怎么在ubuntu下装ORB-SLAM2,并用单目摄像头实时跑起来

最新的是ORB-SLAM2，https://github.com/raulmur/ORB_SLAM2(支持单目、双目和RGB-D接口,最好参照官网安装)

安装必备软件：为了方便.最好先把要下载的库先在windows下下载好(注意下载的版

本).不装在系统盘

（1）更新apt库

sudo apt-get update

（2）安装Git

sudo apt-get install git

（3）安装cmake

sudo apt-get install cmake

（4）安装Pangolin (for visualization and user interface:可视化与用户界面)

Pangolin网址：https://github.com/stevenlovegrove/Pangolin

安装依赖
a、opengl： 
b、GLEW：

sudo apt-get install libglew-dev

c、Boost：

sudo apt-get install libboost-dev libboost-thread-dev libboost-filesystem-dev

d、Python2/Python3：

sudo apt-get install libpython2.7-dev

e、编译基础库

sudo apt-get install build-essential

终端里输入

cd Pangolinmkdir build
cd build
cmake -DCPP11_NO_BOOST=1 ..
make -j 
（建议不要使用make -j，使用make。如果用make -j是使用多处理器编译，可能造成死机）

（5）安装OpenCV (manipulate images and features: 操纵图像和特征点)

安装依赖：

 

a、编译器相关：

sudo apt-get install build-essential

b、必须依赖：

sudo apt-get install cmake git libgtk2.0-dev pkg-config libavcodec-dev
libavformat-dev libswscale-dev

c、可选安装：

sudo apt-get install python-dev python-numpy libtbb2 libtbb-dev libjpeg-dev libpng-dev libtiff-dev libjasper-dev libdc1394-22-dev

安装OpenCV： 

a、官网下载OpenCV 2.4.11 for Linux下载地址，解压到Ubuntu中 

b、进入OpenCV文件夹，配置工程

mkdir release
cd release
cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local ..

c、编译

make
sudo make install

（6）安装Eigen （We use modified versions of theDBoW2 library to perform place recognition--进行位置识别 and g2o library to perform non-linear optimizations--进行非线性优化. Both modified libraries (which are BSD) are included in theThirdparty folder.）
Eigen下载地址，进入到在解压后的Eigen文件夹（例如eigen-eigen-07105f7124f9）下

mkdir build
cd build
cmake ..
make
sudo make install

（7）安装BLAS and LAPACK库

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

ROS (optional)

We provide some examples to process the live input of a monocular, stereo or RGB-D camera using ROS. Building these examples is optional. In case you want to use ROS, a version Hydro or newer is needed.

其实build.sh就是前面第三方库的编译+ORB_SLAM2的编译，以后可以自己写一个 类似的终端命令脚本，就不需要每次编译都手敲一遍。如果发现编译出错，尽量从github或者官网上面git clone源码,里面有很多issus可供参考。

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

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

（2）编译：我们提供一个脚本build.sh建立第三方库和orb-slam2。请确保您已安装所有所需的依赖项

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

• ORB-SLAM2最后编译遇到的问题

1）强制类型转换问题

错误信息：

/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

解决方案：

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

将以下代码template

class LinearSolverEigen: public LinearSolver

{ public: typedef Eigen::SparseMatrix SparseMatrix;

typedef Eigen::Triplet Triplet;

typedef Eigen::PermutationMatrix SparseMatrix::Index> PermutationMatrix;

修改为：

template

class LinearSolverEigen: public LinearSolver

{ public:

typedef Eigen::SparseMatrix SparseMatrix;

typedef Eigen::Triplet Triplet;

typedef Eigen::PermutationMatrix int> PermutationMatrix;

2）usleep未定义：

错误信息：

/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

解决方案：

在source文件的开头增加include#include

需要增加unistd.h的文件有：

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.测试ORB_SLAM2

（1）官网下载测试数据集

 下载内存较大,最好用硬盘下好

Monocular 实例

TUM 数据集

1. Download a sequence from http://vision.in.tum.de/data/datasets/rgbd-dataset/download and uncompress it.
2. 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 数据集

1. Execute download the dataset (grayscale images) from  http://www.cvlibs.net/datasets/kitti/eval_odometry.php
2. 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 实例

KITTI 数据集

1. Download the dataset (grayscale images) from http://www.cvlibs.net/datasets/kitti/eval_odometry.php
2. 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 实例

TUM 数据集

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 实例

处理你自己的实例

      你需要创建一个配置文件来校正你的摄像机。可以参照我们为TUM 和 KITTI 数据集提供的例子在monocular, stereo and RGB-D cameras情况下. 我们使用OpenCV的摄像机校正模型。 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 模式下自由切换

SLAM 模式

      这是默认模式. 此模式下，系统有三个线程并行工作: Tracking, Local Mapping and Loop Closing. 系统不断定位相机，构建新的地图然后试图闭合环形路径。

Localization 模式

       当你有一个比较好的地图的时候，你可以使用此模式。在这种模式下，局部地图构建以及环路闭合将不起作用。 系统在你提供的地图上定位相机 (which is no longer updated), using relocalization if needed.

参考博客:http://blog.csdn.net/zhjm07054115/article/details/51706706

             https://github.com/raulmur/ORB_SLAM2

5.实时用摄像头（可笔记本自带或者外加摄像头）跑数据

（1）安装usb_cam package

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

笔记本自带的摄像头的设备号一般为/dev/video0    外接摄像头一般是

"video_device"value="/dev/video1"/>

（2）把ORB-SLAM2，和 usb_cam放到catkin下src目录下

     $ cd ~/catkin_ws/src  
    $ roscore    //初始化
    $ roslaunch usb_cam usb_cam-test.launch     //启动usb_cam包下的.launch文件启动摄像头。

此时证明摄像头可以正常使用

～～～使用自定义 launch 文件设置摄像头：
usb_cam 给了我们一个默认的 launch 文件在如下目录

1	~/catkin-ws/usb_cam/src/usb_cam/launch/usb_cam-test.launch

如果想要自定义一个我们自己的launch文件，我们可以复制这个文件为一个 usb_cam.launch，然后打开这个文件：

1 2 3 4 5 6 7 8 9 10 11 12 13 14

<launch>   <nodename="usb_cam"pkg="usb_cam"type="usb_cam_node"output="screen">     <paramname="video_device"value="/dev/video0"/>     <paramname="image_width"value="640"/>     <paramname="image_height"value="480"/>     <paramname="pixel_format"value="yuyv"/>     <paramname="camera_frame_id"value="usb_cam"/>     <paramname="io_method"value="mmap"/>   </node>   <nodename="image_view"pkg="image_view"type="image_view"respawn="false"output="screen">     <remapfrom="image"to="/usb_cam/image_raw"/>     <paramname="autosize"value="true"/>   </node> </launch>

其中 /div/video0 表示是第一个摄像头，如果你有多个摄像头，可以将此改为 /div/video1 等等。想要查看当前连接设备，使用如下命令即可：

ls /dev/video*

修改好后运行这个文件：

roslaunch usb_cam usb_cam.launch

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

表明你的 image_view 没有安装，可以执行以下命令安装即可：

sudo apt-get install ros-indigo-image-view

（3）用ORB-SLAM2实时跑数据 （记住:若是用外加摄像头，需要在usb_cam-test.launch和usb_cam_node.cpp把摄像头的设备号改为/dev/video1）

  $ cd ~/catkin_ws/src  
  $ rosrun ORB_SLAM2 Mono /home/ubantu/catkin_ws/src/ORB_SLAM2/Vocabulary/ORBvoc.txt 
       /home/ubantu/catkin_ws/src/ORB_SLAM2/Examples/ROS/ORB_SLAM2/Asus.yaml    
       （即 rosrun ORB-SLAN2 Mono    ORBvoc.txt路径    Asus.yaml路径）

6.标定摄像头（为了防止镜头下的图片发生畸变）

1）摄像头标定时所处的平面位置一旦改变，一般会影响相机内参，需重新标定

2）将标定后的参数替换相机原有的内参，重新跑一遍即可。