Awesome
DynaSLAM
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.
<img src="imgs/teaser.png" width="900px"/>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.
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:
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 folderDynaSLAM/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:
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. ChangePATH_TO_SEQUENCE_FOLDER
to the uncompressed sequence folder. ChangeASSOCIATIONS_FILE
to the path to the corresponding associations file.PATH_TO_MASKS
andPATH_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. ChangePATH_TO_DATASET_FOLDER
to the uncompressed dataset folder. ChangeSEQUENCE_NUMBER
to 00, 01, 02,.., 11. By providing the last argumentPATH_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. ChangePATH_TO_SEQUENCE_FOLDER
to the uncompressed sequence folder. By providing the last argumentPATH_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. ChangePATH_TO_DATASET_FOLDER
to the uncompressed dataset folder. ChangeSEQUENCE_NUMBER
to 00, 01, 02,.., 11. By providing the last argumentPATH_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.