/HSLAM

Primary LanguageC++

HSLAM (Hybrid-SLAM)

This project builds and runs monocular Visual SLAM system which integrates a hybrid approach utilizing both direct and indirect methods.

This project mainly runs on datasets.

For an implementation of HSLAM with live camera feed (utilizing ROS) and and out-of-the-box SLAM system leveraging docker refer to this repository HSLAM_Docker .

Related Publications:

A Unified Hybrid Formulation for Visual SLAM (Doctoral dissertation), Younes, G. (2021).

H-SLAM: Hybrid Direct-Indirect Visual SLAM Younes, G. et al (2024).

Please cite the paper if used in an academic context.

@article{younes2024h,
  title={H-SLAM: Hybrid direct-indirect visual SLAM},
  author={Younes, Georges and Khalil, Douaa and Zelek, John and Asmar, Daniel},
  journal={Robotics and Autonomous Systems},
  pages={104729},
  year={2024},
  publisher={Elsevier}
}

1. License

HSLAM is released under [GPLv3 license]

2. Prerequisites

We have tested the library in Ubuntu 18.04 and 20.04, but it should be easy to compile in other platforms. A powerful computer (e.g. i7) will ensure real-time performance and provide more stable and accurate results.

Dependencies:

[NOTE]: The correct versions will be installed when building project with the provided build.sh script. (See Section 4 for more details)

The project is dependent on specific versions of the following libraries:

  • C++11 or C++0x Compiler: We use the new thread and chrono functionalities of C++11.
  • Pangolin for visualization and user interface. (Included in Thirdparty folder)
  • OpenCV 3.4.6 (Included in Thirdparty folder)
  • DBoW2 and g2o (Included in Thirdparty folder)

3. Building HSLAM library and dependencies

  1. Clone the repository:
git clone https://github.com/8bit-nyk/HSLAM.git
  1. Navigate to the project directory:
cd <your_working_directory>/HSLAM

Before building the main project we need to build the thirdparty dependancies

  1. Navigate to the Thirdparty directory:
cd Thirdparty
  1. We provide a script build.sh to download and install the specific versions of the dependency libraries needed. Execute:
chmod +x build.sh && ./build.sh

  1. Configuring and building main project:

    In terminal navigate back to HSLAM main project directory

    cd ..
or
cd ~/<your_directory>/HSLAM

    Run the following command to create the build directory

    mkdir -p build

    Navigate into the create build directory:

    cd build

    Configure cmake:

    cmake .. -DCMAKE_BUILD_TYPE=RelwithDebInfo

    Build the Project:

    make -j $(nproc)

4. Run Examples

  1. Download one of the sequences from the Monocular Visual Odometry Dataset provided by TUM

  2. Navigate to the build directory of the project

cd ~/HSLAM/build/
  1. Run the following commands:

Note that the following commands assume you downloaded sequence_30 you will need to adjust the files paths according to your local setup

If there are NO photmetric calibration parameters available run:

./bin/HSLAM \
-files=PATH/TO/DATASET/sequence_30/images.zip \
-calib=PATH/TO/DATASET/sequence_30/camera.txt \
-vocabPath=PATH/TO/HSLAM/misc/orbvoc.dbow3 \
-mode=1

If photometric calinration is avaliable run:

  ./bin/HSLAM \
 -files=PATH/TO/DATASET/sequence_30/images.zip \
 -calib=PATH/TO/DATASET/sequence_30/camera.txt \
 -vocabPath=/PATH/TO/HSLAM/misc/orbvoc.dbow3 \
 -mode=0 \
 -gamma=/home/nykvm/datasets/sequence_30/pcalib.txt \
 -vignette=/home/nykvm/datasets/sequence_30/vignette.png \

You should get an output similair to screenshot below showing the SLAM process: hslam-output-screenshot