In this section please find the steps I have to follow to compile DynaSLAM. All the steps were replicated on a clean Ubuntu 18.04 machine. The next section contains the vanillia docs from the original repo.

A blog that help me get started was this one: https://www.ybliu.com/2020/05/how-to-use-dynaslam-using-docker.html And this video helps too: https://www.youtube.com/watch?v=qRhFgEIRs_s

• For ORB-SLAM2 you will need to use my fork: https://github.com/alexs7/ORB_SLAM2/tree/dynaSLAM_compatible
• Similarly for OpenCV: https://github.com/alexs7/opencv/tree/dynaSLAM_compatible
• For Pangolin same too: https://github.com/alexs7/Pangolin/tree/dynaSLAM_compatible

Follow for each repo above the default instructions to install.

For keras, tensoflow, numpy and python please use these versions

• keras = 2.1.6
• tensorflow = 1.13.1
• numpy = 1.16.6
• python = 2.7

And that's it! You should install DynaSLAM with no issues, by following the rest of the default instructions.

PS: I did not get the inpainting working - I think it is not released - please let me know if you get it working.

[Project] [arXiv] [Journal]

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, José M. Fácil, Javier Civera and José Neira
RA-L and IROS, 2018

We provide examples to run the SLAM system in the TUM dataset as RGB-D or monocular, and in the KITTI dataset as stereo or monocular.

• DynaSLAM supports now both OpenCV 2.X and OpenCV 3.X.

• 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:

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/.

• 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:

  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.

• Download the dataset (grayscale images) from http://www.cvlibs.net/datasets/kitti/eval_odometry.php

• Execute the following command. Change KITTIX.yamlto 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)

• 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_FOLDERto 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)

• Download the dataset (grayscale images) from http://www.cvlibs.net/datasets/kitti/eval_odometry.php

• Execute the following command. Change KITTIX.yamlby 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)

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}
 }

Our code builds on ORB-SLAM2.