SalientDSO

Bringing attention to Direct Sparse Odometry

1. What?

This is the accompanying source code for the paper SalientDSO: Bringing attention to Direct Sparse Odometry published in IEEE Transactions in Autonmation Science and Engineering. Visit our project page for more details.

2. Video

3. Citation

If you use this for your research please cite:

@ARTICLE{salientdso, 
author={H. {Liang} and N. J. {Sanket} and C. {Fermüller} and Y. {Aloimonos}}, 
journal={IEEE Transactions on Automation Science and Engineering}, 
title={SalientDSO: Bringing Attention to Direct Sparse Odometry}, 
year={2019}, 
volume={}, 
number={}, 
pages={1-8}, 
keywords={Visualization;Semantics;Optimization;Task analysis;Cameras;Computer vision;Direct sparse odometry (DSO);scene parsing;SLAM;visual saliency.}, 
doi={10.1109/TASE.2019.2900980}, 
ISSN={}, 
month={},}

Also, citing the original DSO paper is appreciated.

@ARTICLE{dso, 
author={J. {Engel} and V. {Koltun} and D. {Cremers}}, 
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
title={Direct Sparse Odometry}, 
year={2018}, 
volume={40}, 
number={3}, 
pages={611-625}, 
keywords={distance measurement;image motion analysis;image sampling;optimisation;probability;direct sparse odometry;visual odometry method;motion formulation;fully direct probabilistic model;consistent optimization;joint optimization;reference frame;camera motion;sample pixels;smooth intensity variations;Cameras;Geometry;Three-dimensional displays;Optimization;Robustness;Computational modeling;Visualization;Visual odometry, SLAM, 3D reconstruction, structure from motion}, 
doi={10.1109/TPAMI.2017.2658577}, 
ISSN={}, 
month={March},}

4. Setup Instructions

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

4.1. Prerequisites

You need to have the following packages on your machine.

• Eigen3 - Eigen
• OpenCV - OpenCV
• Pangolin - Pangolin

4.2. Installing

git clone [email protected]:huaijenliang/SalientDSO-pub.git

4.2.1. Required Dependencies

• suitesparse and eigen3.

Install using

sudo apt-get install libsuitesparse-dev libeigen3-dev libboost-all-dev

• OpenCV

Install using

sudo apt-get install libopencv-dev

• Pangolin

Install from https://github.com/stevenlovegrove/Pangolin

• ziplib

sudo apt-get install zlib1g-dev
cd dso/thirdparty
tar -zxvf libzip-1.1.1.tar.gz
cd libzip-1.1.1/
./configure
make
sudo make install
sudo cp lib/zipconf.h /usr/local/include/zipconf.h   # (no idea why that is needed).

• sse2neon (For ARM builds only) After cloning, just run git submodule update --init to include this. It translates Intel-native SSE functions to ARM-native NEON functions during the compilation process.

4.2.2. Building

Test if the original DSO setting works first. Compile using USE_SALIENCY_SAMPLING=false in setting.h and build using the following instructions.

cd dso
mkdir build
cd build
cmake ..
make -j4

this will compile a library libdso.a, which can be linked from external projects. It will also build a binary dso_dataset, to run SalientDSO on datasets. Once you've tested this you can re-compile using USE_SALIENCY_SAMPLING=true in setting.h to use SalientDSO functionality.

5. Usage

5.1. Test DSO Setting

Run on a dataset from https://vision.in.tum.de/mono-dataset using

DATASETPATH=Path to your dataset
build/bin/dso_dataset files=$DATASETPATH/images.zip calib=$DATASETPATH/camera.txt gamma=$DATASETPATH/pcalib.txt vignette=$DATASETPATH/vignette.png preset=0 mode=0

5.2. Test Saliency Setting

Recompile using USE_SALIENCY_SAMPLING=true in setting.h to use this functionality. First install SalGAN from here and run it on your dataset. Store the saliency images in .png format in a folder.

Run on a dataset from https://vision.in.tum.de/mono-dataset using (this assumes that the saliency files are stored in the dataset in DATASETFOLDER/saliency/ folder).

DATASETPATH=Path to your dataset
build/bin/dso_dataset files=$DATASETPATH/images.zip calib=$DATASETPATH/camera.txt gamma=$DATASETPATH/pcalib.txt vignette=$DATASETPATH/vignette.png saliency=$DATASETPATH/saliency/ preset=0 mode=0 smoothing=1

5.3. Test Semantic Filtered Saliency Setting (SalientDSO's proposed approach)

Recompile using USE_SALIENCY_SAMPLING=true in setting.h to use this functionality. First install SalGAN from here and run it on your dataset. Store the saliency images in .png format in a folder. Also, run scene parsing using PSPNet from here and run it on your dataset. Store the saliency images in .png format in a folder. One can write a simple shell script around the provided inference code to run on the entire dataset. For people with experience in tensorflow, the original python code can be modified accordingly.

Run on a dataset from https://vision.in.tum.de/mono-dataset using (this assumes that the saliency files are stored in the dataset folder in DATASETFOLDER/saliency/ folder and scene parsing is stored in the dataset folder DATASETFOLDER/segmentation).

DATASETPATH=Path to your dataset
build/bin/dso_dataset files=DATASETPATH/images.zip calib=DATASETPATH/camera.txt gamma=DATASETPATH/pcalib.txt vignette=DATASETPATH/vignette.png saliency=DATASETPATH/saliency/ segmentation=DATASETPATH/segmentation/ preset=0 mode=0 smoothing=2

5.4. Running on CVL-UMD dataset

Download the dataset from here. Recompile using USE_SALIENCY_SAMPLING=true in setting.h to use this functionality. Run using the following instructions.

DATASETPATH=Path to your dataset
build/bin/dso_dataset files=DATASETPATH/images calib=DATASETPATH/camera.txt saliency=DATASETPATH/saliency segmentation=DATASETPATH/segmentations/ calib_no_rect=DATASETPATH/camera.txt result=cvl_02_seg_0.txt preset=0 mode=1 quiet=1 saliencyAdd=0 saliencyMeanWeight=60.0 immatureDensity=1000 smoothing=2 patchSize=8 sampleoutput=1 points=pointclouds/pts_cvl_02_seg_0.txt

Additional Dataset Format

The format assumed is that of https://vision.in.tum.de/mono-dataset. However, it should be easy to adapt it to your needs, if required. The binary is run with:

• saliency=XXX where XXX is either a folder or .zip archive containing saliency images. They are sorted alphabetically. for .zip to work, need to comiple with ziplib support.

• segmentation=XXX where XXX is either a folder or .zip archive containing saliency images. They are sorted alphabetically. for .zip to work, need to comiple with ziplib support.

• calib_no_rect=XXX where XXX is a geometric camera calibration file. It is used if saliency images and segmentations is already undistorted.

Commandline Options

there are many command line options available, see main_dso_pangolin.cpp. some examples include

• result=XXX : set the path to the output trajectory file
• smoothing=X : -smoothing=0 : set saliency smoothing term to a constant -smoothing=1 : set saliency smoothing term to the average of the saliency / saliencyMeanWeight -smoothing=2 : apply saliency filtering and set saliency smoothing term to a constant
• saliencyAdd=X : set constant term for saliency smoothing to X
• immatureDensity=X : set the number of candidate points to X
• saliencyMeanWeight=X : set saliencyMeanWeight for saliency smoothing to X
• patchSize=X : set the size KxK of sampling patch to XxX
• points=XXX : set the path to the output 3D point cloud(sampleoutput has to be set to 1)

6. CVL Dataset and other datasets with saliency and scene parsing outputs

The CVL dataset can be downloaded from here. It contains the following directory structure once extracted.

CVL
├── 01 
|   ├── images
|   |	└── *.png
|   ├── saliency
|   |	└── *.jpg
|   ├── segmentations
|   |	└── *.png
|   ├── camera.txt
|   └── times.txt
└── 02
    ├── images
    |	└── *.png
    ├── saliency
    |	└── *.jpg
    ├── segmentations
    | 	└── *.png
    ├── camera.txt
    └── times.txt

7. Acknowledgments

We would like to thank the authors of DSO on which this code is based on.