A-LOAM代码注释学习（五）laserMapping.cpp
目录
- 一、代码注释
-
- 1.1 四个坐标转换函数
- 1.2 回调函数
- 1.3 主程序
- 1.4主处理函数
- 二、重要内容解析
-
- 2.1 PCA算法
- 2.2 TF转换
- 2.3 更新位姿增量
一、代码注释
1.1 四个坐标转换函数
// 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
void transformAssociateToMap()
{
q_w_curr = q_wmap_wodom * q_wodom_curr;
t_w_curr = q_wmap_wodom * t_wodom_curr + t_wmap_wodom;
}
// 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()
{
q_wmap_wodom = q_w_curr * q_wodom_curr.inverse();
t_wmap_wodom = t_w_curr - q_wmap_wodom * t_wodom_curr;
}
// 用Mapping的位姿w_curr，将Lidar坐标系下的点变换到world坐标系下
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;
}
// 这个没有用到，是上面pointAssociateToMap的逆变换，即用Mapping的位姿w_curr，将world坐标系下的点变换到Lidar坐标系下
void pointAssociateTobeMapped(PointType const *const pi, PointType *const po)
{
Eigen::Vector3d point_w(pi->x, pi->y, pi->z);
Eigen::Vector3d point_curr = q_w_curr.inverse() * (point_w - t_w_curr);
po->x = point_curr.x();
po->y = point_curr.y();
po->z = point_curr.z();
po->intensity = pi->intensity;
}
1.2 回调函数
前三个回调函数比较简单，都是将接收到的点云信息加锁并放入到队列里，只有laserOdometryHandler函数功能多一点点。
里程计回调函数是为了以前端的频率向外发布位姿，这里主要提供一个估计的初值。
void laserCloudCornerLastHandler(const sensor_msgs::PointCloud2ConstPtr &laserCloudCornerLast2)
{
mBuf.lock();
cornerLastBuf.push(laserCloudCornerLast2);
mBuf.unlock();
}
void laserCloudSurfLastHandler(const sensor_msgs::PointCloud2ConstPtr &laserCloudSurfLast2)
{
mBuf.lock();
surfLastBuf.push(laserCloudSurfLast2);
mBuf.unlock();
}
void laserCloudFullResHandler(const sensor_msgs::PointCloud2ConstPtr &laserCloudFullRes2)
{
mBuf.lock();
fullResBuf.push(laserCloudFullRes2);
mBuf.unlock();
}
//receive odomtry
void laserOdometryHandler(const nav_msgs::Odometry::ConstPtr &laserOdometry)
{
mBuf.lock();
odometryBuf.push(laserOdometry);
mBuf.unlock();
// high frequence publish
// 获取里程计位姿，laserOdometry来源于/laser_odom_to_init，即laserOdometry的计算结果
// 命名为 q_wodom_curr和t_wodom_curr，把里程计结果解析出来，
// 分别对应 q_w_curr 和 t_w_curr
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;
// 里程计坐标系位姿转化为地图坐标系位姿，就是transformAssociateToMap
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;
// 发布出去
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);
}
1.3 主程序
主程序的框架简单，主要是信息的接收与发布，处理过程在主处理函数mapping_process()里。
订阅四种消息：
- 上一帧的角点集合
- 上一帧的平面点集合
- 当前帧的位姿粗估计
- 当前帧全部点云(经过一次降采样)
发布六种消息：
- 附近5帧组成的降采样子地图
- 所有帧组成的点云地图
- 当前帧原始点云（从velodyne_cloud_3订阅来的点云未经其他处理）
- 经过Map to Map精估计优化后当前帧位姿精估计
- 里程计坐标系位姿转化到世界坐标系位姿（地图坐标系），mapping输出的1Hz位姿，odometry输出的10Hz位姿，整合成10hz作为最终结果
- 经过Map to Map 精估计优化后的当前帧平移
int main(int argc, char **argv)
{
ros::init(argc, argv, "laserMapping");
ros::NodeHandle nh;
//定义体素网格参数值
float lineRes = 0;
float planeRes = 0;
nh.param<float>("mapping_line_resolution", lineRes, 0.4);
nh.param<float>("mapping_plane_resolution", planeRes, 0.8);
printf("line resolution %f plane resolution %f \n", lineRes, planeRes);
//进行体素滤波实现降采样
downSizeFilterCorner.setLeafSize(lineRes, lineRes,lineRes);
downSizeFilterSurf.setLeafSize(planeRes, planeRes, planeRes);
// 订阅了点云以及起始位姿
// 从laserOdometry节点接收角点
ros::Subscriber subLaserCloudCornerLast = nh.subscribe<sensor_msgs::PointCloud2>("/laser_cloud_corner_last", 100, laserCloudCornerLastHandler);
// 从laserOdometry节点接收平面点
ros::Subscriber subLaserCloudSurfLast = nh.subscribe<sensor_msgs::PointCloud2>("/laser_cloud_surf_last", 100, laserCloudSurfLastHandler);
// 从laserOdometry节点接收到的最新帧的位姿，并进行初值赋予
ros::Subscriber subLaserOdometry = nh.subscribe<nav_msgs::Odometry>("/laser_odom_to_init", 100, laserOdometryHandler);
// 从laserOdometry节点接收到的当前帧原始点云（只经过一次降采样）
ros::Subscriber subLaserCloudFullRes = nh.subscribe<sensor_msgs::PointCloud2>("/velodyne_cloud_3", 100, laserCloudFullResHandler);
//注册发布话题
// submap（子地图）所在cube（栅格）中的点云，发布周围5帧的点云（降采样以后的）
pubLaserCloudSurround = nh.advertise<sensor_msgs::PointCloud2>("/laser_cloud_surround", 100);
// map地图
pubLaserCloudMap = nh.advertise<sensor_msgs::PointCloud2>("/laser_cloud_map", 100);
// 当前帧原始点云
pubLaserCloudFullRes = nh.advertise<sensor_msgs::PointCloud2>("/velodyne_cloud_registered", 100);
//经过Map to Map精估计优化后的当前帧位姿
pubOdomAftMapped = nh.advertise<nav_msgs::Odometry>("/aft_mapped_to_init", 100);
// 将里程计坐标系位姿转化到地图坐标系，相当于位姿优化初值，即Odometry odom 到 map
pubOdomAftMappedHighFrec = nh.advertise<nav_msgs::Odometry>("/aft_mapped_to_init_high_frec", 100);
// 经过Map to Map精估计优化后的当前帧平移
pubLaserAfterMappedPath = nh.advertise<nav_msgs::Path>("/aft_mapped_path", 100);
//重置这两个数组，这两数组用于存储所有角点栅格和平面点栅格
for (int i = 0; i < laserCloudNum; i++)
{
laserCloudCornerArray[i].reset(new pcl::PointCloud<PointType>());
laserCloudSurfArray[i].reset(new pcl::PointCloud<PointType>());
}
std::thread mapping_process{process}; //主执行程序
ros::spin(); //不断执行回调函数
return 0;
}
1.4主处理函数
重点在于：
- submap维护
- 高精度位姿求解（点的关联、位姿优化）
void process()
{
//为了保证LOAM算法整体的实时性，Mapping线程每次只处理cornerLastBuf.front()及其他与之时间同步的消息，而因为Mapping线程耗时>100ms导致的历史缓存都会被清空
while(1)
{
// 四个队列分别存放角点、平面点、全部点、和里程计位姿，要确保需要的buffer里都有值
// laserOdometry模块对本节点的执行频率进行了控制，laserOdometry模块publish的位姿是10Hz，点云的publish频率没这么高，限制是2hz ？？？？
while (!cornerLastBuf.empty() && !surfLastBuf.empty() &&
!fullResBuf.empty() && !odometryBuf.empty())
{
mBuf.lock(); //线程加锁，避免线程冲突
// 以cornerLastBuf为基准，把时间戳小于其的全部pop出去，保证其他队列的最新消息与cornerLastBuf.front()最新消息时间戳同步
while (!odometryBuf.empty() && odometryBuf.front()->header.stamp.toSec() < cornerLastBuf.front()->header.stamp.toSec())
odometryBuf.pop();
if (odometryBuf.empty()) //如果没有数据了，则线程解锁
{
mBuf.unlock();
break;
}
while (!surfLastBuf.empty() && surfLastBuf.front()->header.stamp.toSec() < cornerLastBuf.front()->header.stamp.toSec())
surfLastBuf.pop();
if (surfLastBuf.empty())
{
mBuf.unlock();
break;
}
while (!fullResBuf.empty() && fullResBuf.front()->header.stamp.toSec() < cornerLastBuf.front()->header.stamp.toSec())
fullResBuf.pop();
if (fullResBuf.empty())
{
mBuf.unlock();
break;
}
//时间戳
timeLaserCloudCornerLast = cornerLastBuf.front()->header.stamp.toSec();
timeLaserCloudSurfLast = surfLastBuf.front()->header.stamp.toSec();
timeLaserCloudFullRes = fullResBuf.front()->header.stamp.toSec();
timeLaserOdometry = odometryBuf.front()->header.stamp.toSec();
// 原则上取出来的时间戳都是一样的，如果不一样则说明有问题
if (timeLaserCloudCornerLast != timeLaserOdometry ||
timeLaserCloudSurfLast != timeLaserOdometry ||
timeLaserCloudFullRes != timeLaserOdometry)
{
printf("time corner %f surf %f full %f odom %f \n", timeLaserCloudCornerLast, timeLaserCloudSurfLast, timeLaserCloudFullRes, timeLaserOdometry);
printf("unsync messeage!");
mBuf.unlock();
break;
}
// 点云全部转成pcl的数据格式
laserCloudCornerLast->clear();
pcl::fromROSMsg(*cornerLastBuf.front(), *laserCloudCornerLast);
cornerLastBuf.pop();
laserCloudSurfLast->clear();
pcl::fromROSMsg(*surfLastBuf.front(), *laserCloudSurfLast);
surfLastBuf.pop();
laserCloudFullRes->clear();
pcl::fromROSMsg(*fullResBuf.front(), *laserCloudFullRes);
fullResBuf.pop();
// lidar odom的结果转成eigen数据格式
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();
// 考虑到实时性，Mapping线程耗时>100ms导致的队列里缓存的其他边线点都pop出去，不然可能出现处理延时的情况
// 即，odometry信息用完了，清空cornerLastBuf的历史缓存，保证LOAM的整体实时性
while(!cornerLastBuf.empty())
{
cornerLastBuf.pop();
printf("drop lidar frame in mapping for real time performance \n");
}
mBuf.unlock();
TicToc t_whole; //计算这个线程的全部时间
// 根据前端结果，将里程计位姿转化为地图位姿得到后端优化的一个估计初值
transformAssociateToMap();
TicToc t_shift; //计算位姿转换的时间
// 后端地图本质上是一个以当前点为中心的一个珊格地图，根据初始估计值计算寻找当前位姿在地图中的索引，一个格子的边长是50m
int centerCubeI = int((t_w_curr.x() + 25.0) / 50.0) + laserCloudCenWidth;
int centerCubeJ = int((t_w_curr.y() + 25.0) / 50.0) + laserCloudCenHeight;
int centerCubeK = int((t_w_curr.z() + 25.0) / 50.0) + laserCloudCenDepth;
// 如果小于25就向下取整，相当于四舍五入的一个过程
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--;
// 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
// 如果当前珊格索引小于3,就说明当前点快接近地图边界了，需要进行调整，相当于地图整体往x正方向移动
// 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。
// 也就是，移动submap的中心
while (centerCubeI < 3)
{
for (int j = 0; j < laserCloudHeight; j++)
{
for (int k = 0; k < laserCloudDepth; k++)
{
int i = laserCloudWidth - 1;
// 从x最大值开始
pcl::PointCloud<PointType>::Ptr laserCloudCubeCornerPointer =
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k];
pcl::PointCloud<PointType>::Ptr laserCloudCubeSurfPointer =
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k];
// 在I方向cube[I]=cube[I-1],清空最后一个空出来的cube，实现IJK坐标系向I轴负方向移动一个cube的效果
for (; i >= 1; i--)
{
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCornerArray[i - 1 + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k];
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudSurfArray[i - 1 + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k];
}
// 此时i = 0，给到最初一组格子
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCubeCornerPointer;
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCubeSurfPointer;
// 点云清零
laserCloudCubeCornerPointer->clear();
laserCloudCubeSurfPointer->clear();
}
}
//
centerCubeI++;
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];
for (; i < laserCloudWidth - 1; i++)
{
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCornerArray[i + 1 + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k];
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudSurfArray[i + 1 + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k];
}
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCubeCornerPointer;
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCubeSurfPointer;
laserCloudCubeCornerPointer->clear();
laserCloudCubeSurfPointer->clear();
}
}
centerCubeI--;
laserCloudCenWidth--;
}
// 以下是y和z的操作，同理
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--;
}
// 以上操作相当于维护了一个局部地图，保证当前帧不在这个局部地图的边缘，这样才可以从地图中获取足够的约束
int laserCloudValidNum = 0;
int laserCloudSurroundNum = 0;
// 从当前格子为中心，选出地图中一定范围的点云
// 即向IJ坐标轴的正负方向各拓展2个栅格，K坐标轴的正负方向各拓展1个栅格
// 在每一维附近5个栅格(前2个，后2个，中间1个)里进行查找（前后共250米范围），三个维度总共5*5*3=75个栅格
// 在这75个栅格里面进一步筛选在视域范围内的栅格
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，即，xyz都为正，且在有效数量内
{
// 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++;
}
}
}
}
laserCloudCornerFromMap->clear();
laserCloudSurfFromMap->clear();
// 将有效index的cube中的点云叠加到一起组成submap的特征点云
for (int i = 0; i < laserCloudValidNum; i++)
for (int i = 0; i < laserCloudValidNum; i++)
{
*laserCloudCornerFromMap += *laserCloudCornerArray[laserCloudValidInd[i]];
*laserCloudSurfFromMap += *laserCloudSurfArray[laserCloudValidInd[i]];
}
int laserCloudCornerFromMapNum = laserCloudCornerFromMap->points.size();
int laserCloudSurfFromMapNum = laserCloudSurfFromMap->points.size();
// 为了减少运算量，对点云进行降采样
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("map prepare time %f ms\n", t_shift.toc()); //打印位姿转换的时间
printf("map corner num %d surf num %d \n", laserCloudCornerFromMapNum, laserCloudSurfFromMapNum); //打印地图中角点和平面点的数量
// 最终的地图有效点云数目进行判断
if (laserCloudCornerFromMapNum > 10 && laserCloudSurfFromMapNum > 50)
{
TicToc t_opt; //计算建图优化时间
TicToc t_tree; //计算KD-tree建立时间
// 送入kdtree便于最近邻搜索
kdtreeCornerFromMap->setInputCloud(laserCloudCornerFromMap);
kdtreeSurfFromMap->setInputCloud(laserCloudSurfFromMap);
printf("build tree time %f ms \n", t_tree.toc());
// 建立对应关系的优化迭代次数不超过2次
for (int iterCount = 0; iterCount < 2; iterCount++)
{
//ceres::LossFunction *loss_function = NULL;
// 建立ceres问题
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(parameters, 4, q_parameterization);
problem.AddParameterBlock(parameters + 4, 3);
TicToc t_data; //计算建图数据点关联的时间
int corner_num = 0;
// 构建边线点（角点）相关的约束
for (int i = 0; i < laserCloudCornerStackNum; 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
pointOri = laserCloudCornerStack->points[i];
pointAssociateToMap(&pointOri, &pointSel); //转换
// 地图中寻找和该特征点最近的5个点
kdtreeCornerFromMap->nearestKSearch(pointSel, 5, pointSearchInd, pointSearchSqDis);
// 判断最远的点距离不能超过1m，否则就是无效约束，从近往远寻找
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;
Eigen::Matrix3d covMat = Eigen::Matrix3d::Zero();
// 构建5个最近邻点的协方差矩阵
for (int j = 0; j < 5; j++)
{
Eigen::Matrix<double, 3, 1> tmpZeroMean = nearCorners[j] - center;
covMat = covMat + tmpZeroMean * tmpZeroMean.transpose();
}
// 进行特征值分解
Eigen::SelfAdjointEigenSolver<Eigen::Matrix3d> saes(covMat);
// if is indeed line feature
// note Eigen library sort eigenvalues in increasing order
// PCA的原理：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
// 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 unit_direction = saes.eigenvectors().col(2); 因为特征值一般按从小到大排列,所以col(2)就是最大特征值对应的特征向量
Eigen::Vector3d curr_point(pointOri.x, pointOri.y, pointOri.z);
// 最大特征值大于次大特征值的3倍认为是线特征
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
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++;
}
}
}
int surf_num = 0;
// 构建面点约束
for (int i = 0; i < laserCloudSurfStackNum; i++)
{
pointOri = laserCloudSurfStack->points[i];
//double sqrtDis = pointOri.x * pointOri.x + pointOri.y * pointOri.y + pointOri.z * pointOri.z;
pointAssociateToMap(&pointOri, &pointSel);
kdtreeSurfFromMap->nearestKSearch(pointSel, 5, pointSearchInd, pointSearchSqDis);
Eigen::Matrix<double, 5, 3> matA0;
Eigen::Matrix<double, 5, 1> matB0 = -1 * Eigen::Matrix<double, 5, 1>::Ones();
// 构建平面方程Ax + By +Cz + 1 = 0
// 通过构建一个超定方程来求解这个平面方程
// 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;
//printf(" pts %f %f %f ", matA0(j, 0), matA0(j, 1), matA0(j, 2));
}
// find the norm of plane
// 调用eigen接口求解该方程，求解这个最小二乘问题，可得平面的法向量
Eigen::Vector3d norm = matA0.colPivHouseholderQr().solve(matB0); //使用eigen库求解Ax=b
double negative_OA_dot_norm = 1 / norm.norm(); // 法向量的模的倒数
norm.normalize(); // 法向量归一化
// Here n(pa, pb, pc) is unit norm of plane
bool planeValid = true;
// 根据求出来的平面方程进行校验，看看是不是符合平面约束
for (int j = 0; j < 5; j++)
{
// if OX * n > 0.2, then plane is not fit well
// 这里是求解点到平面的距离
// 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; // 点如果距离平面太远，就认为这是一个拟合的不好的平面
break;
}
}
Eigen::Vector3d curr_point(pointOri.x, pointOri.y, pointOri.z);
// 如果平面有效就构建平面约束
if (planeValid)
{
// 利用平面方程构建约束，和前端构建形式稍有不同
ceres::CostFunction *cost_function = LidarPlaneNormFactor::Create(curr_point, norm, negative_OA_dot_norm);
problem.AddResidualBlock(cost_function, loss_function, parameters, parameters + 4);
surf_num++;
}
}
}
//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()); //打印建图数据关联时间
TicToc t_solver; //计算解算时间
// 调用ceres求解
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()); //打印解算时间 t_solver
//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 time %f \n", t_opt.toc()); //打印建图优化的时间 t_opt
}
else
{
ROS_WARN("time Map corner and surf num are not enough");
}
// 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();
TicToc t_add; //计算增加特征点的时间
//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;
// 同样四舍五入一下
if (pointSel.x + 25.0 < 0)
cubeI--;
if (pointSel.y + 25.0 < 0)
cubeJ--;
if (pointSel.z + 25.0 < 0)
cubeK--;
// 如果超过边界的话就算了
if (cubeI >= 0 && cubeI < laserCloudWidth &&
cubeJ >= 0 && cubeJ < laserCloudHeight &&
cubeK >= 0 && cubeK < laserCloudDepth)
{
// 根据xyz的索引计算在一位数组中的索引
int cubeInd = cubeI + laserCloudWidth * cubeJ + laserCloudWidth * laserCloudHeight * cubeK;
laserCloudCornerArray[cubeInd]->push_back(pointSel);
}
}
// 面点也做同样的处理
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);
}
}
printf("add points time %f ms\n", t_add.toc()); //打印增加特征点的时间
TicToc t_filter; //计算降采样的时间
// 因为新增加了点云，对之前已经存有点云的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++)
{
int ind = laserCloudValidInd[i];
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()); //打印降采样的时间
TicToc t_pub; //计算发布地图话题数据的时间
//publish surround map for every 5 frame 每隔5帧对外发布一下
if (frameCount % 5 == 0)
{
laserCloudSurround->clear();
// 把该当前帧相关的局部地图发布出去
for (int i = 0; i < laserCloudSurroundNum; i++)
{
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";
pubLaserCloudSurround.publish(laserCloudSurround3);
}
// 每隔20帧发布全量的局部地图
if (frameCount % 20 == 0)
{
pcl::PointCloud<PointType> laserCloudMap;
for (int i = 0; i < 4851; i++)
{
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);
}
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()); //打印发布地图话题数据的时间
printf("whole mapping time %f ms +++++\n", t_whole.toc()); //打印在全部运行的时间
// 发布当前位姿
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();
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);
// 发布当前轨迹
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
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++;
}
std::chrono::milliseconds dura(2); //延时2ms
std::this_thread::sleep_for(dura);
}
}
二、重要内容解析
2.1 PCA算法
先找五个点，然后求出能够代表这五个点的直线方向，再以此为基础构建两个点，连成线，然后残差函数的形式就跟Odometry部分的一样了，残差距离即点到线的距离。具体方法讲解见参考链接。
2.2 TF转换
固定格式，用来监听camera_init和aft_mapped之间的坐标系转换关系。需要头文件
2.3 更新位姿增量
就是将lidar->odom的位姿变换为lidar->map的位姿，其实就是乘了一个odom->map的变化矩阵，具体地，对姿态四元数和平移向量分别进行变换。可以根据下面这个图理解。
在里程计部分，知道了lidar->landmark（传感器测量），推算出了lidar->odometry，从而知道landmark在odometry下的姿态，现在加一个高精度的lidar->map，就构建出了地图，同时odometry->map就是优化量，对载体位姿进行优化。
参考连接
链接: LOAM笔记及A-LOAM源码阅读
链接: 主成分分析（PCA）原理总结
链接: ALOAM：后端lasermapping 里程计到地图位姿更新维护
链接: ALOAM:后端laserMapping代码结构与数据处理分析
链接: 古月居ROS教程-tf坐标系广播