imuPreintegration.cpp

#include "utility.h"

#include <gtsam/geometry/Rot3.h>
#include <gtsam/geometry/Pose3.h>
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/navigation/GPSFactor.h>
#include <gtsam/navigation/ImuFactor.h>
#include <gtsam/navigation/CombinedImuFactor.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/nonlinear/Marginals.h>
#include <gtsam/nonlinear/Values.h>
#include <gtsam/inference/Symbol.h>

#include <gtsam/nonlinear/ISAM2.h>
#include <gtsam_unstable/nonlinear/IncrementalFixedLagSmoother.h>

using gtsam::symbol_shorthand::X; // Pose3 (x,y,z,r,p,y)
using gtsam::symbol_shorthand::V; // Vel   (xdot,ydot,zdot)
using gtsam::symbol_shorthand::B; // Bias  (ax,ay,az,gx,gy,gz)

class IMUPreintegration : public ParamServer
{
public:

    ros::Subscriber subImu;
    ros::Subscriber subOdometry;
    ros::Publisher pubImuOdometry;
    ros::Publisher pubImuPath;

    // map -> odom
    tf::Transform map_to_odom;
    tf::TransformBroadcaster tfMap2Odom;
    // odom -> base_link
    tf::TransformBroadcaster tfOdom2BaseLink;

    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::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;
    int imuPreintegrationResetId = 0;

    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()));;

    IMUPreintegration()
    {
        subImu      = nh.subscribe<sensor_msgs::Imu>  (imuTopic,                   2000, &IMUPreintegration::imuHandler,      this, ros::TransportHints().tcpNoDelay());
        subOdometry = nh.subscribe<nav_msgs::Odometry>("lio_sam/mapping/odometry", 5,    &IMUPreintegration::odometryHandler, this, ros::TransportHints().tcpNoDelay());

        pubImuOdometry = nh.advertise<nav_msgs::Odometry> (odomTopic, 2000);
        pubImuPath     = nh.advertise<nav_msgs::Path>     ("lio_sam/imu/path", 1);

        map_to_odom    = tf::Transform(tf::createQuaternionFromRPY(0, 0, 0), tf::Vector3(0, 0, 0));

        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::Isotropic::Sigma(6, 1e-2); // meter
        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        
    }

    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;
    }

    void resetParams()
    {
        lastImuT_imu = -1;
        doneFirstOpt = false;
        systemInitialized = false;
    }

    void odometryHandler(const nav_msgs::Odometry::ConstPtr& odomMsg)
    {
        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;
        int currentResetId = round(odomMsg->pose.covariance[0]);
        gtsam::Pose3 lidarPose = gtsam::Pose3(gtsam::Rot3::Quaternion(r_w, r_x, r_y, r_z), gtsam::Point3(p_x, p_y, p_z));

        // correction pose jumped, reset imu pre-integration
        if (currentResetId != imuPreintegrationResetId)
        {
            resetParams();
            imuPreintegrationResetId = currentResetId;
            return;
        }


        // 0. initialize system
        if (systemInitialized == false)
        {
            resetOptimization();

            // pop old IMU message
            while (!imuQueOpt.empty())
            {
                if (ROS_TIME(&imuQueOpt.front()) < currentCorrectionTime - delta_t)
                {
                    lastImuT_opt = ROS_TIME(&imuQueOpt.front());
                    imuQueOpt.pop_front();
                }
                else
                    break;
            }
            // initial pose
            prevPose_ = lidarPose.compose(lidar2Imu);
            gtsam::PriorFactor<gtsam::Pose3> priorPose(X(0), prevPose_, priorPoseNoise);
            graphFactors.add(priorPose);
            // initial velocity
            prevVel_ = gtsam::Vector3(0, 0, 0);
            gtsam::PriorFactor<gtsam::Vector3> priorVel(V(0), prevVel_, priorVelNoise);
            graphFactors.add(priorVel);
            // initial bias
            prevBias_ = gtsam::imuBias::ConstantBias();
            gtsam::PriorFactor<gtsam::imuBias::ConstantBias> priorBias(B(0), prevBias_, priorBiasNoise);
            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();

            imuIntegratorImu_->resetIntegrationAndSetBias(prevBias_);
            imuIntegratorOpt_->resetIntegrationAndSetBias(prevBias_);
            
            key = 1;
            systemInitialized = true;
            return;
        }


        // reset graph for speed
        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();
            // 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);
            if (imuTime < currentCorrectionTime - delta_t)
            {
                double dt = (lastImuT_opt < 0) ? (1.0 / 500.0) : (imuTime - lastImuT_opt);
                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);
                
                lastImuT_opt = imuTime;
                imuQueOpt.pop_front();
            }
            else
                break;
        }
        // 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
        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
        gtsam::Pose3 curPose = lidarPose.compose(lidar2Imu);
        gtsam::PriorFactor<gtsam::Pose3> pose_factor(X(key), curPose, correctionNoise);
        graphFactors.add(pose_factor);
        // insert predicted values
        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
        optimizer.update(graphFactors, graphValues);
        optimizer.update();
        graphFactors.resize(0);
        graphValues.clear();
        // Overwrite the beginning of the preintegration for the next step.
        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
        if (failureDetection(prevVel_, prevBias_))
        {
            resetParams();
            return;
        }


        // 2. after optiization, re-propagate imu odometry preintegration
        prevStateOdom = prevState_;
        prevBiasOdom  = prevBias_;
        // first pop imu message older than current correction data
        double lastImuQT = -1;
        while (!imuQueImu.empty() && ROS_TIME(&imuQueImu.front()) < currentCorrectionTime - delta_t)
        {
            lastImuQT = ROS_TIME(&imuQueImu.front());
            imuQueImu.pop_front();
        }
        // repropogate
        if (!imuQueImu.empty())
        {
            // reset bias use the newly optimized bias
            imuIntegratorImu_->resetIntegrationAndSetBias(prevBiasOdom);
            // integrate imu message from the beginning of this optimization
            for (int i = 0; i < (int)imuQueImu.size(); ++i)
            {
                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;
    }

    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;
    }

    void imuHandler(const sensor_msgs::Imu::ConstPtr& imu_raw)
    {
        sensor_msgs::Imu thisImu = imuConverter(*imu_raw);
        // publish static tf
        tfMap2Odom.sendTransform(tf::StampedTransform(map_to_odom, thisImu.header.stamp, "map", "odom"));

        imuQueOpt.push_back(thisImu);
        imuQueImu.push_back(thisImu);

        if (doneFirstOpt == false)
            return;

        double imuTime = ROS_TIME(&thisImu);
        double dt = (lastImuT_imu < 0) ? (1.0 / 500.0) : (imuTime - lastImuT_imu);
        lastImuT_imu = imuTime;

        // 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);

        // predict odometry
        gtsam::NavState currentState = imuIntegratorImu_->predict(prevStateOdom, prevBiasOdom);

        // publish odometry
        nav_msgs::Odometry odometry;
        odometry.header.stamp = thisImu.header.stamp;
        odometry.header.frame_id = "odom";
        odometry.child_frame_id = "odom_imu";

        // transform imu pose to ldiar
        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();
        odometry.pose.covariance[0] = double(imuPreintegrationResetId);
        pubImuOdometry.publish(odometry);

        // publish imu path
        static nav_msgs::Path imuPath;
        static double last_path_time = -1;
        if (imuTime - last_path_time > 0.1)
        {
            last_path_time = imuTime;
            geometry_msgs::PoseStamped pose_stamped;
            pose_stamped.header.stamp = thisImu.header.stamp;
            pose_stamped.header.frame_id = "odom";
            pose_stamped.pose = odometry.pose.pose;
            imuPath.poses.push_back(pose_stamped);
            while(!imuPath.poses.empty() && abs(imuPath.poses.front().header.stamp.toSec() - imuPath.poses.back().header.stamp.toSec()) > 3.0)
                imuPath.poses.erase(imuPath.poses.begin());
            if (pubImuPath.getNumSubscribers() != 0)
            {
                imuPath.header.stamp = thisImu.header.stamp;
                imuPath.header.frame_id = "odom";
                pubImuPath.publish(imuPath);
            }
        }

        // publish transformation
        tf::Transform tCur;
        tf::poseMsgToTF(odometry.pose.pose, tCur);
        tf::StampedTransform odom_2_baselink = tf::StampedTransform(tCur, thisImu.header.stamp, "odom", "base_link");
        tfOdom2BaseLink.sendTransform(odom_2_baselink);
    }
};


int main(int argc, char** argv)
{
    ros::init(argc, argv, "roboat_loam");
    
    IMUPreintegration ImuP;

    ROS_INFO("\033[1;32m----> IMU Preintegration Started.\033[0m");
    
    ros::spin();
    
    return 0;
}