六.激光SLAM框架学习之A-LOAM框架---项目工程代码介绍---4.laserMapping.cpp--后端建图和帧位姿精估计（优化）
作者：互联网
laserMapping节点订阅了来自laserOdometry的四个话题：当前帧全部点云、上一帧的边线点集合，上一帧的平面点集合，以及当前帧的位姿粗估计。发布了四个话题：附近帧组成的点云子地图（submap），所有帧组成的点云地图，当前帧位姿精估计。
int main(int argc, char **argv)
{
ros::init(argc, argv, "laserMapping");
ros::NodeHandle nh;
float lineRes = 0; // 次极大边线点云体素滤波分辨率
float planeRes = 0; // 次极小平面点云体素滤波分辨率
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);
downSizeFilterCorner.setLeafSize(lineRes, lineRes,lineRes); //进行体素滤波实现降采样
downSizeFilterSurf.setLeafSize(planeRes, planeRes, planeRes);
// 订阅了点云以及起始位姿
// 从laserOdometry节点接收边线点
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T_cur^w
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世界坐标系位姿（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边线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;
}
程序中存在雷达坐标系，地图坐标系，里程计坐标系（laseOdometry节点粗估计得到的odom坐标系），下面是坐标系转换函数。
// set initial guess，里程计位姿转化为地图位姿，作为后端初始估计
void transformAssociateToMap()
{
// T_w_curr = T_w_last * T_last_curr(from lidar odom)
q_w_curr = q_wmap_wodom * q_wodom_curr;
t_w_curr = q_wmap_wodom * t_wodom_curr + t_wmap_wodom;
}
// 更新odom到map之间的位姿变换
void transformUpdate()
{
q_wmap_wodom = q_w_curr * q_wodom_curr.inverse();
t_wmap_wodom = t_w_curr - q_wmap_wodom * t_wodom_curr;
}
//雷达坐标系点转化为地图点
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;
}
//地图点转化到雷达坐标系点
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;
}
下面是回调函数的注释：
// 回调函数中将消息都是送入各自队列，进行线程加锁和解锁
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
//接受前端发送过来的里程计话题，并将位姿转换到世界坐标系下后发布
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
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
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);
}
下面是Scan to Map当前帧位姿精估计，即周围10帧组成的子地图submap与其他所有帧组成的全部地图进行匹配，这种位姿估计方法联系了所有帧的信息，位姿估计更准确。
但是，如果完全使用所有区域的点云进行匹配，这样的效率会很低，而且内存空间可能会爆掉。LOAM采用的是栅格（cube）地图的方法，将整个地图分成21×21×11个珊格，每个珊格是⼀个边⻓50m的正⽅体，当地图逐渐累加时，珊格之外的部分就被舍弃，这样可以保证内存空间不会随着程序的运⾏⽽爆掉，同时保证效率。
// 主处理线程
void process()
{
while(1)
{
// 四个队列分别存放边线点、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，保证其他容器的最新消息与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())
{
mBuf.unlock(); //如果没有数据了，则线程解锁
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;
}
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
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初始估计值
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--;
// 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
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];
// 整体右移
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,也就是最左边的格子赋值给了之前最右边的格子
laserCloudCornerArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCubeCornerPointer;
laserCloudSurfArray[i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k] =
laserCloudCubeSurfPointer;
// 该点云清零，由于是指针操作，相当于最左边的格子清空了
laserCloudCubeCornerPointer->clear();
laserCloudCubeSurfPointer->clear();
}
}
// 索引右移
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250米范围内，总共500米范围），三个维度总共125个栅格
// 在这125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++)
{
// 如果坐标合理
if (i >= 0 && i < laserCloudWidth &&
j >= 0 && j < laserCloudHeight &&
k >= 0 && k < laserCloudDepth)
{
// 把submap子地图的有效栅格索引记录下来
laserCloudValidInd[laserCloudValidNum] = i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k;
laserCloudValidNum++;
laserCloudSurroundInd[laserCloudSurroundNum] = i + laserCloudWidth * j + laserCloudWidth * laserCloudHeight * k;
laserCloudSurroundNum++;
}
}
}
}
laserCloudCornerFromMap->clear();
laserCloudSurfFromMap->clear();
//将有效栅格的点云叠加到一起组成submap子地图的特征点云，构建用来这一帧优化的局部地图
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); //打印地图中边线点和平面点的数量
下面是后端Scan to Map的匹配优化。Submap子地图的网格与全部地图的网格进行匹配时，在laserOdomerty中Scan to Scan之间的匹配方法不适用了。这里的匹配方法如下：
1. 取当前帧的特征点（边线点/平面点）
2. 找到全部地图特征点中，当前特征点的5个最近邻点。
3. 如果是边线点，则以这五个点的均值点为中心，以5个点的主方向向量（类似于PCA方法）为方向，作一条直线，令该边线点与直线距离最短，构建非线性优化问题。
4. 如果是平面点，则寻找五个点的法方向（反向的PCA方法），令这个平面点在法方向上与五个近邻点的距离最小，构建非线性优化问题。
5. 优化变量是雷达位姿，求解能够让以上非线性问题代价函数最小的雷达位姿。
// 最终的地图有效点云数目进行判断
if (laserCloudCornerFromMapNum > 10 && laserCloudSurfFromMapNum > 50)
{
TicToc t_opt; //计算优化时间
TicToc t_tree; //计算KD-tree搜索时间
// 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()); //打印KD-tree搜索时间
// 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++)
{
pointOri = laserCloudCornerStack->points[i];
//double sqrtDis = pointOri.x * pointOri.x + pointOri.y * pointOri.y + pointOri.z * pointOri.z;
// submap子地图中的点云都是在world坐标系下，而接收到的当前帧点云都是Lidar坐标系下，所以要把当前点根据初值投到地图坐标系下去
//即用预测的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);
// 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
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);
}
// 计算这五个点的均值
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);
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])
{
Eigen::Vector3d point_on_line = center;
Eigen::Vector3d point_a, point_b;
// 根据拟合出来的线特征方向，以平均点为中心构建两个虚拟点，代替一条直线
point_a = 0.1 * unit_direction + point_on_line;
point_b = -0.1 * unit_direction + point_on_line;
// 构建约束，和lidar odom约束一致
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;
// 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
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，解就是这个平面的法向量
Eigen::Vector3d norm = matA0.colPivHouseholderQr().solve(matB0);
double negative_OA_dot_norm = 1 / norm.norm(); // 法向量长度的倒数
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++;
}
}
/*
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()); //打印建图数据关联时间
// 调用ceres求解
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 time %f \n", t_opt.toc()); //打印建图优化的时间
}
else
{
ROS_WARN("time Map corner and surf num are not enough");
}
// 完成特征匹配、优化后，用最后匹配计算出的优化变量w_curr，更新增量wmap_wodom，让下一次的Mapping初值更准确
transformUpdate();
下面是一些后处理的工作，即将当前帧的特征点加入到全部地图栅格中，对全部地图栅格中的点进行降采样，刷新附近点云地图，刷新全部点云地图，发布当前帧的精确位姿和平移估计。
TicToc t_add; //计算增加特征点的时间
// 将优化后的当前帧边线点（角点）加到对应的边线点局部地图中去
for (int i = 0; i < laserCloudCornerStackNum; i++)
{
// 该点根据位姿投到地图坐标系
pointAssociateToMap(&laserCloudCornerStack->points[i], &pointSel);
// 算出这个点所在的格子的索引
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
// 把当前帧涉及到的局部地图的珊格做一个降采样
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;
// 21 × 21 × 11 = 4851
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();
// 把当前帧发布出去
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());
// 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);
}
}
lidarFactor.hpp中后端优化用到的ceres模板类如何编写这里就不讲了。在下一章节一起讲解。
下一章讲解A-LOAM原理和一些第三方库调用的细节。
标签：laserCloudHeight,curr,框架,--,pose,++,---,int,laserCloudWidth 来源： https://blog.csdn.net/weixin_36773706/article/details/120872825