SLAM-针对低纹理环境的语义+单目+平面视觉SLAM实现-优质项目实战.zip

# Pop Up SLAM # This code contains a basic implementation for pop-up plane SLAM. Given RGB and ground segmentation image/video, the algorithm pop up walls from each frame then formulate a plane SLAM to optimize both camera pose and plane positions. ```pop_planar_slam``` is main package. Other code is based on and modified from single image. **Related Paper:** * **Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments**, IROS 2016, S. Yang, Y. Song, M. Kaess, S. Scherer [**PDF**](https://shichaoy.github.io/Publications/iros_2016_popslam.pdf) * **Real-time 3D Scene Layout from a Single Image Using Convolutional Neural Networks**, ICRA 2016, S. Yang, D. Maturana, S. Scherer [**PDF**](https://shichaoy.github.io/Publications/icra_2016_sinpop.pdf) If you use the code in your research work, please cite the above paper. Feel free to contact the authors if you have any further questions. # Installation ### Prerequisites This code contains several ros packages. We test it in **ROS indigo/kinetic, Ubuntu 14.04/16.04, Opencv 2/3**. Create or use existing a ros workspace. ```bash mkdir -p ~/popup_ws/src cd ~/popup_ws/src catkin_init_workspace cd 项目 ``` ### Install dependency packages of python ```bash sh install_dependenices.sh ``` ### Download Data ```bash sh download_data.sh ``` if the download link breaks, please download [here](https://drive.google.com/open?id=192wwtxHryaTYMNsdYEu_YI9iQNy1BCbd) and follow sh file to process it. Will fix it later. If ```wget``` not installed, ```sudo apt-get install wget ``` ### Compile ```bash cd ~/popup_ws catkin_make -j4 catkin_make --only-pkg-with-deps pop_up_python -j4 #just double check catkin_make --only-pkg-with-deps pop_planar_slam -j4 #just double check ``` # Running # ```bash source devel/setup.bash roslaunch pop_planar_slam pop_planar_tum_far_example.launch ``` You will see results in Rviz. ### Notes 1. If it shows "NameError: 'pop_up fun...params' is not defined". That is due to python dependency modules are not installed properly. Make sure "from skimage.measure import find_contours,approximate_polygon" can work alone in python. Also 'souce setup.bash' when python changes. There is some pop-up image python function I cannot find C++ replacement therefore we have to use python... All SLAM parts are in C++. 2. The bag file dataset contains the pre-processed dataset with synced rgb and label image. The code just gives an illustration of the core algorithm. No other odometry is used now, which needs to be integrated for large dataset.