SVO-SLAM环境搭建指南

Installation: Plain CMake (No ROS)
首先，建立一个工作目录比如：workspace，然后把下面的需要的都在该目录下进行.

(tip：一定不要使用中文名字，尽管你的系统是中文默认的名字。不然下面的依赖项将会十分困难，cmake找不到配置文件。)

mkdir workspace
cd workspace

Boost - c++ Librairies (thread and system are needed)

sudo apt-<span style="color: #0000ff;">get</span> install libboost-all-dev

Eigen 3 - Linear algebra

apt-<span style="color: #0000ff;">get</span> install libeigen3-dev

OpenCV - Computer vision library for loading and displaying images(我下载的是OpenCV3.0)

<span style="color: #000000;">mkdir build
cd build
cmake ..
make</span>

Sophus - Lie groups

<span style="color: #000000;">cd workspace
git clone https:</span><span style="color: #008000;">//</span><span style="color: #008000;">github.com/strasdat/Sophus.git</span>
<span style="color: #000000;">cd Sophus
git checkout a621ff
mkdir build
cd build
cmake ..
make</span>

如果此时遇到了“unit_complex_.imag() = 0."的错误，需要改代码为：”unit_complex_.imag(0.)“
Fast - Corner Detector

<span style="color: #000000;">cd workspace
git clone https:</span><span style="color: #008000;">//</span><span style="color: #008000;">github.com/uzh-rpg/fast.git</span>
<span style="color: #000000;">cd fast
mkdir build
cd build
cmake ..
make</span>

g2o - General Graph Optimization OPTIONAL
耐心和细心，G2O的每个版本的依赖项很复杂，需要耐心看版本号。不然错误很多都摸不到头脑了。之前在网上也是看了很多博客，并没有真正的解决依赖项的问题。下面我整理自己做的过程，完整正确版本。

首先安装g2o的依赖项：

sudo apt-<span style="color: #0000ff;">get</span>  install cmake libeigen4-dev libsuitesparse-dev, qt4-qmake libqglviewer-dev libsuitesparse-dev libcxsparse3.<span style="color: #800080;">1.2</span>  libcholmod-dev

然后进行下载，编译等：

<span style="color: #000000;">cd workspace
git clone https:</span><span style="color: #008000;">//</span><span style="color: #008000;">github.com/RainerKuemmerle/g2o.git</span>
<span style="color: #000000;">cd g2o
mkdir build
cd build
cmake ..
make
sudo make install</span>

vikit_common - Some useful tools that we need
vikit包含相机模型，SVO需要的一些数学和插值函数。

<span style="color: #000000;">cd workspace
git clone https:</span><span style="color: #008000;">//</span><span style="color: #008000;">github.com/uzh-rpg/rpg_vikit.git</span>

在pg_vikit/vikit_common/CMakeLists.txt 文件中设置 USE_ROS为FALSE.

cd rpg_vikit/<span style="color: #000000;">vikit_common
mkdir build
cd build
cmake ..
make</span>

SVO

<span style="color: #000000;">cd workspace
git clone https:</span><span style="color: #008000;">//</span><span style="color: #008000;">github.com/uzh-rpg/rpg_svo.git</span>
cd rpg_svo/svo

在文件 svo/CMakeLists.txt中，设置USE_ROS为 FALSE.

<span style="color: #000000;">mkdir build
cd build
cmake ..
make</span>

Run SVO without ROS
首先，创建一个存储数据的文件夹：

mkdir Datasets

然后设置一个环境变量去存储路径

export SVO_DATASET_DIR=${HOME}/Datasets

执行脚本.bashrc，然后进去新文件夹下面去下载测试数据

source ~/<span style="color: #000000;">.bashrc
cd ${SVO_DATASET_DIR}
wget http:</span><span style="color: #008000;">//</span><span style="color: #008000;">rpg.ifi.uzh.ch/datasets/sin2_tex2_h1_v8_d.tar.gz -O - | tar -xz</span>

然后在测试数据上面运行SVO即可：

cd svo/<span style="color: #000000;">bin
.</span>/test_pipeline

以上是SVO-SLAM环境搭建指南的详细内容。更多信息请关注PHP中文网其他相关文章！