ORB_SLAM2系列之二:ORB_SLAM2跑单目SLAM
接上一篇ORB_SLAM2系列之一:Ubuntu 14.04 ROS indigo下编译安装ORB_SLAM2,ORB_SLAM2提供了单目，双目和rgbd接口,这里我们先来尝试运行下最简单的单目SLAM(MonoSLAM)。
链接库
首先需要解决个错误：
error while loading shared libraries: libGLEW.so.2.1: cannot open shared obj解决：
ln -s /usr/lib64/libGLEW.so.2.1 /usr/lib/libGLEW.so.2.1准备工作空间
依次执行以下命令：
mkdir -p /home/catkin_ws/src
cd /home/catkin_ws/src/
catkin_init_workspace
cd .. && catkin_make
echo "source /home/catkin_ws/devel/setup.bash" >> ~/.bashrc
chmod 777 catkin_ws/
source /home/catkin_ws/devel/setup.bash
source ~/.bashrc现在/home/catkin_ws目录就是你设定的ROS的工作空间，以后需要其他的库都克隆到/home/catkin_ws/src目录下，然后回退到目录/home/catkin_ws/下，执行catkin_make进行编译。
编译ORB-SLAM2，DBow2和g2o
将ORB-SLAM2克隆到ROS的工作路径/home/catkin_ws/下。DBow和g2o这两个库在ORB-SLAM2的Thirdparty目录中,下载ORB-SLAM2源代码后，使用提供的脚本build.sh即可将它们一并编译了。
cd /home/catkin_ws/src
git clone https://github.com/raulmur/ORB_SLAM2.git
cd ORB_SLAM2
./build.sh
cd ../..
source devel/setup.bash
rospack profile
其中，build.sh内容如下：
echo "Configuring and building Thirdparty/DBoW2 ..."
cd Thirdparty/DBoW2
mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j
cd ../../g2o
echo "Configuring and building Thirdparty/g2o ..."
mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j
cd ../../../
echo "Uncompress vocabulary ..."
cd Vocabulary
tar -xf ORBvoc.txt.tar.gz
cd ..
echo "Configuring and building ORB_SLAM2 ..."
mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release
make -j完成DBow2，g2o，ORB-SLAM的编译，解压DBow2字典文件．ORB-SLAM2启动时，也需要载入这个100多M的文件，比较耗时。
笔记本摄像头驱动安装
如果你使用笔记本自带的摄像头，可以使用博世公司的 “usb_cam”：A ROS Driver for V4L USB Cameras。相似地，将usb_cam克隆到ROS的工作路径/home/catkin_ws/下。
cd /home/catkin_ws/src/
git clone https://github.com/bosch-ros-pkg/usb_cam.git
cd ../..
catkin_make
source devel/setup.bash
rospack profile设定相机参数
#/home/research/ORB_SLAM2/Examples/Monocular/my.yaml
%YAML:1.0
#--------------------------------------------------------------------------------------------
# Camera Parameters. Adjust them!
#--------------------------------------------------------------------------------------------
# Camera calibration and distortion parameters (OpenCV)
Camera.fx: 517.306408
Camera.fy: 516.469215
Camera.cx: 318.643040
Camera.cy: 255.313989
Camera.k1: 0.262383
Camera.k2: -0.953104
Camera.p1: -0.005358
Camera.p2: 0.002628
Camera.k3: 1.163314
Camera.width: 640
Camera.height: 480
# Camera frames per second
Camera.fps: 30.0
# Color order of the images (0: BGR, 1: RGB. It is ignored if images are grayscale)
Camera.RGB: 1
#--------------------------------------------------------------------------------------------
# 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
运行
rosrun ORB_SLAM2 Mono /home/catkin_ws/src/ORB_SLAM2/Vocabulary/ORBvoc.txt /home/research/ORB_SLAM2/Examples/Monocular/my.yaml参考
1.使用自己笔记本摄像头运行orbslam2：http://blog.csdn.net/qq_18661939/article/details/51829157
2.用电脑自带的摄像头跑orb_slam2：https://zhuanlan.zhihu.com/p/29629824
3.ORB-SLAM跑通笔记本摄像头：http://www.cnblogs.com/shang-slam/p/6733322.html
4.在ROS中使用usb摄像头跑ORB SLAM2：http://blog.csdn.net/Goding_learning/article/details/52950993