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readme.md

Awesome SLAM Awesome

Simultaneous Localization and Mapping, also known as SLAM, is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it.

News

Table of Contents

Books

Courses, Lectures and Workshops

Papers

Researchers

United States

Europe

Australia

Datasets

  1. Intel Research Lab (Seattle)

Code

  1. ORB-SLAM
  2. LSD-SLAM
  3. ORB-SLAM2
  4. DVO: Dense Visual Odometry
  5. SVO: Semi-Direct Monocular Visual Odometry
  6. G2O: General Graph Optimization
  7. RGBD-SLAM

Miscellaneous

Contributing

Have anything in mind that you think is awesome and would fit in this list? Feel free to send a pull request.

License

CC0

1、ORBSLAM2

ORBSLAM2在Ubuntu14.04上详细配置流程
http://blog.csdn.net/zzlyw/article/details/54730830
1 安装必要工具
首先，有两个工具是需要提前安装的。即cmake和git。

sudo apt-get install cmake
sudo apt-get install git

2 安装Pangolin，用于可视化和用户接口

Pangolin： https://github.com/stevenlovegrove/Pangolin
官方样例demo https://github.com/stevenlovegrove/Pangolin/tree/master/examples
安装文件夹内
Pangolin函数的使用：
http://docs.ros.org/fuerte/api/pangolin_wrapper/html/namespacepangolin.html

是一款开源的OPENGL显示库，可以用来视频显示、而且开发容易。 是对OpenGL进行封装的轻量级的OpenGL输入/输出和视频显示的库。 可以用于3D视觉和3D导航的视觉图，可以输入各种类型的视频、并且可以保留视频和输入数据用于debug。

安装依赖项： http://www.cnblogs.com/liufuqiang/p/5618335.html Pangolin安装问题 Glew：

sudo apt-get install libglew-dev
CMake：
sudo apt-get install cmake
Boost：
sudo apt-get install libboost-dev libboost-thread-dev libboost-filesystem-dev
Python2 / Python3：
sudo apt-get install libpython2.7-dev
sudo apt-get install build-essential

先转到一个要存储Pangolin的路径下，例如~/Documents，然后

git clone https://github.com/stevenlovegrove/Pangolin.git
cd Pangolin
mkdir build
cd build
cmake ..
make -j
sudo make install

3 安装OpenCV

最低的OpenCV版本为2.4.3，建议采用OpenCV 2.4.11或者OpenCV 3.2.0。从OpenCV官网下载OpenCV2.4.11。然后安装依赖项：

sudo apt-get install libgtk2.0-dev
sudo apt-get install pkg-config

将下载的OpenCV解压到自己的指定目录，然后cd到OpenCV的目录下。

cd ~/Downloads/opencv-2.4.11
mkdir release
cd release
cmake -D CMAKE_BUILD_TYPE=RELEASE -DCMAKE_INSTALL_PREFIX=/usr/local ..
make
sudo make install

4 安装Eigen3

最低要求版本为3.1.0。在http://eigen.tuxfamily.org 下载Eigen3的最新版本， 一般是一个压缩文件，下载后解压，然后cd到Eigen3的根目录下。

mkdir build
cd build
cmake ..
make
sudo make install

5 安装ORBSLAM2

先转到自己打算存储ORBSLAM2工程的路径，然后执行下列命令

git clone https://github.com/raulmur/ORB_SLAM2.git oRB_SLAM2
cd ORB_SLAM2
修改编译 线程数(不然编译时可能会卡住)：
vim build.sh
最后 make -j >>>  make -j4

sudo chmod 777 build.sh
./build.sh

之后会在lib文件夹下生成libORB_SLAM2.so， 并且在Examples文件夹下生成

mono_tum，mono_kitti， mono_euroc  in Examples/Monocular 单目 ，
rgbd_tum   in Examples/Monocular RGB-D，
stereo_kitti 和 stereo_euroc  in Examples/Stereo 双目立体。

数据集：

KITTI dataset 对于 单目 stereo 或者 双目 monocular
http://www.cvlibs.net/datasets/kitti/eval_odometry.php

EuRoC dataset 对于 单目 stereo 或者 双目 monocular
http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets

TUM dataset 对于 RGB-D 或者 单目monocular
https://vision.in.tum.de/data/datasets/rgbd-dataset

论文：

ORB-SLAM: 
[Monocular] Raúl Mur-Artal, J. M. M. Montiel and Juan D. Tardós. ORB-SLAM: A Versatile and Accurate Monocular SLAM System. 
IEEE Transactions on Robotics, vol. 31, no. 5, pp. 1147-1163, 2015. (2015 IEEE Transactions on Robotics Best Paper Award). 
http://webdiis.unizar.es/%7Eraulmur/MurMontielTardosTRO15.pdf

ORB-SLAM2:
[Stereo and RGB-D] Raúl Mur-Artal and Juan D. Tardós. ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras. 
IEEE Transactions on Robotics, vol. 33, no. 5, pp. 1255-1262, 2017. 
https://128.84.21.199/pdf/1610.06475.pdf

词袋模型:
[DBoW2 Place Recognizer] Dorian Gálvez-López and Juan D. Tardós. Bags of Binary Words for Fast Place Recognition in Image Sequences. 
IEEE Transactions on Robotics, vol. 28, no. 5, pp. 1188-1197, 2012. 
http://doriangalvez.com/papers/GalvezTRO12.pdf

单目测试

在http://vision.in.tum.de/data/datasets/rgbd-dataset/download下载一个序列，并解压。
转到ORBSLAM2文件夹下，执行下面的命令。
根据下载的视频序列freiburg1， freiburg2 和 freiburg3将TUMX.yaml分别转换为对应的 TUM1.yaml 或 TUM2.yaml 或 TUM3.yaml
（相机参数文件）。
将PATH_TO_SEQUENCE_FOLDER 更改为解压的视频序列文件夹。
./Examples/Monocular/mono_tum Vocabulary/ORBvoc.txt Examples/Monocular/TUMX.yaml PATH_TO_SEQUENCE_FOLDER 
										  解压的视频序列文件夹

双目测试

在 http://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets 下载一个序列 Vicon Room 1 02  大小1.2GB
./Examples/Stereo/stereo_euroc Vocabulary/ORBvoc.txt Examples/Stereo/EuRoC.yaml PATH_TO_SEQUENCE/cam0/data PATH_TO_SEQUENCE/cam1/data Examples/Stereo/EuRoC_TimeStamps/SEQUENCE.txt

################################### 词带 orb词带txt载入太慢，看到有人转换为binary，速度超快，试了下，确实快. 链接：https://github.com/raulmur/ORB_SLAM2/pull/21/commits/4122702ced85b20bd458d0e74624b9610c19f8cc
Vocabulary/ORBvoc.txt >>> Vocabulary/ORBvoc.bin ################################################################ #CMakeLists.txt 最后添加 ## .txt >>> .bin 文件转换 set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${PROJECT_SOURCE_DIR}/tools) add_executable(bin_vocabulary tools/bin_vocabulary.cc) target_link_libraries(bin_vocabulary ${PROJECT_NAME})

# build.sh   转换 .txt >>> .bin
最后添加
cd ..
echo "Converting vocabulary to binary"
./tools/bin_vocabulary

#### 新建转换文件
tools/bin_vocabulary.cc

#include <time.h>
#include "ORBVocabulary.h"
using namespace std;

bool load_as_text(ORB_SLAM2::ORBVocabulary* voc, const std::string infile) {
  clock_t tStart = clock();
  bool res = voc->loadFromTextFile(infile);
  printf("Loading fom text: %.2fs\n", (double)(clock() - tStart)/CLOCKS_PER_SEC);
  return res;
}

void load_as_xml(ORB_SLAM2::ORBVocabulary* voc, const std::string infile) {
  clock_t tStart = clock();
  voc->load(infile);
  printf("Loading fom xml: %.2fs\n", (double)(clock() - tStart)/CLOCKS_PER_SEC);
}

void load_as_binary(ORB_SLAM2::ORBVocabulary* voc, const std::string infile) {
  clock_t tStart = clock();
  voc->loadFromBinaryFile(infile);
  printf("Loading fom binary: %.2fs\n", (double)(clock() - tStart)/CLOCKS_PER_SEC);
}

void save_as_xml(ORB_SLAM2::ORBVocabulary* voc, const std::string outfile) {
  clock_t tStart = clock();
  voc->save(outfile);
  printf("Saving as xml: %.2fs\n", (double)(clock() - tStart)/CLOCKS_PER_SEC);
}

void save_as_text(ORB_SLAM2::ORBVocabulary* voc, const std::string outfile) {
  clock_t tStart = clock();
  voc->saveToTextFile(outfile);
  printf("Saving as text: %.2fs\n", (double)(clock() - tStart)/CLOCKS_PER_SEC);
}

void save_as_binary(ORB_SLAM2::ORBVocabulary* voc, const std::string outfile) {
  clock_t tStart = clock();
  voc->saveToBinaryFile(outfile);
  printf("Saving as binary: %.2fs\n", (double)(clock() - tStart)/CLOCKS_PER_SEC);
}

int main(int argc, char **argv) {
  cout << "BoW load/save benchmark" << endl;
  ORB_SLAM2::ORBVocabulary* voc = new ORB_SLAM2::ORBVocabulary();

  load_as_text(voc, "Vocabulary/ORBvoc.txt");
  save_as_binary(voc, "Vocabulary/ORBvoc.bin");

  return 0;
}

修改读入文件： Thirdparty/DBoW2/DBoW2/TemplatedVocabulary.h line 248 添加 // WYW ADD 2017.11.4 /** * Loads the vocabulary from a Binary file * @param filename */ bool loadFromBinaryFile(const std::string &filename);

  /**
   * Saves the vocabulary into a Binary file
   * @param filename
   */
  void saveToBinaryFile(const std::string &filename) const;

line 1460
// WYW ADD 2017.11.4  读取二进制 词带
// --------------------------------------------------------------------------
template<class TDescriptor, class F>
bool TemplatedVocabulary<TDescriptor,F>::loadFromBinaryFile(const std::string &filename) {
  fstream f;
  f.open(filename.c_str(), ios_base::in|ios::binary);
  unsigned int nb_nodes, size_node;
  f.read((char*)&nb_nodes, sizeof(nb_nodes));
  f.read((char*)&size_node, sizeof(size_node));
  f.read((char*)&m_k, sizeof(m_k));
  f.read((char*)&m_L, sizeof(m_L));
  f.read((char*)&m_scoring, sizeof(m_scoring));
  f.read((char*)&m_weighting, sizeof(m_weighting));
  createScoringObject();

  m_words.clear();
  m_words.reserve(pow((double)m_k, (double)m_L + 1));
  m_nodes.clear();
  m_nodes.resize(nb_nodes+1);
  m_nodes[0].id = 0;
  char buf[size_node]; int nid = 1;
  while (!f.eof()) {
	f.read(buf, size_node);
	m_nodes[nid].id = nid;
	// FIXME
	const int* ptr=(int*)buf;
	m_nodes[nid].parent = *ptr;
	//m_nodes[nid].parent = *(const int*)buf;
	m_nodes[m_nodes[nid].parent].children.push_back(nid);
	m_nodes[nid].descriptor = cv::Mat(1, F::L, CV_8U);
	memcpy(m_nodes[nid].descriptor.data, buf+4, F::L);
	m_nodes[nid].weight = *(float*)(buf+4+F::L);
	if (buf[8+F::L]) { // is leaf
	  int wid = m_words.size();
	  m_words.resize(wid+1);
	  m_nodes[nid].word_id = wid;
	  m_words[wid] = &m_nodes[nid];
	}
	else
	  m_nodes[nid].children.reserve(m_k);
	nid+=1;
  }
  f.close();
  return true;
}

// --------------------------------------------------------------------------
template<class TDescriptor, class F>
void TemplatedVocabulary<TDescriptor,F>::saveToBinaryFile(const std::string &filename) const {
  fstream f;
  f.open(filename.c_str(), ios_base::out|ios::binary);
  unsigned int nb_nodes = m_nodes.size();
  float _weight;
  unsigned int size_node = sizeof(m_nodes[0].parent) + F::L*sizeof(char) + sizeof(_weight) + sizeof(bool);
  f.write((char*)&nb_nodes, sizeof(nb_nodes));
  f.write((char*)&size_node, sizeof(size_node));
  f.write((char*)&m_k, sizeof(m_k));
  f.write((char*)&m_L, sizeof(m_L));
  f.write((char*)&m_scoring, sizeof(m_scoring));
  f.write((char*)&m_weighting, sizeof(m_weighting));
  for(size_t i=1; i<nb_nodes;i++) {
	const Node& node = m_nodes[i];
	f.write((char*)&node.parent, sizeof(node.parent));
	f.write((char*)node.descriptor.data, F::L);
	_weight = node.weight; f.write((char*)&_weight, sizeof(_weight));
	bool is_leaf = node.isLeaf(); f.write((char*)&is_leaf, sizeof(is_leaf)); // i put this one at the end for alignement....
  }
  f.close();
}
修改slam系统文件 src/System.cc
line 28
// wyw添加 2017.11.4
#include <time.h>
bool has_suffix(const std::string &str, const std::string &suffix) {
  std::size_t index = str.find(suffix, str.size() - suffix.size());
  return (index != std::string::npos);
}

line 68
/////// ////////////////////////////////////
//// wyw 修改 2017.11.4
    clock_t tStart = clock();
    mpVocabulary = new ORBVocabulary();
    //bool bVocLoad = mpVocabulary->loadFromTextFile(strVocFile);
    bool bVocLoad = false; // chose loading method based on file extension
    if (has_suffix(strVocFile, ".txt"))
	  bVocLoad = mpVocabulary->loadFromTextFile(strVocFile);//txt格式打开
    else
	  bVocLoad = mpVocabulary->loadFromBinaryFile(strVocFile);//bin格式打开

    if(!bVocLoad)
    {
	cerr << "Wrong path to vocabulary. " << endl;
	cerr << "Failed to open at: " << strVocFile << endl;
	exit(-1);
    }
    //cout << "Vocabulary loaded!" << endl << endl;  
    printf("Vocabulary loaded in %.2fs\n", (double)(clock() - tStart)/CLOCKS_PER_SEC);//显示文件载入时间

单目SLAM：

例如，我自己的电脑上，该命令变为：
./Examples/Monocular/mono_tum Vocabulary/ORBvoc.txt Examples/Monocular/TUM1.yaml /home/ewenwan/ewenwan/learn/vSLAM/test/vSLAM/ch9project/date/rgbd_dataset_freiburg1_xyz

载入二进制词带
./Examples/Monocular/mono_tum Vocabulary/ORBvoc.bin Examples/Monocular/TUM1.yaml /home/ewenwan/ewenwan/learn/vSLAM/test/vSLAM/ch9project/date/rgbd_dataset_freiburg1_xyz

双目测试

./Examples/Stereo/stereo_euroc Vocabulary/ORBvoc.txt Examples/Stereo/EuRoC.yaml /home/ewenwan/ewenwan/learn/vSLAM/test/vSLAM/ch9project/date/ /cam0/data /home/ewenwan/ewenwan/learn/vSLAM/test/vSLAM/ch9project/date/ /cam1/data Examples/Stereo/EuRoC_TimeStamps/V102.txt
载入二进制词带
./Examples/Stereo/stereo_euroc Vocabulary/ORBvoc.bin Examples/Stereo/EuRoC.yaml /home/ewenwan/ewenwan/learn/vSLAM/test/vSLAM/ch9project/date/ /cam0/data /home/ewenwan/ewenwan/learn/vSLAM/test/vSLAM/ch9project/date/ /cam1/data Examples/Stereo/EuRoC_TimeStamps/V102.txt

ros下的工程:

http://blog.csdn.net/sinat_31802439/article/details/52331465  添加稠密地图
https://pan.baidu.com/s/1miDA952


manifest.xml >>>> package.xml

<package>

  <name>ros_orb</name>     #####包名
  <version>0.0.1</version> #####版本
  <description>ORB_SLAM2</description>#####工程描述
  <author>EWenWan</author> ####作者
  <maintainer email="[email protected]">Raul Mur-Artal</maintainer>##### 维护
  <license>GPLv3</license> ####开源协议

  <buildtool_depend>catkin</buildtool_depend> #### 编译工具以来

  <build_depend>roscpp</build_depend>         #### 编译依赖
  <build_depend>pcl</build_depend>
  <build_depend>tf</build_depend>
  <build_depend>sensor_msgs</build_depend>
  <build_depend>image_transport</build_depend>
  <build_depend>message_filters</build_depend>
  <build_depend>cv_bridge</build_depend>
  <build_depend>cmake_modules</build_depend>

  <run_depend>roscpp</run_depend>             #### 运行依赖
  <run_depend>pcl</run_depend>
  <run_depend>tf</run_depend>
  <run_depend>sensor_msgs</run_depend>
  <run_depend>image_transport</run_depend>
  <run_depend>message_filters</run_depend>
  <run_depend>cv_bridge</run_depend>

</package>


编译信息文件
CMakeLists.txt

cmake_minimum_required(VERSION 2.8.3) ### cmake版本限制

project(ros_orb)##工程名
find_package(catkin REQUIRED COMPONENTS###依赖包
  roscpp
  sensor_msgs
  image_transport
  message_filters
  cv_bridge
  cmake_modules)

set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS}  -Wall  -O3 -march=native ")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall  -O3 -march=native")

### ORB_SLAM2的路径
set(CODE_SOURCE_DIR /home/ewenwan/ewenwan/learn/vSLAM/test/vSLAM/ch9project/oRB_SLAM2/Examples/ROS/ORB_SLAM2)

# Check C++11 or C++0x support
include(CheckCXXCompilerFlag)
CHECK_CXX_COMPILER_FLAG("-std=c++11" COMPILER_SUPPORTS_CXX11)
CHECK_CXX_COMPILER_FLAG("-std=c++0x" COMPILER_SUPPORTS_CXX0X)
if(COMPILER_SUPPORTS_CXX11)
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
   add_definitions(-DCOMPILEDWITHC11)
   message(STATUS "Using flag -std=c++11.")
elseif(COMPILER_SUPPORTS_CXX0X)
   set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++0x")
   add_definitions(-DCOMPILEDWITHC0X)
   message(STATUS "Using flag -std=c++0x.")
else()
   message(FATAL_ERROR "The compiler ${CMAKE_CXX_COMPILER} has no C++11 support. Please use a different C++ compiler.")
endif()

LIST(APPEND CMAKE_MODULE_PATH ${CODE_SOURCE_DIR}/../../../cmake_modules)## ORB_SLAM2的编译文件 FindEigen3.cmake

find_package(OpenCV 2.4.3 REQUIRED)
find_package(Eigen3 3.1.0 REQUIRED)
find_package(Pangolin REQUIRED)
find_package( G2O REQUIRED )
find_package( PCL 1.7 REQUIRED )

catkin_package()                      ###ros包类型说明 

include_directories(
${CODE_SOURCE_DIR}                    ### ORB_SLAM2的路径
${CODE_SOURCE_DIR}/../../../
${CODE_SOURCE_DIR}/../../../include
${Pangolin_INCLUDE_DIRS}
${PCL_INCLUDE_DIRS}
${EIGEN3_INCLUDE_DIR}
)
add_definitions( ${PCL_DEFINITIONS} )
link_directories( ${PCL_LIBRARY_DIRS} )

set(LIBS
${catkin_LIBRARIES}
${OpenCV_LIBS}
${EIGEN3_LIBS}
${PCL_LIBRARIES}
${Pangolin_LIBRARIES}
${CODE_SOURCE_DIR}/../../../Thirdparty/DBoW2/lib/libDBoW2.so
#g2o_core g2o_types_slam3d g2o_solver_csparse g2o_stuff g2o_csparse_extension g2o_types_sim3 g2o_types_sba
${CODE_SOURCE_DIR}/../../../Thirdparty/g2o/lib/libg2o.so
${CODE_SOURCE_DIR}/../../../lib/libORB_SLAM2.so
)

# Node for monocular camera 单目相机
add_executable(mono
src/ros_mono.cc
)
target_link_libraries(mono
${LIBS}
)
# 单目相机 Augmented Reality 增强现实
#add_executable(monoAR
#src/AR/ros_mono_ar.cc
#src/AR/ViewerAR.h
#src/AR/ViewerAR.cc
#)
#target_link_libraries(mono
#${LIBS}
#)

# Node for RGB-D camera 深度相机
add_executable(rgbd
src/ros_rgbd.cc
)
target_link_libraries(rgbd
${LIBS}
)

# Node for stereo camera 双目立体相机
add_executable(stereo
src/ros_stereo.cc
)
target_link_libraries(stereo
${LIBS}
)

cd catkin_ws
catkin_make

运行单目相机SLAM节点
rosrun ros_orb mono Vocabulary/ORBvoc.bin Examples/Monocular/TUM1.yaml /home/ewenwan/ewenwan/learn/vSLAM/test/vSLAM/ch9project/date/rgbd_dataset_freiburg1_xyz

################# ######################## lsd-slam 直接法稠密点云slam Large Scale Direct Monocular ######################################## ####################

http://www.luohanjie.com/2017-03-17/ubuntu-install-lsd-slam.html
https://vision.in.tum.de/research/vslam/lsdslam
https://www.cnblogs.com/hitcm/p/4907536.html
https://github.com/tum-vision/lsd_slam

官方编译方法[1]

rosmake 编译
sudo apt-get install python-rosinstall
sudo apt-get install ros-indigo-libg2o ros-indigo-cv-bridge liblapack-dev libblas-dev freeglut3-dev libqglviewer-dev libsuitesparse-dev libx11-dev
mkdir ~/SLAM/Code/rosbuild_ws
cd ~/SLAM/Code/rosbuild_ws
roses init . /opt/ros/indigo
mkdir package_dir
roses set ~/SLAM/Code/rosbuild_ws/package_dir -t .
echo "source ~/SLAM/Code/rosbuild_ws/setup.bash" >> ~/.bashrc
bash
cd package_dir
git clone https://github.com/tum-vision/lsd_slam.git lsd_slam
rosmake lsd_slam

使用catkin对LSD-SLAM进行编译

mkdir -p ~/catkin_ws/src
git clone https://github.com/tum-vision/lsd_slam.git
cd lsd_slam
git checkout catkin

对lsd_slam/lsd_slam_viewer和lsd_slam/lsd_slam_core文件夹下的package.xml中添加：
<build_depend>cmake_modules</build_depend>
<run_depend>cmake_modules</run_depend>

对lsd_slam/lsd_slam_viewer和lsd_slam/lsd_slam_core文件夹下的CMakeFiles.txt中添加：
find_package(cmake_modules REQUIRED)
find_package(OpenCV 3.0 QUIET) #support opencv3
if(NOT OpenCV_FOUND)
   find_package(OpenCV 2.4.3 QUIET)
   if(NOT OpenCV_FOUND)
      message(FATAL_ERROR "OpenCV > 2.4.3 not found.")
   endif()
endif()


并且在所有的target_link_libraries中添加X11 ${OpenCV_LIBS}，如：
target_link_libraries(lsdslam 
${FABMAP_LIB} 
${G2O_LIBRARIES} 
${catkin_LIBRARIES} 
${OpenCV_LIBS} 
sparse cxsparse X11
)

然后开始编译：

cd ~/catkin_ws/
catkin_make

下载测试数据 474MB 日志回放 vmcremers8.informatik.tu-muenchen.de/lsd/LSD_room.bag.zip 解压

打开一个终端:
roscoe

打开另外一个终端：
cd ~/catkin_ws/
source devel/setup.sh
rosrun lsd_slam_viewer viewer

打开另外一个终端：
cd ~/catkin_ws/
source devel/setup.sh
rosrun lsd_slam_core live_slam image:=/image_raw camera_info:=/camera_info

打开另外一个终端：
cd ~/catkin_ws/
rosbag play ~/LSD_room.bag     ###回放日志   即将之前的数据按话题发布

使用摄像头运行LSD_SLAM 安装驱动[4]： cd ~/catkin_ws/ source devel/setup.sh cd ~/catkin_ws/src git clone https://github.com/ktossell/camera_umd.git cd .. catkin_make roscd uvc_camera/launch/ roslaunch ./camera_node.launch

camera_node.launch文件[5]，如：

<launch>
  <node pkg="uvc_camera" type="uvc_camera_node" name="uvc_camera" output="screen">
    <param name="width" type="int" value="640" />
    <param name="height" type="int" value="480" />
    <param name="fps" type="int" value="30" />
    <param name="frame" type="string" value="wide_stereo" />

    <param name="auto_focus" type="bool" value="False" />
    <param name="focus_absolute" type="int" value="0" />
    <!-- other supported params: auto_exposure, exposure_absolute, brightness, power_line_frequency -->

    <param name="device" type="string" value="/dev/video1" />
    <param name="camera_info_url" type="string" value="file://$(find uvc_camera)/example.yaml" />
  </node>
</launch>

注意官方程序默认分辨率为640*480。

打开一个窗口
运行roscore；

打开另外一个窗口：
cd ~/catkin_ws/
source devel/setup.sh
rosrun lsd_slam_viewer viewer

再打开另外一个窗口：
cd ~/catkin_ws/
source devel/setup.sh
roslaunch uvc_camera camera_node.launch

再打开另外一个窗口：
rosrun lsd_slam_core live_slam /image:=image_raw _calib:=<calibration_file>
校正文件calibration_file可参考lsd_catkin_ws/src/lsd_slam/lsd_slam_core/calib中的cfg文件。

########################### ################################# ##################################### DSO: Direct Sparse Odometry 直接法稀疏点云 SLAM
https://github.com/JakobEngel/dso

１.下载DSO源代码到相应文件路径，比如我的文件路径为/home/hyj/DSO
git clone https://github.com/JakobEngel/dso  dso
２.安装suitesparse and eigen3 (必需)
    sudo apt-get install libsuitesparse-dev libeigen3-dev

３.安装opencv. DSO对opencv依赖很少，仅仅用于读或写图像等一些简单的操作。
    sudo apt-get install libopencv-dev

４.安装pangolin. 强烈推荐安装，考虑到ORB_SLAM中也选择pangolin作为显 示工具，而使用也非常方便，因此建议大家学习。 安装教程请移步pangolin的github主页

５.安装ziplib. 建议安装，DSO用这个库来解压读取数据集压缩包中的图片，这样就不要每次都把下再的图片数据集进行解压了。
    sudo apt-get install zlib1g-dev
    cd thirdparty #找到DSO所在文件路径，切换到thirdparty文件夹下
    tar -zxvf libzip-1.1.1.tar.gz
    cd libzip-1.1.1/./configure
    make
    sudo make install
    sudo cp lib/zipconf.h /usr/local/include/zipconf.h

6.编译DSO.
    cd /home/hyj/DSO/dso
    mkdir build
    cd build
    cmake ..
    make -j
至此，不出意外的话，我们就可以很顺利的完成了DOS的安装。

############################## ################################### Pangolin 可视化库的使用

参考地址：
【1】Pangolin：https://github.com/stevenlovegrove/Pangolin
【2】Pangolin安装问题：http://www.cnblogs.com/liufuqiang/p/5618335.html
【3】Pangolin的Example：https://github.com/stevenlovegrove/Pangolin/tree/master/examples
【4】Pangolin的使用：http://docs.ros.org/fuerte/api/pangolin_wrapper/html/namespacepangolin.html
【5】特性：http://www.stevenlovegrove.com/?id=44

https://www.cnblogs.com/shhu1993/p/6814714.html