A-LOAM源码解读
文件目录
- 代码
-
- 1、kittiHelper.cpp
- 2、scanRegistration.cpp
- 3、laserOdometry.cpp
- 4、laserMapping.cpp
- 5、lidarFactor.cpp
- 参考
代码
1、kittiHelper.cpp
/*
* @Author: your name
* @Date: 2020-09-18 09:28:16
* @LastEditTime: 2020-10-05 21:29:26
* @LastEditors: Please set LastEditors
* @Description: 读取kitti文件，发布odomGT,pathGT
* @FilePath: \A-LOAM-NOTED-devel\src\kittiHelper.cpp
*/
// Author: Tong Qin [email protected]
// Shaozu Cao [email protected]
#include 2、scanRegistration.cpp
scanRegistration的功能就是提取特征点。
- comp函数，比较两个点的曲率
bool comp (int i,int j) {
return (cloudCurvature[i]<cloudCurvature[j]); }
- removeClosedPointCloud函数，丢弃一定距离以内的点
// 丢弃一定距离以内的点
template <typename PointT>
void removeClosedPointCloud(const pcl::PointCloud<PointT> &cloud_in,
pcl::PointCloud<PointT> &cloud_out, float thres)
{
if (&cloud_in != &cloud_out)
{
cloud_out.header = cloud_in.header;
cloud_out.points.resize(cloud_in.points.size());
}
size_t j = 0;
for (size_t i = 0; i < cloud_in.points.size(); ++i)
{
if (cloud_in.points[i].x * cloud_in.points[i].x + cloud_in.points[i].y * cloud_in.points[i].y + cloud_in.points[i].z * cloud_in.points[i].z < thres * thres)
continue;
cloud_out.points[j] = cloud_in.points[i];
j++;
}
if (j != cloud_in.points.size())
{
cloud_out.points.resize(j);
}
cloud_out.height = 1;
cloud_out.width = static_cast<uint32_t>(j);
cloud_out.is_dense = true;
}
- laserCloudHandler函数
这个函数主要就是对接收到的点云数据进行处理，最终发布关键点（laser_cloud_sharp、laser_cloud_less_sharp、laser_cloud_flat、laser_cloud_less_flat），以方便后续使用。
1、首先计算当前帧的第一个点和最后一个点的角度
// 计算起始点和终止点角度
// 解释一下：atan2的范围是[-180,180]，atan的范围是[-90,90]
int cloudSize = laserCloudIn.points.size();
float startOri = -atan2(laserCloudIn.points[0].y, laserCloudIn.points[0].x); // 第一个扫描点的角度
float endOri = -atan2(laserCloudIn.points[cloudSize - 1].y, // 最后一个扫描点的角度
laserCloudIn.points[cloudSize - 1].x) +
2 * M_PI;
// 最后一个点的角度 - 第一个点的角度>PI
if (endOri - startOri > 3 * M_PI)
{
endOri -= 2 * M_PI;
}
else if (endOri - startOri < M_PI)
{
endOri += 2 * M_PI;
}
printf("[scanRegistration] start Ori %f\n",startOri);
printf("[scanRegistration] end Ori %f\n", endOri);
2、对每一个点计算其scanID和该点对应的扫描到的时间，将结果放在intensity中。
// 对每一个点计算scanID和扫描的点的时间
bool halfPassed = false;
int count = cloudSize; // 所有的点数
PointType point;
std::vector<pcl::PointCloud<PointType>> laserCloudScans(N_SCANS); // 每一行扫描视为一个点云
for (int i = 0; i < cloudSize; i++)
{
point.x = laserCloudIn.points[i].x;
point.y = laserCloudIn.points[i].y;
point.z = laserCloudIn.points[i].z;
// 根据激光扫描线的俯仰角获取scanid[0,15]
float angle = atan(point.z / sqrt(point.x * point.x + point.y * point.y)) * 180 / M_PI; // 俯仰角[-90,90]
int scanID = 0;
if (N_SCANS == 16)
{
scanID = int((angle + 15) / 2 + 0.5);
if (scanID > (N_SCANS - 1) || scanID < 0)
{
count--;
continue;
}
}
else if (N_SCANS == 32)
{
scanID = int((angle + 92.0/3.0) * 3.0 / 4.0);
if (scanID > (N_SCANS - 1) || scanID < 0)
{
count--;
continue;
}
}
else if (N_SCANS == 64)
{
if (angle >= -8.83)
scanID = int((2 - angle) * 3.0 + 0.5);
else
scanID = N_SCANS / 2 + int((-8.83 - angle) * 2.0 + 0.5);
// use [0 50] > 50 remove outlies
if (angle > 2 || angle < -24.33 || scanID > 50 || scanID < 0)
{
count--;
continue;
}
}
else
{
printf("[scanRegistration] wrong scan number\n");
ROS_BREAK();
}
//printf("angle %f scanID %d \n", angle, scanID);
// 获取每一个点对应的角度
float ori = -atan2(point.y, point.x); // 每一个点的水平角度
if (!halfPassed) // 判断当前点过没过一半（开始的时候是没过一半的）
{
if (ori < startOri - M_PI / 2)
{
ori += 2 * M_PI;
}
else if (ori > startOri + M_PI * 3 / 2)
{
ori -= 2 * M_PI;
}
if (ori - startOri > M_PI)
{
halfPassed = true;
}
}
else // 后半段时间
{
ori += 2 * M_PI;
if (ori < endOri - M_PI * 3 / 2)
{
ori += 2 * M_PI;
}
else if (ori > endOri + M_PI / 2)
{
ori -= 2 * M_PI;
}
}
float relTime = (ori - startOri) / (endOri - startOri); // 时间占比
point.intensity = scanID + scanPeriod * relTime; // intensity=scanID+点对应的在线上的时间
laserCloudScans[scanID].push_back(point); // 把当前点放在对应的scanID对应的线上
}
3、计算16条scan的起始点的索引和终止点的索引
// 记录16个scan的每一个的startInd，endInd
pcl::PointCloud<PointType>::Ptr laserCloud(new pcl::PointCloud<PointType>());
for (int i = 0; i < N_SCANS; i++)
{
scanStartInd[i] = laserCloud->size() + 5;
*laserCloud += laserCloudScans[i];
scanEndInd[i] = laserCloud->size() - 6;
}
4、对每一个点计算曲率
// 有序地计算曲率
for (int i = 5; i < cloudSize - 5; i++) // 忽略前后的5个点
{
float diffX = laserCloud->points[i - 5].x + laserCloud->points[i - 4].x + laserCloud->points[i - 3].x + laserCloud->points[i - 2].x + laserCloud->points[i - 1].x - 10 * laserCloud->points[i].x + laserCloud->points[i + 1].x + laserCloud->points[i + 2].x + laserCloud->points[i + 3].x + laserCloud->points[i + 4].x + laserCloud->points[i + 5].x;
float diffY = laserCloud->points[i - 5].y + laserCloud->points[i - 4].y + laserCloud->points[i - 3].y + laserCloud->points[i - 2].y + laserCloud->points[i - 1].y - 10 * laserCloud->points[i].y + laserCloud->points[i + 1].y + laserCloud->points[i + 2].y + laserCloud->points[i + 3].y + laserCloud->points[i + 4].y + laserCloud->points[i + 5].y;
float diffZ = laserCloud->points[i - 5].z + laserCloud->points[i - 4].z + laserCloud->points[i - 3].z + laserCloud->points[i - 2].z + laserCloud->points[i - 1].z - 10 * laserCloud->points[i].z + laserCloud->points[i + 1].z + laserCloud->points[i + 2].z + laserCloud->points[i + 3].z + laserCloud->points[i + 4].z + laserCloud->points[i + 5].z;
cloudCurvature[i] = diffX * diffX + diffY * diffY + diffZ * diffZ;
cloudSortInd[i] = i;
cloudNeighborPicked[i] = 0; // 记录这个点是否被选中了
cloudLabel[i] = 0;// Label 2: corner_sharp
// Label 1: corner_less_sharp, ????Label 2
// Label -1: surf_flat
// Label 0: surf_less_flat?? ????Label -1??????????????????
}
5、对每一个scan分为6大块，在每一个块中，分别根据曲率提取角点(2个)和平面点(20个)
pcl::PointCloud<PointType> cornerPointsSharp;
pcl::PointCloud<PointType> cornerPointsLessSharp;
pcl::PointCloud<PointType> surfPointsFlat;
pcl::PointCloud<PointType> surfPointsLessFlat;
// 提取特征点
float t_q_sort = 0;
for (int i = 0; i < N_SCANS; i++)// 0-15
{
if( scanEndInd[i] - scanStartInd[i] < 6)// 每一个scan的start和end
continue;
pcl::PointCloud<PointType>::Ptr surfPointsLessFlatScan(new pcl::PointCloud<PointType>);
for (int j = 0; j < 6; j++)// 把每一个scan均分为6份，0-5
{
int sp = scanStartInd[i] + (scanEndInd[i] - scanStartInd[i]) * j / 6;// subscan?????index
int ep = scanStartInd[i] + (scanEndInd[i] - scanStartInd[i]) * (j + 1) / 6 - 1;// subscan?????index
TicToc t_tmp;
std::sort (cloudSortInd + sp, cloudSortInd + ep + 1, comp);// 对这6份中的每一份按照曲率小到大排序
t_q_sort += t_tmp.toc();
// 提取角点
int largestPickedNum = 0;
for (int k = ep; k >= sp; k--)// 在subscan中 倒序查找
{
int ind = cloudSortInd[k];
if (cloudNeighborPicked[ind] == 0 &&
cloudCurvature[ind] > 0.1)// ????????б??????????????????0.1
{
largestPickedNum++;
if (largestPickedNum <= 2)// 一个subscan中最多有 2个corner_sharp
{
cloudLabel[ind] = 2; //corner_sharp
cornerPointsSharp.push_back(laserCloud->points[ind]);
cornerPointsLessSharp.push_back(laserCloud->points[ind]);
}
else if (largestPickedNum <= 20)// 一个subscan中最多有20个corner_less_sharp,corner_less_sharp包括corner_sharp
{
cloudLabel[ind] = 1; // corner_less_sharp
cornerPointsLessSharp.push_back(laserCloud->points[ind]);
}
else // corner_less_sharp>20了
{
break;
}
cloudNeighborPicked[ind] = 1; // 该点已经被处理过了，记为1
// 与临近点的距离的平方 <= 0.05的点标记为选择过，避免特征点密集分布
for (int l = 1; l <= 5; l++)
{
float diffX = laserCloud->points[ind + l].x - laserCloud->points[ind + l - 1].x;
float diffY = laserCloud->points[ind + l].y - laserCloud->points[ind + l - 1].y;
float diffZ = laserCloud->points[ind + l].z - laserCloud->points[ind + l - 1].z;
if (diffX * diffX + diffY * diffY + diffZ * diffZ > 0.05) // 如果相邻两根scan的距离大于0.05，那么退出
{
break;
}
cloudNeighborPicked[ind + l] = 1;
}
// 与临近点的距离的平方 <= 0.05的点标记为选择过，避免特征点密集分布，越往上scanID越大越远，所以可以直接break
for (int l = -1; l >= -5; l--)
{
float diffX = laserCloud->points[ind + l].x - laserCloud->points[ind + l + 1].x;
float diffY = laserCloud->points[ind + l].y - laserCloud->points[ind + l + 1].y;
float diffZ = laserCloud->points[ind + l].z - laserCloud->points[ind + l + 1].z;
if (diffX * diffX + diffY * diffY + diffZ * diffZ > 0.05)
{
break;
}
cloudNeighborPicked[ind + l] = 1;
}
}
}
// 提取平面点
// 提取surf平面feature，与上述类似，选取该subscan曲率最小的前4个点为surf_flat
int smallestPickedNum = 0;
for (int k = sp; k <= ep; k++) // 寻找平的，曲率由小到大排序
{
int ind = cloudSortInd[k];
// 判断是否满足平面点的条件
if (cloudNeighborPicked[ind] == 0 &&
cloudCurvature[ind] < 0.1)
{
cloudLabel[ind] = -1;
surfPointsFlat.push_back(laserCloud->points[ind]);
smallestPickedNum++;
if (smallestPickedNum >= 4)
{
break;
}
cloudNeighborPicked[ind] = 1;
// 根据scanID往上查找，防止关键点密集
for (int l = 1; l <= 5; l++)
{
float diffX = laserCloud->points[ind + l].x - laserCloud->points[ind + l - 1].x;
float diffY = laserCloud->points[ind + l].y - laserCloud->points[ind + l - 1].y;
float diffZ = laserCloud->points[ind + l].z - laserCloud->points[ind + l - 1].z;
if (diffX * diffX + diffY * diffY + diffZ * diffZ > 0.05)
{
break;
}
cloudNeighborPicked[ind + l] = 1;
}
for (int l = -1; l >= -5; l--)
{
float diffX = laserCloud->points[ind + l].x - laserCloud->points[ind + l + 1].x;
float diffY = laserCloud->points[ind + l].y - laserCloud->points[ind + l + 1].y;
float diffZ = laserCloud->points[ind + l].z - laserCloud->points[ind + l + 1].z;
if (diffX * diffX + diffY * diffY + diffZ * diffZ > 0.05)
{
break;
}
cloudNeighborPicked[ind + l] = 1;
}
}
}
// 对剩下的点提取为lessFlat
for (int k = sp; k <= ep; k++)
{
if (cloudLabel[k] <= 0)
{
surfPointsLessFlatScan->push_back(laserCloud->points[k]);
}
}
}
// 对lessFlat进行降采样
pcl::PointCloud<PointType> surfPointsLessFlatScanDS;
pcl::VoxelGrid<PointType> downSizeFilter;
downSizeFilter.setInputCloud(surfPointsLessFlatScan);
downSizeFilter.setLeafSize(0.2, 0.2, 0.2);
downSizeFilter.filter(surfPointsLessFlatScanDS);
surfPointsLessFlat += surfPointsLessFlatScanDS;
}
3、laserOdometry.cpp
laserOdometry的作用就是对相邻帧的两帧点云做帧间匹配得到位姿的变换
1、当前帧的在world世界坐标系下的位姿
// Lidar Odometry线程估计的frame在world坐标系的位姿P，Transformation from current frame to world frame
Eigen::Quaterniond q_w_curr(1, 0, 0, 0);
Eigen::Vector3d t_w_curr(0, 0, 0);
2、待优化变量
// 点云特征匹配时的优化变量
double para_q[4] = {
0, 0, 0, 1};
double para_t[3] = {
0, 0, 0};
// 下面的2个分别是优化变量para_q和para_t的映射：表示的是两个world坐标系下的位姿P之间的增量，例如△P = P0.inverse() * P1
Eigen::Map<Eigen::Quaterniond> q_last_curr(para_q); // 相当于引用
Eigen::Map<Eigen::Vector3d> t_last_curr(para_t);
3、将当前帧点云转换到上一帧，根据运动模型对点云去畸变
TransformToStart：将当前帧Lidar坐标系下的点云变换到上一帧Lidar坐标系下（也就是当前帧的初始位姿，起始位姿，所以函数名是TransformToStart），因为kitti点云已经去除了畸变，所以不再考虑运动补偿。（如果点云没有去除畸变，用slerp差值的方式计算出每个点在fire时刻的位姿，然后进行TransformToStart的坐标变换，一方面实现了变换到上一帧Lidar坐标系下；另一方面也可以理解成点都将fire时刻统一到了开始时刻，即去除了畸变，完成了运动补偿）
// undistort lidar point
// 将当前帧的点云转为上一帧，其中根据运动模型对点云进行了去畸变
void TransformToStart(PointType const *const pi, PointType *const po)
{
//interpolation ratio
double s;
if (DISTORTION)
s = (pi->intensity - int(pi->intensity)) / SCAN_PERIOD; // 获取每一点在周期中的位置
else
s = 1.0;
//s = 1;
Eigen::Quaterniond q_point_last = Eigen::Quaterniond::Identity().slerp(s, q_last_curr); // 四元数插值
Eigen::Vector3d t_point_last = s * t_last_curr; // 平移量插值
Eigen::Vector3d point(pi->x, pi->y, pi->z); // 当前帧的点
Eigen::Vector3d un_point = q_point_last * point + t_point_last; // 将当前帧的点变换到上一帧坐标系下
po->x = un_point.x();
po->y = un_point.y();
po->z = un_point.z();
po->intensity = pi->intensity;
}
4、接收上游的5个topic函数，将消息放在queue中，方便后续处理
// 存放数据的queue
std::queue<sensor_msgs::PointCloud2ConstPtr> cornerSharpBuf; // 只能访问 queue 容器适配器的第一个和最后一个元素。只能在容器的末尾添加新元素，只能从头部移除元素。
std::queue<sensor_msgs::PointCloud2ConstPtr> cornerLessSharpBuf;
std::queue<sensor_msgs::PointCloud2ConstPtr> surfFlatBuf;
std::queue<sensor_msgs::PointCloud2ConstPtr> surfLessFlatBuf;
std::queue<sensor_msgs::PointCloud2ConstPtr> fullPointsBuf;
//...
// 之后的5个Handler函数为接受上游5个topic的回调函数，作用是将消息缓存到对应的queue中，以便后续处理。
void laserCloudSharpHandler(const sensor_msgs::PointCloud2ConstPtr &cornerPointsSharp2)
{
mBuf.lock();
cornerSharpBuf.push(cornerPointsSharp2);
mBuf.unlock();
}
void laserCloudLessSharpHandler(const sensor_msgs::PointCloud2ConstPtr &cornerPointsLessSharp2)
{
mBuf.lock();
cornerLessSharpBuf.push(cornerPointsLessSharp2);
mBuf.unlock();
}
void laserCloudFlatHandler(const sensor_msgs::PointCloud2ConstPtr &surfPointsFlat2)
{
mBuf.lock();
surfFlatBuf.push(surfPointsFlat2);
mBuf.unlock();
}
void laserCloudLessFlatHandler(const sensor_msgs::PointCloud2ConstPtr &surfPointsLessFlat2)
{
mBuf.lock();
surfLessFlatBuf.push(surfPointsLessFlat2);
mBuf.unlock();
}
//receive all point cloud
void laserCloudFullResHandler(const sensor_msgs::PointCloud2ConstPtr &laserCloudFullRes2)
{
mBuf.lock();
fullPointsBuf.push(laserCloudFullRes2);
mBuf.unlock();
}
5、main函数中的发布者
ros::Publisher pubLaserCloudCornerLast = nh.advertise<sensor_msgs::PointCloud2>("/laser_cloud_corner_last", 100);
ros::Publisher pubLaserCloudSurfLast = nh.advertise<sensor_msgs::PointCloud2>("/laser_cloud_surf_last", 100);
ros::Publisher pubLaserCloudFullRes = nh.advertise<sensor_msgs::PointCloud2>("/velodyne_cloud_3", 100);
ros::Publisher pubLaserOdometry = nh.advertise<nav_msgs::Odometry>("/laser_odom_to_init", 100);
ros::Publisher pubLaserPath = nh.advertise<nav_msgs::Path>("/laser_odom_path", 100);
5、消息同步机制
ros::spinOnce();
if (!cornerSharpBuf.empty() && !cornerLessSharpBuf.empty() &&
!surfFlatBuf.empty() && !surfLessFlatBuf.empty() &&
!fullPointsBuf.empty())
{
timeCornerPointsSharp = cornerSharpBuf.front()->header.stamp.toSec();
timeCornerPointsLessSharp = cornerLessSharpBuf.front()->header.stamp.toSec();
timeSurfPointsFlat = surfFlatBuf.front()->header.stamp.toSec();
timeSurfPointsLessFlat = surfLessFlatBuf.front()->header.stamp.toSec();
timeLaserCloudFullRes = fullPointsBuf.front()->header.stamp.toSec();
// 如果关键点的时间戳和完整数据的时间戳有一个不相等，那么报错
if (timeCornerPointsSharp != timeLaserCloudFullRes ||
timeCornerPointsLessSharp != timeLaserCloudFullRes ||
timeSurfPointsFlat != timeLaserCloudFullRes ||
timeSurfPointsLessFlat != timeLaserCloudFullRes)
{
printf("unsync messeage!");
ROS_BREAK();
}
// 5个点云的时间同步
mBuf.lock();
cornerPointsSharp->clear();
pcl::fromROSMsg(*cornerSharpBuf.front(), *cornerPointsSharp);
cornerSharpBuf.pop();
cornerPointsLessSharp->clear();
pcl::fromROSMsg(*cornerLessSharpBuf.front(), *cornerPointsLessSharp);
cornerLessSharpBuf.pop();
surfPointsFlat->clear();
pcl::fromROSMsg(*surfFlatBuf.front(), *surfPointsFlat);
surfFlatBuf.pop();
surfPointsLessFlat->clear();
pcl::fromROSMsg(*surfLessFlatBuf.front(), *surfPointsLessFlat);
surfLessFlatBuf.pop();
laserCloudFullRes->clear();
pcl::fromROSMsg(*fullPointsBuf.front(), *laserCloudFullRes);
fullPointsBuf.pop();
mBuf.unlock();
6、帧间匹配优化机制
// initializing
if (!systemInited)// 第一帧不进行匹配，仅仅将 cornerPointsLessSharp 保存至 laserCloudCornerLast
// 将 surfPointsLessFlat 保存至 laserCloudSurfLast
// 为下次匹配提供target
{
systemInited = true;
std::cout << "Initialization finished \n";
}
else // 第二帧开始
{
int cornerPointsSharpNum = cornerPointsSharp->points.size();
int surfPointsFlatNum = surfPointsFlat->points.size();
TicToc t_opt;
for (size_t opti_counter = 0; opti_counter < 2; ++opti_counter)// 点到线以及点到面的ICP，迭代2次
{
corner_correspondence = 0; // 角点的误差项数量
plane_correspondence = 0; // 平面点的误差项数量
//ceres::LossFunction *loss_function = NULL;
ceres::LossFunction *loss_function = new ceres::HuberLoss(0.1);
ceres::LocalParameterization *q_parameterization =
new ceres::EigenQuaternionParameterization();
ceres::Problem::Options problem_options;
ceres::Problem problem(problem_options);
problem.AddParameterBlock(para_q, 4, q_parameterization);
problem.AddParameterBlock(para_t, 3);
pcl::PointXYZI pointSel;
std::vector<int> pointSearchInd;
std::vector<float> pointSearchSqDis;
TicToc t_data;
// 基于最近邻原理建立corner特征点之间关联，find correspondence for corner features
for (int i = 0; i < cornerPointsSharpNum; ++i)
{
TransformToStart(&(cornerPointsSharp->points[i]), &pointSel);// 将当前帧的corner_sharp特征点O_cur，从当前帧的Lidar坐标系下变换到上一帧的Lidar坐标系下（记为点O，注意与前面的点O_cur不同），以利于寻找corner特征点的correspondence
kdtreeCornerLast->nearestKSearch(pointSel, 1, pointSearchInd, pointSearchSqDis);// kdtree中的点云是上一帧的corner_less_sharp，所以这是在上一帧
// 的corner_less_sharp中寻找当前帧corner_sharp特征点O的最近邻点（记为A）
int closestPointInd = -1, minPointInd2 = -1;
if (pointSearchSqDis[0] < DISTANCE_SQ_THRESHOLD)// 如果最近邻的corner特征点之间距离平方小于阈值，则最近邻点A有效
{
closestPointInd = pointSearchInd[0];
int closestPointScanID = int(laserCloudCornerLast->points[closestPointInd].intensity); // 最近点的scanID，第几条线
double minPointSqDis2 = DISTANCE_SQ_THRESHOLD;
// 寻找点O的另外一个最近邻的点（记为点B） in the direction of increasing scan line
// 顺着index增加的方向查找
// 遍历范围内的点，选择距离pointSel最近的点（除掉kd-tree直接检索到的最近点之外）
for (int j = closestPointInd + 1; j < (int)laserCloudCornerLast->points.size(); ++j)// laserCloudCornerLast 来自上一帧的corner_less_sharp特征点,由于提取特征时是
{
// 按照scan的顺序提取的，所以laserCloudCornerLast中的点也是按照scanID递增的顺序存放的
// if in the same scan line, continue
// 如果查找到的scanID在closestPointScanID下面，直接跳过
if (int(laserCloudCornerLast->points[j].intensity) <= closestPointScanID)// intensity整数部分存放的是scanID
continue;
// 第二近的点距离closestPointScanID也不能太远
// if not in nearby scans, end the loop
if (int(laserCloudCornerLast->points[j].intensity) > (closestPointScanID + NEARBY_SCAN))
break;
double pointSqDis = (laserCloudCornerLast->points[j].x - pointSel.x) *
(laserCloudCornerLast->points[j].x - pointSel.x) +
(laserCloudCornerLast->points[j].y - pointSel.y) *
(laserCloudCornerLast->points[j].y - pointSel.y) +
(laserCloudCornerLast->points[j].z - pointSel.z) *
(laserCloudCornerLast->points[j].z - pointSel.z);
if (pointSqDis < minPointSqDis2)// 第二个最近邻点有效,，更新点B
{
// find nearer point
minPointSqDis2 = pointSqDis;
minPointInd2 = j;
}
}
// 寻找点O的另外一个最近邻的点B in the direction of decreasing scan line
for (int j = closestPointInd - 1; j >= 0; --j)
{
// if in the same scan line, continue
if (int(laserCloudCornerLast->points[j].intensity) >= closestPointScanID)
continue;
// if not in nearby scans, end the loop
if (int(laserCloudCornerLast->points[j].intensity) < (closestPointScanID - NEARBY_SCAN))
break;
double pointSqDis = (laserCloudCornerLast->points[j].x - pointSel.x) *
(laserCloudCornerLast->points[j].x - pointSel.x) +
(laserCloudCornerLast->points[j].y - pointSel.y) *
(laserCloudCornerLast->points[j].y - pointSel.y) +
(laserCloudCornerLast->points[j].z - pointSel.z) *
(laserCloudCornerLast->points[j].z - pointSel.z);
if (pointSqDis < minPointSqDis2)// 第二个最近邻点有效，更新点B
{
// find nearer point
minPointSqDis2 = pointSqDis;
minPointInd2 = j;
}
}
}
// 构造误差项
if (minPointInd2 >= 0) // both closestPointInd and minPointInd2 is valid
{
// 即特征点O的两个最近邻点A和B都有效
Eigen::Vector3d curr_point(cornerPointsSharp->points[i].x,
cornerPointsSharp->points[i].y,
cornerPointsSharp->points[i].z);
Eigen::Vector3d last_point_a(laserCloudCornerLast->points[closestPointInd].x,
laserCloudCornerLast->points[closestPointInd].y,
laserCloudCornerLast->points[closestPointInd].z);
Eigen::Vector3d last_point_b(laserCloudCornerLast->points[minPointInd2].x,
laserCloudCornerLast->points[minPointInd2].y,
laserCloudCornerLast->points[minPointInd2].z);
double s;// 运动补偿系数，kitti数据集的点云已经被补偿过，所以s = 1.0
if (DISTORTION)
s = (cornerPointsSharp->points[i].intensity - int(cornerPointsSharp->points[i].intensity)) / SCAN_PERIOD;
else
s = 1.0;
// 用点O，A，B构造点到线的距离的残差项，注意这三个点都是在上一帧的Lidar坐标系下，即，残差 = 点O到直线AB的距离
// 具体到介绍lidarFactor.cpp时再说明该残差的具体计算方法
ceres::CostFunction *cost_function = LidarEdgeFactor::Create(curr_point, last_point_a, last_point_b, s);
problem.AddResidualBlock(cost_function, loss_function, para_q, para_t);
corner_correspondence++;
}
}
// 下面说的点符号与上述相同
// 与上面的建立corner特征点之间的关联类似，寻找平面特征点O的最近邻点ABC，即基于最近邻原理建立surf特征点之间的关联，find correspondence for plane features
for (int i = 0; i < surfPointsFlatNum; ++i)
{
TransformToStart(&(surfPointsFlat->points[i]), &pointSel);
kdtreeSurfLast->nearestKSearch(pointSel, 1, pointSearchInd, pointSearchSqDis);
int closestPointInd = -1, minPointInd2 = -1, minPointInd3 = -1;
if (pointSearchSqDis[0] < DISTANCE_SQ_THRESHOLD)// 找到的最近邻点A有效
{
closestPointInd = pointSearchInd[0];
// get closest point's scan ID
int closestPointScanID = int(laserCloudSurfLast->points[closestPointInd].intensity);
double minPointSqDis2 = DISTANCE_SQ_THRESHOLD, minPointSqDis3 = DISTANCE_SQ_THRESHOLD;
// search in the direction of increasing scan line
for (int j = closestPointInd + 1; j < (int)laserCloudSurfLast->points.size(); ++j)
{
// if not in nearby scans, end the loop
if (int(laserCloudSurfLast->points[j].intensity) > (closestPointScanID + NEARBY_SCAN))
break;
double pointSqDis = (laserCloudSurfLast->points[j].x - pointSel.x) *
(laserCloudSurfLast->points[j].x - pointSel.x) +
(laserCloudSurfLast->points[j].y - pointSel.y) *
(laserCloudSurfLast->points[j].y - pointSel.y) +
(laserCloudSurfLast->points[j].z - pointSel.z) *
(laserCloudSurfLast->points[j].z - pointSel.z);
// if in the same or lower scan line
if (int(laserCloudSurfLast->points[j].intensity) <= closestPointScanID && pointSqDis < minPointSqDis2)
{
minPointSqDis2 = pointSqDis;// 找到的第2个最近邻点有效，更新点B，注意如果scanID准确的话，一般点A和点B的scanID相同
minPointInd2 = j;
}
// if in the higher scan line
else if (int(laserCloudSurfLast->points[j].intensity) > closestPointScanID && pointSqDis < minPointSqDis3)
{
minPointSqDis3 = pointSqDis;// 找到的第3个最近邻点有效，更新点C，注意如果scanID准确的话，一般点A和点B的scanID相同,且与点C的scanID不同，与LOAM的paper叙述一致
minPointInd3 = j;
}
}
// search in the direction of decreasing scan line
for (int j = closestPointInd - 1; j >= 0; --j)
{
// if not in nearby scans, end the loop
if (int(laserCloudSurfLast->points[j].intensity) < (closestPointScanID - NEARBY_SCAN))
break;
double pointSqDis = (laserCloudSurfLast->points[j].x - pointSel.x) *
(laserCloudSurfLast->points[j].x - pointSel.x) +
(laserCloudSurfLast->points[j].y - pointSel.y) *
(laserCloudSurfLast->points[j].y - pointSel.y) +
(laserCloudSurfLast->points[j].z - pointSel.z) *
(laserCloudSurfLast->points[j].z - pointSel.z);
// if in the same or higher scan line
if (int(laserCloudSurfLast->points[j].intensity) >= closestPointScanID && pointSqDis < minPointSqDis2)
{
minPointSqDis2 = pointSqDis;
minPointInd2 = j;
}
else if (int(laserCloudSurfLast->points[j].intensity) < closestPointScanID && pointSqDis < minPointSqDis3)
{
// find nearer point
minPointSqDis3 = pointSqDis;
minPointInd3 = j;
}
}
}
// 如果三个近邻点都有效，构造误差项
if (minPointInd2 >= 0 && minPointInd3 >= 0)// 如果三个最近邻点都有效
{
Eigen::Vector3d curr_point(surfPointsFlat->points[i].x,
surfPointsFlat->points[i].y,
surfPointsFlat->points[i].z);
Eigen::Vector3d last_point_a(laserCloudSurfLast->points[closestPointInd].x,
laserCloudSurfLast->points[closestPointInd].y,
laserCloudSurfLast->points[closestPointInd].z);
Eigen::Vector3d last_point_b(laserCloudSurfLast->points[minPointInd2].x,
laserCloudSurfLast->points[minPointInd2].y,
laserCloudSurfLast->points[minPointInd2].z);
Eigen::Vector3d last_point_c(laserCloudSurfLast->points[minPointInd3].x,
laserCloudSurfLast->points[minPointInd3].y,
laserCloudSurfLast->points[minPointInd3].z);
double s;
if (DISTORTION)
s = (surfPointsFlat->points[i].intensity - int(surfPointsFlat->points[i].intensity)) / SCAN_PERIOD;
else
s = 1.0;
// 用点O，A，B，C构造点到面的距离的残差项，注意这三个点都是在上一帧的Lidar坐标系下，即，残差 = 点O到平面ABC的距离
// 同样的，具体到介绍lidarFactor.cpp时再说明该残差的具体计算方法
ceres::CostFunction *cost_function = LidarPlaneFactor::Create(curr_point, last_point_a, last_point_b, last_point_c, s);
problem.AddResidualBlock(cost_function, loss_function, para_q, para_t);
plane_correspondence++;
}
}
printf("data association time %f ms \n", t_data.toc());
if ((corner_correspondence + plane_correspondence) < 10)
{
printf("less correspondence! *************************************************\n");
}
TicToc t_solver;
ceres::Solver::Options options;
options.linear_solver_type = ceres::DENSE_QR;
options.max_num_iterations = 4;
options.minimizer_progress_to_stdout = false; // 输出到cout
ceres::Solver::Summary summary;
// 基于构建的所有残差项，求解最优的当前帧位姿与上一帧位姿的位姿增量：para_q和para_t
ceres::Solve(options, &problem, &summary);
printf("solver time %f ms \n", t_solver.toc());
}
printf("optimization twice time %f \n", t_opt.toc());
// 用最新计算出的位姿增量，更新上一帧的位姿，得到当前帧的位姿，注意这里说的位姿都指的是世界坐标系下的位姿
t_w_curr = t_w_curr + q_w_curr * t_last_curr;
q_w_curr = q_w_curr * q_last_curr;
}
7、发布odom和path
// publish odometry
nav_msgs::Odometry laserOdometry;
laserOdometry.header.frame_id = "/camera_init";
laserOdometry.child_frame_id = "/laser_odom";
laserOdometry.header.stamp = ros::Time().fromSec(timeSurfPointsLessFlat);
laserOdometry.pose.pose.orientation.x = q_w_curr.x();
laserOdometry.pose.pose.orientation.y = q_w_curr.y();
laserOdometry.pose.pose.orientation.z = q_w_curr.z();
laserOdometry.pose.pose.orientation.w = q_w_curr.w();
laserOdometry.pose.pose.position.x = t_w_curr.x();
laserOdometry.pose.pose.position.y = t_w_curr.y();
laserOdometry.pose.pose.position.z = t_w_curr.z();
pubLaserOdometry.publish(laserOdometry);
geometry_msgs::PoseStamped laserPose;
laserPose.header = laserOdometry.header;
laserPose.pose = laserOdometry.pose.pose;
laserPath.header.stamp = laserOdometry.header.stamp;
laserPath.poses.push_back(laserPose);
laserPath.header.frame_id = "/camera_init";
pubLaserPath.publish(laserPath);
8、更新配准的source
// 当前帧变上一帧
pcl::PointCloud<PointType>::Ptr laserCloudTemp = cornerPointsLessSharp;
cornerPointsLessSharp = laserCloudCornerLast;
laserCloudCornerLast = laserCloudTemp;
laserCloudTemp = surfPointsLessFlat;
surfPointsLessFlat = laserCloudSurfLast;
laserCloudSurfLast = laserCloudTemp;
laserCloudCornerLastNum = laserCloudCornerLast->points.size();
laserCloudSurfLastNum = laserCloudSurfLast->points.size();
// std::cout << "the size of corner last is " << laserCloudCornerLastNum << ", and the size of surf last is " << laserCloudSurfLastNum << '\n';
// 使用上一帧的点云更新kd-tree，如果是第一帧的话是直接将其保存为这个的
kdtreeCornerLast->setInputCloud(laserCloudCornerLast);// 更新kdtree的点云
kdtreeSurfLast->setInputCloud(laserCloudSurfLast);
9、每5帧执行一次发布
// 每隔5帧执行一次
if (frameCount % skipFrameNum == 0)
{
frameCount = 0;
// 发布上一帧的Corner点
sensor_msgs::PointCloud2 laserCloudCornerLast2;
pcl::toROSMsg(*laserCloudCornerLast, laserCloudCornerLast2);
laserCloudCornerLast2.header.stamp = ros::Time().fromSec(timeSurfPointsLessFlat);
laserCloudCornerLast2.header.frame_id = "/camera";
pubLaserCloudCornerLast.publish(laserCloudCornerLast2);
// 发布上一帧的Surf点
sensor_msgs::PointCloud2 laserCloudSurfLast2;
pcl::toROSMsg(*laserCloudSurfLast, laserCloudSurfLast2);
laserCloudSurfLast2.header.stamp = ros::Time().fromSec(timeSurfPointsLessFlat);
laserCloudSurfLast2.header.frame_id = "/camera";
pubLaserCloudSurfLast.publish(laserCloudSurfLast2);
// 发布全部完整点云
sensor_msgs::PointCloud2 laserCloudFullRes3;
pcl::toROSMsg(*laserCloudFullRes, laserCloudFullRes3);
laserCloudFullRes3.header.stamp = ros::Time().fromSec(timeSurfPointsLessFlat);
laserCloudFullRes3.header.frame_id = "/camera";
pubLaserCloudFullRes.publish(laserCloudFullRes3);
}
4、laserMapping.cpp
0、submap的维护
参考：LOAM笔记及A-LOAM源码阅读
1、优化的变量，是当前帧在世界坐标系下的pose
// 点云特征匹配时的优化变量
double parameters[7] = {
0, 0, 0, 1, 0, 0, 0};
// Mapping线程估计的frame在world坐标系的位姿P,因为Mapping的算法耗时很有可能会超过100ms，所以
// 这个位姿P不是实时的，LOAM最终输出的实时位姿P_realtime,需要Mapping线程计算的相对低频位姿和
// Odometry线程计算的相对高频位姿做整合，详见后面laserOdometryHandler函数分析。此外需要注意
// 的是，不同于Odometry线程，这里的位姿P，即q_w_curr和t_w_curr，本身就是匹配时的优化变量。
// mapping线程计算的scan-to-localmap匹配后得到的位姿
Eigen::Map<Eigen::Quaterniond> q_w_curr(parameters); // map计算后的在world下的pose
Eigen::Map<Eigen::Vector3d> t_w_curr(parameters + 4);
2、mapping线程到Odometry线程的pose变换，Odometry线程计算得到的当前帧在world坐标系下的pose
// 下面的两个变量是world坐标系下的Odometry计算的位姿和Mapping计算的位姿之间的增量（也即变换，transformation）
// wmap_odom * wodom_curr = wmap_curr(即前面的q/t_w_curr)
// transformation between odom's world and map's world frame
Eigen::Quaterniond q_wmap_wodom(1, 0, 0, 0); // map到odom的变换
Eigen::Vector3d t_wmap_wodom(0, 0, 0);
// Odometry线程计算的frame在world坐标系的位姿
Eigen::Quaterniond q_wodom_curr(1, 0, 0, 0); // odom计算的在world坐标系下的pose
Eigen::Vector3d t_wodom_curr(0, 0, 0);
3、得到当前帧的在world世界坐标系下的pose
// set initial guess，上一帧的Transform(增量)wmap_wodom * 本帧Odometry位姿wodom_curr，旨在为本帧Mapping位姿w_curr设置一个初始值
void transformAssociateToMap() // 获取Mapping的初值
{
q_w_curr = q_wmap_wodom * q_wodom_curr;
t_w_curr = q_wmap_wodom * t_wodom_curr + t_wmap_wodom;
}
4、利用mapping计算得到的pose，计算mapping线程和Odometry线程之间的pose变换
// 用在最后，当Mapping的位姿w_curr计算完毕后，更新增量wmap_wodom，旨在为下一次执行transformAssociateToMap函数时做准备
void transformUpdate() // 获取map到odom的变换
{
q_wmap_wodom = q_w_curr * q_wodom_curr.inverse();
t_wmap_wodom = t_w_curr - q_wmap_wodom * t_wodom_curr;
}
5、用mapping的位姿将Lidar坐标系下的点转换到world坐标系下
// 用Mapping的位姿w_curr，将Lidar坐标系下的点变换到world坐标系下.q_w_curr为优化量，代表lidar在世界坐标系中的pose
void pointAssociateToMap(PointType const *const pi, PointType *const po)
{
Eigen::Vector3d point_curr(pi->x, pi->y, pi->z);
Eigen::Vector3d point_w = q_w_curr * point_curr + t_w_curr;
po->x = point_w.x();
po->y = point_w.y();
po->z = point_w.z();
po->intensity = pi->intensity;
//po->intensity = 1.0;
}
6、Odometry的回调函数
// receive odomtry
void laserOdometryHandler(const nav_msgs::Odometry::ConstPtr &laserOdometry)
{
mBuf.lock();
odometryBuf.push(laserOdometry);
mBuf.unlock();
// high frequence publish
Eigen::Quaterniond q_wodom_curr;
Eigen::Vector3d t_wodom_curr;
q_wodom_curr.x() = laserOdometry->pose.pose.orientation.x;
q_wodom_curr.y() = laserOdometry->pose.pose.orientation.y;
q_wodom_curr.z() = laserOdometry->pose.pose.orientation.z;
q_wodom_curr.w() = laserOdometry->pose.pose.orientation.w;
t_wodom_curr.x() = laserOdometry->pose.pose.position.x;
t_wodom_curr.y() = laserOdometry->pose.pose.position.y;
t_wodom_curr.z() = laserOdometry->pose.pose.position.z;
// 为了保证LOAM整体的实时性，防止Mapping线程耗时>100ms导致丢帧，用上一次的增量wmap_wodom来更新
// Odometry的位姿，旨在用Mapping位姿的初始值（也可以理解为预测值）来实时输出，进而实现LOAM整体的实时性
// 这里为啥不直接用transformAssociateToMap()函数来获取mapping的初始值？
Eigen::Quaterniond q_w_curr = q_wmap_wodom * q_wodom_curr;
Eigen::Vector3d t_w_curr = q_wmap_wodom * t_wodom_curr + t_wmap_wodom;
nav_msgs::Odometry odomAftMapped;
odomAftMapped.header.frame_id = "/camera_init";
odomAftMapped.child_frame_id = "/aft_mapped";
odomAftMapped.header.stamp = laserOdometry->header.stamp;
odomAftMapped.pose.pose.orientation.x = q_w_curr.x();
odomAftMapped.pose.pose.orientation.y = q_w_curr.y();
odomAftMapped.pose.pose.orientation.z = q_w_curr.z();
odomAftMapped.pose.pose.orientation.w = q_w_curr.w();
odomAftMapped.pose.pose.position.x = t_w_curr.x();
odomAftMapped.pose.pose.position.y = t_w_curr.y();
odomAftMapped.pose.pose.position.z = t_w_curr.z();
pubOdomAftMappedHighFrec.publish(odomAftMapped);
}
7、process函数
- 先读取Odometry线程的pose
// odom线程得到的pose
q_wodom_curr.x() = odometryBuf.front()->pose.pose.orientation.x;
q_wodom_curr.y() = odometryBuf.front()->pose.pose.orientation.y;
q_wodom_curr.z() = odometryBuf.front()->pose.pose.orientation.z;
q_wodom_curr.w() = odometryBuf.front()->pose.pose.orientation.w;
t_wodom_curr.x() = odometryBuf.front()->pose.pose.position.x;
t_wodom_curr.y() = odometryBuf.front()->pose.pose.position.y;
t_wodom_curr.z() = odometryBuf.front()->pose.pose.position.z;
odometryBuf.pop();
- 更新当前帧在mapping线程中的pose
// 上一帧的增量wmap_wodom * 本帧Odometry位姿wodom_curr，旨在为本帧Mapping位姿w_curr设置一个初始值
transformAssociateToMap(); // 第一次运行时，wmap_wodom=E
- 地图维护
// 地图维护
TicToc t_shift;
// 下面这是计算当前帧位置t_w_curr（在上图中用红色五角星表示的位置）IJK坐标（见上图中的坐标轴），
// 参照LOAM_NOTED的注释，下面有关25呀，50啥的运算，等效于以50m为单位进行缩放，因为LOAM用1维数组
// 进行cube的管理，而数组的index只用是正数，所以要保证IJK坐标都是正数，所以加了laserCloudCenWidth/Heigh/Depth
// 的偏移，来使得当前位置尽量位于submap的中心处，也就是使得上图中的五角星位置尽量处于所有格子的中心附近，
// 偏移laserCloudCenWidth/Heigh/Depth会动态调整，来保证当前位置尽量位于submap的中心处。
// 当前帧在world中的IJK坐标
int centerCubeI = int((t_w_curr.x() + 25.0) / 50.0) + laserCloudCenWidth; // 加上偏移
int centerCubeJ = int((t_w_curr.y() + 25.0) / 50.0) + laserCloudCenHeight;
int centerCubeK = int((t_w_curr.z() + 25.0) / 50.0) + laserCloudCenDepth;
// 由于计算机求余是向零取整，为了不使（-50.0,50.0）求余后都向零偏移，当被求余数为负数时求余结果统一向左偏移一个单位，也即减一
if (t_w_curr.x() + 25.0 < 0)
centerCubeI--;
if (t_w_curr.y() + 25.0 < 0)
centerCubeJ--;
if (t_w_curr.z() + 25.0 < 0)
centerCubeK--;
// 输出偏移量
printf("new----laserCloudCenWidth:%d,laserCloudCenHeight:%d,laserCloudCenDepth:%d",laserCloudCenWidth,laserCloudCenHeight,laserCloudCenDepth);
printf("I:%f, J:%f, K:%f",centerCubeI,centerCubeJ,centerCubeK);
// 以下注释部分参照LOAM_NOTED，结合我画的submap的示意图说明下面的6个while loop的作用：要
// 注意世界坐标系下的点云地图是固定的，但是IJK坐标系我们是可以移动的，所以这6个while loop
// 的作用就是调整IJK坐标系（也就是调整所有cube位置），使得五角星在IJK坐标系的坐标范围处于
// 3 <= centerCubeI < 18， 3 < centerCubeJ < 8, 3 < centerCubeK < 18，目的是为了防止后续向
// 四周拓展cube（图中的黄色cube就是拓展的cube）时，index（即IJK坐标）成为负数。
while (centerCubeI < 3) // 以i轴为例，如果当前帧的（五角星）坐标小于3，那么要将其向I轴的正方向移动，
{
for (int j = 0; j < laserCloudHeight; j++)
{
for (int k = 0; k < laserCloudDepth; k++)
{
int i = laserCloudWidth - 1; // 先把最大的i的对应的点云取出来
pcl::PointCloud<PointType>::Ptr laserCloudCubeCornerPointer =
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k];
pcl::PointCloud<PointType>::Ptr laserCloudCubeSurfPointer =
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k];
for (; i >= 1; i--)// 在I方向上，将cube[I] = cube[I-1],最后一个空出来的cube清空点云，实现IJK坐标系向I轴负方向移动一个cube的
// 效果，从相对运动的角度看，就是图中的五角星在IJK坐标系下向I轴正方向移动了一个cube，如下面的动图所示，所
// 以centerCubeI最后++，laserCloudCenWidth也会++，为下一帧Mapping时计算五角星的IJK坐标做准备。
{
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCornerArray[i - 1 + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k]; // i处的点云被i-1处的点云替代
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudSurfArray[i - 1 + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k];
}
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCubeCornerPointer; // 把之前最大的i对应的点云赋值给最小的i
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCubeSurfPointer;
laserCloudCubeCornerPointer->clear();
laserCloudCubeSurfPointer->clear();
}
}
centerCubeI++;
// 这里为啥要对laserCloudCenWidth加一行？
// 答：不是加一行，因为laserCloudCenWidth代表的当前帧坐标在IJK坐标系下的偏移量，而不是submap的尺寸，这个偏移量是可变的，目的就是为了使得submap位于中心，submap的尺寸是固定的5*5*5
laserCloudCenWidth++;
}
while (centerCubeI >= laserCloudWidth - 3)
{
for (int j = 0; j < laserCloudHeight; j++)
{
for (int k = 0; k < laserCloudDepth; k++)
{
int i = 0;
pcl::PointCloud<PointType>::Ptr laserCloudCubeCornerPointer =
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k];
pcl::PointCloud<PointType>::Ptr laserCloudCubeSurfPointer =
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k]; // 把最上面一行的点云取出来
for (; i < laserCloudWidth - 1; i++)
{
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCornerArray[i + 1 + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k]; // i行的点云被i+1行的点云替代
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudSurfArray[i + 1 + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k];
}
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCubeCornerPointer; // 把最小的行的点云放在新增加的一行
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCubeSurfPointer;
laserCloudCubeCornerPointer->clear();
laserCloudCubeSurfPointer->clear();
}
}
centerCubeI--;
laserCloudCenWidth--;
}
while (centerCubeJ < 3)
{
for (int i = 0; i < laserCloudWidth; i++)
{
for (int k = 0; k < laserCloudDepth; k++)
{
int j = laserCloudHeight - 1;
pcl::PointCloud<PointType>::Ptr laserCloudCubeCornerPointer =
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k];
pcl::PointCloud<PointType>::Ptr laserCloudCubeSurfPointer =
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k];
for (; j >= 1; j--)
{
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCornerArray[i + laserCloudWidth * (j - 1) + laserCloudWidth * laserCloudHeight * k];
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudSurfArray[i + laserCloudWidth * (j - 1) + laserCloudWidth * laserCloudHeight * k];
}
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCubeCornerPointer;
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCubeSurfPointer;
laserCloudCubeCornerPointer->clear();
laserCloudCubeSurfPointer->clear();
}
}
centerCubeJ++;
laserCloudCenHeight++;
}
while (centerCubeJ >= laserCloudHeight - 3)
{
for (int i = 0; i < laserCloudWidth; i++)
{
for (int k = 0; k < laserCloudDepth; k++)
{
int j = 0;
pcl::PointCloud<PointType>::Ptr laserCloudCubeCornerPointer =
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k];
pcl::PointCloud<PointType>::Ptr laserCloudCubeSurfPointer =
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k];
for (; j < laserCloudHeight - 1; j++)
{
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCornerArray[i + laserCloudWidth * (j + 1) + laserCloudWidth * laserCloudHeight * k];
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudSurfArray[i + laserCloudWidth * (j + 1) + laserCloudWidth * laserCloudHeight * k];
}
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCubeCornerPointer;
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCubeSurfPointer;
laserCloudCubeCornerPointer->clear();
laserCloudCubeSurfPointer->clear();
}
}
centerCubeJ--;
laserCloudCenHeight--;
}
while (centerCubeK < 3)
{
for (int i = 0; i < laserCloudWidth; i++)
{
for (int j = 0; j < laserCloudHeight; j++)
{
int k = laserCloudDepth - 1;
pcl::PointCloud<PointType>::Ptr laserCloudCubeCornerPointer =
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k];
pcl::PointCloud<PointType>::Ptr laserCloudCubeSurfPointer =
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k];
for (; k >= 1; k--)
{
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * (k - 1)];
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * (k - 1)];
}
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCubeCornerPointer;
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCubeSurfPointer;
laserCloudCubeCornerPointer->clear();
laserCloudCubeSurfPointer->clear();
}
}
centerCubeK++;
laserCloudCenDepth++;
}
while (centerCubeK >= laserCloudDepth - 3)
{
for (int i = 0; i < laserCloudWidth; i++)
{
for (int j = 0; j < laserCloudHeight; j++)
{
int k = 0;
pcl::PointCloud<PointType>::Ptr laserCloudCubeCornerPointer =
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k];
pcl::PointCloud<PointType>::Ptr laserCloudCubeSurfPointer =
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k];
for (; k < laserCloudDepth - 1; k++)
{
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * (k + 1)];
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * (k + 1)];
}
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCubeCornerPointer;
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCubeSurfPointer;
laserCloudCubeCornerPointer->clear();
laserCloudCubeSurfPointer->clear();
}
}
centerCubeK--;
laserCloudCenDepth--;
}
// 输出最终的偏移量
printf("new----laserCloudCenWidth:%d,laserCloudCenHeight:%d,laserCloudCenDepth:%d",laserCloudCenWidth,laserCloudCenHeight,laserCloudCenDepth);
// 输出最终的当前帧坐标
printf("new-I:%f, new-J:%f, new-K:%f",centerCubeI,centerCubeJ,centerCubeK);
// -------------------至此，IJK坐标系维护完成，当前帧位于IJK坐标系中心-------------------
- 以当前帧为中心，扩展(5,5,3)的黄色小地图
int laserCloudValidNum = 0;
int laserCloudSurroundNum = 0;
// 向IJ坐标轴的正负方向各拓展2个cube，K坐标轴的正负方向各拓展1个cube，上图中五角星所在的蓝色cube就是当前位置
// 所处的cube，拓展的cube就是黄色的cube，这些cube就是submap的范围 submap的大小就是5,5,3
for (int i = centerCubeI - 2; i <= centerCubeI + 2; i++)
{
for (int j = centerCubeJ - 2; j <= centerCubeJ + 2; j++)
{
for (int k = centerCubeK - 1; k <= centerCubeK + 1; k++)
{
if (i >= 0 && i < laserCloudWidth &&
j >= 0 && j < laserCloudHeight &&
k >= 0 && k < laserCloudDepth)// 如果坐标合法 [0,21), [0,21),[0,10]
{
// 记录submap中的所有cube的index，记为有效index
laserCloudValidInd[laserCloudValidNum] = i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k;
laserCloudValidNum++;
laserCloudSurroundInd[laserCloudSurroundNum] = i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k;
laserCloudSurroundNum++;
}
}
}
}
// submap大小
printf("submap size: %d\n",laserCloudValidNum);
// -------------------至此，得到当前帧的局部地图索引-------------------
- 将submap的点云加在一起
laserCloudCornerFromMap->clear();
laserCloudSurfFromMap->clear();
for (int i = 0; i < laserCloudValidNum; i++)
{
// 将有效index的cube中的点云叠加到一起组成submap的特征点云
*laserCloudCornerFromMap += *laserCloudCornerArray[laserCloudValidInd[i]];
*laserCloudSurfFromMap += *laserCloudSurfArray[laserCloudValidInd[i]];
}
int laserCloudCornerFromMapNum = laserCloudCornerFromMap->points.size();
int laserCloudSurfFromMapNum = laserCloudSurfFromMap->points.size();
// -------------------至此，得到当前帧的局部地图的特征点云-------------------
// 对上一帧的特征点云下采样
pcl::PointCloud<PointType>::Ptr laserCloudCornerStack(new pcl::PointCloud<PointType>());
downSizeFilterCorner.setInputCloud(laserCloudCornerLast);
downSizeFilterCorner.filter(*laserCloudCornerStack);
int laserCloudCornerStackNum = laserCloudCornerStack->points.size();
pcl::PointCloud<PointType>::Ptr laserCloudSurfStack(new pcl::PointCloud<PointType>());
downSizeFilterSurf.setInputCloud(laserCloudSurfLast);
downSizeFilterSurf.filter(*laserCloudSurfStack);
int laserCloudSurfStackNum = laserCloudSurfStack->points.size();
printf("submap prepare time %f ms\n", t_shift.toc());
printf("submap corner num %d surf num %d \n", laserCloudCornerFromMapNum, laserCloudSurfFromMapNum); // 输出(5,5,3)个cube中的点云的总数量
- 如果小地图的点足够多，开始优化匹配
if (laserCloudCornerFromMapNum > 10 && laserCloudSurfFromMapNum > 50) // 如果submap的特征点云数足够多，对其构建kd-tree
{
TicToc t_opt;
TicToc t_tree;
kdtreeCornerFromMap->setInputCloud(laserCloudCornerFromMap); // (5,5,3)的submap构建kd-tree
kdtreeSurfFromMap->setInputCloud(laserCloudSurfFromMap);
printf("build tree time %f ms \n", t_tree.toc());
for (int iterCount = 0; iterCount < 2; iterCount++) // 迭代两次
{
//ceres::LossFunction *loss_function = NULL;
ceres::LossFunction *loss_function = new ceres::HuberLoss(0.1); // LossFunction
ceres::LocalParameterization *q_parameterization =
new ceres::EigenQuaternionParameterization();
ceres::Problem::Options problem_options;
ceres::Problem problem(problem_options); // Problem
problem.AddParameterBlock(parameters, 4, q_parameterization); //添加参数块
problem.AddParameterBlock(parameters + 4, 3);
TicToc t_data;
int corner_num = 0;
for (int i = 0; i < laserCloudCornerStackNum; i++) // 遍历上一帧的所有Corner点
{
pointOri = laserCloudCornerStack->points[i];
// 需要注意的是submap中的点云都是world坐标系，而当前帧的点云都是Lidar坐标系，所以
// 在搜寻最近邻点时，先用预测的Mapping位姿w_curr，将Lidar坐标系下的特征点变换到world坐标系下
pointAssociateToMap(&pointOri, &pointSel);
// 在submap的corner特征点（target）中，寻找距离当前帧corner特征点（source）最近的5个点
kdtreeCornerFromMap->nearestKSearch(pointSel, 5, pointSearchInd, pointSearchSqDis);
if (pointSearchSqDis[4] < 1.0)
{
std::vector<Eigen::Vector3d> nearCorners;
Eigen::Vector3d center(0, 0, 0);
for (int j = 0; j < 5; j++)
{
Eigen::Vector3d tmp(laserCloudCornerFromMap->points[pointSearchInd[j]].x,
laserCloudCornerFromMap->points[pointSearchInd[j]].y,
laserCloudCornerFromMap->points[pointSearchInd[j]].z);
center = center + tmp;
nearCorners.push_back(tmp);
}
// 计算这个5个最近邻点的中心
center = center / 5.0;
// 协方差矩阵
Eigen::Matrix3d covMat = Eigen::Matrix3d::Zero();
for (int j = 0; j < 5; j++)
{
Eigen::Matrix<double, 3, 1> tmpZeroMean = nearCorners[j] - center;
covMat = covMat + tmpZeroMean * tmpZeroMean.transpose();
}
// 计算协方差矩阵的特征值和特征向量，用于判断这5个点是不是呈线状分布，此为PCA的原理
Eigen::SelfAdjointEigenSolver<Eigen::Matrix3d> saes(covMat);
// if is indeed line feature
// note Eigen library sort eigenvalues in increasing order
Eigen::Vector3d unit_direction = saes.eigenvectors().col(2);// 如果5个点呈线状分布，最大的特征值对应的特征向量就是该线的方向向量
Eigen::Vector3d curr_point(pointOri.x, pointOri.y, pointOri.z);
if (saes.eigenvalues()[2] > 3 * saes.eigenvalues()[1])// 如果最大的特征值 >> 其他特征值，则5个点确实呈线状分布，否则认为直线“不够直”
{
Eigen::Vector3d point_on_line = center;
Eigen::Vector3d point_a, point_b;
// 从中心点沿着方向向量向两端移动0.1m，构造线上的两个点
point_a = 0.1 * unit_direction + point_on_line;
point_b = -0.1 * unit_direction + point_on_line;
// 然后残差函数的形式就跟Odometry一样了，残差距离即点到线的距离，到介绍lidarFactor.cpp时再说明具体计算方法
ceres::CostFunction *cost_function = LidarEdgeFactor::Create(curr_point, point_a, point_b, 1.0);
problem.AddResidualBlock(cost_function, loss_function, parameters, parameters + 4);
corner_num++;
}
}
/*
else if(pointSearchSqDis[4] < 0.01 * sqrtDis)
{
Eigen::Vector3d center(0, 0, 0);
for (int j = 0; j < 5; j++)
{
Eigen::Vector3d tmp(laserCloudCornerFromMap->points[pointSearchInd[j]].x,
laserCloudCornerFromMap->points[pointSearchInd[j]].y,
laserCloudCornerFromMap->points[pointSearchInd[j]].z);
center = center + tmp;
}
center = center / 5.0;
Eigen::Vector3d curr_point(pointOri.x, pointOri.y, pointOri.z);
ceres::CostFunction *cost_function = LidarDistanceFactor::Create(curr_point, center);
problem.AddResidualBlock(cost_function, loss_function, parameters, parameters + 4);
}
*/
}
int surf_num = 0;
for (int i = 0; i < laserCloudSurfStackNum; i++)
{
pointOri = laserCloudSurfStack->points[i];
pointAssociateToMap(&pointOri, &pointSel);
kdtreeSurfFromMap->nearestKSearch(pointSel, 5, pointSearchInd, pointSearchSqDis);
// 求面的法向量就不是用的PCA了（虽然论文中说还是PCA），使用的是最小二乘拟合，是为了提效？不确定
// 假设平面不通过原点，则平面的一般方程为Ax + By + Cz + 1 = 0，用这个假设可以少算一个参数，提效。
Eigen::Matrix<double, 5, 3> matA0;
Eigen::Matrix<double, 5, 1> matB0 = -1 * Eigen::Matrix<double, 5, 1>::Ones();
// 用上面的2个矩阵表示平面方程就是 matA0 * norm（A, B, C） = matB0，这是个超定方程组，因为数据个数超过未知数的个数
if (pointSearchSqDis[4] < 1.0)
{
for (int j = 0; j < 5; j++)
{
matA0(j, 0) = laserCloudSurfFromMap->points[pointSearchInd[j]].x;
matA0(j, 1) = laserCloudSurfFromMap->points[pointSearchInd[j]].y;
matA0(j, 2) = laserCloudSurfFromMap->points[pointSearchInd[j]].z;
}
// 求解这个最小二乘问题，可得平面的法向量，find the norm of plane
Eigen::Vector3d norm = matA0.colPivHouseholderQr().solve(matB0);
// Ax + By + Cz + 1 = 0，全部除以法向量的模长，方程依旧成立，而且使得法向量归一化了
double negative_OA_dot_norm = 1 / norm.norm();
norm.normalize();
// Here n(pa, pb, pc) is unit norm of plane
bool planeValid = true;
for (int j = 0; j < 5; j++)
{
// 点(x0, y0, z0)到平面Ax + By + Cz + D = 0 的距离公式 = fabs(Ax0 + By0 + Cz0 + D) / sqrt(A^2 + B^2 + C^2)
if (fabs(norm(0) * laserCloudSurfFromMap->points[pointSearchInd[j]].x +
norm(1) * laserCloudSurfFromMap->points[pointSearchInd[j]].y +
norm(2) * laserCloudSurfFromMap->points[pointSearchInd[j]].z + negative_OA_dot_norm) > 0.2)
{
planeValid = false;// 平面没有拟合好，平面“不够平”
break;
}
}
Eigen::Vector3d curr_point(pointOri.x, pointOri.y, pointOri.z);
if (planeValid)
{
// 构造点到面的距离残差项，同样的，具体到介绍lidarFactor.cpp时再说明该残差的具体计算方法
ceres::CostFunction *cost_function = LidarPlaneNormFactor::Create(curr_point, norm, negative_OA_dot_norm);
problem.AddResidualBlock(cost_function, loss_function, parameters, parameters + 4);
surf_num++;
}
}
/*
else if(pointSearchSqDis[4] < 0.01 * sqrtDis)
{
Eigen::Vector3d center(0, 0, 0);
for (int j = 0; j < 5; j++)
{
Eigen::Vector3d tmp(laserCloudSurfFromMap->points[pointSearchInd[j]].x,
laserCloudSurfFromMap->points[pointSearchInd[j]].y,
laserCloudSurfFromMap->points[pointSearchInd[j]].z);
center = center + tmp;
}
center = center / 5.0;
Eigen::Vector3d curr_point(pointOri.x, pointOri.y, pointOri.z);
ceres::CostFunction *cost_function = LidarDistanceFactor::Create(curr_point, center);
problem.AddResidualBlock(cost_function, loss_function, parameters, parameters + 4);
}
*/
}
//printf("corner num %d used corner num %d \n", laserCloudCornerStackNum, corner_num);
//printf("surf num %d used surf num %d \n", laserCloudSurfStackNum, surf_num);
printf("mapping data assosiation time %f ms \n", t_data.toc());
TicToc t_solver;
ceres::Solver::Options options;
options.linear_solver_type = ceres::DENSE_QR;
options.max_num_iterations = 4;
options.minimizer_progress_to_stdout = false;
options.check_gradients = false;
options.gradient_check_relative_precision = 1e-4;
ceres::Solver::Summary summary;
ceres::Solve(options, &problem, &summary);
printf("mapping solver time %f ms \n", t_solver.toc());
//printf("time %f \n", timeLaserOdometry);
//printf("corner factor num %d surf factor num %d\n", corner_num, surf_num);
//printf("result q %f %f %f %f result t %f %f %f\n", parameters[3], parameters[0], parameters[1], parameters[2],
// parameters[4], parameters[5], parameters[6]);
}
printf("mapping optimization twice time %f \n", t_opt.toc());
// -------------------至此，两次迭代完成了，得到w_curr，当前帧在map坐标系下的pose----------------------------
}
else // submap的点数量不够
{
ROS_WARN("time Map corner and surf num are not enough");
}
- 更新mapping到Odometry线程的T
// 完成ICP（迭代2次）的特征匹配后，用最后匹配计算出的优化变量w_curr，更新增量wmap_wodom，为下一次
// Mapping做准备
transformUpdate(); // 获取map到odom的变换Transform
- 更新submap的点云
TicToc t_add;
// 创建两个indices，用来存放当前帧的点所处cube的index
std::vector<int> CornerInd();
std::vector<int> SurfInd();
// 下面两个for loop的作用就是将当前帧的特征点云，逐点进行操作：转换到world坐标系并添加到对应位置的cube中
for (int i = 0; i < laserCloudCornerStackNum; i++)
{
// Lidar坐标系转到world坐标系
pointAssociateToMap(&laserCloudCornerStack->points[i], &pointSel);
// 计算本次的特征点的IJK坐标，进而确定添加到哪个cube中
int cubeI = int((pointSel.x + 25.0) / 50.0) + laserCloudCenWidth;
int cubeJ = int((pointSel.y + 25.0) / 50.0) + laserCloudCenHeight;
int cubeK = int((pointSel.z + 25.0) / 50.0) + laserCloudCenDepth;
if (pointSel.x + 25.0 < 0)
cubeI--;
if (pointSel.y + 25.0 < 0)
cubeJ--;
if (pointSel.z + 25.0 < 0)
cubeK--;
if (cubeI >= 0 && cubeI < laserCloudWidth &&
cubeJ >= 0 && cubeJ < laserCloudHeight &&
cubeK >= 0 && cubeK < laserCloudDepth)
{
int cubeInd = cubeI + laserCloudWidth * cubeJ + laserCloudWidth * laserCloudHeight * cubeK;
laserCloudCornerArray[cubeInd]->push_back(pointSel); // PointCloud.push_back
CornerInd.append(cubeInd);
}
}
for (int i = 0; i < laserCloudSurfStackNum; i++)
{
pointAssociateToMap(&laserCloudSurfStack->points[i], &pointSel);
int cubeI = int((pointSel.x + 25.0) / 50.0) + laserCloudCenWidth;
int cubeJ = int((pointSel.y + 25.0) / 50.0) + laserCloudCenHeight;
int cubeK = int((pointSel.z + 25.0) / 50.0) + laserCloudCenDepth;
if (pointSel.x + 25.0 < 0)
cubeI--;
if (pointSel.y + 25.0 < 0)
cubeJ--;
if (pointSel.z + 25.0 < 0)
cubeK--;
if (cubeI >= 0 && cubeI < laserCloudWidth &&
cubeJ >= 0 && cubeJ < laserCloudHeight &&
cubeK >= 0 && cubeK < laserCloudDepth)
{
int cubeInd = cubeI + laserCloudWidth * cubeJ + laserCloudWidth * laserCloudHeight * cubeK;
laserCloudSurfArray[cubeInd]->push_back(pointSel);
SurfInd.append(cubeInd);
}
}
printf("add current points to submap time %f ms\n", t_add.toc());
TicToc t_filter;
// 对submap的cube点云进行下采样
// 因为新增加了点云，对之前已经存有点云的submap cube全部重新进行一次降采样
// 这个地方可以简单优化一下：如果之前的cube没有新添加点就不需要再降采样
for (int i = 0; i < laserCloudValidNum; i++) // 遍历submap的所有cube
{
int ind = laserCloudValidInd[i]; // submap中每一个cube的索引
// 判断当前的submap的cube id 是否在当前帧的索引的vector中
pcl::PointCloud<PointType>::Ptr tmpCorner(new pcl::PointCloud<PointType>());
downSizeFilterCorner.setInputCloud(laserCloudCornerArray[ind]);
downSizeFilterCorner.filter(*tmpCorner);
laserCloudCornerArray[ind] = tmpCorner;
pcl::PointCloud<PointType>::Ptr tmpSurf(new pcl::PointCloud<PointType>());
downSizeFilterSurf.setInputCloud(laserCloudSurfArray[ind]);
downSizeFilterSurf.filter(*tmpSurf);
laserCloudSurfArray[ind] = tmpSurf;
}
printf("filter time %f ms \n", t_filter.toc());
- 发布
// 每5帧发布一次submap (5,5,3)的cube大小
TicToc t_pub;
//publish surround map for every 5 frames
if (frameCount % 5 == 0)
{
laserCloudSurround->clear();
for (int i = 0; i < laserCloudSurroundNum; i++) // 遍历submap
{
int ind = laserCloudSurroundInd[i];
*laserCloudSurround += *laserCloudCornerArray[ind];
*laserCloudSurround += *laserCloudSurfArray[ind];
}
sensor_msgs::PointCloud2 laserCloudSurround3;
pcl::toROSMsg(*laserCloudSurround, laserCloudSurround3);
laserCloudSurround3.header.stamp = ros::Time().fromSec(timeLaserOdometry);
laserCloudSurround3.header.frame_id = "/camera_init"; // world坐标系
pubLaserCloudSurround.publish(laserCloudSurround3);
}
// 每20帧发布IJK局部地图
// pub laserCloudMap for every 20 frames
if (frameCount % 20 == 0)
{
pcl::PointCloud<PointType> laserCloudMap;
for (int i = 0; i < 4851; i++) // 遍历IJK坐标系
{
laserCloudMap += *laserCloudCornerArray[i];
laserCloudMap += *laserCloudSurfArray[i];
}
sensor_msgs::PointCloud2 laserCloudMsg;
pcl::toROSMsg(laserCloudMap, laserCloudMsg);
laserCloudMsg.header.stamp = ros::Time().fromSec(timeLaserOdometry);
laserCloudMsg.header.frame_id = "/camera_init";
pubLaserCloudMap.publish(laserCloudMsg);
}
// 全部点云转化到world坐标系，并发布
int laserCloudFullResNum = laserCloudFullRes->points.size();
for (int i = 0; i < laserCloudFullResNum; i++)
{
pointAssociateToMap(&laserCloudFullRes->points[i], &laserCloudFullRes->points[i]);
}
sensor_msgs::PointCloud2 laserCloudFullRes3;
pcl::toROSMsg(*laserCloudFullRes, laserCloudFullRes3);
laserCloudFullRes3.header.stamp = ros::Time().fromSec(timeLaserOdometry);
laserCloudFullRes3.header.frame_id = "/camera_init";
pubLaserCloudFullRes.publish(laserCloudFullRes3);
printf("mapping pub time %f ms \n", t_pub.toc());
printf("whole mapping time %f ms +++++\n", t_whole.toc());
// Odometry
nav_msgs::Odometry odomAftMapped;
odomAftMapped.header.frame_id = "/camera_init";
odomAftMapped.child_frame_id = "/aft_mapped";
odomAftMapped.header.stamp = ros::Time().fromSec(timeLaserOdometry);
odomAftMapped.pose.pose.orientation.x = q_w_curr.x(); // 当前帧在world下的pose,ceres的优化变量
odomAftMapped.pose.pose.orientation.y = q_w_curr.y();
odomAftMapped.pose.pose.orientation.z = q_w_curr.z();
odomAftMapped.pose.pose.orientation.w = q_w_curr.w();
odomAftMapped.pose.pose.position.x = t_w_curr.x();
odomAftMapped.pose.pose.position.y = t_w_curr.y();
odomAftMapped.pose.pose.position.z = t_w_curr.z();
pubOdomAftMapped.publish(odomAftMapped);
// Path
geometry_msgs::PoseStamped laserAfterMappedPose;
laserAfterMappedPose.header = odomAftMapped.header;
laserAfterMappedPose.pose = odomAftMapped.pose.pose;
laserAfterMappedPath.header.stamp = odomAftMapped.header.stamp;
laserAfterMappedPath.header.frame_id = "/camera_init";
laserAfterMappedPath.poses.push_back(laserAfterMappedPose);
pubLaserAfterMappedPath.publish(laserAfterMappedPath);
// 发布tf变换,(world)/camera_init--->(当前帧)/aft_mapped
static tf::TransformBroadcaster br;
tf::Transform transform;
tf::Quaternion q;
transform.setOrigin(tf::Vector3(t_w_curr(0),
t_w_curr(1),
t_w_curr(2)));
q.setW(q_w_curr.w());
q.setX(q_w_curr.x());
q.setY(q_w_curr.y());
q.setZ(q_w_curr.z());
transform.setRotation(q);
br.sendTransform(tf::StampedTransform(transform, odomAftMapped.header.stamp, "/camera_init", "/aft_mapped"));
frameCount++;
5、lidarFactor.cpp
1、构建误差项
// 点到线的残差距离计算
struct LidarEdgeFactor
{
// 构造函数
LidarEdgeFactor(Eigen::Vector3d curr_point_, Eigen::Vector3d last_point_a_,
Eigen::Vector3d last_point_b_, double s_)
: curr_point(curr_point_), last_point_a(last_point_a_), last_point_b(last_point_b_), s(s_) {
}
// 括号运算符重载
template <typename T>
bool operator()(const T *q, const T *t, T *residual) const
{
Eigen::Matrix<T, 3, 1> cp{
T(curr_point.x()), T(curr_point.y()), T(curr_point.z())};
Eigen::Matrix<T, 3, 1> lpa{
T(last_point_a.x()), T(last_point_a.y()), T(last_point_a.z())};
Eigen::Matrix<T, 3, 1> lpb{
T(last_point_b.x()), T(last_point_b.y()), T(last_point_b.z())};
//Eigen::Quaternion q_last_curr{q[3], T(s) * q[0], T(s) * q[1], T(s) * q[2]};
Eigen::Quaternion<T> q_last_curr{
q[3], q[0], q[1], q[2]}; // last到curr的旋转四元数
Eigen::Quaternion<T> q_identity{
T(1), T(0), T(0), T(0)};
// 考虑运动补偿（四元数插值），ktti点云已经补偿过所以可以忽略下面的对四元数slerp插值以及对平移的线性插值
q_last_curr = q_identity.slerp(T(s), q_last_curr);
Eigen::Matrix<T, 3, 1> t_last_curr{
T(s) * t[0], T(s) * t[1], T(s) * t[2]};
Eigen::Matrix<T, 3, 1> lp;
// Odometry线程时，下面是将当前帧Lidar坐标系下的cp点变换到上一帧的Lidar坐标系下，然后在上一帧的Lidar坐标系计算点到线的残差距离
// Mapping线程时，下面是将当前帧Lidar坐标系下的cp点变换到world坐标系下，然后在world坐标系下计算点到线的残差距离
lp = q_last_curr * cp + t_last_curr;
// 点到线的计算如下图所示
Eigen::Matrix<T, 3, 1> nu = (lp - lpa).cross(lp - lpb);
Eigen::Matrix<T, 3, 1> de = lpa - lpb;
// 最终的残差本来应该是residual[0] = nu.norm() / de.norm(); 为啥也分成3个，我也不知
// 道，从我试验的效果来看，确实是下面的残差函数形式，最后输出的pose精度会好一点点，这里需要
// 注意的是，所有的residual都不用加fabs，因为Ceres内部会对其求 平方 作为最终的残差项
residual[0] = nu.x() / de.norm();
residual[1] = nu.y() / de.norm();
residual[2] = nu.z() / de.norm();
return true;
}
// 返回函数的指针
static ceres::CostFunction *Create(const Eigen::Vector3d curr_point_, const Eigen::Vector3d last_point_a_,
const Eigen::Vector3d last_point_b_, const double s_)
{
return (new ceres::AutoDiffCostFunction<
LidarEdgeFactor, 3, 4, 3>(
// ^ ^ ^
// | | |
// 残差的维度 ____| | |
// 优化变量q的维度 _______| |
// 优化变量t的维度 __________|
new LidarEdgeFactor(curr_point_, last_point_a_, last_point_b_, s_)));
}
// 一些变量
Eigen::Vector3d curr_point, last_point_a, last_point_b;
double s;
};
// 计算Odometry线程中点到面的残差距离
struct LidarPlaneFactor
{
LidarPlaneFactor(Eigen::Vector3d curr_point_, Eigen::Vector3d last_point_j_,
Eigen::Vector3d last_point_l_, Eigen::Vector3d last_point_m_, double s_)
: curr_point(curr_point_), last_point_j(last_point_j_), last_point_l(last_point_l_),
last_point_m(last_point_m_), s(s_)
{
// 点l、j、m就是搜索到的最近邻的3个点，下面就是计算出这三个点构成的平面ljlm的法向量
ljm_norm = (last_point_j - last_point_l).cross(last_point_j - last_point_m);
// 归一化法向量
ljm_norm.normalize();
}
template <typename T>
bool operator()(const T *q, const T *t, T *residual) const
{
Eigen::Matrix<T, 3, 1> cp{
T(curr_point.x()), T(curr_point.y()), T(curr_point.z())}; // cp
Eigen::Matrix<T, 3, 1> lpj{
T(last_point_j.x()), T(last_point_j.y()), T(last_point_j.z())}; // last point j
//Eigen::Matrix lpl{T(last_point_l.x()), T(last_point_l.y()), T(last_point_l.z())};
//Eigen::Matrix lpm{T(last_point_m.x()), T(last_point_m.y()), T(last_point_m.z())};
Eigen::Matrix<T, 3, 1> ljm{
T(ljm_norm.x()), T(ljm_norm.y()), T(ljm_norm.z())}; // 法向量
//Eigen::Quaternion q_last_curr{q[3], T(s) * q[0], T(s) * q[1], T(s) * q[2]};
Eigen::Quaternion<T> q_last_curr{
q[3], q[0], q[1], q[2]};
Eigen::Quaternion<T> q_identity{
T(1), T(0), T(0), T(0)};
q_last_curr = q_identity.slerp(T(s), q_last_curr);
Eigen::Matrix<T, 3, 1> t_last_curr{
T(s) * t[0], T(s) * t[1], T(s) * t[2]};
Eigen::Matrix<T, 3, 1> lp;
lp = q_last_curr * cp + t_last_curr;
// 计算点到平面的残差距离，如下图所示
residual[0] = (lp - lpj).dot(ljm);
return true;
}
static ceres::CostFunction *Create(const Eigen::Vector3d curr_point_, const Eigen::Vector3d last_point_j_,
const Eigen::Vector3d last_point_l_, const Eigen::Vector3d last_point_m_,
const double s_)
{
return (new ceres::AutoDiffCostFunction<
LidarPlaneFactor, 1, 4, 3>(
// ^ ^ ^
// | | |
// 残差的维度 ____| | |
// 优化变量q的维度 _______| |
// 优化变量t的维度 __________|
new LidarPlaneFactor(curr_point_, last_point_j_, last_point_l_, last_point_m_, s_)));
}
Eigen::Vector3d curr_point, last_point_j, last_point_l, last_point_m;
Eigen::Vector3d ljm_norm;
double s;
};
struct LidarPlaneNormFactor
{
LidarPlaneNormFactor(Eigen::Vector3d curr_point_, Eigen::Vector3d plane_unit_norm_,
double negative_OA_dot_norm_) : curr_point(curr_point_), plane_unit_norm(plane_unit_norm_),
negative_OA_dot_norm(negative_OA_dot_norm_) {
}
template <typename T>
bool operator()(const T *q, const T *t, T *residual) const
{
Eigen::Quaternion<T> q_w_curr{
q[3], q[0], q[1], q[2]};
Eigen::Matrix<T, 3, 1> t_w_curr{
t[0], t[1], t[2]};
Eigen::Matrix<T, 3, 1> cp{
T(curr_point.x()), T(curr_point.y()), T(curr_point.z())};
Eigen::Matrix<T, 3, 1> point_w;
point_w = q_w_curr * cp + t_w_curr;
Eigen::Matrix<T, 3, 1> norm(T(plane_unit_norm.x()), T(plane_unit_norm.y()), T(plane_unit_norm.z()));
residual[0] = norm.dot(point_w) + T(negative_OA_dot_norm);
return true;
}
static ceres::CostFunction *Create(const Eigen::Vector3d curr_point_, const Eigen::Vector3d plane_unit_norm_,
const double negative_OA_dot_norm_)
{
return (new ceres::AutoDiffCostFunction<
LidarPlaneNormFactor, 1, 4, 3>(
new LidarPlaneNormFactor(curr_point_, plane_unit_norm_, negative_OA_dot_norm_)));
}
Eigen::Vector3d curr_point;
Eigen::Vector3d plane_unit_norm;
double negative_OA_dot_norm;
};
struct LidarDistanceFactor
{
LidarDistanceFactor(Eigen::Vector3d curr_point_, Eigen::Vector3d closed_point_)
: curr_point(curr_point_), closed_point(closed_point_){
}
template <typename T>
bool operator()(const T *q, const T *t, T *residual) const
{
Eigen::Quaternion<T> q_w_curr{
q[3], q[0], q[1], q[2]};
Eigen::Matrix<T, 3, 1> t_w_curr{
t[0], t[1], t[2]};
Eigen::Matrix<T, 3, 1> cp{
T(curr_point.x()), T(curr_point.y()), T(curr_point.z())};
Eigen::Matrix<T, 3, 1> point_w;
point_w = q_w_curr * cp + t_w_curr;
residual[0] = point_w.x() - T(closed_point.x());
residual[1] = point_w.y() - T(closed_point.y());
residual[2] = point_w.z() - T(closed_point.z());
return true;
}
static ceres::CostFunction *Create(const Eigen::Vector3d curr_point_, const Eigen::Vector3d closed_point_)
{
return (new ceres::AutoDiffCostFunction<
LidarDistanceFactor, 3, 4, 3>(
new LidarDistanceFactor(curr_point_, closed_point_)));
}
Eigen::Vector3d curr_point;
Eigen::Vector3d closed_point;
};
参考
LOAM笔记及A-LOAM源码阅读