（7）安裝BLAS and LAPACK庫

sudo apt-get install libblas-dev
sudo apt-get install liblapack-dev

ROS (optional)

其實build.sh就是前面第三方庫的編譯+ORB_SLAM2的編譯，以後可以自己寫一個 類似的終端命令指令碼，就不需要每次編譯都手敲一遍。如果發現編譯出錯，儘量從github或者官網上面git clone原始碼,裡面有很多issus可供參考。

3.安裝ORB_SLAM： 
（1）Clone the repository:

git clone https://github.com/raulmur/ORB_SLAM2.git ORB_SLAM2

（2）編譯：我們提供一個指令碼build.sh建立第三方庫和orb-slam2。請確保您已安裝所有所需的依賴項

cd ORB_SLAM2
chmod +x build.sh
 
./build.sh

• ORB-SLAM2最後編譯遇到的問題

1）強制型別轉換問題

錯誤資訊：

/home/melanie/tools/eigen/Eigen/src/Core/AssignEvaluator.h:817:3: error: static assertion failed: YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY EIGEN_CHECK_BINARY_COMPATIBILIY(Func,typename ActualDstTypeCleaned::Scalar,typename Src::Scalar); ^CMakeFiles/ORB_SLAM2.dir/build.make:350: recipe for target 'CMakeFiles/ORB_SLAM2.dir/src/Optimizer.cc.o' failedmake[2]: *** [CMakeFiles/ORB_SLAM2.dir/src/Optimizer.cc.o] Error 1CMakeFiles/Makefile2:178: recipe for target 'CMakeFiles/ORB_SLAM2.dir/all' failedmake[1]: *** [CMakeFiles/ORB_SLAM2.dir/all] Error 2Makefile:83: recipe for target 'all' failedmake: *** [all] Error 2

解決方案：

開啟Thirdparty/g2o/g2o/solvers/linear_solver_eigen.h，

將以下程式碼template <typename MatrixType>

class LinearSolverEigen: public LinearSolver<MatrixType>

{ public: typedef Eigen::SparseMatrix<double, Eigen::ColMajor> SparseMatrix;

typedef Eigen::Triplet<double> Triplet;

typedef Eigen::PermutationMatrix<Eigen::Dynamic, Eigen::Dynamic, SparseMatrix::Index> PermutationMatrix;

修改為：

template <typename MatrixType>

class LinearSolverEigen: public LinearSolver<MatrixType>

{ public:

typedef Eigen::SparseMatrix<double, Eigen::ColMajor> SparseMatrix;

typedef Eigen::Triplet<double> Triplet;

typedef Eigen::PermutationMatrix<Eigen::Dynamic, Eigen::Dynamic, int> PermutationMatrix;

2）usleep未定義：

錯誤資訊：

/home/melanie/source/SmartCar/ORM_SLAM2/ORB_SLAM2/src/Viewer.cc:159:28:

error: ‘usleep’ was not declared in this scope usleep(3000);

^CMakeFiles/ORB_SLAM2.dir/build.make:494: recipe for target

'CMakeFiles/ORB_SLAM2.dir/src/Viewer.cc.o' failedmake[2]:

*** [CMakeFiles/ORB_SLAM2.dir/src/Viewer.cc.o]

Error 1CMakeFiles/Makefile2:178: recipe for target 'CMakeFiles/ORB_SLAM2.dir/all' failedmake[1]: *** [CMakeFiles/ORB_SLAM2.dir/all]

Error 2Makefile:83: recipe for target 'all' failed

make: *** [all] Error 2

解決方案：

在source檔案的開頭增加include#include <unistd.h>

需要增加unistd.h的檔案有：

Examples/Monocular/mono_euroc.cc

Examples/Monocular/mono_kitti.cc

Examples/Monocular/mono_tum.cc

Examples/RGB-D/rgbd_tum.cc

Examples/Stereo/stereo_euroc.cc

Examples/Stereo/stereo_kitti.cc

src/LocalMapping.cc

src/LoopClosing.cc

src/System.cc

src/Tracking.cc

src/Viewer.cc

4.測試ORB_SLAM2

（1）官網下載測試資料集

 下載記憶體較大,最好用硬碟下好

Monocular 例項

TUM 資料集

1. 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.

./Examples/Monocular/mono_tum Vocabulary/ORBvoc.txt Examples/Monocular/TUMX.yaml PATH_TO_SEQUENCE_FOLDER

KITTI 資料集

1. e the following command. Change KITTIX.yaml by KITTI00-02.yaml, KITTI03.yaml or KITTI04-12.yaml for sequence 0 to 2, 3, and 4 to 12 respectively. Change PATH_TO_DATASET_FOLDER to the uncompressed dataset folder. Change SEQUENCE_NUMBER to 00, 01, 02,.., 11.

./Examples/Monocular/mono_kitti Vocabulary/ORBvoc.txt Examples/Monocular/KITTIX.yaml PATH_TO_DATASET_FOLDER/dataset/sequences/SEQUENCE_NUMBER

Stereo 例項

KITTI 資料集

1. Execute the following command. Change KITTIX.yamlto KITTI00-02.yaml, KITTI03.yaml or KITTI04-12.yaml for sequence 0 to 2, 3, and 4 to 12 respectively. Change PATH_TO_DATASET_FOLDER to the uncompressed dataset folder. Change SEQUENCE_NUMBER to 00, 01, 02,.., 11.

./Examples/Stereo/stereo_kitti Vocabulary/ORBvoc.txt Examples/Stereo/KITTIX.yaml PATH_TO_DATASET_FOLDER/dataset/sequences/SEQUENCE_NUMBER

RGB-D 例項

TUM 資料集

1. Associate RGB images and depth images using the python script associate.py. We already provide associations for some of the sequences in Examples/RGB-D/associations download a sequence fromhttp://vision.in.tum.de/data/datasets/rgbd-dataset/download and uncompress it.
2. /. You can generate your own associations file executing:
3. python associate.py PATH_TO_SEQUENCE/rgb.txt PATH_TO_SEQUENCE/depth.txt > associations.txt
4. 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

ROS 例項

處理你自己的例項

      你需要建立一個配置檔案來校正你的攝像機。可以參照我們為TUM 和 KITTI 資料集提供的例子在monocular, stereo and RGB-D cameras情況下. 我們使用OpenCV的攝像機校正模型。 See the examples to learn how to create a program that makes use of the ORB-SLAM2 library and how to pass images to the SLAM system. Stereo input must be synchronized and rectified. RGB-D input must be synchronized and depth registered.

SLAM 和 Localization 模式

你可以使用GUI在SLAM 和 Localization 模式下自由切換

SLAM 模式

      這是預設模式. 此模式下，系統有三個執行緒並行工作: Tracking, Local Mapping and Loop Closing. 系統不斷定位相機，構建新的地圖然後試圖閉合環形路徑。

Localization 模式

       當你有一個比較好的地圖的時候，你可以使用此模式。在這種模式下，區域性地圖構建以及環路閉合將不起作用。 系統在你提供的地圖上定位相機 (which is no longer updated), using relocalization if needed.

參考部落格:http://blog.csdn.net/zhjm07054115/article/details/51706706

https://github.com/raulmur/ORB_SLAM2

5.實時用攝像頭（可筆記本自帶或者外加攝像頭）跑資料

（1）安裝usb_cam package

    $ cd ~/catkin_ws/src  
    $ git clone https://github.com/bosch-ros-pkg/usb_cam.git  
    $ cd ~/catkin_ws  
    $ catkin_make  

     $ cd ~/catkin_ws/src  
    $ roscore    //初始化
    $ roslaunch usb_cam usb_cam-test.launch     //啟動usb_cam包下的.launch檔案啟動攝像頭。

此時證明攝像頭可以正常使用

～～～使用自定義 launch 檔案設定攝像頭：
usb_cam 給了我們一個預設的 launch 檔案在如下目錄

如果想要自定義一個我們自己的launch檔案，我們可以複製這個檔案為一個 usb_cam.launch，然後開啟這個檔案：

其中 /div/video0 表示是第一個攝像頭，如果你有多個攝像頭，可以將此改為 /div/video1 等等。想要檢視當前連線裝置，使用如下命令即可：

修改好後執行這個檔案：

Error1:[rospack] Error: package 'image_view' not found

表明你的 image_view 沒有安裝，可以執行以下命令安裝即可：