本文阅读的代码为2020年11月1日下载的github的最新master。
如果代码后续更新了请以github为准。

1 CMakelist.txt 与 package.xml

1.1 package.xml

package.xml 里的依赖：除了ros的常规依赖外，还有cv_bridge, pcl_conversions, GTSAM 三者。

1.2 CMakelist.txt

CMaKelist.txt里还指明了其他的依赖。

find_package(OpenMP REQUIRED)
find_package(PCL REQUIRED QUIET)
find_package(OpenCV REQUIRED QUIET)
find_package(GTSAM REQUIRED QUIET)

没有写install部分。

还生成了一个msg，其内容如下

# Cloud Info
Header header int32[] startRingIndex
int32[] endRingIndexint32[]  pointColInd # point column index in range image
float32[] pointRange # point range int64 imuAvailable
int64 odomAvailable# Attitude for LOAM initialization
float32 imuRollInit
float32 imuPitchInit
float32 imuYawInit# Initial guess from imu pre-integration
float32 initialGuessX
float32 initialGuessY
float32 initialGuessZ
float32 initialGuessRoll
float32 initialGuessPitch
float32 initialGuessYaw# Point cloud messages
sensor_msgs/PointCloud2 cloud_deskewed  # 去畸变后的雷达数据　original cloud deskewed
sensor_msgs/PointCloud2 cloud_corner    # 提取处的角点特征　extracted corner feature
sensor_msgs/PointCloud2 cloud_surface   # 提取处的面特征　extracted surface feature

2 include/utility.h

这个文件的内容很简单，可以分为3个部分。
第一部分: 各种 #include
第二部分: 定义了一个类 ParamServer。

类的成员变量就是 config/param.yml 里所有的参数。
只有2个成员函数
– 构造函数: 从ros的参数服务器里读取所有参数，并赋值给成员变量
– imuConverter() : 将ros下的imu数据根据imu的外参进行数值变换。

第三部分: 将ros的imu数据解析处理的函数，发布ros topic的函数，以及2个计算点距离的函数。

3 src/imageProjection.cpp

3.1 首先声明了 2 个数据类型

// Velodyne
struct PointXYZIRT
{PCL_ADD_POINT4DPCL_ADD_INTENSITY;uint16_t ring;　// 使用了 ring 信息，雷达数据位于第几条扫描线上float time;EIGEN_MAKE_ALIGNED_OPERATOR_NEW // Eigen的字段对齐
} EIGEN_ALIGN16;// 将 PointXYZIRT 在pcl中注册，其包括的字段为 x,y,z,intensity,ring,time
POINT_CLOUD_REGISTER_POINT_STRUCT (PointXYZIRT,  (float, x, x) (float, y, y) (float, z, z) (float, intensity, intensity)(uint16_t, ring, ring) (float, time, time)// Ouster　雷达的数据类型
struct PointXYZIRT {PCL_ADD_POINT4D;float intensity;uint32_t t;uint16_t reflectivity;uint8_t ring;uint16_t noise;uint32_t range;EIGEN_MAKE_ALIGNED_OPERATOR_NEW
}EIGEN_ALIGN16;POINT_CLOUD_REGISTER_POINT_STRUCT(PointXYZIRT,(float, x, x) (float, y, y) (float, z, z) (float, intensity, intensity)(uint32_t, t, t) (uint16_t, reflectivity, reflectivity)(uint8_t, ring, ring) (uint16_t, noise, noise) (uint32_t, range, range)
)

3.2 main()

可以看到，使用了3个线程，这3个线程是ros自己管理的，灵活调用，有可能一个函数同时占用2个线程，并不是为每个回调分配1个线程。

int main(int argc, char** argv)
{ros::init(argc, argv, "lio_sam");ImageProjection IP;ROS_INFO("\033[1;32m----> Image Projection Started.\033[0m");ros::MultiThreadedSpinner spinner(3);spinner.spin();return 0;
}

3.3 ImageProjection类

类的成员变量为

// imu deque 的数据长度
const int queueLength = 2000;class ImageProjection : public ParamServer
{private:std::mutex imuLock;std::mutex odoLock;ros::Subscriber subLaserCloud;ros::Publisher  pubLaserCloud;ros::Publisher pubExtractedCloud;ros::Publisher pubLaserCloudInfo;// 保存imu数据ros::Subscriber subImu;std::deque<sensor_msgs::Imu> imuQueue;// 保存 odom 数据ros::Subscriber subOdom;std::deque<nav_msgs::Odometry> odomQueue;// 保存雷达数据std::deque<sensor_msgs::PointCloud2> cloudQueue;sensor_msgs::PointCloud2 currentCloudMsg;double *imuTime = new double[queueLength];double *imuRotX = new double[queueLength];double *imuRotY = new double[queueLength];double *imuRotZ = new double[queueLength];int imuPointerCur;bool firstPointFlag;Eigen::Affine3f transStartInverse;pcl::PointCloud<PointXYZIRT>::Ptr laserCloudIn;pcl::PointCloud<PointType>::Ptr   fullCloud;    // 一帧雷达所有的数据pcl::PointCloud<PointType>::Ptr   extractedCloud;int deskewFlag;cv::Mat rangeMat;bool odomDeskewFlag;float odomIncreX;float odomIncreY;float odomIncreZ;// 自己定义的消息类型lio_sam::cloud_info cloudInfo;double timeScanCur;double timeScanEnd;std_msgs::Header cloudHeader;};

4 ImageProjection类的成员函数

4.1 构造函数

    ImageProjection() : deskewFlag(0){// 订阅topic, 指定hints到roscpp的传输层,　使用 使用没延迟的TCP通讯subImu        = nh.subscribe<sensor_msgs::Imu>(imuTopic, 2000, &ImageProjection::imuHandler, this, ros::TransportHints().tcpNoDelay());subOdom       = nh.subscribe<nav_msgs::Odometry>(odomTopic+"_incremental", 2000, &ImageProjection::odometryHandler, this, ros::TransportHints().tcpNoDelay());subLaserCloud = nh.subscribe<sensor_msgs::PointCloud2>(pointCloudTopic, 5, &ImageProjection::cloudHandler, this, ros::TransportHints().tcpNoDelay());pubExtractedCloud = nh.advertise<sensor_msgs::PointCloud2> ("lio_sam/deskew/cloud_deskewed", 1);pubLaserCloudInfo = nh.advertise<lio_sam::cloud_info> ("lio_sam/deskew/cloud_info", 1);allocateMemory();       // 为各个指针分配内存resetParameters();      // 为其他参数进行初始化与重置// pcl控制台输出error信息pcl::console::setVerbosityLevel(pcl::console::L_ERROR);}// 为3个指针分配内存void allocateMemory(){// 估计是考虑到这3个指针从头到尾都持续存在吧，所以并没有delete...laserCloudIn.reset(new pcl::PointCloud<PointXYZIRT>());fullCloud.reset(new pcl::PointCloud<PointType>());extractedCloud.reset(new pcl::PointCloud<PointType>());fullCloud->points.resize(N_SCAN*Horizon_SCAN);cloudInfo.startRingIndex.assign(N_SCAN, 0);cloudInfo.endRingIndex.assign(N_SCAN, 0);cloudInfo.pointColInd.assign(N_SCAN*Horizon_SCAN, 0);cloudInfo.pointRange.assign(N_SCAN*Horizon_SCAN, 0);// 怎么调用了2遍 resetParameters()resetParameters();}// 为其他参数进行初始化与重置void resetParameters(){laserCloudIn->clear();extractedCloud->clear();// 深度图像的初始化　reset range matrix for range image projectionrangeMat = cv::Mat(N_SCAN, Horizon_SCAN, CV_32F, cv::Scalar::all(FLT_MAX));imuPointerCur = 0;firstPointFlag = true;odomDeskewFlag = false;for (int i = 0; i < queueLength; ++i){imuTime[i] = 0;imuRotX[i] = 0;imuRotY[i] = 0;imuRotZ[i] = 0;}}

4.2 imu与odom的回调

    // imu的回调，先转换坐标系，再保存数据void imuHandler(const sensor_msgs::Imu::ConstPtr& imuMsg){// 将imu的数据 转换到 lidar 坐标系下 ???sensor_msgs::Imu thisImu = imuConverter(*imuMsg);std::lock_guard<std::mutex> lock1(imuLock);imuQueue.push_back(thisImu);    // 保存数据}// odom的回调，仅仅保存数据void odometryHandler(const nav_msgs::Odometry::ConstPtr& odometryMsg){std::lock_guard<std::mutex> lock2(odoLock);odomQueue.push_back(*odometryMsg);}

4.3 雷达点云的回调

    // 雷达点云的回调void cloudHandler(const sensor_msgs::PointCloud2ConstPtr& laserCloudMsg){// 首先检查点云的队列是否少于等于2个// 之后将ros格式的点云 转成 自定义的pcl格式的点云，并获取雷达数据的起始和终止时间，以及检查是否存在nan// 当第三个雷达数据到达的时候，会检查第一个雷达数据的field，是否存在ring与time字段，这个检查只会进行一次if (!cachePointCloud(laserCloudMsg))return;// 获取当前点云持续时间的旋转平移// 旋转的角速度由imu积分得到，平移量由里程计得到if (!deskewInfo())return;// 将校正畸变后的点云转换成range image，并存入fullCloudprojectPointCloud();cloudExtraction();// 发布 cloud_deskewed 和 cloudInfopublishClouds();// 参数进行初始化与重置resetParameters();}

5

5.1 cachePointCloud()

    // 首先检查点云的队列是否少于等于2个// 之后将ros格式的点云 转成 自定义的pcl格式的点云，并获取雷达数据的起始和终止时间，以及检查是否存在nan// 当第三个雷达数据到达的时候，会检查第一个雷达数据的field，是否存在ring与time字段，这个检查只会进行一次bool cachePointCloud(const sensor_msgs::PointCloud2ConstPtr& laserCloudMsg){// cache point cloudcloudQueue.push_back(*laserCloudMsg);// 点云的个数少于等于2个，不做处理，没法匹配if (cloudQueue.size() <= 2)return false;// convert cloudcurrentCloudMsg = cloudQueue.front();cloudQueue.pop_front();// 将ros格式的点云 转成 自定义的pcl格式的点云pcl::fromROSMsg(currentCloudMsg, *laserCloudIn);// get timestampcloudHeader = currentCloudMsg.header;timeScanCur = cloudHeader.stamp.toSec(); // 认为ros中header的时间为这一帧雷达数据的起始时间timeScanEnd = timeScanCur + laserCloudIn->points.back().time; // Velodyne// timeScanEnd = timeScanCur + (float)laserCloudIn->points.back().t / 1000000000.0; // Ouster数据的时间格式与vlp不一样// check dense flag　点云数据不能包含 nanif (laserCloudIn->is_dense == false){ROS_ERROR("Point cloud is not in dense format, please remove NaN points first!");ros::shutdown();}// check ring channel 检查是否存在 ring 字段static int ringFlag = 0;if (ringFlag == 0){ringFlag = -1;for (int i = 0; i < (int)currentCloudMsg.fields.size(); ++i){if (currentCloudMsg.fields[i].name == "ring"){ringFlag = 1;break;}}if (ringFlag == -1){ROS_ERROR("Point cloud ring channel not available, please configure your point cloud data!");ros::shutdown();}}   // check point time　检查是否存在 time 字段if (deskewFlag == 0){deskewFlag = -1;for (int i = 0; i < (int)currentCloudMsg.fields.size(); ++i){if (currentCloudMsg.fields[i].name == timeField){deskewFlag = 1;break;}}if (deskewFlag == -1)ROS_WARN("Point cloud timestamp not available, deskew function disabled, system will drift significantly!");}return true;}

5.2 deskewInfo()

    // 获取当前点云持续时间的旋转平移// 旋转的角速度由imu积分得到，平移量由里程计得到bool deskewInfo(){std::lock_guard<std::mutex> lock1(imuLock);std::lock_guard<std::mutex> lock2(odoLock);// make sure IMU data available for the scan// imu数据队列的头尾的时间戳要在雷达数据的时间段之外if (imuQueue.empty() || imuQueue.front().header.stamp.toSec() > timeScanCur || imuQueue.back().header.stamp.toSec() < timeScanEnd){ROS_DEBUG("Waiting for IMU data ...");return false;}imuDeskewInfo();odomDeskewInfo();return true;}// 修剪imu的数据队列，直到imu的时间处于这帧点云的时间内// 之后对当前队列中的imu进行积分，角加速度乘时间，求得旋转的角速度void imuDeskewInfo(){// cloudInfo 中imu的数据队列正在处理，暂时不可用cloudInfo.imuAvailable = false;// 修剪imu的数据队列，直到imu的时间处于这帧点云的时间前0.01秒内while (!imuQueue.empty()){if (imuQueue.front().header.stamp.toSec() < timeScanCur - 0.01)imuQueue.pop_front();elsebreak;}if (imuQueue.empty())return;imuPointerCur = 0;for (int i = 0; i < (int)imuQueue.size(); ++i){sensor_msgs::Imu thisImuMsg = imuQueue[i];double currentImuTime = thisImuMsg.header.stamp.toSec();// get roll, pitch, and yaw estimation for this scan// imu的时间合适，转变成 rpy if (currentImuTime <= timeScanCur)imuRPY2rosRPY(&thisImuMsg, &cloudInfo.imuRollInit, &cloudInfo.imuPitchInit, &cloudInfo.imuYawInit);if (currentImuTime > timeScanEnd + 0.01)break;if (imuPointerCur == 0){imuRotX[0] = 0;imuRotY[0] = 0;imuRotZ[0] = 0;imuTime[0] = currentImuTime;++imuPointerCur;continue;}// get angular velocitydouble angular_x, angular_y, angular_z;imuAngular2rosAngular(&thisImuMsg, &angular_x, &angular_y, &angular_z);// integrate rotation// 对imu的角加速度进行积分，角加速度 * （　当前帧imu的时间 - 上一帧imu的时间　）double timeDiff = currentImuTime - imuTime[imuPointerCur-1];imuRotX[imuPointerCur] = imuRotX[imuPointerCur-1] + angular_x * timeDiff;imuRotY[imuPointerCur] = imuRotY[imuPointerCur-1] + angular_y * timeDiff;imuRotZ[imuPointerCur] = imuRotZ[imuPointerCur-1] + angular_z * timeDiff;imuTime[imuPointerCur] = currentImuTime;++imuPointerCur;}--imuPointerCur;if (imuPointerCur <= 0)return;cloudInfo.imuAvailable = true;}// 获取这帧点云时间内的　第一个和最后一个里程计信息，将第一个odom当成初始值放入cloudInfo// 求得第一个和最后一个里程计间的平移量void odomDeskewInfo(){cloudInfo.odomAvailable = false;// 修剪odom的数据队列，直到odom的时间处于这帧点云的时间前0.01秒内while (!odomQueue.empty()){if (odomQueue.front().header.stamp.toSec() < timeScanCur - 0.01)odomQueue.pop_front();elsebreak;}if (odomQueue.empty())return;if (odomQueue.front().header.stamp.toSec() > timeScanCur)return;// get start odometry at the beinning of the scannav_msgs::Odometry startOdomMsg;// 获取odom的时间　大于等于　雷达当前时间的　odomfor (int i = 0; i < (int)odomQueue.size(); ++i){startOdomMsg = odomQueue[i];if (ROS_TIME(&startOdomMsg) < timeScanCur)continue;elsebreak;}tf::Quaternion orientation;tf::quaternionMsgToTF(startOdomMsg.pose.pose.orientation, orientation);double roll, pitch, yaw;tf::Matrix3x3(orientation).getRPY(roll, pitch, yaw);// Initial guess used in mapOptimizationcloudInfo.initialGuessX = startOdomMsg.pose.pose.position.x;cloudInfo.initialGuessY = startOdomMsg.pose.pose.position.y;cloudInfo.initialGuessZ = startOdomMsg.pose.pose.position.z;cloudInfo.initialGuessRoll  = roll;cloudInfo.initialGuessPitch = pitch;cloudInfo.initialGuessYaw   = yaw;cloudInfo.odomAvailable = true;// get end odometry at the end of the scanodomDeskewFlag = false;// odom最后一个数据的时间　比　雷达结束的时间早，不能覆盖点云的时间if (odomQueue.back().header.stamp.toSec() < timeScanEnd)return;// 获取第一个比　雷达结束的时间　晚的　odomnav_msgs::Odometry endOdomMsg;for (int i = 0; i < (int)odomQueue.size(); ++i){endOdomMsg = odomQueue[i];if (ROS_TIME(&endOdomMsg) < timeScanEnd)continue;elsebreak;}if (int(round(startOdomMsg.pose.covariance[0])) != int(round(endOdomMsg.pose.covariance[0])))return;Eigen::Affine3f transBegin = pcl::getTransformation(startOdomMsg.pose.pose.position.x, startOdomMsg.pose.pose.position.y, startOdomMsg.pose.pose.position.z, roll, pitch, yaw);tf::quaternionMsgToTF(endOdomMsg.pose.pose.orientation, orientation);tf::Matrix3x3(orientation).getRPY(roll, pitch, yaw);Eigen::Affine3f transEnd = pcl::getTransformation(endOdomMsg.pose.pose.position.x, endOdomMsg.pose.pose.position.y, endOdomMsg.pose.pose.position.z, roll, pitch, yaw);Eigen::Affine3f transBt = transBegin.inverse() * transEnd;// 通过　transBt　获取　odomIncreX等，每一帧点云数据的odom变化量float rollIncre, pitchIncre, yawIncre;pcl::getTranslationAndEulerAngles(transBt, odomIncreX, odomIncreY, odomIncreZ, rollIncre, pitchIncre, yawIncre);odomDeskewFlag = true;}

5.3 projectPointCloud()

    // 将校正畸变后的点云转换成range image，并存入fullCloudvoid projectPointCloud(){int cloudSize = laserCloudIn->points.size();// range image projectionfor (int i = 0; i < cloudSize; ++i){PointType thisPoint;thisPoint.x = laserCloudIn->points[i].x;thisPoint.y = laserCloudIn->points[i].y;thisPoint.z = laserCloudIn->points[i].z;thisPoint.intensity = laserCloudIn->points[i].intensity;// 求得当前点对应 range image 的　rangefloat range = pointDistance(thisPoint);if (range < lidarMinRange || range > lidarMaxRange)continue;// 求得当前点对应 range image 的　行索引int rowIdn = laserCloudIn->points[i].ring;if (rowIdn < 0 || rowIdn >= N_SCAN)continue;if (rowIdn % downsampleRate != 0)continue;// 求得点的水平角（deg)float horizonAngle = atan2(thisPoint.x, thisPoint.y) * 180 / M_PI;// 水平分辨率static float ang_res_x = 360.0/float(Horizon_SCAN);// 求得当前点对应 range image 的　列索引int columnIdn = -round((horizonAngle-90.0)/ang_res_x) + Horizon_SCAN/2;if (columnIdn >= Horizon_SCAN)columnIdn -= Horizon_SCAN;if (columnIdn < 0 || columnIdn >= Horizon_SCAN)continue;if (rangeMat.at<float>(rowIdn, columnIdn) != FLT_MAX)continue;// 对点云中的每个点进行　畸变校正thisPoint = deskewPoint(&thisPoint, laserCloudIn->points[i].time); // Velodyne// thisPoint = deskewPoint(&thisPoint, (float)laserCloudIn->points[i].t / 1000000000.0); // OusterrangeMat.at<float>(rowIdn, columnIdn) = range;// 并且　将这个点存到 fullCloud 中int index = columnIdn + rowIdn * Horizon_SCAN;fullCloud->points[index] = thisPoint;}}// 根据点云中某点的时间戳赋予其对应插值得到的旋转量void findRotation(double pointTime, float *rotXCur, float *rotYCur, float *rotZCur){*rotXCur = 0; *rotYCur = 0; *rotZCur = 0;// 找到在　pointTime　之后的imu数据int imuPointerFront = 0;while (imuPointerFront < imuPointerCur){if (pointTime < imuTime[imuPointerFront])break;++imuPointerFront;}// 如果上边的循环没进去或者到了最大执行次数，则只能近似的将当前的旋转　赋值过去if (pointTime > imuTime[imuPointerFront] || imuPointerFront == 0){*rotXCur = imuRotX[imuPointerFront];*rotYCur = imuRotY[imuPointerFront];*rotZCur = imuRotZ[imuPointerFront];} else {int imuPointerBack = imuPointerFront - 1;// 算一下该点时间戳在本组imu中的位置，线性插值double ratioFront = (pointTime - imuTime[imuPointerBack]) / (imuTime[imuPointerFront] - imuTime[imuPointerBack]);double ratioBack = (imuTime[imuPointerFront] - pointTime) / (imuTime[imuPointerFront] - imuTime[imuPointerBack]);// 按前后百分比赋予旋转量*rotXCur = imuRotX[imuPointerFront] * ratioFront + imuRotX[imuPointerBack] * ratioBack;*rotYCur = imuRotY[imuPointerFront] * ratioFront + imuRotY[imuPointerBack] * ratioBack;*rotZCur = imuRotZ[imuPointerFront] * ratioFront + imuRotZ[imuPointerBack] * ratioBack;}}// 如果运动慢，点云由于运动导致的矫正不如没有void findPosition(double relTime, float *posXCur, float *posYCur, float *posZCur){*posXCur = 0; *posYCur = 0; *posZCur = 0;// If the sensor moves relatively slow, like walking speed, positional deskew seems to have little benefits. Thus code below is commented.// if (cloudInfo.odomAvailable == false || odomDeskewFlag == false)//     return;// float ratio = relTime / (timeScanEnd - timeScanCur);// *posXCur = ratio * odomIncreX;// *posYCur = ratio * odomIncreY;// *posZCur = ratio * odomIncreZ;}// 对点云的每个点进行畸变校正PointType deskewPoint(PointType *point, double relTime){if (deskewFlag == -1 || cloudInfo.imuAvailable == false)return *point;double pointTime = timeScanCur + relTime;float rotXCur, rotYCur, rotZCur;findRotation(pointTime, &rotXCur, &rotYCur, &rotZCur);float posXCur, posYCur, posZCur;findPosition(relTime, &posXCur, &posYCur, &posZCur);// 点云中的第一个点 求 transStartInverse，之后在这帧数据畸变过程中不再改变if (firstPointFlag == true){transStartInverse = (pcl::getTransformation(posXCur, posYCur, posZCur, rotXCur, rotYCur, rotZCur)).inverse();firstPointFlag = false;}// transform points to startEigen::Affine3f transFinal = pcl::getTransformation(posXCur, posYCur, posZCur, rotXCur, rotYCur, rotZCur);// 该点相对第一个点的变换矩阵　=　第一个点在lidar世界坐标系下的变换矩阵的逆 × 当前点时lidar世界坐标系下变换矩阵Eigen::Affine3f transBt = transStartInverse * transFinal;// 根据lidar位姿变换，修正点云位置PointType newPoint;newPoint.x = transBt(0,0) * point->x + transBt(0,1) * point->y + transBt(0,2) * point->z + transBt(0,3);newPoint.y = transBt(1,0) * point->x + transBt(1,1) * point->y + transBt(1,2) * point->z + transBt(1,3);newPoint.z = transBt(2,0) * point->x + transBt(2,1) * point->y + transBt(2,2) * point->z + transBt(2,3);newPoint.intensity = point->intensity;return newPoint;}

5.4 publishClouds()

// 发布 cloud_deskewed 和 cloudInfo
void publishClouds()
{cloudInfo.header = cloudHeader;cloudInfo.cloud_deskewed  = publishCloud(&pubExtractedCloud, extractedCloud, cloudHeader.stamp, lidarFrame);pubLaserCloudInfo.publish(cloudInfo);
}sensor_msgs::PointCloud2 publishCloud(ros::Publisher *thisPub, pcl::PointCloud<PointType>::Ptr thisCloud, ros::Time thisStamp, std::string thisFrame)
{sensor_msgs::PointCloud2 tempCloud;pcl::toROSMsg(*thisCloud, tempCloud);tempCloud.header.stamp = thisStamp;tempCloud.header.frame_id = thisFrame;if (thisPub->getNumSubscribers() != 0)thisPub->publish(tempCloud);return tempCloud;
}

总结一下：

这个类做了点云的畸变纠正，在速度慢的情况下只用imu进行畸变校正。要是用odom的话要把代码的注释取消掉。

之后进行range image的制作，并提取了有效点的索引，暂时还没用到。

将处理好的２个点云发布出去。

REERENCES

https://blog.csdn.net/unlimitedai/article/details/107378759#t6

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