使用ORBSLAM2进行kineticV2稠密建图，实时转octomap建图以及导航

决定总结最近一个月的工作，这个月在orbslam2的基础上，使用kineticV2完成了稠密点云地图的重建，实现了点云的回环，并使用octomap转换成实时的八叉树地图，导航部分已经有了思路，打算下个月所一个基于octomap的航迹生成能用在视觉的导航上。

一、传感器和依赖包安装

PC性能：Dell xps13 内存16GB 硬盘SSD：500GB 显卡：Intel iris集显

操作系统：ubuntu16.04 ROS：kinetic版本

依赖库版本：eigen3.1.2 、pcl-1.7、opencv3.2、vtk6.2、octomap1.9、

安装顺序：

1、先安装eigen3.1.2（涉及到很多东西，所以先解决eigen问题）

2、安装pcl1.7、再安装opencv3.2

3、安装kineticV2的libfreenect2、iai_kinect2

4、最后安装octomap

安装eigen3.1.2

cd eigen-eigen-5097c01bcdc4
mkdir build &&cd build
cmake ..
sudo make install

查看eigen版本

pkg-config --modversion eigen3

注：安装eigen不要更改安装路径，这样更换版本时可以自动覆盖原来的路径

2、pcl
本代码使用pcl-1.7版本开发，删除其他版本pcl
locate pcl查看其他版本的pcl安装在哪里，一般存于像/usr/local/share/pcl-1.8 、/usr/local/lib/pkgconfig/等区域，sudo rm -rf 文件路径删除。
例：

sudo rm -rf /usr/local/share/pcl-1.8  /usr/local/lib/pkgconfig/pcl*

locate pcl后如果还有这个文件，打开文件夹的形式打开到那个目录下再看看。有时候多余文件夹或文件已经删了，但是通过命令行locate的还是会有。

cd pcl-pcl-1.7.2
mkdir build&&cd build
cmake ..
make -j8 （编译大概30分钟）
sudo make install

编译有问题的话百度下，基本上都是eigen或者各种依赖库版本不对导致的。

3、下载安装libfreenect(Kinect开源驱动)

安装方式参考https://github.com/OpenKinect/libfreenect2

git clone https://github.com/OpenKinect/libfreenect2.gitcd libfreenect2sudo apt-get install build-essential cmake pkg-configsudo apt-get install libusb-1.0-0-devsudo apt-get install libturbojpeg libjpeg-turbo8-devsudo apt-get install libglfw3-devsudo apt-get install libopenni2-devcd ..mkdir build && cd buildcmake .. -DCMAKE_INSTALL_PREFIX=$HOME/freenect2makemake install

设定udev rules:

libfreenect2目录下执行

sudo cp ../platform/linux/udev/90-kinect2.rules /etc/udev/rules.d/

重新插拔设备

运行Demo程序: libfreenect2目录下执行 ./build/bin/Protonect，不出意外，应该能够看到如下效果：

注意：这里要分别测测cpu、opengl、opencl模型下的情况

./build/bin/Protonect cpu./build/bin/Protonect gl./build/bin/Protonect cl

尤其是使用opengl和opencl跑的，NVIDIA和Intel需要先安装NVIDIA的cuda后再执行，opencl执行不过关会影响后面iai_kinect2安装后执行roslaunch kinect2_bridge kinect2_bridge.launch的效果，这里我们先测一下，只要有图像就行，如果gl、或者cl执行不出来问题先保留，在iai_kinect2安装后再给出对应解决方案。

4、iai_kinect2

利用命令行从Github上面下载工程源码到工作空间内src文件夹内:

cd ~/catkin_ws/src/git clone https://github.com/code-iai/iai_kinect2.gitcd iai_kinect2rosdep install -r --from-paths .cd ~/catkin_wscatkin_make -DCMAKE_BUILD_TYPE="Release"

安装iai-kinect2操作这一步"rosdep install -r --from-paths 出现错误
ERROR: the following packages/stacks could not have their rosdep keys resolved
to system dependencies:
kinect2_viewer: Cannot locate rosdep definition for [kinect2_bridge]
kinect2_calibration: Cannot locate rosdep definition for [kinect2_bridge]
kinect2_bridge: Cannot locate rosdep definition for [kinect2_registration]
Continuing to install resolvable dependencies…
解决办法：命令改写为：

rosdep install --from-paths ~/catkin_ws/src/iai_kinect2 --ignore-src -r

执行下面命令查看能否正常执行kineticV2

roslaunch kinect2_bridge kinect2_bridge.launch

如果安装正常是可以执行的，

…

[ INFO] [1565591147.113376730]: [DepthRegistration::New] Using CPU registration method!
[ INFO] [1565591147.113685492]: [DepthRegistration::New] Using CPU registration method!
[ INFO] [1565591147.192329239]: [Kinect2Bridge::main] waiting for clients to connect
这里最后一行显示等待客户端连接，这个是正常的，因为会产生大量的计算量，因此默认不会自动打开显示窗口，

执行rostopic list明显看到是有话题的，当订阅相关话题时才会有数据。执行：

rosrun rviz rviz

左下角add —— image 在Image Topic中选/kinect2/qhd/image_color_rect ，可以看到图像，则kinect2可以正常使用了

5、出错排雷

好，关于kineticV2该安装的都安装完了，接下来我讲讲我遇到过的问题，供各位朋友们参考

a、其实我遇到的核心问题就是双显卡状态下，cl不能执行的问题。一开始在我的台式机（双显卡）上执行./build/bin/Protonect cl，报错，找不到opencl设备；执行roslaunch kinect2_bridge kinect2_bridge.launch。报错如下：

[ INFO] [1565590436.239968384]: [DepthRegistration::New] Using OpenCL registration method!
[ INFO] [1565590436.240130258]: [DepthRegistration::New] Using OpenCL registration method!
beignet-opencl-icd: no supported GPU found, this is probably the wrong opencl-icd package for this hardware
(If you have multiple ICDs installed and OpenCL works, you can ignore this message)
[ INFO] [1565590436.245914876]: [DepthRegistrationOpenCL::init] devices:
[ERROR] [1565590436.245966385]: [DepthRegistrationOpenCL::init] could not find any suitable device
[Info] [Freenect2DeviceImpl] closing…
[Info] [Freenect2DeviceImpl] releasing usb interfaces…
[Info] [Freenect2DeviceImpl] deallocating usb transfer pools…
[Info] [Freenect2DeviceImpl] closing usb device…
[Info] [Freenect2DeviceImpl] closed
[ERROR] [1565590436.247492556]: [Kinect2Bridge::start] Initialization failed!

…
[Error] [OpenCLDepthPacketProcessorImpl] OpenCLDepthPacketProcessor is not initialized!
[Error] [OpenCLDepthPacketProcessorImpl] OpenCLDepthPacketProcessor is not initialized!
[Error] [OpenCLDepthPacketProcessorImpl] OpenCLDepthPacketProcessor is not initialized!
[Info] [Freenect2DeviceImpl] submitting rgb transfers…
[Info] [Freenect2DeviceImpl] submitting depth transfers…
[Error] [DepthPacketStreamParser] Packet buffer is NULL
[Error] [DepthPacketStreamParser] Packet buffer is NULL

查看错误信息我们可以得知问题出在opencl上，找不到opencl设备

解决方案：

a、查看https://github.com/OpenKinect/libfreenect2里关于双显卡的安装依赖包，下载nvidia对应显卡的cuda，两个显卡都安装后,重新编译，再执行其他操作。在xps13的笔记本上只有一个显卡，所以一遍通过。

b、如果不安装opencl，则可以通过opengl+cpu的形式执行，opengl用来计算深度图（depth），cpu用来计算（color）的方式，解决。

修改iai_kinect2/kinect2_bridge/launch下kinect2_bridge.launch
将

修改为

再次执行roslaunch kinect2_bridge kinect2_bridge.launch，报错

[Kinect2Bridge::initRegistration] CPU registration is not available! ".
参考解决方案：https://github.com/code-iai/iai_kinect2/issues/447
这里找不到cpu是因为eigen找不到的原因

locate FindEigen3.cmake

locate找到FindEigen3.cmake复制到iai_kinect2/kinect2_registration/cmake下，重新catkin_make整个iai_kinect2工程可解决问题。

6、安装octomap1.9
源码下载git clone https://github.com/OctoMap/octomap.git
cd octomap
mkdir build&&cd build
cmake …
make
sudo make install

传感器安装部分结束。安装参考博客https://blog.csdn.net/wuguangbin1230/article/details/77184032

二、基于ORBSLAM2的pcl-1.7点云拼接与三维稠密点云重建

先进行个稠密点云的三维重建，感谢高博做出的工作！https://github.com/gaoxiang12/ORBSLAM2_with_pointcloud_map

在高博基础上，另一位大佬给稠密地图加了回环https://github.com/tiantiandabaojian/ORB-SLAM2_RGBD_DENSE_MAP.git

我的工作是将kineticV2相机的稠密点云实时转换成octomap，并在rviz里进行展示。

原理：用单目、双目、RGBD都可以进行稠密地图的建立，建立全局地图是我们实现导航的第一步，通过相机图像将像素转换为点云（pointcloud）数据，进而进行拼接，在此基础上如果要恢复物体外观轮廓，就需要使用三角网格（mesh）、面片（surfel）进行建图，这样的生成的pcd点云地图往往很大，跑tum生成的数据集都可达到5、600MB的大小，用于导航的话非常不利于我们设备进行导航地图的导入，所以亦可以通过体素（voxel）占据网格地图（Occupancy Map）。

点云包含xyz和rgb信息

外点滤波器以及降采样滤波器。

数据集实现效果:

先抛出代码，后面解释

pointcloudmapping.c文件

/** <one line to give the program's name and a brief idea of what it does.>* Copyright (C) 2016  <copyright holder> <email>* * This program is free software: you can redistribute it and/or modify* it under the terms of the GNU General Public License as published by* the Free Software Foundation, either version 3 of the License, or* (at your option) any later version.* * This program is distributed in the hope that it will be useful,* but WITHOUT ANY WARRANTY; without even the implied warranty of* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the* GNU General Public License for more details.* * You should have received a copy of the GNU General Public License* along with this program.  If not, see <http://www.gnu.org/licenses/>.* */#include "pointcloudmapping.h"
#include <KeyFrame.h>
#include <opencv2/highgui/highgui.hpp>
#include <pcl/visualization/cloud_viewer.h>
#include <pcl/filters/statistical_outlier_removal.h>
#include "Converter.h"
#include "PointCloude.h"
#include "System.h"int currentloopcount = 0;
/*** @param resolution_ :体素大小分辨率，分辨率越小，单个体素越小* @param meank_ ： meank_ 为在进行统计时考虑查询邻近点个数* @param thresh_：设置距离阈值，其公式是 mean + global stddev_mult * global stddev，即mean+1.0*stddev* @return ：无*/
PointCloudMapping::PointCloudMapping(double resolution_,double meank_,double thresh_)
{this->resolution = resolution_;//分辨率this->meank = thresh_;this->thresh = thresh_;statistical_filter.setMeanK(meank);//统计估计滤波参数statistical_filter.setStddevMulThresh(thresh);voxel.setLeafSize( resolution, resolution, resolution);//设置每个体素子叶分辨率globalMap = boost::make_shared< PointCloud >( );viewerThread = make_shared<thread>( bind(&PointCloudMapping::viewer, this ) );
}/** void PointCloudMapping::shutdown()* \brief 关闭建图线程*/
void PointCloudMapping::shutdown()
{{unique_lock<mutex> lck(shutDownMutex);shutDownFlag = true;keyFrameUpdated.notify_one();}//等待PointCloudMapping_viewer 本线程执行结束再执行系统主线程viewerThread->join();
}//插入关键帧
/*** @param kf    关键帧* @param color 关键帧彩色图* @param depth 关键帧深度图* @param idk   第idk个关键帧* @param vpKFs 获取全部关键帧* @function    在点云地图里插入关键帧*/
void PointCloudMapping::insertKeyFrame(KeyFrame* kf, cv::Mat& color, cv::Mat& depth,int idk,vector<KeyFrame*> vpKFs)
{cout<<"receive a keyframe, id = "<<idk<<" 第"<<kf->mnId<<"个"<<endl;//cout<<"vpKFs数量"<<vpKFs.size()<<endl;unique_lock<mutex> lck(keyframeMutex);keyframes.push_back( kf );currentvpKFs = vpKFs;//colorImgs.push_back( color.clone() );//depthImgs.push_back( depth.clone() );PointCloude pointcloude;pointcloude.pcID = idk;pointcloude.T = ORB_SLAM2::Converter::toSE3Quat( kf->GetPose() );//获取关键帧位姿pointcloude.pcE = generatePointCloud(kf,color,depth);//迭代关键帧点云pointcloud.push_back(pointcloude);keyFrameUpdated.notify_one();//通知线程开锁
}/**** @param kf    关键帧* @param color 彩色图* @param depth 深度图* @return 关键帧点云*/
pcl::PointCloud< PointCloudMapping::PointT >::Ptr PointCloudMapping::generatePointCloud(KeyFrame* kf, cv::Mat& color, cv::Mat& depth)//,Eigen::Isometry3d T
{//新建一个点云// point cloud is null ptrPointCloud::Ptr tmp( new PointCloud() );//对点云进行for ( int m=0; m<depth.rows; m+=2 ){for ( int n=0; n<depth.cols; n+=2 ){float d = depth.ptr<float>(m)[n];//获取（m,n）处的深度值if (d < 0.01 || d>5)//滤除设备可靠深度范围之外点continue;PointT p;//相机模型，只计算关键帧的点云//座标系与pcl座标系相反，所以可以p.z=-dp.z = d;p.x = ( n - kf->cx) * p.z / kf->fx;p.y = ( m - kf->cy) * p.z / kf->fy;//彩色图计算点云颜色p.b = color.ptr<uchar>(m)[n*3];p.g = color.ptr<uchar>(m)[n*3+1];p.r = color.ptr<uchar>(m)[n*3+2];tmp->points.push_back(p);}}//cout<<"generate point cloud for kf "<<kf->mnId<<", size="<<cloud->points.size()<<endl;return tmp;
}/** @brief 显示点云线程*/
void PointCloudMapping::viewer()
{//创建显示点云窗口pcl::visualization::CloudViewer viewer("viewer");while(1){{unique_lock<mutex> lck_shutdown( shutDownMutex );if (shutDownFlag){break;}}{unique_lock<mutex> lck_keyframeUpdated( keyFrameUpdateMutex );keyFrameUpdated.wait( lck_keyframeUpdated );}// keyframe is updatedsize_t N=0;{unique_lock<mutex> lck( keyframeMutex );N = keyframes.size();}if(loopbusy || bStop){//cout<<"loopbusy || bStop"<<endl;continue;}//cout<<lastKeyframeSize<<"    "<<N<<endl;if(lastKeyframeSize == N)cloudbusy = false;//cout<<"待处理点云个数 = "<<N<<endl;cloudbusy = true;for ( size_t i=lastKeyframeSize; i<N ; i++ ){PointCloud::Ptr p (new PointCloud);//将点云数据转换成ascii码形式存储在pcd文件中//1、源点云   2、转变后的点云   3、位姿变换矩阵pcl::transformPointCloud( *(pointcloud[i].pcE), *p, pointcloud[i].T.inverse().matrix());//  转换后的点云叠加存储在globalMap中*globalMap += *p;}// depth filter and statistical removal//这里的滤波只是显示上的滤波PointCloud::Ptr tmp1 ( new PointCloud );statistical_filter.setInputCloud(globalMap);    //对globalMap进行统计学去噪statistical_filter.filter( *tmp1 );             // 执行去噪计算并保存点到 tmp1//体素滤波器voxel filter进行降采样PointCloud::Ptr tmp(new PointCloud());voxel.setInputCloud( tmp1 );voxel.filter( *globalMap );//globalMap->swap( *tmp );viewer.showCloud( globalMap );cout<<"show global map, size="<<N<<"   "<<globalMap->points.size()<<endl;lastKeyframeSize = N;cloudbusy = false;}
}/** 保存pcd地图*/
void PointCloudMapping::save()
{pcl::io::savePCDFile( "/home/linker/catkin_make/src/MYNT-EYE-ORB-SLAM2-Sample/result.pcd", *globalMap );cout<<"globalMap save finished"<<endl;
}/** 更新点云*/
void PointCloudMapping::updatecloud()
{if(!cloudbusy){loopbusy = true;cout<<"startloopmappoint"<<endl;PointCloud::Ptr tmp1(new PointCloud);for (int i=0;i<currentvpKFs.size();i++){for (int j=0;j<pointcloud.size();j++){if(pointcloud[j].pcID==currentvpKFs[i]->mnFrameId){Eigen::Isometry3d T = ORB_SLAM2::Converter::toSE3Quat(currentvpKFs[i]->GetPose() );PointCloud::Ptr cloud(new PointCloud);pcl::transformPointCloud( *pointcloud[j].pcE, *cloud, T.inverse().matrix());*tmp1 +=*cloud;continue;}}}cout<<"finishloopmap"<<endl;PointCloud::Ptr tmp2(new PointCloud());voxel.setInputCloud( tmp1 );voxel.filter( *tmp2 );globalMap->swap( *tmp2 );loopbusy = false;loopcount++;}
}//获取全局点云地图点，智能指针，return 回来
pcl::PointCloud<PointCloudMapping::PointT>::Ptr PointCloudMapping::getGlobalMap() {return this->globalMap;
}

pointcloudmapping.h

/** <one line to give the program's name and a brief idea of what it does.>* Copyright (C) 2016  <copyright holder> <email>* * This program is free software: you can redistribute it and/or modify* it under the terms of the GNU General Public License as published by* the Free Software Foundation, either version 3 of the License, or* (at your option) any later version.* * This program is distributed in the hope that it will be useful,* but WITHOUT ANY WARRANTY; without even the implied warranty of* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the* GNU General Public License for more details.* * You should have received a copy of the GNU General Public License* along with this program.  If not, see <http://www.gnu.org/licenses/>.* */#ifndef POINTCLOUDMAPPING_H
#define POINTCLOUDMAPPING_H#include "System.h"
#include "PointCloude.h"
#include <pcl/common/transforms.h>
#include <pcl/point_types.h>
#include <pcl/filters/voxel_grid.h>
#include <condition_variable>
#include <pcl/io/pcd_io.h>
#include <pcl/filters/statistical_outlier_removal.h>using namespace std;
using namespace ORB_SLAM2;class PointCloudMapping
{public://定义点云类型typedef pcl::PointXYZRGBA PointT;typedef pcl::PointCloud<PointT> PointCloud;PointCloudMapping( double resolution_,double meank_,double thresh_ );void save();// 插入一个keyframe，会更新一次地图void insertKeyFrame( KeyFrame* kf, cv::Mat& color, cv::Mat& depth,int idk,vector<KeyFrame*> vpKFs );void shutdown();void viewer();void inserttu( cv::Mat& color, cv::Mat& depth,int idk);int loopcount = 0;vector<KeyFrame*> currentvpKFs;bool cloudbusy;bool loopbusy;void updatecloud();bool bStop = false;PointCloud::Ptr getGlobalMap();
protected:PointCloud::Ptr globalMap;PointCloud::Ptr generatePointCloud(KeyFrame* kf, cv::Mat& color, cv::Mat& depth);//PointCloud::Ptr globalMap;shared_ptr<thread>  viewerThread;bool    shutDownFlag    =false;mutex   shutDownMutex;condition_variable  keyFrameUpdated;mutex               keyFrameUpdateMutex;vector<PointCloude>     pointcloud;// data to generate point cloudsvector<KeyFrame*>       keyframes;vector<cv::Mat>         colorImgs;vector<cv::Mat>         depthImgs;vector<cv::Mat>         colorImgks;vector<cv::Mat>         depthImgks;vector<int>             ids;mutex                   keyframeMutex;uint16_t                lastKeyframeSize =0;double resolution = 0.04;double meank = 50;double thresh = 1;pcl::VoxelGrid<PointT>  voxel;pcl::StatisticalOutlierRemoval<PointT> statistical_filter;
};#endif // POINTCLOUDMAPPING_H

system.cc

void System::Shutdown()
{mpLocalMapper->RequestFinish();mpLoopCloser->RequestFinish();mpPointCloudMapping->shutdown();if(mpViewer){mpViewer->RequestFinish();while(!mpViewer->isFinished())usleep(5000);}// Wait until all thread have effectively stoppedwhile(!mpLocalMapper->isFinished() || !mpLoopCloser->isFinished() || mpLoopCloser->isRunningGBA()){usleep(5000);}if(mpViewer)pangolin::BindToContext("ORB-SLAM2: Map Viewer");
}

void System::save()
{mpPointCloudMapping->save();
}pcl::PointCloud<PointCloudMapping::PointT>::Ptr System::getGlobalMap() {return mpPointCloudMapping->getGlobalMap();
}
int System::getloopcount()
{return mpLoopCloser->loopcount;
}
}

track.cc中void Tracking::CreateNewKeyFrame()函数添加

    // insert Key Frame into point cloud viewervector<KeyFrame*> vpKFs = mpMap->GetAllKeyFrames();mpPointCloudMapping->insertKeyFrame( pKF, this->mImRGB, this->mImDepth  ,idk,vpKFs);

LoopClousing.cc的void LoopClosing::RunGlobalBundleAdjustment(unsigned long nLoopKF)添加代码

//稠密建图loopcount++;while(loopcount!=mpPointCloudMapping->loopcount)mpPointCloudMapping->updatecloud();cout<<"mpPointCloudMapping->loopcount="<<mpPointCloudMapping->loopcount<<endl;

接下来我将生成的稠密点云通过ros_octomap映射到ros话题中，octomap原理高博在书中已经讲的很详细了。

在ros里进行展示

ros_rgbd.cc

int main(int argc, char **argv)
{ros::init(argc, argv, "RGBD");ros::start();if(argc != 3){cerr << endl << "Usage: rosrun ORB_SLAM2 RGBD path_to_vocabulary path_to_settings" << endl;ros::shutdown();return 1;}// Create SLAM system. It initializes all system threads and gets ready to process frames.ORB_SLAM2::System SLAM(argv[1],argv[2],ORB_SLAM2::System::RGBD,true);ImageGrabber igb(&SLAM);ros::NodeHandle nh;//原代码
//    message_filters::Subscriber<sensor_msgs::Image> rgb_sub(nh, "/camera/rgb/image_raw", 1);
//    message_filters::Subscriber<sensor_msgs::Image> depth_sub(nh, "camera/depth_registered/image_raw", 1);//修改为kinect2message_filters::Subscriber<sensor_msgs::Image> rgb_sub(nh, "/kinect2/qhd/image_color", 1);message_filters::Subscriber<sensor_msgs::Image> depth_sub(nh, "/kinect2/qhd/image_depth_rect", 1);typedef message_filters::sync_policies::ApproximateTime<sensor_msgs::Image, sensor_msgs::Image> sync_pol;message_filters::Synchronizer<sync_pol> sync(sync_pol(10), rgb_sub,depth_sub);sync.registerCallback(boost::bind(&ImageGrabber::GrabRGBD,&igb,_1,_2));//TODO OCTOMAP添加pcl::PointCloud<pcl::PointXYZRGBA>::Ptr global_map(new pcl::PointCloud<pcl::PointXYZRGBA>);global_map = SLAM.mpPointCloudMapping->getGlobalMap();pcl::PointCloud<pcl::PointXYZRGB>::Ptr global_map_copy(new pcl::PointCloud<pcl::PointXYZRGB>);//数据格式转换cout<<"-----------------------------------------------------------"<<endl;cout <<"ros is running "<<endl;while (ros::ok()){pcl::copyPointCloud(*global_map, *global_map_copy);ros::Publisher pcl_pub = nh.advertise<sensor_msgs::PointCloud2> ("/orbslam2_with_kinect2/output", 10);sensor_msgs::PointCloud2 output;pcl::toROSMsg(*global_map_copy,output);// 转换成ROS下的数据类型 最终通过topic发布output.header.stamp=ros::Time::now();output.header.frame_id  ="camera_rgb_frame";//output.header.frame_id  ="map";ros::Rate loop_rate(10);pcl_pub.publish(output);ros::spinOnce();loop_rate.sleep();}//TODO 结束//ros::spin();SLAM.save();// Stop all threadsSLAM.Shutdown();// Save camera trajectorySLAM.SaveKeyFrameTrajectoryTUM("KeyFrameTrajectory.txt");ros::shutdown();return 0;
}

之后存在几种方法去实现导航：

1、octomap_server是ROS中的一个基于octomap的功能包。我在查阅资料的时候，发现所有的介绍、博客等资料都是在介绍其将点云地图转化为基于Octree的OctoMap的功能。由于之前一直在查找三维点云地图转化为二维地图的方法，所以之前试过这个包的三维转换功能后就没有在继续使用，由于之前使用其他方法将二维占据栅格地图生成了，然后准备回过头来再看一下octomap_server的三维概率地图，然后在不经意间就发现了它也有转化为二维地图的功能

首先简单介绍下octomap_server【设置】的安装。

打开一个终端.(ctrl+alt+T)输入下面指令安装octomap.
sudo apt-get install ros-kinetic-octomap-ros #安装octomap
sudo apt-get install ros-kinetic-octomap-msgs
sudo apt-get install ros-kinetic-octomap-server安装octomap 在 rviz 中的插件
sudo apt-get install ros-kinetic-octomap-rviz-plugins

安装上这个插件以后你可以启动 rviz ,这时候点开Add选项，会多一个octomap_rviz_plugins模组.如下图所示:

其中的OccupancyGrid是显示三维概率地图，也就是octomap地图。OccupancyMap是显示二维占据栅格地图。

可以通过一个launch文件启动octomap_server节点，如下：

其中的param都是可以修改的，具体的修改细节见这里。/octotree_map修改为自己的PointCloud2点云即可。

运行此launch文件会有如下话题：

打开rviz，在里面添加OccupancyGrid，OccupancyMap，Map显示选项,显示话题选择octomap_full或者octomap_binary。

注意：param中的frame_id要和rviz的Fixed Frame一致。

Map:

OccupancyMap:

OccupancyGrid:

对应的参考代码：https://github.com/306327680/PointCloud-to-grid-map【参考注释】

或者北达科他大学( North Dakota State University)cloud_to_map学习代码：https://download.csdn.net/download/sru_alo/12277545

2、使用3D稠密点云图，并使用octomap进行压缩滤除地面信息。然后通过2D投影生成占据格地图最后利用costmap进行全局和局部路径规划导航实时避障。【参考代码】

然后参考OctoMap中3D-RRT路径规划

#include "ros/ros.h"
#include <octomap_msgs/Octomap.h>
#include <octomap_msgs/conversions.h>
#include <octomap_ros/conversions.h>
#include <octomap/octomap.h>
#include <message_filters/subscriber.h>
#include "visualization_msgs/Marker.h"
#include <trajectory_msgs/MultiDOFJointTrajectory.h>
#include <nav_msgs/Odometry.h>
#include <geometry_msgs/Pose.h>
#include <nav_msgs/Path.h>
#include <geometry_msgs/PoseStamped.h>#include <ompl/base/spaces/SE3StateSpace.h>
#include <ompl/base/spaces/SE3StateSpace.h>
#include <ompl/base/OptimizationObjective.h>
#include <ompl/base/objectives/PathLengthOptimizationObjective.h>
// #include <ompl/geometric/planners/rrt/RRTstar.h>
#include <ompl/geometric/planners/rrt/InformedRRTstar.h>
#include <ompl/geometric/SimpleSetup.h>#include <ompl/config.h>
#include <iostream>#include "fcl/config.h"
#include "fcl/octree.h"
#include "fcl/traversal/traversal_node_octree.h"
#include "fcl/collision.h"
#include "fcl/broadphase/broadphase.h"
#include "fcl/math/transform.h"namespace ob = ompl::base;
namespace og = ompl::geometric;// Declear some global variables//ROS publishers
ros::Publisher vis_pub;
ros::Publisher traj_pub;class planner {public:void setStart(double x, double y, double z){ob::ScopedState<ob::SE3StateSpace> start(space);start->setXYZ(x,y,z);start->as<ob::SO3StateSpace::StateType>(1)->setIdentity();pdef->clearStartStates();pdef->addStartState(start);}void setGoal(double x, double y, double z){ob::ScopedState<ob::SE3StateSpace> goal(space);goal->setXYZ(x,y,z);goal->as<ob::SO3StateSpace::StateType>(1)->setIdentity();pdef->clearGoal();pdef->setGoalState(goal);std::cout << "goal set to: " << x << " " << y << " " << z << std::endl;}void updateMap(std::shared_ptr<fcl::CollisionGeometry> map){tree_obj = map;}// Constructorplanner(void){//四旋翼的障碍物几何形状Quadcopter = std::shared_ptr<fcl::CollisionGeometry>(new fcl::Box(0.8, 0.8, 0.3));//分辨率参数设置fcl::OcTree* tree = new fcl::OcTree(std::shared_ptr<const octomap::OcTree>(new octomap::OcTree(0.15)));tree_obj = std::shared_ptr<fcl::CollisionGeometry>(tree);//解的状态空间space = ob::StateSpacePtr(new ob::SE3StateSpace());// create a start stateob::ScopedState<ob::SE3StateSpace> start(space);// create a goal stateob::ScopedState<ob::SE3StateSpace> goal(space);// set the bounds for the R^3 part of SE(3)// 搜索的三维范围设置ob::RealVectorBounds bounds(3);bounds.setLow(0,-5);bounds.setHigh(0,5);bounds.setLow(1,-5);bounds.setHigh(1,5);bounds.setLow(2,0);bounds.setHigh(2,3);space->as<ob::SE3StateSpace>()->setBounds(bounds);// construct an instance of  space information from this state spacesi = ob::SpaceInformationPtr(new ob::SpaceInformation(space));start->setXYZ(0,0,0);start->as<ob::SO3StateSpace::StateType>(1)->setIdentity();// start.random();goal->setXYZ(0,0,0);goal->as<ob::SO3StateSpace::StateType>(1)->setIdentity();// goal.random();// set state validity checking for this spacesi->setStateValidityChecker(std::bind(&planner::isStateValid, this, std::placeholders::_1 ));// create a problem instancepdef = ob::ProblemDefinitionPtr(new ob::ProblemDefinition(si));// set the start and goal statespdef->setStartAndGoalStates(start, goal);// set Optimizattion objectivepdef->setOptimizationObjective(planner::getPathLengthObjWithCostToGo(si));std::cout << "Initialized: " << std::endl;}// Destructor~planner(){}void replan(void){std::cout << "Total Points:" << path_smooth->getStateCount () << std::endl;if(path_smooth->getStateCount () <= 2)plan();else{for (std::size_t idx = 0; idx < path_smooth->getStateCount (); idx++){if(!replan_flag)replan_flag = !isStateValid(path_smooth->getState(idx));elsebreak;}if(replan_flag)plan();elsestd::cout << "Replanning not required" << std::endl;}}void plan(void){// create a planner for the defined spaceog::InformedRRTstar* rrt = new og::InformedRRTstar(si);//设置rrt的参数rangerrt->setRange(0.2);ob::PlannerPtr plan(rrt);// set the problem we are trying to solve for the plannerplan->setProblemDefinition(pdef);// perform setup steps for the plannerplan->setup();// print the settings for this spacesi->printSettings(std::cout);std::cout << "problem setting\n";// print the problem settingspdef->print(std::cout);// attempt to solve the problem within one second of planning timeob::PlannerStatus solved = plan->solve(1);if (solved){// get the goal representation from the problem definition (not the same as the goal state)// and inquire about the found pathstd::cout << "Found solution:" << std::endl;ob::PathPtr path = pdef->getSolutionPath();og::PathGeometric* pth = pdef->getSolutionPath()->as<og::PathGeometric>();pth->printAsMatrix(std::cout);// print the path to screen// path->print(std::cout);nav_msgs::Path msg;msg.header.stamp = ros::Time::now();msg.header.frame_id = "map";for (std::size_t path_idx = 0; path_idx < pth->getStateCount (); path_idx++){const ob::SE3StateSpace::StateType *se3state = pth->getState(path_idx)->as<ob::SE3StateSpace::StateType>();// extract the first component of the state and cast it to what we expectconst ob::RealVectorStateSpace::StateType *pos = se3state->as<ob::RealVectorStateSpace::StateType>(0);// extract the second component of the state and cast it to what we expectconst ob::SO3StateSpace::StateType *rot = se3state->as<ob::SO3StateSpace::StateType>(1);geometry_msgs::PoseStamped pose;//             pose.header.frame_id = "/world"pose.pose.position.x = pos->values[0];pose.pose.position.y = pos->values[1];pose.pose.position.z = pos->values[2];pose.pose.orientation.x = rot->x;pose.pose.orientation.y = rot->y;pose.pose.orientation.z = rot->z;pose.pose.orientation.w = rot->w;msg.poses.push_back(pose);}traj_pub.publish(msg);//Path smoothing using bspline//B样条曲线优化og::PathSimplifier* pathBSpline = new og::PathSimplifier(si);path_smooth = new og::PathGeometric(dynamic_cast<const og::PathGeometric&>(*pdef->getSolutionPath()));pathBSpline->smoothBSpline(*path_smooth,3);// std::cout << "Smoothed Path" << std::endl;// path_smooth.print(std::cout);//Publish path as markersnav_msgs::Path smooth_msg;smooth_msg.header.stamp = ros::Time::now();smooth_msg.header.frame_id = "map";for (std::size_t idx = 0; idx < path_smooth->getStateCount (); idx++){// cast the abstract state type to the type we expectconst ob::SE3StateSpace::StateType *se3state = path_smooth->getState(idx)->as<ob::SE3StateSpace::StateType>();// extract the first component of the state and cast it to what we expectconst ob::RealVectorStateSpace::StateType *pos = se3state->as<ob::RealVectorStateSpace::StateType>(0);// extract the second component of the state and cast it to what we expectconst ob::SO3StateSpace::StateType *rot = se3state->as<ob::SO3StateSpace::StateType>(1);geometry_msgs::PoseStamped point;//             pose.header.frame_id = "/world"point.pose.position.x = pos->values[0];point.pose.position.y = pos->values[1];point.pose.position.z = pos->values[2];point.pose.orientation.x = rot->x;point.pose.orientation.y = rot->y;point.pose.orientation.z = rot->z;point.pose.orientation.w = rot->w;smooth_msg.poses.push_back(point);std::cout << "Published marker: " << idx << std::endl;}vis_pub.publish(smooth_msg);// ros::Duration(0.1).sleep();// Clear memorypdef->clearSolutionPaths();replan_flag = false;}elsestd::cout << "No solution found" << std::endl;}
private:// construct the state space we are planning inob::StateSpacePtr space;// construct an instance of  space information from this state spaceob::SpaceInformationPtr si;// create a problem instanceob::ProblemDefinitionPtr pdef;og::PathGeometric* path_smooth;bool replan_flag = false;std::shared_ptr<fcl::CollisionGeometry> Quadcopter;std::shared_ptr<fcl::CollisionGeometry> tree_obj;bool isStateValid(const ob::State *state){// cast the abstract state type to the type we expectconst ob::SE3StateSpace::StateType *se3state = state->as<ob::SE3StateSpace::StateType>();// extract the first component of the state and cast it to what we expectconst ob::RealVectorStateSpace::StateType *pos = se3state->as<ob::RealVectorStateSpace::StateType>(0);// extract the second component of the state and cast it to what we expectconst ob::SO3StateSpace::StateType *rot = se3state->as<ob::SO3StateSpace::StateType>(1);fcl::CollisionObject treeObj((tree_obj));fcl::CollisionObject aircraftObject(Quadcopter);// check validity of state defined by pos & rotfcl::Vec3f translation(pos->values[0],pos->values[1],pos->values[2]);fcl::Quaternion3f rotation(rot->w, rot->x, rot->y, rot->z);aircraftObject.setTransform(rotation, translation);fcl::CollisionRequest requestType(1,false,1,false);fcl::CollisionResult collisionResult;fcl::collide(&aircraftObject, &treeObj, requestType, collisionResult);return(!collisionResult.isCollision());}// Returns a structure representing the optimization objective to use// for optimal motion planning. This method returns an objective which// attempts to minimize the length in configuration space of computed// paths.ob::OptimizationObjectivePtr getThresholdPathLengthObj(const ob::SpaceInformationPtr& si){ob::OptimizationObjectivePtr obj(new ob::PathLengthOptimizationObjective(si));// obj->setCostThreshold(ob::Cost(1.51));return obj;}ob::OptimizationObjectivePtr getPathLengthObjWithCostToGo(const ob::SpaceInformationPtr& si){ob::OptimizationObjectivePtr obj(new ob::PathLengthOptimizationObjective(si));obj->setCostToGoHeuristic(&ob::goalRegionCostToGo);return obj;}};void octomapCallback(const octomap_msgs::Octomap::ConstPtr &msg, planner* planner_ptr)
{//loading octree from binary// const std::string filename = "/home/xiaopeng/dense.bt";// octomap::OcTree temp_tree(0.1);// temp_tree.readBinary(filename);// fcl::OcTree* tree = new fcl::OcTree(std::shared_ptr<const octomap::OcTree>(&temp_tree));// convert octree to collision objectoctomap::OcTree* tree_oct = dynamic_cast<octomap::OcTree*>(octomap_msgs::msgToMap(*msg));fcl::OcTree* tree = new fcl::OcTree(std::shared_ptr<const octomap::OcTree>(tree_oct));// Update the octree used for collision checkingplanner_ptr->updateMap(std::shared_ptr<fcl::CollisionGeometry>(tree));planner_ptr->replan();
}void odomCb(const nav_msgs::Odometry::ConstPtr &msg, planner* planner_ptr)
{planner_ptr->setStart(msg->pose.pose.position.x, msg->pose.pose.position.y, msg->pose.pose.position.z);
}void startCb(const geometry_msgs::PointStamped::ConstPtr &msg, planner* planner_ptr)
{planner_ptr->setStart(msg->point.x, msg->point.y, msg->point.z);
}void goalCb(const geometry_msgs::PointStamped::ConstPtr &msg, planner* planner_ptr)
{planner_ptr->setGoal(msg->point.x, msg->point.y, msg->point.z);planner_ptr->plan();
}int main(int argc, char **argv)
{ros::init(argc, argv, "octomap_planner");ros::NodeHandle n;planner planner_object;ros::Subscriber octree_sub = n.subscribe<octomap_msgs::Octomap>("/octomap_binary", 1, boost::bind(&octomapCallback, _1, &planner_object));// ros::Subscriber odom_sub = n.subscribe<nav_msgs::Odometry>("/rovio/odometry", 1, boost::bind(&odomCb, _1, &planner_object));ros::Subscriber goal_sub = n.subscribe<geometry_msgs::PointStamped>("/goal/clicked_point", 1, boost::bind(&goalCb, _1, &planner_object));ros::Subscriber start_sub = n.subscribe<geometry_msgs::PointStamped>("/start/clicked_point", 1, boost::bind(&startCb, _1, &planner_object));//   vis_pub = n.advertise<visualization_msgs::Marker>( "visualization_marker", 0 );vis_pub = n.advertise<nav_msgs::Path>( "visualization_marker", 0 );
//  traj_pub = n.advertise<trajectory_msgs::MultiDOFJointTrajectory>("waypoints",1);traj_pub = n.advertise<nav_msgs::Path>("waypoints",1);std::cout << "OMPL version: " << OMPL_VERSION << std::endl;ros::spin();return 0;
}

3、利用OMPL实现的ROS turtlebot 路径规划(需要安装OMPL库，从而可以实现上述两种方式的导航)

此外对应两个纯ORB的开源项目：
https://github.com/Ewenwan/Active-ORB-SLAM2(Ubuntu14.04)
https://github.com/abhineet123/ORB_SLAM2(Ubuntu16.04)

一个RGBD进阶版建图
https://github.com/was48i/IndoorMapping