LIFT-SLAM

LIFT SLAM is a hybrid SLAM that combines a learned feature detector and descriptor along with a classical SLAM back-end such as ORB SLAM.

Read our report here

Installation

Things to apt install

sudo apt install libgl1-mesa-dev libglew-dev pkg-config libegl1-mesa-dev libwayland-dev libxkbcommon-dev wayland-protocols libeigen3-dev

ORB-SLAM2

• OpenCV 3

• Pangolin

  cd third-party/Pangolin-0.6
  mkdir build
  cd build
  cmake ..
  cmake --build .
  sudo make install
  

• Build

  cd SLAM
  chmod +x build.sh
  ./build.sh
  

• Test

  cd workspace
  ./SLAM/Examples/Monocular/mono_kitti_orb SLAM/Vocabulary/ORBvoc.txt SLAM/Examples/Monocular/KITTI04-12.yaml data/04-Straight-Line-Drive
  

LIFT

• Create a separate conda env for this

• OpenCV 3

• Several Python Packages

  pip install -r requirements.txt
  

• cudatoolkit (choose one)

  conda install -c anaconda cudatoolkit=10.2
  conda install -c anaconda cudatoolkit=11.0
  

• Build (No need, all Python)

• Test

  python lift_features.py
  

Running LIFT SLAM

• Your KITTI data should be under data/<sequence> from the root of this repo, e.g. data/04-Straight-Line-Drive

• Go to lift_parser.py and change the folder directory to the sequence you want and run it

  main_dir = "data/04-Straight-Line-Drive"
  

• Run LIFT SLAM

  cd workspace
  ./SLAM/Examples/Monocular/mono_kitti_lift SLAM/Vocabulary/ORBvoc.txt SLAM/Examples/Monocular/KITTI04-12.yaml data/04-Straight-Line-Drive
  ./SLAM/Examples/Monocular/mono_kitti_lift SLAM/Vocabulary/ORBvoc.txt SLAM/Examples/Monocular/KITTI04-12.yaml data/06-2U-turns-same-road
  

• Evaluate

  python evaluation.py