lio_sam代码阅读之mapOptimization

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基本转自别人

/**
 * 当前帧位姿初始化
 * 1、如果是第一帧,用原始imu数据的RPY初始化当前帧位姿(旋转部分)
 * 2、后续帧,用imu里程计计算两帧之间的增量位姿变换,作用于前一帧的激光位姿,得到当前帧激光位姿
*/
    void updateInitialGuess()
    {
        // 前一帧的位姿,注:这里指lidar的位姿,后面都简写成位姿
        //transformTobeMapped 初始值为0;
        incrementalOdometryAffineFront = trans2Affine3f(transformTobeMapped);

        // 前一帧的初始化姿态角(来自原始imu数据),用于估计第一帧的位姿(旋转部分)
        static Eigen::Affine3f lastImuTransformation;
        
        // 如果关键帧集合为空,继续进行初始化
        if (cloudKeyPoses3D->points.empty())
        {
            // 当前帧位姿的旋转部分,用激光帧信息中的RPY(来自imu原始数据)初始化
            transformTobeMapped[0] = cloudInfo.imuRollInit;
            transformTobeMapped[1] = cloudInfo.imuPitchInit;
            transformTobeMapped[2] = cloudInfo.imuYawInit;

            if (!useImuHeadingInitialization)
                transformTobeMapped[2] = 0;

            lastImuTransformation = pcl::getTransformation(0, 0, 0, cloudInfo.imuRollInit, cloudInfo.imuPitchInit, cloudInfo.imuYawInit); 
            return;
        }

        // 用当前帧和前一帧对应的imu里程计计算相对位姿变换,再用前一帧的位姿与相对变换,计算当前帧的位姿,存transformTobeMapped
        static bool lastImuPreTransAvailable = false;
        static Eigen::Affine3f lastImuPreTransformation;
        if (cloudInfo.odomAvailable == true)
        {
            // 当前帧的初始估计位姿(来自imu里程计),后面用来计算增量位姿变换
            Eigen::Affine3f transBack = pcl::getTransformation(cloudInfo.initialGuessX,    cloudInfo.initialGuessY,     cloudInfo.initialGuessZ, 
                                                               cloudInfo.initialGuessRoll, cloudInfo.initialGuessPitch, cloudInfo.initialGuessYaw);
            if (lastImuPreTransAvailable == false)
            {
                // 赋值给前一帧
                lastImuPreTransformation = transBack;
                lastImuPreTransAvailable = true;
            } else {
                // 当前帧相对于前一帧的位姿变换,imu里程计计算得到
                Eigen::Affine3f transIncre = lastImuPreTransformation.inverse() * transBack;
                // 前一帧的位姿
                Eigen::Affine3f transTobe = trans2Affine3f(transformTobeMapped);
                // 当前帧的位姿
                Eigen::Affine3f transFinal = transTobe * transIncre;
                // 更新当前帧位姿transformTobeMapped
                pcl::getTranslationAndEulerAngles(transFinal, transformTobeMapped[3], transformTobeMapped[4], transformTobeMapped[5], 
                                                              transformTobeMapped[0], transformTobeMapped[1], transformTobeMapped[2]);
                // 赋值给前一帧
                lastImuPreTransformation = transBack;

                lastImuTransformation = pcl::getTransformation(0, 0, 0, cloudInfo.imuRollInit, cloudInfo.imuPitchInit, cloudInfo.imuYawInit); // save imu before return;
                return;
            }
        }

        // 只在第一帧调用(注意上面的return),用imu数据初始化当前帧位姿,仅初始化旋转部分
        if (cloudInfo.imuAvailable == true)
        {
            // 当前帧的姿态角(来自原始imu数据)
            Eigen::Affine3f transBack = pcl::getTransformation(0, 0, 0, cloudInfo.imuRollInit, cloudInfo.imuPitchInit, cloudInfo.imuYawInit);
            // 当前帧相对于前一帧的姿态变换
            Eigen::Affine3f transIncre = lastImuTransformation.inverse() * transBack;

            // 前一帧的位姿
            Eigen::Affine3f transTobe = trans2Affine3f(transformTobeMapped);
            // 当前帧的位姿
            Eigen::Affine3f transFinal = transTobe * transIncre;
            // 更新当前帧位姿transformTobeMapped
            pcl::getTranslationAndEulerAngles(transFinal, transformTobeMapped[3], transformTobeMapped[4], transformTobeMapped[5], 
                                                          transformTobeMapped[0], transformTobeMapped[1], transformTobeMapped[2]);

            lastImuTransformation = pcl::getTransformation(0, 0, 0, cloudInfo.imuRollInit, cloudInfo.imuPitchInit, cloudInfo.imuYawInit); // save imu before return;
            return;
        }
    }
/**
 * 提取局部角点、平面点云集合,加入局部map
 * 1、对最近的一帧关键帧,搜索时空维度上相邻的关键帧集合,降采样一下
 * 2、对关键帧集合中的每一帧,提取对应的角点、平面点,加入局部map中
*/
    void extractSurroundingKeyFrames()
    {
        if (cloudKeyPoses3D->points.empty() == true)
            return; 
        
        // if (loopClosureEnableFlag == true)
        // {
        //     extractForLoopClosure();    
        // } else {
        //     extractNearby();
        // }

        // 提取局部角点、平面点云集合,加入局部map
        // 1、对最近的一帧关键帧,搜索时空维度上相邻的关键帧集合,降采样一下
        // 2、对关键帧集合中的每一帧,提取对应的角点、平面点,加入局部map中
        extractNearby();
    }

    void extractNearby()
    {
        //这里面存放的是关键帧的位姿点,用一个3d点表示
        pcl::PointCloud<PointType>::Ptr surroundingKeyPoses(new pcl::PointCloud<PointType>());
        //这里是降采样的3d点
        pcl::PointCloud<PointType>::Ptr surroundingKeyPosesDS(new pcl::PointCloud<PointType>());
        //下面这些参数是pcl中保存结果的变量
        std::vector<int> pointSearchInd;
        std::vector<float> pointSearchSqDis;

        // kdtree的输入,全局关键帧位姿集合(历史所有关键帧集合)
        kdtreeSurroundingKeyPoses->setInputCloud(cloudKeyPoses3D); 
        // 对最近的一帧关键帧,在半径区域内搜索空间区域上相邻的关键帧集合
        // surroundingKeyframeSearchRadius = 50;
        kdtreeSurroundingKeyPoses->radiusSearch(cloudKeyPoses3D->back(), (double)surroundingKeyframeSearchRadius, pointSearchInd, pointSearchSqDis);
        // 遍历搜索结果,pointSearchInd存的是结果在cloudKeyPoses3D下面的索引
        for (int i = 0; i < (int)pointSearchInd.size(); ++i)
        {
            int id = pointSearchInd[i];
            // 加入相邻关键帧位姿集合中
            surroundingKeyPoses->push_back(cloudKeyPoses3D->points[id]);
        }

        // 降采样一下
        downSizeFilterSurroundingKeyPoses.setInputCloud(surroundingKeyPoses);
        downSizeFilterSurroundingKeyPoses.filter(*surroundingKeyPosesDS);

        // 加入时间上相邻的一些关键帧,比如当载体在原地转圈,这些帧加进来是合理的
        int numPoses = cloudKeyPoses3D->size();
        for (int i = numPoses-1; i >= 0; --i)
        {
            if (timeLaserInfoCur - cloudKeyPoses6D->points[i].time < 10.0)
                surroundingKeyPosesDS->push_back(cloudKeyPoses3D->points[i]);
            else
                break;
        }

        // 将相邻关键帧集合对应的角点、平面点,加入到局部map中,作为scan-to-map匹配的局部点云地图
        extractCloud(surroundingKeyPosesDS);
    }

void extractCloud(pcl::PointCloud<PointType>::Ptr cloudToExtract)
    {
        // 相邻关键帧集合对应的角点、平面点,加入到局部map中;注:称之为局部map,后面进行scan-to-map匹配,所用到的map就是这里的相邻关键帧对应点云集合
        laserCloudCornerFromMap->clear();
        laserCloudSurfFromMap->clear(); 
        // 遍历当前帧(实际是取最近的一个关键帧来找它相邻的关键帧集合)时空维度上相邻的关键帧集合
        for (int i = 0; i < (int)cloudToExtract->size(); ++i)
        {
            // 距离超过阈值,丢弃
            if (pointDistance(cloudToExtract->points[i], cloudKeyPoses3D->back()) > surroundingKeyframeSearchRadius)
                continue;

            // 相邻关键帧索引,之所以保存到laserCloudMapContainer变量中,是为了牺牲空间节省查找时间
            int thisKeyInd = (int)cloudToExtract->points[i].intensity;
            if (laserCloudMapContainer.find(thisKeyInd) != laserCloudMapContainer.end()) 
            {
                *laserCloudCornerFromMap += laserCloudMapContainer[thisKeyInd].first;
                *laserCloudSurfFromMap   += laserCloudMapContainer[thisKeyInd].second;
            } else {
                // 相邻关键帧对应的角点、平面点云,通过6D位姿变换到世界坐标系下
                pcl::PointCloud<PointType> laserCloudCornerTemp = *transformPointCloud(cornerCloudKeyFrames[thisKeyInd],  &cloudKeyPoses6D->points[thisKeyInd]);
                pcl::PointCloud<PointType> laserCloudSurfTemp = *transformPointCloud(surfCloudKeyFrames[thisKeyInd],    &cloudKeyPoses6D->points[thisKeyInd]);
                // 加入局部map
                *laserCloudCornerFromMap += laserCloudCornerTemp;
                *laserCloudSurfFromMap   += laserCloudSurfTemp;
                laserCloudMapContainer[thisKeyInd] = make_pair(laserCloudCornerTemp, laserCloudSurfTemp);
            }
            
        }

        // 降采样局部角点map
        downSizeFilterCorner.setInputCloud(laserCloudCornerFromMap);
        downSizeFilterCorner.filter(*laserCloudCornerFromMapDS);
        laserCloudCornerFromMapDSNum = laserCloudCornerFromMapDS->size();
        // 降采样局部平面点map
        downSizeFilterSurf.setInputCloud(laserCloudSurfFromMap);
        downSizeFilterSurf.filter(*laserCloudSurfFromMapDS);
        laserCloudSurfFromMapDSNum = laserCloudSurfFromMapDS->size();

        // 太大了,清空一下内存
        if (laserCloudMapContainer.size() > 1000)
            laserCloudMapContainer.clear();
    }
/**
 * 当前激光帧角点、平面点集合降采样
*/
    void downsampleCurrentScan()
    {
        // 当前激光帧角点集合降采样
        laserCloudCornerLastDS->clear();
        downSizeFilterCorner.setInputCloud(laserCloudCornerLast);
        downSizeFilterCorner.filter(*laserCloudCornerLastDS);
        laserCloudCornerLastDSNum = laserCloudCornerLastDS->size();

        // 当前激光帧平面点集合降采样
        laserCloudSurfLastDS->clear();
        downSizeFilterSurf.setInputCloud(laserCloudSurfLast);
        downSizeFilterSurf.filter(*laserCloudSurfLastDS);
        laserCloudSurfLastDSNum = laserCloudSurfLastDS->size();
    }
/**
 * scan-to-map优化当前帧位姿
 * 1、要求当前帧特征点数量足够多,且匹配的点数够多,才执行优化
 * 2、迭代30次(上限)优化
 *   1) 当前激光帧角点寻找局部map匹配点
 *      a.更新当前帧位姿,将当前帧角点坐标变换到map系下,在局部map中查找5个最近点,距离小于1m,且5个点构成直线(用距离中心点的协方差矩阵,特征值进行判断),则认为匹配上了
 *      b.计算当前帧角点到直线的距离、垂线的单位向量,存储为角点参数
 *   2) 当前激光帧平面点寻找局部map匹配点
 *      a.更新当前帧位姿,将当前帧平面点坐标变换到map系下,在局部map中查找5个最近点,距离小于1m,且5个点构成平面(最小二乘拟合平面),则认为匹配上了
 *      b.计算当前帧平面点到平面的距离、垂线的单位向量,存储为平面点参数
 *   3) 提取当前帧中与局部map匹配上了的角点、平面点,加入同一集合
 *   4) 对匹配特征点计算Jacobian矩阵,观测值为特征点到直线、平面的距离,构建高斯牛顿方程,迭代优化当前位姿,存transformTobeMapped
 * 3、用imu原始RPY数据与scan-to-map优化后的位姿进行加权融合,更新当前帧位姿的roll、pitch,约束z坐标
*/
    void scan2MapOptimization()
    {
        // 要求有关键帧
        if (cloudKeyPoses3D->points.empty())
            return;

        // 当前激光帧的角点、平面点数量足够多
        // edgeFeatureMinValidNum = 10;
        // surfFeatureMinValidNum = 100;
        if (laserCloudCornerLastDSNum > edgeFeatureMinValidNum && laserCloudSurfLastDSNum > surfFeatureMinValidNum)
        {
            // kdtree输入为局部map点云
            kdtreeCornerFromMap->setInputCloud(laserCloudCornerFromMapDS);
            kdtreeSurfFromMap->setInputCloud(laserCloudSurfFromMapDS);

            // 迭代30次
            for (int iterCount = 0; iterCount < 30; iterCount++)
            {
                // 每次迭代清空特征点集合
                laserCloudOri->clear();
                coeffSel->clear();

                // 当前激光帧角点寻找局部map匹配点
                // 1、更新当前帧位姿,将当前帧角点坐标变换到map系下,在局部map中查找5个最近点,距离小于1m,且5个点构成直线(用距离中心点的协方差矩阵,特征值进行判断),则认为匹配上了
                // 2、计算当前帧角点到直线的距离、垂线的单位向量,存储为角点参数
                cornerOptimization();

                // 当前激光帧平面点寻找局部map匹配点
                // 1、更新当前帧位姿,将当前帧平面点坐标变换到map系下,在局部map中查找5个最近点,距离小于1m,且5个点构成平面(最小二乘拟合平面),则认为匹配上了
                // 2、计算当前帧平面点到平面的距离、垂线的单位向量,存储为平面点参数
                surfOptimization();

                // 提取当前帧中与局部map匹配上了的角点、平面点,加入同一集合
                combineOptimizationCoeffs();

                // scan-to-map优化
                // 对匹配特征点计算Jacobian矩阵,观测值为特征点到直线、平面的距离,构建高斯牛顿方程,迭代优化当前位姿,存transformTobeMapped
                if (LMOptimization(iterCount) == true)
                    break;              
            }
            // 用imu原始RPY数据与scan-to-map优化后的位姿进行加权融合,更新当前帧位姿的roll、pitch,约束z坐标
            transformUpdate();
        } else {
            ROS_WARN("Not enough features! Only %d edge and %d planar features available.", laserCloudCornerLastDSNum, laserCloudSurfLastDSNum);
        }
    }

/**
* 当前激光帧角点寻找局部map匹配点
* 1、更新当前帧位姿,将当前帧角点坐标变换到map系下,在局部map中查找5个最近点,距离小于1m,且5个点构成直线(用距离中心点的协方差矩阵,特征值进行判断),则认为匹配上了
* 2、计算当前帧角点到直线的距离、垂线的单位向量,存储为角点参数
*/

    void cornerOptimization()
    {
        // 更新当前帧位姿
        updatePointAssociateToMap();

        // 遍历当前帧角点集合
        // numberOfCores = 4;
        #pragma omp parallel for num_threads(numberOfCores)
        for (int i = 0; i < laserCloudCornerLastDSNum; i++)
        {
            PointType pointOri, pointSel, coeff;
            std::vector<int> pointSearchInd;
            std::vector<float> pointSearchSqDis;

            // 角点(坐标还是lidar系)
            pointOri = laserCloudCornerLastDS->points[i];
            // 根据当前帧位姿,变换到世界坐标系(map系)下
            pointAssociateToMap(&pointOri, &pointSel);
            // 在局部角点map中查找当前角点相邻的5个角点
            kdtreeCornerFromMap->nearestKSearch(pointSel, 5, pointSearchInd, pointSearchSqDis);

            cv::Mat matA1(3, 3, CV_32F, cv::Scalar::all(0));
            cv::Mat matD1(1, 3, CV_32F, cv::Scalar::all(0));
            cv::Mat matV1(3, 3, CV_32F, cv::Scalar::all(0));
            
            // 要求距离都小于1m
            if (pointSearchSqDis[4] < 1.0) {
                // 计算5个点的均值坐标,记为中心点
                float cx = 0, cy = 0, cz = 0;
                for (int j = 0; j < 5; j++) {
                    cx += laserCloudCornerFromMapDS->points[pointSearchInd[j]].x;
                    cy += laserCloudCornerFromMapDS->points[pointSearchInd[j]].y;
                    cz += laserCloudCornerFromMapDS->points[pointSearchInd[j]].z;
                }
                cx /= 5; cy /= 5;  cz /= 5;

                // 计算协方差
                float a11 = 0, a12 = 0, a13 = 0, a22 = 0, a23 = 0, a33 = 0;
                for (int j = 0; j < 5; j++) {
                    // 计算点与中心点之间的距离
                    float ax = laserCloudCornerFromMapDS->points[pointSearchInd[j]].x - cx;
                    float ay = laserCloudCornerFromMapDS->points[pointSearchInd[j]].y - cy;
                    float az = laserCloudCornerFromMapDS->points[pointSearchInd[j]].z - cz;

                    a11 += ax * ax; a12 += ax * ay; a13 += ax * az;
                    a22 += ay * ay; a23 += ay * az;
                    a33 += az * az;
                }
                a11 /= 5; a12 /= 5; a13 /= 5; a22 /= 5; a23 /= 5; a33 /= 5;

                // 构建协方差矩阵
                matA1.at<float>(0, 0) = a11; matA1.at<float>(0, 1) = a12; matA1.at<float>(0, 2) = a13;
                matA1.at<float>(1, 0) = a12; matA1.at<float>(1, 1) = a22; matA1.at<float>(1, 2) = a23;
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