# Fast-Dynamic-ORB-SLAM

**Repository Path**: wind_x/Fast-Dynamic-ORB-SLAM

## Basic Information

- **Project Name**: Fast-Dynamic-ORB-SLAM
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-10-21
- **Last Updated**: 2024-09-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# FAST Dynamic ORB-SLAM2
**Authors:** Justin Bi, Li Chen, Pradeep Suresh, Yicheng Tao, Yuliang Zhu

FAST Dynamic ORB-SLAM2 is a near real-time SLAM library that is built on [ORB-SLAM 2](https://github.com/raulmur/ORB_SLAM2) by Mur-Artal et al. It attempts to mitigate the error introduced by dynamic objects in the SLAM system on RGB-D cameras. See our other repository for related work: https://github.com/bijustin/YOLO-DynaSLAM

Our paper is located here: https://github.com/bijustin/Fast-Dynamic-ORB-SLAM/blob/master/dynamic-orb-slam.pdf

Our video is located here: https://www.youtube.com/watch?v=sazkFVVjITQ


## Moving Object Detection
In the project,  Firstly, ORB featurematches between two close frames are found. Secondly,the  fundamental  matrix  is  solved  using  the  RANSACalgorithm. The third step is calculating the epipolar lines(corresponding  to  the  outlier  matches)  in  the  secondframe,  and  calculate  the  distance  between  the  feature points  and  the  epipolar  lines.  If  the  distance  is  greaterthan some threshold, then we consider its corresponding feature point to be part of the moving object.

Finally, we utilize optical flow's angle's relation to optimize our results.

The following are the results using TUM dataset.  

![avatar](https://github.com/bijustin/Fast-Dynamic-ORB-SLAM/blob/master/indoor2.gif)

![avatar](https://github.com/bijustin/Fast-Dynamic-ORB-SLAM/blob/master/indoor3.gif)

# 1. License

FAST Dynamic ORB-SLAM2 is based on ORB-SLAM2, and as such is released under a [GPLv3 license](https://github.com/bijustin/Fast-Dynamic-ORB-SLAM/blob/master/License-gpl.txt). For a list of all code/library dependencies (and associated licenses), please see [Dependencies.md](https://github.com/bijustin/Fast-Dynamic-ORB-SLAM/blob/master/Dependencies.md).


# 2. Prerequisites

FAST Dynamic ORB-SLAM 2 has been tested in Ubuntu 18.04, but should be fine to compile on other platforms. A powerful CPU will result in real-time results.


## Libraries
FAST Dynamic ORB-SLAM 2 is developed based on ORB-SLAM 2, and uses no additional libraries. Thus, the required libraries are the same, as follows:

## 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 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](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.

# 3. Building FAST Dynamic ORB-SLAM2 library and examples

Clone the repository:
```
git clone https://github.com/bijustin/Fast-Dynamic-ORB-SLAM.git FASTORB_SLAM2
```

We provide a script `build.sh` to build the *Thirdparty* libraries and *FAST Dynamic ORB-SLAM2*. Please make sure you have installed all required dependencies (see section 2). Execute:
```
cd FASTORB_SLAM2
chmod +x build.sh
./build.sh
```

This will create **libORB_SLAM2.so**  at *lib* folder and the executable **rgbd_tum** in *Examples* folder.


# 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](http://vision.in.tum.de/data/datasets/rgbd-dataset/tools). 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
  ```

3. 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_FOLDER`to 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
  ```
  
  
## Acknowledgements
Our code builds on [ORB-SLAM2](https://github.com/raulmur/ORB_SLAM2) and [DynaSLAM](https://github.com/BertaBescos/DynaSLAM).