上一次我们已经看完了特征提取,这次我们进入特征匹配的内容!!!
/**
2. Feature Association
*/
if (!systemInitedLM) {
checkSystemInitialization();
return;
}
updateInitialGuess();
updateTransformation();
integrateTransformation();
publishOdometry();
publishCloudsLast(); // cloud to mapOptimization
}
systemInitedLM = false; systemInitedLM的初始值为false。
一、checkSystemInitialization()
void checkSystemInitialization(){
pcl::PointCloud<PointType>::Ptr laserCloudTemp = cornerPointsLessSharp;
cornerPointsLessSharp = laserCloudCornerLast;
laserCloudCornerLast = laserCloudTemp;
laserCloudTemp = surfPointsLessFlat;
surfPointsLessFlat = laserCloudSurfLast;
laserCloudSurfLast = laserCloudTemp;
kdtreeCornerLast->setInputCloud(laserCloudCornerLast);
kdtreeSurfLast->setInputCloud(laserCloudSurfLast);
// 使用交换后的 laserCloudCornerLast 和 laserCloudSurfLast 更新两个KD树(kdtreeCornerLast 和 kdtreeSurfLast)的输入云数据。
// KD树是用于加速最近邻搜索的数据结构,这里可能用于后续的点云匹配和处理。
laserCloudCornerLastNum = laserCloudCornerLast->points.size();
laserCloudSurfLastNum = laserCloudSurfLast->points.size();
sensor_msgs::PointCloud2 laserCloudCornerLast2;
pcl::toROSMsg(*laserCloudCornerLast, laserCloudCornerLast2);
laserCloudCornerLast2.header.stamp = cloudHeader.stamp;
laserCloudCornerLast2.header.frame_id = "camera";
pubLaserCloudCornerLast.publish(laserCloudCornerLast2);
sensor_msgs::PointCloud2 laserCloudSurfLast2;
pcl::toROSMsg(*laserCloudSurfLast, laserCloudSurfLast2);
laserCloudSurfLast2.header.stamp = cloudHeader.stamp;
laserCloudSurfLast2.header.frame_id = "camera";
pubLaserCloudSurfLast.publish(laserCloudSurfLast2);
transformSum[0] += imuPitchStart;
transformSum[2] += imuRollStart;
systemInitedLM = true;
}
将cornerPointsLessSharp的值存入laserCloudCornerLast,并发布。
将surfPointsLessFlat的值存入laserCloudSurfLast,并发布。
transformSun[i]=0,原来初值为0。
更新transformSum的值,有个问题:为什么不更新transformSum【1】??
transformSum[0] += imuPitchStart;
transformSum[2] += imuRollStart;
将systemInitedLM设置为true1,然后进入到特征匹配。
二、updateInitialGuess()
void updateInitialGuess(){
imuPitchLast = imuPitchCur;
imuYawLast = imuYawCur;
imuRollLast = imuRollCur;
imuShiftFromStartX = imuShiftFromStartXCur;
imuShiftFromStartY = imuShiftFromStartYCur;
imuShiftFromStartZ = imuShiftFromStartZCur;
imuVeloFromStartX = imuVeloFromStartXCur;
imuVeloFromStartY = imuVeloFromStartYCur;
imuVeloFromStartZ = imuVeloFromStartZCur;
if (imuAngularFromStartX != 0 || imuAngularFromStartY != 0 || imuAngularFromStartZ != 0){
transformCur[0] = - imuAngularFromStartY;
transformCur[1] = - imuAngularFromStartZ;
transformCur[2] = - imuAngularFromStartX;
}
if (imuVeloFromStartX != 0 || imuVeloFromStartY != 0 || imuVeloFromStartZ != 0){
transformCur[3] -= imuVeloFromStartX * scanPeriod;
transformCur[4] -= imuVeloFromStartY * scanPeriod;
transformCur[5] -= imuVeloFromStartZ * scanPeriod;
//extern const float scanPeriod = 0.1;
}
}
更新imu的值,imuPitchCur、imuYawCur、imuRollCur等值都是在adjustDistortion()函数中,通过插值得到的。
注意这里坐标轴的变换与对应:
transformCur[0] = - imuAngularFromStartY;
transformCur[1] = - imuAngularFromStartZ;
transformCur[2] = - imuAngularFromStartX;
这里的imuAngularFromStartY和imuVeloFromStartX都是累积值,但是累积的方式很简单,很粗糙:
imuShiftX[imuPointerLast] = imuShiftX[imuPointerBack] + imuVeloX[imuPointerBack] * timeDiff + accX * timeDiff * timeDiff / 2;
imuShiftY[imuPointerLast] = imuShiftY[imuPointerBack] + imuVeloY[imuPointerBack] * timeDiff + accY * timeDiff * timeDiff / 2;
imuShiftZ[imuPointerLast] = imuShiftZ[imuPointerBack] + imuVeloZ[imuPointerBack] * timeDiff + accZ * timeDiff * timeDiff / 2;
imuVeloX[imuPointerLast] = imuVeloX[imuPointerBack] + accX * timeDiff;
imuVeloY[imuPointerLast] = imuVeloY[imuPointerBack] + accY * timeDiff;
imuVeloZ[imuPointerLast] = imuVeloZ[imuPointerBack] + accZ * timeDiff;
imuAngularRotationX[imuPointerLast] = imuAngularRotationX[imuPointerBack] + imuAngularVeloX[imuPointerBack] * timeDiff;
imuAngularRotationY[imuPointerLast] = imuAngularRotationY[imuPointerBack] + imuAngularVeloY[imuPointerBack] * timeDiff;
imuAngularRotationZ[imuPointerLast] = imuAngularRotationZ[imuPointerBack] + imuAngularVeloZ[imuPointerBack] * timeDiff;
三、更新变换矩阵
updateTransformation()
void updateTransformation(){
if (laserCloudCornerLastNum < 10 || laserCloudSurfLastNum < 100)
return;
for (int iterCount1 = 0; iterCount1 < 25; iterCount1++) {
laserCloudOri->clear();
coeffSel->clear();
findCorrespondingSurfFeatures(iterCount1);
if (laserCloudOri->points.size() < 10)
continue;
if (calculateTransformationSurf(iterCount1) == false)
break;
}
for (int iterCount2 = 0; iterCount2 < 25; iterCount2++) {
laserCloudOri->clear();
coeffSel->clear();
findCorrespondingCornerFeatures(iterCount2);
if (laserCloudOri->points.size() < 10)
continue;
if (calculateTransformat