安装配置Intel Realsense D435i并在D435i上跑ORB-SLAM2

    # Register the server's public key
    sudo apt-key adv --keyserver keys.gnupg.net --recv-key C8B3A55A6F3EFCDE || sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv-key C8B3A55A6F3EFCDE
    # Add the server to the list of repositories
    sudo add-apt-repository "deb http://realsense-hw-public.s3.amazonaws.com/Debian/apt-repo xenial main" -u
    # Install the libraries
    sudo apt install librealsense2-dkms
    sudo apt install librealsense2-utils
    # Optionally install the developer and debug packages
    sudo apt install librealsense2-dev
    sudo apt install librealsense2-dbg
    # Reconnect the Intel RealSense depth camera and run: realsense-viewer to verify the installation.

    # ***************************Prerequisites***************************** #
    # Update Ubuntu distribution, including getting the latest stable kernel
    sudo apt update && sudo apt upgrade && sudo apt dist-upgrade
    # Download the complete source tree with git
    git clone https://github.com/IntelRealSense/librealsense.git
    # Navigate to librealsense root directory and Unplug any connected Intel RealSense camera
    # Install the core packages required to build librealsense binaries and the affected kernel modules
    sudo apt install git libssl-dev libusb-1.0-0-dev pkg-config libgtk-3-dev
    sudo apt install libglfw3-dev libgl1-mesa-dev libglu1-mesa-dev
    # certain librealsense CMAKE flags (e.g. CUDA) require version 3.8+(check: cmake --version)
    # Run Intel Realsense permissions script located from librealsense root directory
    ./scripts/setup_udev_rules.sh
    # Build and apply patched kernel modules
    ./scripts/patch-realsense-ubuntu-lts.sh
    # Tracking Module requires hid_sensor_custom kernel module to operate properly.
    echo 'hid_sensor_custom' | sudo tee -a /etc/modules
    
    # ***************************Building and Installing librealsense2 SDK***************************** #
    # Navigate to librealsense root directory
    mkdir build && cd build
    # Builds librealsense along with the demos and tutorials
    cmake ../ -DBUILD_EXAMPLES=true
    # Recompile and install librealsense binaries
    sudo make uninstall && make clean && make -jX && sudo make install
    # Use make -jX for parallel compilation, where X stands for the number of CPU cores available
    # The shared object will be installed in /usr/local/lib, header files in /usr/local/include
    # The binary demos, tutorials and test files will be copied into /usr/local/bin
    
    # ***************************Testing librealsense2 SDK***************************** #
    # Navigate to librealsense root directory
     cd build/examples/capture
    ./rs-capture

    # Step 1: Install the latest Intel RealSense SDK
    
    # Step 2: Install the ROS Kinetic --- http://wiki.ros.org/kinetic/Installation/Ubuntu
    
    # Step 3: Install Intel RealSense ROS from Sources
    # Create a catkin workspace
    mkdir -p ~/catkin_ws/src
    cd ~/catkin_ws/src/
    # Clone the latest Intel RealSense ROS into 'catkin_ws/src/'
    git clone https://github.com/IntelRealSense/realsense-ros.git
    cd realsense-ros/
    git checkout `git tag | sort -V | grep -P "^\d+\.\d+\.\d+" | tail -1`
    cd ../..
    # Make sure all dependent ros packages are installed. You can check .travis.yml file for reference.
    sudo apt install ros-kinetic-cv-bridge ros-kinetic-image-transport ros-kinetic-tf ros-kinetic-diagnostic-updater ros-kinetic-ddynamic-reconfigure
    catkin_make -DCATKIN_ENABLE_TESTING=False -DCMAKE_BUILD_TYPE=Release
    catkin_make install
    echo source $(pwd)/devel/setup.bash >> ~/.bashrc
    source ~/.bashrc
    
    # Step 4:检验是否能在ros使用realsense相机：
    # 通过usb连接相机到电脑
    sudo apt install ros-kinetic-rgbd-launch
    roslaunch realsense2_camera rs_rgbd.launch
    # 查看一下相机发布的topic
    rostopic list
    # 查看相机内参信息的两种方式
    rostopic echo /camera/color/camera_info
    rostopic echo /camera/aligned_depth_to_color/camera_info
    # 再打开一个终端
    rviz
    # 此时并不能看到什么结果，左上角 Displays 中 Fixed Frame 选项中，下拉菜单选择 camera_link，这是主要到Global Status变成了绿色
    # 点击该框中的Add -> 上方点击 By topic -> /depth_registered 下的 /points 下的/PointCloud2
    # 点击该框中的Add -> 上方点击 By topic -> /color 下的 /image_raw 下的image

    # Prerequisites
    # Pangolin, OpenCV, Eigen3 安装见我的另一篇文章：
    
    # Building ORB-SLAM2 library and examples
    git clone https://github.com/raulmur/ORB_SLAM2.git ORB_SLAM2
    cd ORB_SLAM2 && chmod +x build.sh && ./build.sh
    # 编译时可能遇到的错误参考：https://github.com/raulmur/ORB_SLAM2/issues/337
    
    # 使用网上公开的数据集测试ORB_SLAM2
    
    # 1、Monocular Examples --- TUM Dataset
    # Download a sequence from http://vision.in.tum.de/data/datasets/rgbd-dataset/download and uncompress it.
    # Change TUMX.yaml to TUM1.yaml,TUM2.yaml or TUM3.yaml for freiburg1, freiburg2 and freiburg3 sequences respectively. Change PATH_TO_SEQUENCE_FOLDERto the uncompressed sequence folder.
    ./Examples/Monocular/mono_tum Vocabulary/ORBvoc.txt Examples/Monocular/TUMX.yaml PATH_TO_SEQUENCE_FOLDER
    
    # 3、RGB-D Example --- TUM Dataset
    # Download a sequence from http://vision.in.tum.de/data/datasets/rgbd-dataset/download and uncompress it.
    # We already provide associations for some of the sequences in Examples/RGB-D/associations/. You can generate your own associations file executing:
    python associate.py PATH_TO_SEQUENCE/rgb.txt PATH_TO_SEQUENCE/depth.txt > associations.txt
    # Execute the following command. Change TUMX.yaml to TUM1.yaml,TUM2.yaml or TUM3.yaml for freiburg1, freiburg2 and freiburg3 sequences respectively. Change PATH_TO_SEQUENCE_FOLDERto the uncompressed sequence folder. Change ASSOCIATIONS_FILE to the path to the corresponding associations file.
    ./Examples/RGB-D/rgbd_tum Vocabulary/ORBvoc.txt Examples/RGB-D/TUMX.yaml PATH_TO_SEQUENCE_FOLDER ASSOCIATIONS_FILE

    # Add the path including Examples/ROS/ORB_SLAM2 to the ROS_PACKAGE_PATH environment variable. 
    # Open .bashrc file and add at the end the following line. Replace PATH by the folder where you cloned ORB_SLAM2
    export ROS_PACKAGE_PATH=${ROS_PACKAGE_PATH}:PATH/ORB_SLAM2/Examples/ROS
    # Execute build_ros.sh script
    chmod +x build_ros.sh
    ./build_ros.sh
    # 编译时可能遇到的错误参考：https://github.com/raulmur/ORB_SLAM2/issues/337

    set(LIBS 
    ${OpenCV_LIBS} 
    ${EIGEN3_LIBS}
    ${Pangolin_LIBRARIES}
    ${PROJECT_SOURCE_DIR}/../../../Thirdparty/DBoW2/lib/libDBoW2.so
    ${PROJECT_SOURCE_DIR}/../../../Thirdparty/g2o/lib/libg2o.so
    ${PROJECT_SOURCE_DIR}/../../../lib/libORB_SLAM2.so
    -lboost_system              # here
    )

    roslaunch realsense2_camera rs_rgbd.launch
    rostopic echo /camera/color/camera_info

    ---
    header: 
      seq: 17
      stamp: 
    secs: 1560907148
    nsecs: 588988566
      frame_id: "camera_color_optical_frame"
    height: 480
    width: 640
    distortion_model: "plumb_bob"
    D: [0.0, 0.0, 0.0, 0.0, 0.0]
    K: [615.9417724609375, 0.0, 322.3533630371094, 0.0, 616.0935668945312, 240.44674682617188, 0.0, 0.0, 1.0]
    R: [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]
    P: [615.9417724609375, 0.0, 322.3533630371094, 0.0, 0.0, 616.0935668945312, 240.44674682617188, 0.0, 0.0, 0.0, 1.0, 0.0]
    binning_x: 0
    binning_y: 0
    roi: 
      x_offset: 0
      y_offset: 0
      height: 0
      width: 0
      do_rectify: False
    ---

    %YAML:1.0
    
    #--------------------------------------------------------------------------------------------
    # Camera Parameters. Adjust them!
    #--------------------------------------------------------------------------------------------
    
    # Camera calibration and distortion parameters (OpenCV) 
    Camera.fx: 615.9417724609375
    Camera.fy: 616.0935668945312
    Camera.cx: 322.3533630371094
    Camera.cy: 240.44674682617188
    
    Camera.k1: 0.0
    Camera.k2: 0.0
    Camera.p1: 0.0
    Camera.p2: 0.0
    Camera.p3: 0.0
    
    Camera.width: 640
    Camera.height: 480
    
    # Camera frames per second 
    Camera.fps: 30.0
    
    # IR projector baseline times fx (aprox.)
    # bf = baseline (in meters) * fx, D435i的 baseline = 50 mm 
    Camera.bf: 30.797   
    
    # Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
    Camera.RGB: 1
    
    # Close/Far threshold. Baseline times.
    ThDepth: 40.0
    
    # Deptmap values factor
    DepthMapFactor: 1000.0
    
    #--------------------------------------------------------------------------------------------
    # ORB Parameters
    #--------------------------------------------------------------------------------------------
    
    # ORB Extractor: Number of features per image
    ORBextractor.nFeatures: 1000
    
    # ORB Extractor: Scale factor between levels in the scale pyramid 	
    ORBextractor.scaleFactor: 1.2
    
    # ORB Extractor: Number of levels in the scale pyramid	
    ORBextractor.nLevels: 8
    
    # ORB Extractor: Fast threshold
    # Image is divided in a grid. At each cell FAST are extracted imposing a minimum response.
    # Firstly we impose iniThFAST. If no corners are detected we impose a lower value minThFAST
    # You can lower these values if your images have low contrast			
    ORBextractor.iniThFAST: 20
    ORBextractor.minThFAST: 7
    
    #--------------------------------------------------------------------------------------------
    # Viewer Parameters
    #--------------------------------------------------------------------------------------------
    Viewer.KeyFrameSize: 0.05
    Viewer.KeyFrameLineWidth: 1
    Viewer.GraphLineWidth: 0.9
    Viewer.PointSize:2
    Viewer.CameraSize: 0.08
    Viewer.CameraLineWidth: 3
    Viewer.ViewpointX: 0
    Viewer.ViewpointY: -0.7
    Viewer.ViewpointZ: -1.8
    Viewer.ViewpointF: 500

    message_filters::Subscriber<sensor_msgs::Image> rgb_sub(nh, "/camera/color/image_raw", 1);
    message_filters::Subscriber<sensor_msgs::Image> depth_sub(nh, "/camera/aligned_depth_to_color/image_raw", 1);

    # 重新编译build_ros.sh
    chmod +x build_ros.sh
    ./build_ros.sh
    # 运行ORB_SLAM2
    rosrun ORB_SLAM2 RGBD Vocabulary/ORBvoc.txt Examples/RGB-D/D435i.yaml