ORB_SLAM: A C++ repository from Ultramarine1939-syujie

针对ubuntu20.04系统做了验证，gcc/g++版本默认分别为9和11，窗口环境为x11

首先添加ROS环境变量，然后分别在第三方文件下编译好各个库

环境配置

1、打开ORB_SLAM/src文件夹下的ORBextractor.cc文件，添加两个头文件：

#include <opencv2/features2d/features2d.hpp>
#include <opencv2/imgproc/imgproc.hpp>

2、打开ORB_SLAM/Thirdparty/g2o/g2o/solvers文件夹下的linear_solver_eigen.h文件，找到

#51～56
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;

3、打开ORB_SLAM文件夹下的CMakeLists.txt，添加以下内容：

rosbuild_add_boost_directories()
rosbuild_link_boost(${PROJECT_NAME} thread)

target_link_libraries(${PROJECT_NAME}
${OpenCV_LIBS}
${EIGEN3_LIBS}
${PROJECT_SOURCE_DIR}/Thirdparty/DBoW2/lib/libDBoW2.so
${PROJECT_SOURCE_DIR}/Thirdparty/g2o/lib/libg2o.so
/usr/lib/x86_64-linux-gnu/libboost_system.so
/usr/lib/x86_64-linux-gnu/libboost_filesystem.so
)

4、修改manifest.xml

#删除
<depend package="opencv2"/>

5、解压Data文件夹中的词袋库文件

6、修改ORB_SLAM/src/MapPublisher.cc

#31
    const char* MAP_FRAME_ID = "ORB_SLAM/World";

7、安装一些必要的库

sudo apt-get install libboost-all-dev
sudo apt-get install libsuitesparse-dev
sudo apt-get install libblas-dev
sudo apt-get install liblapack-dev
sudo apt-get install libeigen3-dev

8、修改ORB_SLAM/include/ORBextractor.h

#include <vector>
#include <list>
#include <opencv2/opencv.hpp>

9、修改ORB_SLAM/include/PnPsolver.h

#include <opencv2/opencv.hpp>
#include<opencv2/core/core_c.h>
#include <opencv2/core/types_c.h>
#include "MapPoint.h"
#include "Frame.h"
using namespace cv;

10、修改ORB_SLAM/src/ORBextractor.cc

#include <opencv2/features2d/features2d.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <vector>

11、修改ORB_SLAM/src/PnPsolver.cc

#include <vector>
#include <cmath>
#include <opencv2/opencv.hpp>
#include<opencv2/core/core_c.h>
#include <opencv2/core/types_c.h>
#include "Thirdparty/DBoW2/DUtils/Random.h"
#include <ros/ros.h>
#include <algorithm>

using namespace cv;
using namespace std;

12、修改ORB_SLAM/src/Sim3Solver.cc

#include <vector>
#include <cmath>
#include <opencv2/opencv.hpp>
#include <opencv2/core/core_c.h>
#include <opencv2/core/types_c.h>


#include <ros/ros.h>

#include "KeyFrame.h"
#include "ORBmatcher.h"

#include "Thirdparty/DBoW2/DUtils/Random.h"
using namespace cv;

13、修改Thirdparty/DBoW2/DBoW2/FClass.h

#include <opencv2/core/core.hpp>
#include <vector>
#include <string>

14、修改Thirdparty/DBoW2/DBoW2/FORB.h

#include <opencv2/core/core.hpp>
#include <vector>
#include <string>

15、修改Thirdparty/DBoW2/DBoW2/TemplatedVocabulary.h

#include <algorithm>
#include <opencv2/core/core.hpp>
#include <limits>

16、Thirdparty/DBoW2/CMakeLists.txt

find_package(OpenCV 4.2 QUIET)

PS：如有遗漏参考仓库文件，此处不再赘述

编译

Make sure you have installed ROS and all library dependencies (boost, eigen3, opencv, blas, lapack).

Clone the repository:

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

Add the path where you cloned ORB-SLAM to the ROS_PACKAGE_PATH environment variable. To do this, modify your .bashrc and add at the bottom the following line (replace PATH_TO_PARENT_OF_ORB_SLAM):
```
export ROS_PACKAGE_PATH=${ROS_PACKAGE_PATH}:PATH_TO_PARENT_OF_ORB_SLAM
```
Build g2o. Go into Thirdparty/g2o/ and execute:
```
mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make 
```
Tip: To achieve the best performance in your computer, set your favorite compilation flags in line 61 and 62 of Thirdparty/g2o/CMakeLists.txt (by default -03 -march=native)
Build DBoW2. Go into Thirdparty/DBoW2/ and execute:
```
mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make  
```
Tip: Set your favorite compilation flags in line 4 and 5 of Thirdparty/DBoW2/CMakeLists.txt (by default -03 -march=native)
Build ORB_SLAM. In the ORB_SLAM root execute:

If you use ROS Indigo, remove the depency of opencv2 in the manifest.xml.
```
mkdir build
cd build
cmake .. -DROS_BUILD_TYPE=Release
make
```
Tip: Set your favorite compilation flags in line 12 and 13 of ./CMakeLists.txt (by default -03 -march=native)

运行

Launch ORB-SLAM from the terminal (roscore should have been already executed):

rosrun ORB_SLAM ORB_SLAM PATH_TO_VOCABULARY PATH_TO_SETTINGS_FILE

You have to provide the path to the ORB vocabulary and to the settings file. The paths must be absolute or relative to the ORB_SLAM directory.
We already provide the vocabulary file we use in ORB_SLAM/Data/ORBvoc.txt.tar.gz. Uncompress the file, as it will be loaded much faster.

The last processed frame is published to the topic /ORB_SLAM/Frame. You can visualize it using image_view:

rosrun image_view image_view image:=/ORB_SLAM/Frame _autosize:=true
The map is published to the topic /ORB_SLAM/Map, the current camera pose and global world coordinate origin are sent through /tf in frames /ORB_SLAM/Camera and /ORB_SLAM/World respectively. Run rviz to visualize the map:

in ROS Fuerte:

rosrun rviz rviz -d Data/rviz.vcg

in ROS Groovy or a newer version:

rosrun rviz rviz -d Data/rviz.rviz
ORB_SLAM will receive the images from the topic /camera/image_raw. You can now play your rosbag or start your camera node. If you have a sequence with individual image files, you will need to generate a bag from them. We provide a tool to do that: https://github.com/raulmur/BagFromImages.

Ultramarine1939-syujie/ORB_SLAM

环境配置

编译

运行