README.md · onlyGavin/DynaSLAM-YoloV5

# DynaSLAM

[[Project]](https://bertabescos.github.io/DynaSLAM/)   [[arXiv]](https://arxiv.org/abs/1806.05620)   [[Journal]](https://ieeexplore.ieee.org/document/8421015)

DynaSLAM is a visual SLAM system that is robust in dynamic scenarios for monocular, stereo and RGB-D configurations. Having a static map of the scene allows inpainting the frame background that has been occluded by such dynamic objects.

DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes   
[Berta Bescos](http://bertabescos.github.io), [José M. Fácil](http://webdiis.unizar.es/~jmfacil/), [Javier Civera](http://webdiis.unizar.es/~jcivera/) and [José Neira](http://webdiis.unizar.es/~jneira/)   
RA-L and IROS, 2018

We provide examples to run the SLAM system in the [TUM dataset](http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets) as RGB-D or monocular, and in the [KITTI dataset](http://www.cvlibs.net/datasets/kitti/eval_odometry.php) as stereo or monocular.

## News
- DynaSLAM supports now both OpenCV 2.X and OpenCV 3.X.

## Getting Started
- Install ORB-SLAM2 prerequisites: C++11 or C++0x Compiler, Pangolin, OpenCV and Eigen3  (https://github.com/raulmur/ORB_SLAM2).
- Install boost libraries with the command `sudo apt-get install libboost-all-dev`.
- Install python 2.7, keras and tensorflow, and download the `mask_rcnn_coco.h5` model from this GitHub repository: https://github.com/matterport/Mask_RCNN/releases. 
- Clone this repo:
```bash
git clone https://github.com/BertaBescos/DynaSLAM.git
cd DynaSLAM
```
```
cd DynaSLAM
	chmod +x build.sh
./build.sh
```
- Place the `mask_rcnn_coco.h5` model in the folder `DynaSLAM/src/python/`.

## RGB-D Example on TUM Dataset
- Download a sequence from http://vision.in.tum.de/data/datasets/rgbd-dataset/download and uncompress it.

- Associate RGB images and depth images executing the python script [associate.py](http://vision.in.tum.de/data/datasets/rgbd-dataset/tools):

```
  python associate.py PATH_TO_SEQUENCE/rgb.txt PATH_TO_SEQUENCE/depth.txt > associations.txt
  ```
These associations files are given in the folder `./Examples/RGB-D/associations/` for the TUM dynamic sequences.

- 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. `PATH_TO_MASKS` and `PATH_TO_OUTPUT` are optional parameters.

```
  ./Examples/RGB-D/rgbd_tum Vocabulary/ORBvoc.txt Examples/RGB-D/TUMX.yaml PATH_TO_SEQUENCE_FOLDER ASSOCIATIONS_FILE (PATH_TO_MASKS) (PATH_TO_OUTPUT)
  ```
  
If `PATH_TO_MASKS` and `PATH_TO_OUTPUT` are **not** provided, only the geometrical approach is used to detect dynamic objects.

If `PATH_TO_MASKS` is provided, Mask R-CNN is used to segment the potential dynamic content of every frame. These masks are saved in the provided folder `PATH_TO_MASKS`. If this argument is `no_save`, the masks are used but not saved. If it finds the Mask R-CNN computed dynamic masks in `PATH_TO_MASKS`, it uses them but does not compute them again.

If `PATH_TO_OUTPUT` is provided, the inpainted frames are computed and saved in `PATH_TO_OUTPUT`.

## Stereo Example on KITTI Dataset
- Download the dataset (grayscale images) from http://www.cvlibs.net/datasets/kitti/eval_odometry.php

- Execute the following command. Change `KITTIX.yaml`to KITTI00-02.yaml, KITTI03.yaml or KITTI04-12.yaml for sequence 0 to 2, 3, and 4 to 12 respectively. Change `PATH_TO_DATASET_FOLDER` to the uncompressed dataset folder. Change `SEQUENCE_NUMBER` to 00, 01, 02,.., 11. By providing the last argument `PATH_TO_MASKS`, dynamic objects are detected with Mask R-CNN.
```
./Examples/Stereo/stereo_kitti Vocabulary/ORBvoc.txt Examples/Stereo/KITTIX.yaml PATH_TO_DATASET_FOLDER/dataset/sequences/SEQUENCE_NUMBER (PATH_TO_MASKS)
```

## Monocular Example on TUM Dataset
- Download a sequence from http://vision.in.tum.de/data/datasets/rgbd-dataset/download and uncompress it.

- 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. By providing the last argument `PATH_TO_MASKS`, dynamic objects are detected with Mask R-CNN.
```
./Examples/Monocular/mono_tum Vocabulary/ORBvoc.txt Examples/Monocular/TUMX.yaml PATH_TO_SEQUENCE_FOLDER (PATH_TO_MASKS)
```

## Monocular Example on KITTI Dataset
- Download the dataset (grayscale images) from http://www.cvlibs.net/datasets/kitti/eval_odometry.php

- Execute the following command. Change `KITTIX.yaml`by KITTI00-02.yaml, KITTI03.yaml or KITTI04-12.yaml for sequence 0 to 2, 3, and 4 to 12 respectively. Change `PATH_TO_DATASET_FOLDER` to the uncompressed dataset folder. Change `SEQUENCE_NUMBER` to 00, 01, 02,.., 11. By providing the last argument `PATH_TO_MASKS`, dynamic objects are detected with Mask R-CNN.
```
./Examples/Monocular/mono_kitti Vocabulary/ORBvoc.txt Examples/Monocular/KITTIX.yaml PATH_TO_DATASET_FOLDER/dataset/sequences/SEQUENCE_NUMBER (PATH_TO_MASKS)
```

## Citation

If you use DynaSLAM in an academic work, please cite:

@article{bescos2018dynaslam,
      title={{DynaSLAM}: Tracking, Mapping and Inpainting in Dynamic Environments},
      author={Bescos, Berta, F\'acil, JM., Civera, Javier and Neira, Jos\'e},
      journal={IEEE RA-L},
      year={2018}
     }

## Acknowledgements
Our code builds on [ORB-SLAM2](https://github.com/raulmur/ORB_SLAM2).

# DynaSLAM

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