ORB-SLAM2 环境搭建和运行测试
本文档是记录orb-slam
buildup的记录，基本参考如下文章：
http://blog.csdn.net/u010128736/article/details/52808650
使用文档记录，一是为了加深印象，二则防止忘记，三则记录文档中不能满足的情况需要再次记录。
http://blog.csdn.net/gubenpeiyuan/article/details/54945356
由于本机已经安装Python，因此此处忽略了参考文章中的Python安装步骤。
在安装Pangolin时候，由于参考文章1和2中不同，按照2中操作，最后一步增加sudo make install
安装OpenCV方法：
1. 到官网下载OpenCV的source code： opencv-2.4.11.zip
2. unzip opencv-2.4.11.zip 解压到Ubuntu
3.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 ..
4.编译和安装
make
sudo
make install
安装Eigen
1.
到官网
下载Eigen：eigen-eigen-5a0156e40feb.zip
2. 解压文件 unzip eigen-eigen-5a0156e40feb.zip
3. 编译安装
mkdir
build
cd
build
cmake
..
make
sudo
make install
Eigen简介：
是一个涉及线性代数：矩阵，向量，数值计算等相关的算法的C++模板库。
Eigen编译错误，问题出在cmake ..
使用sudo apt-get install 直接安装。
ORB-SLAM2下载路径：https://github.com/raulmur/ORB_SLAM2
git
clone https://github.com/raulmur/ORB_SLAM2.git
ORB-SLAM官网：http://webdiis.unizar.es/~raulmur/orbslam/
编译ORB-SLAM2
编译时候出错，查问题是属于swap空间不足。调整swap空间，编译OK。（but，很慢）
virtual
memory exhaustedvirtual memory exhausted
下载测试集。
运行命令：
./Examples/Monocular/mono_tum Vocabulary/ORBvoc.txt Examples/Monocular/TUMX.yaml PATH_TO_SEQUENCE_FOLDER1测试用例带三个参数，其中两个源码包里有，另外一个需要到网上下载测试集。
https://vision.in.tum.de/data/datasets/rgbd-dataset/download
第一次运行成功！（很兴奋～～～浪里格朗）
mechmqx@mechmqx-R467-R464-P467:~/mechmqx/slam/ORB_SLAM2-master$
./Examples/Monocular/mono_tum Vocabulary/ORBvoc.txt
Examples/Monocular/TUM1.yaml
/home/mechmqx/mechmqx/slam/rgbd_dataset_freiburg1_rpy
ORB-SLAM2
Copyright (C) 2014-2016 Raul Mur-Artal, University of Zaragoza.
This
program comes with ABSOLUTELY NO WARRANTY;
This
is free software, and you are welcome to redistribute it
under
certain conditions. See LICENSE.txt.
Input
sensor was set to: Monocular
Loading
ORB Vocabulary. This could take a while...
Vocabulary
loaded!
Camera
Parameters:
-
fx: 517.306
-
fy: 516.469
-
cx: 318.643
-
cy: 255.314
-
k1: 0.262383
-
k2: -0.953104
-
k3: 1.16331
-
p1: -0.005358
-
p2: 0.002628
-
fps: 30
-
color order: RGB (ignored if grayscale)
ORB
Extractor Parameters:
-
Number of Features: 1000
-
Scale Levels: 8
-
Scale Factor: 1.2
-
Initial Fast Threshold: 20
-
Minimum Fast Threshold: 7
-------
Start
processing sequence ...
Images
in the sequence: 723
New
Map created with 91 points
Wrong
initialization, reseting...
System
Reseting
Reseting
Local Mapper... done
Reseting
Loop Closing... done
Reseting
Database... done
New
Map created with 107 points
-------
median
tracking time: 0.0494655
mean
tracking time: 0.0552974
Saving
keyframe trajectory to KeyFrameTrajectory.txt ...
trajectory
saved!
运行时画面：