superpoint ile orbslam birlikte kullanılmıştır.
In the SUPERSLAM3 pipeline, input images are converted to grayscale and fed into the Superpoint detector pipeline (A). The Superpoint encoder-decoder pipeline consists of a learned encoder, utilizing several convolutional layers, and two non-learned decoders for joint feature and descriptor extraction. The detected features are then processed by the ORB-SLAM3 backend, which comprises three primary components operating in parallel threads: the Tracking, Local Mapping, and Loop & Map Merging threads (B). The backend extracts keyframes, initializes and updates the map, and performs both local and global motion and pose estimation within the Local Mapping Thread and Loop & Map Merging thread. If a loop closure is detected, the pose estimation is further refined.
This repository was forked from ORB-SLAM3. The pre-trained model of SuperPoint come from the official MagicLeap repository.
We have tested the libraries and executables on Ubuntu 20.04.
ORBSLAM3 uses the new thread and chrono functionalities of C++17.
We use OpenCV to manipulate images and features. Dowload and install instructions can be found at: http://opencv.org. Required at least 3.0. Tested with OpenCV 3.4.11.
sudo apt-get update
sudo apt-get install build-essential cmake git pkg-config libgtk-3-dev libavcodec-dev libavformat-dev libswscale-dev libv4l-dev libxvidcore-dev libx264-dev libjpeg-dev libpng-dev libtiff-dev gfortran openexr libatlas-base-dev python3-dev python3-numpy libtbb2 libtbb-dev libdc1394-22-dev
cd ~
git clone https://github.com/opencv/opencv.git
cd opencv
git checkout 3.4.16
cd ~
git clone https://github.com/opencv/opencv_contrib.git
cd opencv_contrib
git checkout 3.4.16
cd opencv
mkdir build && cd build
cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local -D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules -D BUILD_EXAMPLES=ON ..
make -j$(nproc)
sudo make install
sudo ldconfig
Required by g2o (see below). Download and install instructions can be found at: http://eigen.tuxfamily.org. Required at least 3.1.0. Tested with Eigen3 3.4.0.
sudo apt install libeigen3-dev
We use a BOW vocabulary based on the BOW3 library to perform place recognition, and g2o library is used to perform non-linear optimizations. All these libraries are included in the Thirdparty folder.
For more informations please refer to this repo.
Please, follow these instructions for the installation of the Cuda Toolkit 12.2.
If not installed during the Cuda Toolkit installation process, please install the nvidia driver 535:
sudo apt-get install nvidia-driver-535Export Cuda paths
echo 'export PATH=/usr/local/cuda-12.2/bin:$PATH' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=/usr/local/cuda-12.2/lib64:$LD_LIBRARY_PATH' >> ~/.bashrc
source ~/.bashrc
sudo ldconfig Verify the Nvidia driver availability:
nvidia-smiIf only CPU can be used, install cpu-version LibTorch. Some code change about tensor device should be required.
wget -O LibTorch.zip wget https://download.pytorch.org/libtorch/cu121/libtorch-cxx11-abi-shared-with-deps-2.1.0%2Bcu121.zip -O libtorch.zip
sudo unzip libtorch.zip -d /usr/localClone the repository:
git clone --recursive https://github.com/fthbng77/SP_SLAM3.gitBuild the project:
cd SP_SLAM3
chmod +x build.sh
./build.shBu şekilde superpoint kullanılarak çalıştırılabilir yaml dosyasındaki threshold
cd SP_SLAM3
export LD_LIBRARY_PATH=/home/fatih/SP_SLAM3/lib:$LD_LIBRARY_PATH
./Examples/Monocular/mono_webcam Vocabulary/ORBvoc.txt Examples/Monocular/EuRoC.yaml
# or with superpoint Vocabulary
./Examples/Monocular/mono_webcam Vocabulary/superpoint_voc.yml Examples/Monocular/EuRoC.yamlfeature matching işlemi için LightGlue eklenecektir.