LIO-SAM探秘第三章之代码解析（五） --- imuPreintegration.cpp
本文阅读的代码为2020年11月1日下载的github的最新master。
如果代码后续更新了请以github为准。
1 main()
int main(int argc, char** argv)
{
ros::init(argc, argv, "roboat_loam");
IMUPreintegration ImuP;
TransformFusion TF;
ROS_INFO("\033[1;32m----> IMU Preintegration Started.\033[0m");
ros::MultiThreadedSpinner spinner(4);
spinner.spin();
return 0;
}
2 IMUPreintegration类
2.1 成员变量
class IMUPreintegration : public ParamServer
{
public:
std::mutex mtx;
ros::Subscriber subImu;
ros::Subscriber subOdometry;
ros::Publisher pubImuOdometry;
bool systemInitialized = false;
gtsam::noiseModel::Diagonal::shared_ptr priorPoseNoise;
gtsam::noiseModel::Diagonal::shared_ptr priorVelNoise;
gtsam::noiseModel::Diagonal::shared_ptr priorBiasNoise;
gtsam::noiseModel::Diagonal::shared_ptr correctionNoise;
gtsam::noiseModel::Diagonal::shared_ptr correctionNoise2;
gtsam::Vector noiseModelBetweenBias;
gtsam::PreintegratedImuMeasurements *imuIntegratorOpt_;
gtsam::PreintegratedImuMeasurements *imuIntegratorImu_;
std::deque<sensor_msgs::Imu> imuQueOpt;
std::deque<sensor_msgs::Imu> imuQueImu;
gtsam::Pose3 prevPose_;
gtsam::Vector3 prevVel_;
gtsam::NavState prevState_;
gtsam::imuBias::ConstantBias prevBias_;
gtsam::NavState prevStateOdom;
gtsam::imuBias::ConstantBias prevBiasOdom;
bool doneFirstOpt = false;
double lastImuT_imu = -1;
double lastImuT_opt = -1;
gtsam::ISAM2 optimizer;
gtsam::NonlinearFactorGraph graphFactors;
gtsam::Values graphValues;
const double delta_t = 0;
int key = 1;
gtsam::Pose3 imu2Lidar = gtsam::Pose3(gtsam::Rot3(1, 0, 0, 0), gtsam::Point3(-extTrans.x(), -extTrans.y(), -extTrans.z()));
gtsam::Pose3 lidar2Imu = gtsam::Pose3(gtsam::Rot3(1, 0, 0, 0), gtsam::Point3(extTrans.x(), extTrans.y(), extTrans.z()));
};
2.2 构造函数
IMUPreintegration()
{
// imuTopic 为 imu_correct, imu原始数据
subImu = nh.subscribe<sensor_msgs::Imu>(imuTopic, 2000, &IMUPreintegration::imuHandler, this, ros::TransportHints().tcpNoDelay());
// lio_sam/mapping/odometry_incremental 是mapOptmization发出的
subOdometry = nh.subscribe<nav_msgs::Odometry>("lio_sam/mapping/odometry_incremental", 5, &IMUPreintegration::odometryHandler, this, ros::TransportHints().tcpNoDelay());
// 发布 odometry/imu_incremental
pubImuOdometry = nh.advertise<nav_msgs::Odometry>(odomTopic + "_incremental", 2000);
// 下面是预积分使用到的gtsam的一些参数配置
boost::shared_ptr<gtsam::PreintegrationParams> p = gtsam::PreintegrationParams::MakeSharedU(imuGravity);
p->accelerometerCovariance = gtsam::Matrix33::Identity(3, 3) * pow(imuAccNoise, 2); // acc white noise in continuous
p->gyroscopeCovariance = gtsam::Matrix33::Identity(3, 3) * pow(imuGyrNoise, 2); // gyro white noise in continuous
p->integrationCovariance = gtsam::Matrix33::Identity(3, 3) * pow(1e-4, 2); // error committed in integrating position from velocities
gtsam::imuBias::ConstantBias prior_imu_bias((gtsam::Vector(6) << 0, 0, 0, 0, 0, 0).finished());
; // assume zero initial bias
priorPoseNoise = gtsam::noiseModel::Diagonal::Sigmas((gtsam::Vector(6) << 1e-2, 1e-2, 1e-2, 1e-2, 1e-2, 1e-2).finished()); // rad,rad,rad,m, m, m
priorVelNoise = gtsam::noiseModel::Isotropic::Sigma(3, 1e4); // m/s
priorBiasNoise = gtsam::noiseModel::Isotropic::Sigma(6, 1e-3); // 1e-2 ~ 1e-3 seems to be good
correctionNoise = gtsam::noiseModel::Diagonal::Sigmas((gtsam::Vector(6) << 0.05, 0.05, 0.05, 0.1, 0.1, 0.1).finished()); // rad,rad,rad,m, m, m
correctionNoise2 = gtsam::noiseModel::Diagonal::Sigmas((gtsam::Vector(6) << 1, 1, 1, 1, 1, 1).finished()); // rad,rad,rad,m, m, m
noiseModelBetweenBias = (gtsam::Vector(6) << imuAccBiasN, imuAccBiasN, imuAccBiasN, imuGyrBiasN, imuGyrBiasN, imuGyrBiasN).finished();
imuIntegratorImu_ = new gtsam::PreintegratedImuMeasurements(p, prior_imu_bias); // setting up the IMU integration for IMU message thread
imuIntegratorOpt_ = new gtsam::PreintegratedImuMeasurements(p, prior_imu_bias); // setting up the IMU integration for optimization
}
2.3 resetOptimization()
// gtsam相关优化参数重置与初始化
void resetOptimization()
{
gtsam::ISAM2Params optParameters;
optParameters.relinearizeThreshold = 0.1;
optParameters.relinearizeSkip = 1;
optimizer = gtsam::ISAM2(optParameters);
gtsam::NonlinearFactorGraph newGraphFactors;
graphFactors = newGraphFactors;
gtsam::Values NewGraphValues;
graphValues = NewGraphValues;
}
2.4 resetParams()
// 对这几个变量进行重置
void resetParams()
{
lastImuT_imu = -1;
doneFirstOpt = false;
systemInitialized = false;
}
2.5 imuHandler()
// 使用 gtsam 对imu进行预积分，之后对odom进行预测，发布预测后的odometry/imu_incremental
void imuHandler(const sensor_msgs::Imu::ConstPtr &imu_raw)
{
std::lock_guard<std::mutex> lock(mtx);
// imu数据转换到雷达坐标系下
sensor_msgs::Imu thisImu = imuConverter(*imu_raw);
// 两个双端队列分别装着优化前后的imu数据
imuQueOpt.push_back(thisImu);
imuQueImu.push_back(thisImu);
// 执行一次优化之后才会进行后续的预测
if (doneFirstOpt == false)
return;
// 获得imu的时间间隔, 第一次为 1/500,之后是两次imuTime间的差
double imuTime = ROS_TIME(&thisImu);
double dt = (lastImuT_imu < 0) ? (1.0 / 500.0) : (imuTime - lastImuT_imu);
lastImuT_imu = imuTime;
// 进行预积分 integrate this single imu message
imuIntegratorImu_->integrateMeasurement(gtsam::Vector3(thisImu.linear_acceleration.x, thisImu.linear_acceleration.y, thisImu.linear_acceleration.z),
gtsam::Vector3(thisImu.angular_velocity.x, thisImu.angular_velocity.y, thisImu.angular_velocity.z),
dt);
// 根据预计分结果, 预测odom
gtsam::NavState currentState = imuIntegratorImu_->predict(prevStateOdom, prevBiasOdom);
// publish odometry 发布预测的odom
nav_msgs::Odometry odometry;
odometry.header.stamp = thisImu.header.stamp;
odometry.header.frame_id = odometryFrame;
odometry.child_frame_id = "odom_imu";
// transform imu pose to ldiar
// 预测值currentState获得imu位姿, 再由imu到雷达变换, 获得雷达位姿 lidarPose
gtsam::Pose3 imuPose = gtsam::Pose3(currentState.quaternion(), currentState.position());
gtsam::Pose3 lidarPose = imuPose.compose(imu2Lidar);
odometry.pose.pose.position.x = lidarPose.translation().x();
odometry.pose.pose.position.y = lidarPose.translation().y();
odometry.pose.pose.position.z = lidarPose.translation().z();
odometry.pose.pose.orientation.x = lidarPose.rotation().toQuaternion().x();
odometry.pose.pose.orientation.y = lidarPose.rotation().toQuaternion().y();
odometry.pose.pose.orientation.z = lidarPose.rotation().toQuaternion().z();
odometry.pose.pose.orientation.w = lidarPose.rotation().toQuaternion().w();
odometry.twist.twist.linear.x = currentState.velocity().x();
odometry.twist.twist.linear.y = currentState.velocity().y();
odometry.twist.twist.linear.z = currentState.velocity().z();
odometry.twist.twist.angular.x = thisImu.angular_velocity.x + prevBiasOdom.gyroscope().x();
odometry.twist.twist.angular.y = thisImu.angular_velocity.y + prevBiasOdom.gyroscope().y();
odometry.twist.twist.angular.z = thisImu.angular_velocity.z + prevBiasOdom.gyroscope().z();
pubImuOdometry.publish(odometry);
}
2.6 odometryHandler()
//
void odometryHandler(const nav_msgs::Odometry::ConstPtr &odomMsg)
{
std::lock_guard<std::mutex> lock(mtx);
// 里程计消息的当前时间戳
double currentCorrectionTime = ROS_TIME(odomMsg);
// make sure we have imu data to integrate
if (imuQueOpt.empty())
return;
float p_x = odomMsg->pose.pose.position.x;
float p_y = odomMsg->pose.pose.position.y;
float p_z = odomMsg->pose.pose.position.z;
float r_x = odomMsg->pose.pose.orientation.x;
float r_y = odomMsg->pose.pose.orientation.y;
float r_z = odomMsg->pose.pose.orientation.z;
float r_w = odomMsg->pose.pose.orientation.w;
bool degenerate = (int)odomMsg->pose.covariance[0] == 1 ? true : false;
// 得到雷达的位姿
gtsam::Pose3 lidarPose = gtsam::Pose3(gtsam::Rot3::Quaternion(r_w, r_x, r_y, r_z), gtsam::Point3(p_x, p_y, p_z));
// 0. initialize system
// 只执行一次, 初始化系统
if (systemInitialized == false)
{
// 优化参数重置
resetOptimization();
// pop old IMU message
// 推出相对较旧的imu消息 保证imu与odometry消息时间同步 因为imu是高频数据所以这是必要的
// 整个LIO-SAM中作者对时间同步这块的思想都是这样的
while (!imuQueOpt.empty())
{
// delta_t = 0
if (ROS_TIME(&imuQueOpt.front()) < currentCorrectionTime - delta_t)
{
lastImuT_opt = ROS_TIME(&imuQueOpt.front());
imuQueOpt.pop_front();
}
else
break;
}
// initial pose
// 将激光里程计提供的位姿 转到imu坐标系下
prevPose_ = lidarPose.compose(lidar2Imu);
// 一元因子，系统先验 - 添加位姿因子
gtsam::PriorFactor<gtsam::Pose3> priorPose(X(0), prevPose_, priorPoseNoise);
graphFactors.add(priorPose);
// initial velocity - 添加速度因子
prevVel_ = gtsam::Vector3(0, 0, 0);
gtsam::PriorFactor<gtsam::Vector3> priorVel(V(0), prevVel_, priorVelNoise);
graphFactors.add(priorVel);
// initial bias - 添加偏差因子
prevBias_ = gtsam::imuBias::ConstantBias();
gtsam::PriorFactor<gtsam::imuBias::ConstantBias> priorBias(B(0), prevBias_, priorBiasNoise);
graphFactors.add(priorBias);
// 除了因子外 还要有节点value
// add values
graphValues.insert(X(0), prevPose_);
graphValues.insert(V(0), prevVel_);
graphValues.insert(B(0), prevBias_);
// optimize once 进行一次优化
optimizer.update(graphFactors, graphValues);
graphFactors.resize(0);
graphValues.clear();
// 积分器重置
imuIntegratorImu_->resetIntegrationAndSetBias(prevBias_);
imuIntegratorOpt_->resetIntegrationAndSetBias(prevBias_);
key = 1;
systemInitialized = true;
return;
}
// reset graph for speed
// 如果key达到100 重置整个图 减小计算压力 加快速度
// 保存最后的噪声值
if (key == 100)
{
// get updated noise before reset
gtsam::noiseModel::Gaussian::shared_ptr updatedPoseNoise = gtsam::noiseModel::Gaussian::Covariance(optimizer.marginalCovariance(X(key - 1)));
gtsam::noiseModel::Gaussian::shared_ptr updatedVelNoise = gtsam::noiseModel::Gaussian::Covariance(optimizer.marginalCovariance(V(key - 1)));
gtsam::noiseModel::Gaussian::shared_ptr updatedBiasNoise = gtsam::noiseModel::Gaussian::Covariance(optimizer.marginalCovariance(B(key - 1)));
// reset graph
resetOptimization();
// 重置之后还有类似与初始化的过程 区别在于噪声值不同
// add pose
gtsam::PriorFactor<gtsam::Pose3> priorPose(X(0), prevPose_, updatedPoseNoise);
graphFactors.add(priorPose);
// add velocity
gtsam::PriorFactor<gtsam::Vector3> priorVel(V(0), prevVel_, updatedVelNoise);
graphFactors.add(priorVel);
// add bias
gtsam::PriorFactor<gtsam::imuBias::ConstantBias> priorBias(B(0), prevBias_, updatedBiasNoise);
graphFactors.add(priorBias);
// add values
graphValues.insert(X(0), prevPose_);
graphValues.insert(V(0), prevVel_);
graphValues.insert(B(0), prevBias_);
// optimize once
optimizer.update(graphFactors, graphValues);
graphFactors.resize(0);
graphValues.clear();
key = 1;
}
// 1. integrate imu data and optimize
while (!imuQueOpt.empty())
{
// pop and integrate imu data that is between two optimizations
sensor_msgs::Imu *thisImu = &imuQueOpt.front();
double imuTime = ROS_TIME(thisImu);
// 对早于当前odom数据的imu数据进行积分，imu为观测值
if (imuTime < currentCorrectionTime - delta_t) // delta_t = 0
{
double dt = (lastImuT_opt < 0) ? (1.0 / 500.0) : (imuTime - lastImuT_opt);
// 进行预积分得到新的状态值 注意用到的是imu数据的加速度 角速度
// 作者要求的9轴imu数据中欧拉角在本程序文件中没有任何用到 全在地图优化里用到的
// 这个integrateMeasurement还不懂 ？？？
imuIntegratorOpt_->integrateMeasurement(
gtsam::Vector3(thisImu->linear_acceleration.x, thisImu->linear_acceleration.y, thisImu->linear_acceleration.z),
gtsam::Vector3(thisImu->angular_velocity.x, thisImu->angular_velocity.y, thisImu->angular_velocity.z),
dt);
// 在推出一次数据前保存上一个数据的时间戳
lastImuT_opt = imuTime;
imuQueOpt.pop_front();
}
else
break;
}
// 利用两帧之间的IMU数据完成了预积分后 增加imu因子 到因子图中
// add imu factor to graph
const gtsam::PreintegratedImuMeasurements &preint_imu =
dynamic_cast<const gtsam::PreintegratedImuMeasurements &>(*imuIntegratorOpt_);
gtsam::ImuFactor imu_factor(X(key - 1), V(key - 1), X(key), V(key), B(key - 1), preint_imu);
graphFactors.add(imu_factor);
// add imu bias between factor 二元因子，位姿之间，回环之间
graphFactors.add(gtsam::BetweenFactor<gtsam::imuBias::ConstantBias>(B(key - 1), B(key), gtsam::imuBias::ConstantBias(),
gtsam::noiseModel::Diagonal::Sigmas(
sqrt(imuIntegratorOpt_->deltaTij()) * noiseModelBetweenBias)));
// add pose factor
// 还加入了pose factor 其实对应于作者论文中的因子图结构 就是与imu因子一起的 Lidar odometry factor
gtsam::Pose3 curPose = lidarPose.compose(lidar2Imu);
gtsam::PriorFactor<gtsam::Pose3> pose_factor(X(key), curPose, degenerate ? correctionNoise2 : correctionNoise);
graphFactors.add(pose_factor);
// insert predicted values
// 插入预测的值 即因子图中x0 x1 x2 ……节点
gtsam::NavState propState_ = imuIntegratorOpt_->predict(prevState_, prevBias_);
graphValues.insert(X(key), propState_.pose());
graphValues.insert(V(key), propState_.v());
graphValues.insert(B(key), prevBias_);
// optimize 进行优化
optimizer.update(graphFactors, graphValues);
optimizer.update();
// 优化完成后重置
graphFactors.resize(0);
graphValues.clear();
// Overwrite the beginning of the preintegration for the next step.
// 用这次的优化结果重写或者说是覆盖相关初始值 为下一次优化准备
gtsam::Values result = optimizer.calculateEstimate();
prevPose_ = result.at<gtsam::Pose3>(X(key));
prevVel_ = result.at<gtsam::Vector3>(V(key));
prevState_ = gtsam::NavState(prevPose_, prevVel_);
prevBias_ = result.at<gtsam::imuBias::ConstantBias>(B(key));
// Reset the optimization preintegration object.
imuIntegratorOpt_->resetIntegrationAndSetBias(prevBias_);
// check optimization
// 检查是否有失败情况 如有则重置参数
if (failureDetection(prevVel_, prevBias_))
{
resetParams();
return;
}
// 2. after optiization, re-propagate imu odometry preintegration
/*为了维持实时性imuIntegrateImu就得在每次odom触发优化后立刻获取最新的bias
同时对imu测量值imuQueImu执行bias改变的状态重传播处理
这样可以最大限度的保证实时性和准确性。*/
prevStateOdom = prevState_;
prevBiasOdom = prevBias_;
// first pop imu message older than current correction data
double lastImuQT = -1;
// 上边的while弹出了imuQueOpt中时间更早的，这里是对imuQueImu中时间更早的进行弹出
while (!imuQueImu.empty() && ROS_TIME(&imuQueImu.front()) < currentCorrectionTime - delta_t)
{
lastImuQT = ROS_TIME(&imuQueImu.front());
imuQueImu.pop_front();
}
// repropogate
// 使用同样的imu数据进行了2次预积分，bias值不一样了
if (!imuQueImu.empty())
{
// reset bias use the newly optimized bias
// 使用最新的优化后的bias更新bias值
imuIntegratorImu_->resetIntegrationAndSetBias(prevBiasOdom);
// integrate imu message from the beginning of this optimization
for (int i = 0; i < (int)imuQueImu.size(); ++i)
{
// 利用imuQueImu中的数据进行预积分 主要区别旧在于上一行的更新了bias
sensor_msgs::Imu *thisImu = &imuQueImu[i];
double imuTime = ROS_TIME(thisImu);
double dt = (lastImuQT < 0) ? (1.0 / 500.0) : (imuTime - lastImuQT);
imuIntegratorImu_->integrateMeasurement(gtsam::Vector3(thisImu->linear_acceleration.x, thisImu->linear_acceleration.y, thisImu->linear_acceleration.z),
gtsam::Vector3(thisImu->angular_velocity.x, thisImu->angular_velocity.y, thisImu->angular_velocity.z),
dt);
lastImuQT = imuTime;
}
}
++key;
doneFirstOpt = true;
}
2.7 failureDetection()
// 速度或者偏差过大，认为优化失败了，需要重置优化参数
bool failureDetection(const gtsam::Vector3 &velCur, const gtsam::imuBias::ConstantBias &biasCur)
{
Eigen::Vector3f vel(velCur.x(), velCur.y(), velCur.z());
if (vel.norm() > 30)
{
ROS_WARN("Large velocity, reset IMU-preintegration!");
return true;
}
Eigen::Vector3f ba(biasCur.accelerometer().x(), biasCur.accelerometer().y(), biasCur.accelerometer().z());
Eigen::Vector3f bg(biasCur.gyroscope().x(), biasCur.gyroscope().y(), biasCur.gyroscope().z());
if (ba.norm() > 1.0 || bg.norm() > 1.0)
{
ROS_WARN("Large bias, reset IMU-preintegration!");
return true;
}
return false;
}
3 TransformFusion类
3.1 成员变量
class TransformFusion : public ParamServer
{
public:
std::mutex mtx;
ros::Subscriber subImuOdometry;
ros::Subscriber subLaserOdometry;
ros::Publisher pubImuOdometry;
ros::Publisher pubImuPath;
Eigen::Affine3f lidarOdomAffine;
Eigen::Affine3f imuOdomAffineFront;
Eigen::Affine3f imuOdomAffineBack;
tf::TransformListener tfListener;
tf::StampedTransform lidar2Baselink;
double lidarOdomTime = -1;
deque<nav_msgs::Odometry> imuOdomQueue;
};
3.2 构造函数
{
if (lidarFrame != baselinkFrame)
{
try
{
tfListener.waitForTransform(lidarFrame, baselinkFrame, ros::Time(0), ros::Duration(3.0));
tfListener.lookupTransform(lidarFrame, baselinkFrame, ros::Time(0), lidar2Baselink);
}
catch (tf::TransformException ex)
{
ROS_ERROR("%s", ex.what());
}
}
// 订阅激光里程计lio_sam/mapping/odometry 和 imu数据odometry/imu_incremental
subLaserOdometry = nh.subscribe<nav_msgs::Odometry>("lio_sam/mapping/odometry", 5, &TransformFusion::lidarOdometryHandler, this, ros::TransportHints().tcpNoDelay());
subImuOdometry = nh.subscribe<nav_msgs::Odometry>(odomTopic + "_incremental", 2000, &TransformFusion::imuOdometryHandler, this, ros::TransportHints().tcpNoDelay());
// publisher 发布融合后的imu path和预积分完成优化后预测的 odometry/imu
pubImuOdometry = nh.advertise<nav_msgs::Odometry>(odomTopic, 2000);
pubImuPath = nh.advertise<nav_msgs::Path>("lio_sam/imu/path", 1);
}
3.3 odom2affine()
// 将odom数据转换成Affine3f
Eigen::Affine3f odom2affine(nav_msgs::Odometry odom)
{
double x, y, z, roll, pitch, yaw;
x = odom.pose.pose.position.x;
y = odom.pose.pose.position.y;
z = odom.pose.pose.position.z;
tf::Quaternion orientation;
tf::quaternionMsgToTF(odom.pose.pose.orientation, orientation);
tf::Matrix3x3(orientation).getRPY(roll, pitch, yaw);
return pcl::getTransformation(x, y, z, roll, pitch, yaw);
}
3.4 lidarOdometryHandler()
// 保存当前的雷达里程计数据与时间
void lidarOdometryHandler(const nav_msgs::Odometry::ConstPtr &odomMsg)
{
std::lock_guard<std::mutex> lock(mtx);
lidarOdomAffine = odom2affine(*odomMsg);
lidarOdomTime = odomMsg->header.stamp.toSec();
}
3.5 imuOdometryHandler()
// 将imu_odom中的xyz, 用odom乘以imu的变换求得的xyz进行替换
// 发布map->odom->base_link 的 tf
void imuOdometryHandler(const nav_msgs::Odometry::ConstPtr &odomMsg)
{
// static tf 初始时刻的map->odom的tf为 (0,0,0)
static tf::TransformBroadcaster tfMap2Odom;
static tf::Transform map_to_odom = tf::Transform(tf::createQuaternionFromRPY(0, 0, 0),
tf::Vector3(0, 0, 0));
// 发布map->odom的tf，map_to_odom这个值在之后没有进行改变
// 也就是说map->odom的tf始终为 (0,0,0)
tfMap2Odom.sendTransform(tf::StampedTransform(map_to_odom, odomMsg->header.stamp, mapFrame, odometryFrame));
std::lock_guard<std::mutex> lock(mtx);
// 存入队列
imuOdomQueue.push_back(*odomMsg);
// get latest odometry (at current IMU stamp)
if (lidarOdomTime == -1)
return;
// 时间同步，找到imuOdomQueue的时间在lidarOdomTime之后的数据
while (!imuOdomQueue.empty())
{
if (imuOdomQueue.front().header.stamp.toSec() <= lidarOdomTime)
imuOdomQueue.pop_front();
else
break;
}
// 利用imu队列首尾之间的增量式变换获得最终里程计仿射矩阵（地图优化程序中发出的里程计*imu里程计增量）
Eigen::Affine3f imuOdomAffineFront = odom2affine(imuOdomQueue.front());
Eigen::Affine3f imuOdomAffineBack = odom2affine(imuOdomQueue.back());
Eigen::Affine3f imuOdomAffineIncre = imuOdomAffineFront.inverse() * imuOdomAffineBack;
Eigen::Affine3f imuOdomAffineLast = lidarOdomAffine * imuOdomAffineIncre;
float x, y, z, roll, pitch, yaw;
pcl::getTranslationAndEulerAngles(imuOdomAffineLast, x, y, z, roll, pitch, yaw);
// publish latest odometry 发布最新的里程计
nav_msgs::Odometry laserOdometry = imuOdomQueue.back();
laserOdometry.pose.pose.position.x = x;
laserOdometry.pose.pose.position.y = y;
laserOdometry.pose.pose.position.z = z;
laserOdometry.pose.pose.orientation = tf::createQuaternionMsgFromRollPitchYaw(roll, pitch, yaw);
pubImuOdometry.publish(laserOdometry);
// publish tf
// 根据算出来的里程计，发布odom->base_link的tf
static tf::TransformBroadcaster tfOdom2BaseLink;
tf::Transform tCur;
tf::poseMsgToTF(laserOdometry.pose.pose, tCur);
if (lidarFrame != baselinkFrame)
tCur = tCur * lidar2Baselink;
tf::StampedTransform odom_2_baselink = tf::StampedTransform(tCur, odomMsg->header.stamp, odometryFrame, baselinkFrame);
tfOdom2BaseLink.sendTransform(odom_2_baselink);
// publish IMU path
// 发布所有数据融合后的path
static nav_msgs::Path imuPath;
static double last_path_time = -1;
double imuTime = imuOdomQueue.back().header.stamp.toSec();
if (imuTime - last_path_time > 0.1)
{
last_path_time = imuTime;
geometry_msgs::PoseStamped pose_stamped;
pose_stamped.header.stamp = imuOdomQueue.back().header.stamp;
pose_stamped.header.frame_id = odometryFrame;
pose_stamped.pose = laserOdometry.pose.pose;
imuPath.poses.push_back(pose_stamped);
while (!imuPath.poses.empty() &&
imuPath.poses.front().header.stamp.toSec() < lidarOdomTime - 1.0)
imuPath.poses.erase(imuPath.poses.begin());
if (pubImuPath.getNumSubscribers() != 0)
{
imuPath.header.stamp = imuOdomQueue.back().header.stamp;
imuPath.header.frame_id = odometryFrame;
pubImuPath.publish(imuPath);
}
}
}
总结一下
IMUPreintegration 订阅 lio_sam/mapping/odometry_incremental 和 imu_correct , 使用gtsam进行imu与激光里程计的紧耦合优化，发布优化后的 odometry/imu_incremental。
TransformFusion 订阅 IMUPreintegration发布的odometry/imu_incremental，以及激光里程计lio_sam/mapping/odometry 。
imuOdometryHandler 只是将 odometry/imu_incremental 中的xyz值，使用lio_sam/mapping/odometry中的值，乘以相应时间内的odometry/imu_incrementa的变化量，得到新的xyz进行替代; 并发布map->odom->base_link的tf。
gstam优化部分还没有看懂，回头看懂了再来更新
REFERENCES
LIO-SAM源码阅读与分析（3）–imuPreintegration.cpp
https://zhuanlan.zhihu.com/p/183190393