SLAM本质剖析-Open3D

点击上方“3D视觉工坊”，选择“星标”

干货第一时间送达

作者丨lovely_yoshino

来源丨古月居

0. 前言

在深入剖析了Ceres、Eigen、Sophus、G2O后，以V-SLAM为代表的计算方式基本已经全部讲完。就L-SLAM而言，本系列也讲述了PCL、与GTSAM点云计算部分。

之前的系列部分作者本以为已经基本讲完，但是近期突然发现还有关于Open3D的部分还没有写。趁着这次不全来形成一整个系列，方便自己回顾以及后面的人一起学习。

1. Open3D环境安装

这里将Open3D的环境安装分为两个部分：非ROS和ROS环境

非ROS环境

//下载源码
git clone git@github.com:intel-isl/Open3D.git
git submodule update --init --recursive
//安装依赖
cd open3d
util/scripts/install-deps-ubuntu.sh
//编译安装
mkdir build


cd build


sudo cmake -DCMAKE_INSTALL_PREFIX=/opt/Open3D/ -DBUILD_EIGEN3=ON -DBUILD_GLEW=ON -DBUILD_GLFW=ON -DBUILD_JSONCPP=ON -DBUILD_PNG=ON -DPYTHON_EXECUTABLE=/usr/bin/python ..


sudo make -j8


sudo make install

ROS环境

//更新cmake
sudo apt-add-repository 'deb https://apt.kitware.com/ubuntu/ bionic main'
sudo apt-get update
sudo apt-get install cmake
//安装Open3D
git clone --recursive https://github.com/intel-isl/Open3D
cd Open3D && source util/scripts/install-deps-ubuntu.sh
mkdir build && cd build
cmake -DBUILD_EIGEN3=ON -DBUILD_GLEW=ON -DBUILD_GLFW=ON -DBUILD_JSONCPP=ON -DBUILD_PNG=ON -DGLIBCXX_USE_CXX11_ABI=ON -DPYTHON_EXECUTABLE=/usr/bin/python -DBUILD_UNIT_TESTS=ON ..
make -j4
sudo make install
//基于Open3D的ros程序
mkdir -p catkin_ws/src
cd catkin_ws/src
git clone git@github.com:unr-arl/open3d_ros.git
cd ..
catkin config -DCMAKE_BUILD_TYPE=Release
catkin build open3d_ros

2. Open3D示例

Open3D的操作和PCL类似，都是利用源码读取ply点云后, 并做ransec平面分割的操作。

//Open3D
#include "Open3D/Open3D.h"


//Eigen
#include "Eigen/Dense"


/* RANSAC平面分割 */
void testOpen3D::pcPlaneRANSAC(const QString &pcPath)
{
    int width = 700, height = 500;
    auto cloud_ptr = std::make_shared();
    if (!open3d::io::ReadPointCloud(pcPath.toStdString(), *cloud_ptr)) { return; }
    open3d::visualization::DrawGeometries({ cloud_ptr }, "Point Cloud 1", width, height);




    /***** 距离阈值，平面最小点数，最大迭代次数。返回平面参数和内点 *****/
    double tDis = 0.05;
    int minNum = 3;
    int numIter = 100;
    std::tuple vRes = cloud_ptr->SegmentPlane(tDis, minNum, numIter);


    //ABCD
    Eigen::Vector4d para = std::get(vRes);
    //内点索引
    std::vectorselectedIndex = std::get(vRes);




    //内点赋红色
    std::shared_ptrinPC = cloud_ptr->SelectByIndex(selectedIndex, false);
    const Eigen::Vector3d colorIn = { 255,0,0 };
    inPC->PaintUniformColor(colorIn);


    //外点赋黑色
    std::shared_ptroutPC = cloud_ptr->SelectByIndex(selectedIndex, true);
    const Eigen::Vector3d colorOut = { 0,0,0 };
    outPC->PaintUniformColor(colorOut);




    //显示 
    open3d::visualization::DrawGeometries({ inPC,outPC }, "RANSAC平面分割", width, height);
}

对于Open3D而言，PCL可以做到的，其自身也可以做到，下面部分代码为Open3D的ICP匹配

#include 
#include#include#include#include#include "Open3D/Registration/GlobalOptimization.h"
#include "Open3D/Registration/PoseGraph.h"
#include "Open3D/Registration/ColoredICP.h"
#include "Open3D/Open3D.h"
#include "Open3D/Registration/FastGlobalRegistration.h"




using namespace open3d;
using namespace std;
using namespace registration;
using namespace geometry;
using namespace cv;


void main()
{


    open3d::geometry::PointCloud source, target;
    open3d::io::ReadPointCloud("C:/Users/chili1080/Desktop/Augmented ICL-NUIM Dataset-jyx/livingroom1-fragments-ply/cloud_bin_0.ply", source);
    open3d::io::ReadPointCloud("C:/Users/chili1080/Desktop/Augmented ICL-NUIM Dataset-jyx/livingroom1-fragments-ply/cloud_bin_1.ply", target);


    Eigen::Vector3d color_source(1, 0.706, 0);
    Eigen::Vector3d color_target(0, 0.651, 0.929);


    source.PaintUniformColor(color_source);
    target.PaintUniformColor(color_target);


    double th = 0.02;
    open3d::registration::RegistrationResult res = open3d::registration::RegistrationICP(source, target, th, Eigen::Matrix4d::Identity(), 
        TransformationEstimationPointToPoint(false), ICPConvergenceCriteria(1e-6, 1e-6, 300));
    //显示配准结果  fitness 算法对这次配准的打分
    //inlier_rmse 表示的是 root of covariance, 也就是所有匹配点之间的距离的总和除以所有点的数量的平方根
    //correspondence_size 代表配准后吻合的点云的数量
    cout << "fitness: "<