.sh

```

### Run ORB-SLAM3:
Before running, you should change the path in tum_vi.sh where you save the dataset, such as:
```
pathDatasetTUM_VI='/home/sph/Downloads' #Example, it is necesary to change it by the dataset path
```

Work in shells path
```
cd shells
./tum_vi.sh
```

要注意的是,目前只有单目带IMU的被激活,里面的配置需要自己对应自己的电脑更新

原版ros的编译会出现ORBSLAM2的错误
```
error: ‘ORB_SLAM2’ has not been declared
     ImageGrabber(ORB_SLAM2::System* pSLAM):mpSLAM(pSLAM){}
```

需要用指令修复：
```
sed -i "s/ORB_SLAM2/ORB_SLAM3/g" `grep -rl "ORB_SLAM2"`
```

原版ros的编译也有可能出现找不到文件的错误:
```
fatal error: GeometricCamera.h: No such file or directory #include "GeometricCamera.h"
```
需要在CMakeList添加文件路径:
```
${PROJECT_SOURCE_DIR}/../../../include/CameraModels
```

### Run ORB-SLAM3 in ros
Build ros version
```
cd shells
./build_ros.sh
```
Set ROS_PACKAGE_PATH:
```c++
export ROS_PACKAGE_PATH=${ROS_PACKAGE_PATH}:/home/sph/Documents/ORB_SLAM3_Fixed/Examples/ROS
```
Run ros-version ORB-SLAM3 in root path:
```
cd ORB_SLAM3_Fixed
rosrun ORB_SLAM3 Mono Vocabulary/ORBvoc.txt Examples/Monocular-Inertial/TUM_512.yaml
```
But!!!! You can`t run ORB-SLAM3 without run the camera_node!!!!
So, if you want to test ros-version, just use your computer camera(wish you have)
### Use usb_cam to run camera_node
```
git clone https://github.com/bosch-ros-pkg/usb_cam.git
```
Build and launch it, so you can see the /usb_cam/image_raw in rostopic.
But, that is not enough!!!!!
You should change the rostopic name in ORB-SLAM3, which is in Line 62, ros_mono.cc
```
ros::Subscriber sub = nodeHandler.subscribe("/usb_cam/image_raw", 1, &ImageGrabber::GrabImage,&igb);
```

After the steps up, it work finally!

##### A problem
When I first run it, error come out:
```
Failed to load module "canberra-gtk-module"
```
To solve this problem, install the module:
```
sudo apt-get install libcanberra-gtk-module
```

### 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)).

<a href="https://youtu.be/HyLNq-98LRo" target="_blank"><img src="https://img.youtube.com/vi/HyLNq-98LRo/0.jpg" 
alt="ORB-SLAM2" 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**, 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
```