在ORBSLAM2的定位基础上，结合摄像机点云数据实现三维环境的稠密重构，代码在下面的项目上进行修改

https://github.com/tiantiandabaojian/ORB-SLAM2_RGBD_DENSE_MAP.git

修改后的代码

https://github.com/huang-xiaoliang/ORB-SLAM2_RGBD_DENSE_LOOP

该项目源码编译后运行出现错误，经过分析为存储每帧点云信息的pointcloud容器出现问题，取消存储位姿T即可

（后来发现可以是代码版本问题，里面eigen版本太高了，使用旧版本就不会存在这个问题）

因此不再使用其在回环线程的点云模型重置功能，只是在ORBSLAM2的基础上，在tracking线程中创建关键帧处添加点云提取和融合功能

融合后点云在view线程中使用pcl的cloudview进行实时的稠密点云模型显示

如下图所示，融合模型相比原生的点云模型模糊（未回环），这是在低速运动下扫描得到的模型，高速时由于笔记本cpu运算能力有限处理的帧数少，定位误差大

作者回环后重构模型

PART1：添加新的稠密点云模型类pointcloudmapping

class PointCloudMapping
{
public:
    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 insertKeyFrame2( KeyFrame* kf, cv::Mat& color, cv::Mat& depth,int idk);
    void shutdown();
    void viewer();

protected:
    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;
    // data to generate point clouds

    mutex                   keyframeMutex;
    
    double resolution = 0.01;
    double meank = 50;
    double thresh = 1;
    pcl::VoxelGrid<PointT>  voxel;
    pcl::StatisticalOutlierRemoval<PointT> statistical_filter;
};

//初始化时启动视图view线程展示点云模型
PointCloudMapping::PointCloudMapping(double resolution_,double meank_,double thresh_)
{
    this->resolution = resolution_;
    this->meank = thresh_;
    this->thresh = thresh_;
    statistical_filter.setMeanK(meank);
    statistical_filter.setStddevMulThresh(thresh);
    voxel.setLeafSize( resolution, resolution, resolution);
    globalMap = boost::make_shared< PointCloud >( );
    
    viewerThread = make_shared<thread>( bind(&PointCloudMapping::viewer, this ) );
}

void PointCloudMapping::shutdown()
{
    {
        unique_lock<mutex> lck(shutDownMutex);
        shutDownFlag = true;
        keyFrameUpdated.notify_one();
    }
    viewerThread->join();
}


void PointCloudMapping::insertKeyFrame2(KeyFrame* kf, cv::Mat& color, cv::Mat& depth,int idk)
{
    cout<<"receive a keyframe, id = "<<idk<<" 第"<<kf->mnId<<"个"<<endl;
    PointCloude pointcloude;
    pointcloude.pcID = idk;
    pointcloude.T = ORB_SLAM2::Converter::toSE3Quat( kf->GetPose() );
    pointcloude.pcE = generatePointCloud(kf,color,depth);
    PointCloud::Ptr p (new PointCloud);
    pcl::transformPointCloud( *(pointcloude.pcE), *p, pointcloude.T.inverse().matrix());
    *globalMap += *p;
    //过滤
    PointCloud::Ptr tmp1 ( new PointCloud );
    statistical_filter.setInputCloud(globalMap);
    statistical_filter.filter( *tmp1 );
    //融合
    PointCloud::Ptr tmp(new PointCloud());
    voxel.setInputCloud( tmp1 );
    voxel.filter( *globalMap );
    //pointcloud2.push_back(pointcloude);
    cout<<"当前关键帧点云数量为："<<pointcloude.pcE->points.size()<<endl;
    cout<<"融合后关键帧点云数量为："<<globalMap->points.size()<<endl;
    //keyFrameUpdated.notify_one();
    if((kf->mnId)%20==10)
    {
      globalMap->clear();
      cout<<"清空global点云数据，×××××××××××××××××××××××××××××××××××××××××××"<<endl;
    }
    if((kf->mnId)%20==5)
    {
      cout<<"唤醒view线程更新点云模型，×××××××××××××××××××××××××××××××××××××××××××"<<endl;
      keyFrameUpdated.notify_one();
    }
}

pcl::PointCloud< PointCloudMapping::PointT >::Ptr PointCloudMapping::generatePointCloud(KeyFrame* kf, cv::Mat& color, cv::Mat& depth)//,Eigen::Isometry3d T
{
    PointCloud::Ptr tmp( new PointCloud() );
    // point cloud is null ptr
    for ( int m=0; m<depth.rows; m+=3 )
    {
        for ( int n=0; n<depth.cols; n+=3 )
        {
            float d = depth.ptr<float>(m)[n];
            if (d < 0.01 || d>5)
                continue;
            PointT p;
            p.z = d;
            p.x = ( n - kf->cx) * p.z / kf->fx;
            p.y = ( m - kf->cy) * p.z / kf->fy;
            
            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);
        }
    }
    
    //Eigen::Isometry3d T = ORB_SLAM2::Converter::toSE3Quat( kf->GetPose() );
    //PointCloud::Ptr cloud(new PointCloud);
    //pcl::transformPointCloud( *tmp, *cloud, T.inverse().matrix());
    //cloud->is_dense = false;
    
    //cout<<"generate point cloud for kf "<<kf->mnId<<", size="<<cloud->points.size()<<endl;
    return tmp;
}


void PointCloudMapping::viewer()
{
    pcl::visualization::CloudViewer viewer("viewer");
    while(1)
    {
        
        {
	    
            unique_lock<mutex> lck_shutdown( shutDownMutex );
            if (shutDownFlag)
            {
                break;
            }
        }
        {
	    cout<<"view线程进入睡眠××××××××××××××××××××××××××××××××××××"<<endl;
            unique_lock<mutex> lck_keyframeUpdated( keyFrameUpdateMutex );
            keyFrameUpdated.wait( lck_keyframeUpdated );
        }
        
        // keyframe is updated 
        cout<<"在view线程中显示点云模型×××××××××××××××××××××××××"<<endl;
        viewer.showCloud( globalMap );
    }
}
void PointCloudMapping::save()
{
	pcl::io::savePCDFile( "result.pcd", *globalMap );
	cout<<"globalMap save finished"<<endl;
}

PART2：在system类的构造函数中初始化PointCloudMapping并传入tracking线程

    // Initialize pointcloud mapping
    mpPointCloudMapping = make_shared<PointCloudMapping>( resolution,meank,thresh );
	
    //Initialize the Tracking thread
    //(it will live in the main thread of execution, the one that called this constructor)
    mpTracker = new Tracking(this, mpVocabulary, mpFrameDrawer, mpMapDrawer,
        mpMap, mpPointCloudMapping, mpKeyFrameDatabase, strSettingsFile, mSensor);

PART3：tracking线程中存储关键帧点云数据

    //mpPointCloudMapping中插入关键帧，并提取关键帧信息存入pointcloud
    //如果关键帧信息和回环帧id相同，则添加至点云模型
    mpPointCloudMapping->insertKeyFrame2( pKF, this->mImRGB, this->mImDepth  ,idk);

点云模型的显示线程view在PointCloudMapping的构造函数中实现，此处不再单独列出，由于计算机性能有限，点云模型融合后仍会影响orbslam2的追踪性能，后期进行回环融合后，再对建模效果进行进一步评价。