Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/BertaBescos/DynaSLAM

DynaSLAM is a SLAM system robust in dynamic environments for monocular, stereo and RGB-D setups
https://github.com/BertaBescos/DynaSLAM

dynamic inpainting monocular rgb-d slam stereo

Last synced: about 2 months ago
JSON representation

DynaSLAM is a SLAM system robust in dynamic environments for monocular, stereo and RGB-D setups

Lists

README 

        
# 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