LOAM源码解析（一）ScanRegistration
代码中文注释版本：https://github.com/daobilige-su/loam_velodyne
简单概述
一、主函数
二、imuHandler()
三、AccumulateIMUShift()
四、TransformToStartIMU()
五、laserCloudHandle()
LOAM代码框架如下
主要思路就以两个并行算法LidarOdometry（高频低精度）、LidarMapping（低频高精度）实现低漂移和低计算复杂度。
简单概述
ScanRegistraion 主要提取特征点，并对点云进行运动补偿。
一、主函数
功能：申明订阅、发布内容。
代码没什么特别的，就不展开说了。
二、imuHandler()
功能：
- 接收imu消息；
- 减去重力影响，求出xyz方向的加速度实际值；
- 进行坐标轴变换（从RPY — XYZ 变换到 RPY — ZXY）
下面解释如果消除重力加速度的影响(变化方式就按照代码中的来)。
上式表示IMU到世界坐标系旋转矩阵(从下向上看)，其中
由世界坐标系到IMU的变换为，对原来的旋转矩阵求个逆(求逆还是比较简单的，因为旋转矩阵是正交矩阵，逆就等于转置)
那么重力在IMU坐标系下的分量就可以表示为
其中
这里为9.8，是因为IMU所获数值与重力方向相反。然后根据以上公式计算得到重力影响
float accX = imuIn->linear_acceleration.y - sin(roll) * cos(pitch) * 9.81;
float accY = imuIn->linear_acceleration.z - cos(roll) * cos(pitch) * 9.81;
float accZ = imuIn->linear_acceleration.x + sin(pitch) * 9.81;
再用IMU数据的分量减去就好，如上述代码所示。注意这里同时做了坐标轴变换(我听说...坐标轴变换是为了协调工作，可能是和相机融合什么的...母鸡)，accZ -- x，获得原来x轴方向上的数据。
{
double roll, pitch, yaw;
tf::Quaternion orientation;
//convert Quaternion msg to Quaternion
tf::quaternionMsgToTF(imuIn->orientation, orientation);
//That's R = Rz(yaw)*Ry(pitch)*Rx(roll).
//这里的yaw pitch roll是世界坐标系下
tf::Matrix3x3(orientation).getRPY(roll, pitch, yaw);
//减去重力的影响,求出xyz方向的加速度实际值，并进行坐标轴交换，统一到z轴向前,x轴向左的右手坐标系, 交换过后RPY对应fixed axes ZXY(RPY---ZXY)。Now R = Ry(yaw)*Rx(pitch)*Rz(roll).
float accX = imuIn->linear_acceleration.y - sin(roll) * cos(pitch) * 9.81;
float accY = imuIn->linear_acceleration.z - cos(roll) * cos(pitch) * 9.81;
float accZ = imuIn->linear_acceleration.x + sin(pitch) * 9.81;
//循环移位效果，形成环形数组
imuPointerLast = (imuPointerLast + 1) % imuQueLength;
//存入信息
imuTime[imuPointerLast] = imuIn->header.stamp.toSec();
imuRoll[imuPointerLast] = roll;
imuPitch[imuPointerLast] = pitch;
imuYaw[imuPointerLast] = yaw;
imuAccX[imuPointerLast] = accX;
imuAccY[imuPointerLast] = accY;
imuAccZ[imuPointerLast] = accZ;
//积分速度与位移
AccumulateIMUShift();
}三、AccumulateIMUShift()
功能：积分出当前最新IMU时刻下的速度和位移
做过坐标轴变换的旋转矩阵表示为
，同样表示IMU到世界坐标系下的变换(可能有点绕)。按照下面的变换依次左乘得到世界坐标系下的加速度值。
//将当前时刻的加速度值绕交换过的ZXY固定轴（原XYZ）分别旋转(roll, pitch, yaw)角，转换得到世界坐标系下的加速度值(right hand rule)
//绕z轴旋转(roll)
float x1 = cos(roll) * accX - sin(roll) * accY;
float y1 = sin(roll) * accX + cos(roll) * accY;
float z1 = accZ;
//绕x轴旋转(pitch)
float x2 = x1;
float y2 = cos(pitch) * y1 - sin(pitch) * z1;
float z2 = sin(pitch) * y1 + cos(pitch) * z1;
//绕y轴旋转(yaw)
accX = cos(yaw) * x2 + sin(yaw) * z2;
accY = y2;
accZ = -sin(yaw) * x2 + cos(yaw) * z2;四、TransformToStartIMU()
功能：补偿加减速产生的位移畸变
ShiftTostartIMU() 与 VeloToStartIMU()代码中的旋转逻辑同上，看看就差不多了。只不过就是注意下求逆的时候看成 Rz(pitch).inverse = Rz(-pitch)，因为旋转矩阵的逆等于转置了，再结合一下三角函数的奇偶性就正好。
代码逻辑：将当前时刻的点云变换到世界坐标系，以世界坐标系为中间媒介，再把点云变换第一个点云坐标系下。根据运动模型
，当前时刻点与开始时刻点只相差加减速产生的位移（ ShiftTostartIMU()得到加减速产生的位移 ），把这一部分补偿上即可。
{
/********************************************************************************
Ry*Rx*Rz*Pl, transform point to the global frame
*********************************************************************************/
//绕z轴旋转(imuRollCur)
float x1 = cos(imuRollCur) * p->x - sin(imuRollCur) * p->y;
float y1 = sin(imuRollCur) * p->x + cos(imuRollCur) * p->y;
float z1 = p->z;
//绕x轴旋转(imuPitchCur)
float x2 = x1;
float y2 = cos(imuPitchCur) * y1 - sin(imuPitchCur) * z1;
float z2 = sin(imuPitchCur) * y1 + cos(imuPitchCur) * z1;
//绕y轴旋转(imuYawCur)
float x3 = cos(imuYawCur) * x2 + sin(imuYawCur) * z2;
float y3 = y2;
float z3 = -sin(imuYawCur) * x2 + cos(imuYawCur) * z2;
/********************************************************************************
Rz(pitch).inverse * Rx(pitch).inverse * Ry(yaw).inverse * Pg
transfrom global points to the local frame
*********************************************************************************/
//绕y轴旋转(-imuYawStart)
float x4 = cos(imuYawStart) * x3 - sin(imuYawStart) * z3;
float y4 = y3;
float z4 = sin(imuYawStart) * x3 + cos(imuYawStart) * z3;
//绕x轴旋转(-imuPitchStart)
float x5 = x4;
float y5 = cos(imuPitchStart) * y4 + sin(imuPitchStart) * z4;
float z5 = -sin(imuPitchStart) * y4 + cos(imuPitchStart) * z4;
//绕z轴旋转(-imuRollStart)，然后叠加平移量
p->x = cos(imuRollStart) * x5 + sin(imuRollStart) * y5 + imuShiftFromStartXCur;
p->y = -sin(imuRollStart) * x5 + cos(imuRollStart) * y5 + imuShiftFromStartYCur;
p->z = z5 + imuShiftFromStartZCur;
}五、laserCloudHandle()
功能：提取边缘点，次边缘点，平面点以及次平面点；发布点云。
这一部分就是主要内容，可能需要注意的内容直接写在如下代码注释中。
{
if (!systemInited) {//丢弃前20个点云数据
systemInitCount++;
if (systemInitCount >= systemDelay) {
systemInited = true;
}
return;
}
//记录每个scan有曲率的点的开始和结束索引
std::vector scanStartInd(N_SCANS, 0);
std::vector scanEndInd(N_SCANS, 0);
//当前点云时间
double timeScanCur = laserCloudMsg->header.stamp.toSec();
pcl::PointCloud laserCloudIn;
//消息转换成pcl数据存放
pcl::fromROSMsg(*laserCloudMsg, laserCloudIn);
std::vector indices;
//移除空点
pcl::removeNaNFromPointCloud(laserCloudIn, laserCloudIn, indices);
//点云点的数量
int cloudSize = laserCloudIn.points.size();
//lidar scan开始点的旋转角,atan2范围[-pi,+pi],计算旋转角时取负号是因为velodyne是顺时针旋转
float startOri = -atan2(laserCloudIn.points[0].y, laserCloudIn.points[0].x);
//lidar scan结束点的旋转角，加2*pi使点云旋转周期为2*pi
//这里加上 2M_PI 方便后续计算，最后一个点旋转角度在第一个点后
float endOri = -atan2(laserCloudIn.points[cloudSize - 1].y,
laserCloudIn.points[cloudSize - 1].x) + 2 * M_PI;
//结束方位角与开始方位角差值控制在(PI,3*PI)范围，允许lidar不是一个圆周扫描
//正常情况下在这个范围内：pi < endOri - startOri < 3*pi，异常则修正
if (endOri - startOri > 3 * M_PI) {
endOri -= 2 * M_PI;
} else if (endOri - startOri < M_PI) {
endOri += 2 * M_PI;
}
//lidar扫描线是否旋转过半
bool halfPassed = false;
int count = cloudSize;
PointType point;
//按照激光光束存储点云
std::vector > laserCloudScans(N_SCANS);
for (int i = 0; i < cloudSize; i++) {
//坐标轴交换，velodyne lidar的坐标系也转换到z轴向前，x轴向左的右手坐标系
point.x = laserCloudIn.points[i].y;
point.y = laserCloudIn.points[i].z;
point.z = laserCloudIn.points[i].x;
//计算点的仰角(根据lidar文档垂直角计算公式),根据仰角排列激光线号，velodyne每两个scan之间间隔2度
//具体可看velodyne官方的用户手册
float angle = atan(point.y / sqrt(point.x * point.x + point.z * point.z)) * 180 / M_PI;
int scanID;
//仰角四舍五入(加减0.5截断效果等于四舍五入)
int roundedAngle = int(angle + (angle<0.0?-0.5:+0.5));
if (roundedAngle > 0){
scanID = roundedAngle;
}
else {
scanID = roundedAngle + (N_SCANS - 1);
}
//过滤点，只挑选[-15度，+15度]范围内的点,scanID属于[0,15]
if (scanID > (N_SCANS - 1) || scanID < 0 ){
count--;
continue;
}
//该点的旋转角
float ori = -atan2(point.x, point.z);
if (!halfPassed) {//根据扫描线是否旋转过半选择与起始位置还是终止位置进行差值计算，从而进行补偿
//确保-pi/2 < ori - startOri < 3*pi/2 -- 这里我不是很懂，有明白可以留言下
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 {
ori += 2 * M_PI;
//确保-3*pi/2 < ori - endOri < pi/2
if (ori < endOri - M_PI * 3 / 2) {
ori += 2 * M_PI;
} else if (ori > endOri + M_PI / 2) {
ori -= 2 * M_PI;
}
}
//-0.5 < relTime < 1.5（点旋转的角度与整个周期旋转角度的比率, 即点云中点的相对时间）
float relTime = (ori - startOri) / (endOri - startOri);
//点强度=线号+点相对时间（即一个整数+一个小数，整数部分是线号，小数部分是该点的相对时间）,匀速扫描：根据当前扫描的角度和扫描周期计算相对扫描起始位置的时间
//todo 因为retTime是一个旋转角度的比率，必然是小于1的，而scanPeriod也是小于1的，相乘还是小1，所以小数部分可以存储该点的旋转时间(本周期相对第一个点的时间)
point.intensity = scanID + scanPeriod * relTime;
//点时间=点云时间+周期时间
if (imuPointerLast >= 0) {//如果收到IMU数据,使用IMU矫正点云畸变
float pointTime = relTime * scanPeriod;//计算点的周期时间
//寻找是否有点云的时间戳小于IMU的时间戳的IMU位置:imuPointerFront
//todo 即imuPointerFront 是该点云的下一时刻
while (imuPointerFront != imuPointerLast) {
if (timeScanCur + pointTime < imuTime[imuPointerFront]) {
break;
}
imuPointerFront = (imuPointerFront + 1) % imuQueLength;
}
if (timeScanCur + pointTime > imuTime[imuPointerFront]) {//没找到,此时imuPointerFront==imtPointerLast,只能以当前收到的最新的IMU的速度，位移，欧拉角作为当前点的速度，位移，欧拉角使用
imuRollCur = imuRoll[imuPointerFront];
imuPitchCur = imuPitch[imuPointerFront];
imuYawCur = imuYaw[imuPointerFront];
imuVeloXCur = imuVeloX[imuPointerFront];
imuVeloYCur = imuVeloY[imuPointerFront];
imuVeloZCur = imuVeloZ[imuPointerFront];
imuShiftXCur = imuShiftX[imuPointerFront];
imuShiftYCur = imuShiftY[imuPointerFront];
imuShiftZCur = imuShiftZ[imuPointerFront];
} else {//找到了点云时间戳小于IMU时间戳的IMU位置,则该点必处于imuPointerBack和imuPointerFront之间，据此线性插值，计算点云点的速度，位移和欧拉角
int imuPointerBack = (imuPointerFront + imuQueLength - 1) % imuQueLength;
//按时间距离计算权重分配比率,也即线性插值
float ratioFront = (timeScanCur + pointTime - imuTime[imuPointerBack])
/ (imuTime[imuPointerFront] - imuTime[imuPointerBack]);
float ratioBack = (imuTime[imuPointerFront] - timeScanCur - pointTime)
/ (imuTime[imuPointerFront] - imuTime[imuPointerBack]);
imuRollCur = imuRoll[imuPointerFront] * ratioFront + imuRoll[imuPointerBack] * ratioBack;
imuPitchCur = imuPitch[imuPointerFront] * ratioFront + imuPitch[imuPointerBack] * ratioBack;
if (imuYaw[imuPointerFront] - imuYaw[imuPointerBack] > M_PI) {
imuYawCur = imuYaw[imuPointerFront] * ratioFront + (imuYaw[imuPointerBack] + 2 * M_PI) * ratioBack;
} else if (imuYaw[imuPointerFront] - imuYaw[imuPointerBack] < -M_PI) {
imuYawCur = imuYaw[imuPointerFront] * ratioFront + (imuYaw[imuPointerBack] - 2 * M_PI) * ratioBack;
} else {
imuYawCur = imuYaw[imuPointerFront] * ratioFront + imuYaw[imuPointerBack] * ratioBack;
}
//本质:imuVeloXCur = imuVeloX[imuPointerback] + (imuVelX[imuPointerFront]-imuVelX[imuPoniterBack])*ratioFront
imuVeloXCur = imuVeloX[imuPointerFront] * ratioFront + imuVeloX[imuPointerBack] * ratioBack;
imuVeloYCur = imuVeloY[imuPointerFront] * ratioFront + imuVeloY[imuPointerBack] * ratioBack;
imuVeloZCur = imuVeloZ[imuPointerFront] * ratioFront + imuVeloZ[imuPointerBack] * ratioBack;
imuShiftXCur = imuShiftX[imuPointerFront] * ratioFront + imuShiftX[imuPointerBack] * ratioBack;
imuShiftYCur = imuShiftY[imuPointerFront] * ratioFront + imuShiftY[imuPointerBack] * ratioBack;
imuShiftZCur = imuShiftZ[imuPointerFront] * ratioFront + imuShiftZ[imuPointerBack] * ratioBack;
}
if (i == 0) {//如果是第一个点,记住点云起始位置的速度，位移，欧拉角
imuRollStart = imuRollCur;
imuPitchStart = imuPitchCur;
imuYawStart = imuYawCur;
imuVeloXStart = imuVeloXCur;
imuVeloYStart = imuVeloYCur;
imuVeloZStart = imuVeloZCur;
imuShiftXStart = imuShiftXCur;
imuShiftYStart = imuShiftYCur;
imuShiftZStart = imuShiftZCur;
} else {//计算之后每个点相对于第一个点的由于加减速非匀速运动产生的位移速度畸变，并对点云中的每个点位置信息重新补偿矫正
ShiftToStartIMU(pointTime);
VeloToStartIMU();
TransformToStartIMU(&point);
}
}
laserCloudScans[scanID].push_back(point);//将每个补偿矫正的点放入对应线号的容器
}
//获得有效范围内的点的数量
cloudSize = count;
pcl::PointCloud::Ptr laserCloud(new pcl::PointCloud());
for (int i = 0; i < N_SCANS; i++) {//将所有的点按照线号从小到大放入一个容器
*laserCloud += laserCloudScans[i];
}
int scanCount = -1;
for (int i = 5; i < cloudSize - 5; i++) {
//使用每个点的前后五个点计算曲率，因此前五个与最后五个点跳过，论文中c的计算
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;
//曲率计算
cloudCurvature[i] = diffX * diffX + diffY * diffY + diffZ * diffZ;
//记录曲率点的索引
cloudSortInd[i] = i;
//初始时，点全未筛选过
cloudNeighborPicked[i] = 0;
//初始化为less flat点
cloudLabel[i] = 0;
//每个scan，只有第一个符合的点会进来，因为每个scan的点都在一起存放
//这里是为了获得每一个线号激光束的开始点和结尾点索引(用于后续对光束分段处理)
if (int(laserCloud->points[i].intensity) != scanCount) {
scanCount = int(laserCloud->points[i].intensity);//控制每个scan只进入第一个点
//曲率只取同一个scan计算出来的，跨scan计算的曲率非法，排除，也即排除每个scan的前后五个点
if (scanCount > 0 && scanCount < N_SCANS) {
scanStartInd[scanCount] = i + 5;
scanEndInd[scanCount - 1] = i - 5;
}
}
}
//第一个scan曲率点有效点序从第5个开始，最后一个激光线结束点序size-5
scanStartInd[0] = 5;
scanEndInd.back() = cloudSize - 5;
//挑选点，排除容易被斜面挡住的点以及离群点，有些点容易被斜面挡住，而离群点可能出现带有偶然性，这些情况都可能导致前后两次扫描不能被同时看到
for (int i = 5; i < cloudSize - 6; i++) {//与后一个点差值，所以减6
float diffX = laserCloud->points[i + 1].x - laserCloud->points[i].x;
float diffY = laserCloud->points[i + 1].y - laserCloud->points[i].y;
float diffZ = laserCloud->points[i + 1].z - laserCloud->points[i].z;
//计算有效曲率点与后一个点之间的距离平方和
float diff = diffX * diffX + diffY * diffY + diffZ * diffZ;
if (diff > 0.1) {//前提:两个点之间距离要大于0.1
//点的深度
float depth1 = sqrt(laserCloud->points[i].x * laserCloud->points[i].x +
laserCloud->points[i].y * laserCloud->points[i].y +
laserCloud->points[i].z * laserCloud->points[i].z);
//后一个点的深度
float depth2 = sqrt(laserCloud->points[i + 1].x * laserCloud->points[i + 1].x +
laserCloud->points[i + 1].y * laserCloud->points[i + 1].y +
laserCloud->points[i + 1].z * laserCloud->points[i + 1].z);
//按照两点的深度的比例，将深度较大的点拉回后计算距离
if (depth1 > depth2) {
diffX = laserCloud->points[i + 1].x - laserCloud->points[i].x * depth2 / depth1;
diffY = laserCloud->points[i + 1].y - laserCloud->points[i].y * depth2 / depth1;
diffZ = laserCloud->points[i + 1].z - laserCloud->points[i].z * depth2 / depth1;
//边长比也即是弧度值，若小于0.1，说明夹角比较小，斜面比较陡峭,点深度变化比较剧烈,点处在近似与激光束平行的斜面上
//todo 近似计算，（弧度 = 弧长 / 半径）所以将深度大的拉回一点，是让两个点近似在一个圆上 0.1弧近似对应 (0.1 * 180 / M_PI) 5.7度
//todo 夹角小，说明斜面比较陡峭，点深度变化比较剧烈
if (sqrt(diffX * diffX + diffY * diffY + diffZ * diffZ) / depth2 < 0.1) {//排除容易被斜面挡住的点
//该点及前面五个点（大致都在斜面上）全部置为筛选过
cloudNeighborPicked[i - 5] = 1;
cloudNeighborPicked[i - 4] = 1;
cloudNeighborPicked[i - 3] = 1;
cloudNeighborPicked[i - 2] = 1;
cloudNeighborPicked[i - 1] = 1;
cloudNeighborPicked[i] = 1;
}
} else {
diffX = laserCloud->points[i + 1].x * depth1 / depth2 - laserCloud->points[i].x;
diffY = laserCloud->points[i + 1].y * depth1 / depth2 - laserCloud->points[i].y;
diffZ = laserCloud->points[i + 1].z * depth1 / depth2 - laserCloud->points[i].z;
//筛选后面几个点，自身不应该也排除吗？
if (sqrt(diffX * diffX + diffY * diffY + diffZ * diffZ) / depth1 < 0.1) {
cloudNeighborPicked[i + 1] = 1;
cloudNeighborPicked[i + 2] = 1;
cloudNeighborPicked[i + 3] = 1;
cloudNeighborPicked[i + 4] = 1;
cloudNeighborPicked[i + 5] = 1;
cloudNeighborPicked[i + 6] = 1;
}
}
}
float diffX2 = laserCloud->points[i].x - laserCloud->points[i - 1].x;
float diffY2 = laserCloud->points[i].y - laserCloud->points[i - 1].y;
float diffZ2 = laserCloud->points[i].z - laserCloud->points[i - 1].z;
//与前一个点的距离平方和
float diff2 = diffX2 * diffX2 + diffY2 * diffY2 + diffZ2 * diffZ2;
//点深度的平方和
float dis = laserCloud->points[i].x * laserCloud->points[i].x
+ laserCloud->points[i].y * laserCloud->points[i].y
+ laserCloud->points[i].z * laserCloud->points[i].z;
//与前后点的平方和都大于深度平方和的万分之二，这些点视为离群点，包括陡斜面上的点，强烈凸凹点和空旷区域中的某些点，置为筛选过，弃用
//这个0.0002系数可能是实验获得，个人猜的
if (diff > 0.0002 * dis && diff2 > 0.0002 * dis) {
cloudNeighborPicked[i] = 1;
}
}
pcl::PointCloud cornerPointsSharp;
pcl::PointCloud cornerPointsLessSharp;
pcl::PointCloud surfPointsFlat;
pcl::PointCloud surfPointsLessFlat;
//将每条线上的点分入相应的类别：边沿点和平面点
for (int i = 0; i < N_SCANS; i++) {
pcl::PointCloud::Ptr surfPointsLessFlatScan(new pcl::PointCloud);
//将每个scan的曲率点分成6等份处理,确保周围都有点被选作特征点，保证提取的特征点分布均匀
for (int j = 0; j < 6; j++) {
//六等份起点：sp = scanStartInd + (scanEndInd - scanStartInd)*j/6
int sp = (scanStartInd[i] * (6 - j) + scanEndInd[i] * j) / 6;
//六等份终点：ep = scanStartInd - 1 + (scanEndInd - scanStartInd)*(j+1)/6
int ep = (scanStartInd[i] * (5 - j) + scanEndInd[i] * (j + 1)) / 6 - 1;
//按曲率从小到大冒泡排序
for (int k = sp + 1; k <= ep; k++) {
for (int l = k; l >= sp + 1; l--) {
if (cloudCurvature[cloudSortInd[l]] < cloudCurvature[cloudSortInd[l - 1]]) {
int temp = cloudSortInd[l - 1];
cloudSortInd[l - 1] = cloudSortInd[l];
cloudSortInd[l] = temp;
}
}
}
//挑选每个分段的曲率很大和比较大的点
int largestPickedNum = 0;
for (int k = ep; k >= sp; k--) {
int ind = cloudSortInd[k]; //曲率最大点的点序
//如果曲率大的点，曲率的确比较大，并且未被筛选过滤掉
if (cloudNeighborPicked[ind] == 0 &&
cloudCurvature[ind] > 0.1) {
largestPickedNum++;
if (largestPickedNum <= 2) {//挑选曲率最大的前2个点放入sharp点集合
cloudLabel[ind] = 2;//2代表点曲率很大
cornerPointsSharp.push_back(laserCloud->points[ind]);
cornerPointsLessSharp.push_back(laserCloud->points[ind]);
} else if (largestPickedNum <= 20) {//挑选曲率最大的前20个点放入less sharp点集合
cloudLabel[ind] = 1;//1代表点曲率比较尖锐
cornerPointsLessSharp.push_back(laserCloud->points[ind]);
} else {
break;
}
cloudNeighborPicked[ind] = 1;//筛选标志置位
//将曲率比较大的点的前后各5个连续距离比较近的点筛选出去(计算相邻两点距离)，防止特征点聚集，使得特征点在每个方向上尽量分布均匀
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;
}
//同理
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;
}
}
}
//挑选每个分段的曲率很小比较小的点
int smallestPickedNum = 0;
for (int k = sp; k <= ep; k++) {
int ind = cloudSortInd[k];
//如果曲率的确比较小，并且未被筛选出
if (cloudNeighborPicked[ind] == 0 &&
cloudCurvature[ind] < 0.1) {
cloudLabel[ind] = -1;//-1代表曲率很小的点
surfPointsFlat.push_back(laserCloud->points[ind]);
smallestPickedNum++;
if (smallestPickedNum >= 4) {//只选最小的四个，剩下的Label==0,就都是曲率比较小的
break;
}
cloudNeighborPicked[ind] = 1;
for (int l = 1; l <= 5; l++) {//同样防止特征点聚集
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;
}
}
}
//将剩余的点（包括之前被排除的点）全部归入平面点中less flat类别中
for (int k = sp; k <= ep; k++) {
if (cloudLabel[k] <= 0) {
surfPointsLessFlatScan->push_back(laserCloud->points[k]);
}
}
}
//由于less flat点最多，对每个分段less flat的点进行体素栅格滤波
pcl::PointCloud surfPointsLessFlatScanDS;
pcl::VoxelGrid downSizeFilter;
downSizeFilter.setInputCloud(surfPointsLessFlatScan);
downSizeFilter.setLeafSize(0.2, 0.2, 0.2);
downSizeFilter.filter(surfPointsLessFlatScanDS);
//less flat点汇总
surfPointsLessFlat += surfPointsLessFlatScanDS;
}
} 剩下还有一些简单的发布，就不详细解释了。
如果有问题，欢迎下方留言。
LOAM源码结合论文解析（二）laserOdometry