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estimator.cpp
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#include "estimator.h"
Estimator::Estimator(): f_manager{Rs}
{
printf("init begins\n");
clearState();
}
void Estimator::setParameter()
{
for (int i = 0; i < NUM_OF_CAM; i++)
{
tic[i] = TIC[i];
ric[i] = RIC[i];
}
f_manager.setRic(ric);
ProjectionFactor::sqrt_info = FOCAL_LENGTH / 1.5 * Matrix2d::Identity();
ProjectionTdFactor::sqrt_info = FOCAL_LENGTH / 1.5 * Matrix2d::Identity();
td = TD;
}
void Estimator::clearState()
{
for (int i = 0; i < WINDOW_SIZE + 1; i++)
{
Rs[i].setIdentity();
Ps[i].setZero();
Vs[i].setZero();
Bas[i].setZero();
Bgs[i].setZero();
dt_buf[i].clear();
linear_acceleration_buf[i].clear();
angular_velocity_buf[i].clear();
if (pre_integrations[i] != nullptr)
{
delete pre_integrations[i];
}
pre_integrations[i] = nullptr;
}
for (int i = 0; i < NUM_OF_CAM; i++)
{
tic[i] = Vector3d::Zero();
ric[i] = Matrix3d::Identity();
}
solver_flag = INITIAL;
first_imu = false,
sum_of_back = 0;
sum_of_front = 0;
frame_count = 0;
solver_flag = INITIAL;
initial_timestamp = 0;
all_image_frame.clear();
td = TD;
if (tmp_pre_integration != nullptr)
delete tmp_pre_integration;
if (last_marginalization_info != nullptr)
delete last_marginalization_info;
tmp_pre_integration = nullptr;
last_marginalization_info = nullptr;
last_marginalization_parameter_blocks.clear();
f_manager.clearState();
failure_occur = 0;
relocalization_info = 0;
drift_correct_r = Matrix3d::Identity();
drift_correct_t = Vector3d::Zero();
}
void Estimator::processIMU(double dt, const Vector3d &linear_acceleration, const Vector3d &angular_velocity)
{
if (!first_imu)
{
first_imu = true;
acc_0 = linear_acceleration;
gyr_0 = angular_velocity;
}
if (!pre_integrations[frame_count])
{
pre_integrations[frame_count] = new IntegrationBase{acc_0, gyr_0, Bas[frame_count], Bgs[frame_count]};
}
if (frame_count != 0)
{
pre_integrations[frame_count]->push_back(dt, linear_acceleration, angular_velocity);
//if(solver_flag != NON_LINEAR)
tmp_pre_integration->push_back(dt, linear_acceleration, angular_velocity);
dt_buf[frame_count].push_back(dt);
linear_acceleration_buf[frame_count].push_back(linear_acceleration);
angular_velocity_buf[frame_count].push_back(angular_velocity);
int j = frame_count;
Vector3d un_acc_0 = Rs[j] * (acc_0 - Bas[j]) - g;
Vector3d un_gyr = 0.5 * (gyr_0 + angular_velocity) - Bgs[j];
Rs[j] *= Utility::deltaQ(un_gyr * dt).toRotationMatrix();
Vector3d un_acc_1 = Rs[j] * (linear_acceleration - Bas[j]) - g;
Vector3d un_acc = 0.5 * (un_acc_0 + un_acc_1);
Ps[j] += dt * Vs[j] + 0.5 * dt * dt * un_acc;
Vs[j] += dt * un_acc;
}
acc_0 = linear_acceleration;
gyr_0 = angular_velocity;
}
void Estimator::processImage(const map<int, vector<pair<int, Eigen::Matrix<double, 7, 1>>>> &image, const std_msgs::Header &header)
{
printf("new image coming ------------------------------------------\n");
printf("Adding feature points %lu\n", image.size());
if (f_manager.addFeatureCheckParallax(frame_count, image, td))
marginalization_flag = MARGIN_OLD;
else
marginalization_flag = MARGIN_SECOND_NEW;
printf("this frame is--------------------%s\n", marginalization_flag ? "reject" : "accept");
printf("%s", marginalization_flag ? "Non-keyframe" : "Keyframe");
printf("Solving %d\n", frame_count);
printf("number of feature: %d\n", f_manager.getFeatureCount());
Headers[frame_count] = header;
ImageFrame imageframe(image, header.stamp.toSec());
imageframe.pre_integration = tmp_pre_integration;
all_image_frame.insert(make_pair(header.stamp.toSec(), imageframe));
tmp_pre_integration = new IntegrationBase{acc_0, gyr_0, Bas[frame_count], Bgs[frame_count]};
if(ESTIMATE_EXTRINSIC == 2)
{
printf("calibrating extrinsic param, rotation movement is needed\n");
if (frame_count != 0)
{
vector<pair<Vector3d, Vector3d>> corres = f_manager.getCorresponding(frame_count - 1, frame_count);
Matrix3d calib_ric;
if (initial_ex_rotation.CalibrationExRotation(corres, pre_integrations[frame_count]->delta_q, calib_ric))
{
printf("initial extrinsic rotation calib success\n");
cout<<"initial extrinsic rotation: " << endl << calib_ric<<endl;
ric[0] = calib_ric;
RIC[0] = calib_ric;
ESTIMATE_EXTRINSIC = 1;
}
}
}
if (solver_flag == INITIAL)
{
if (frame_count == WINDOW_SIZE)
{
bool result = false;
if( ESTIMATE_EXTRINSIC != 2 && (header.stamp.toSec() - initial_timestamp) > 0.1)
{
result = initialStructure();
initial_timestamp = header.stamp.toSec();
}
if(result)
{
solver_flag = NON_LINEAR;
solveOdometry();
slideWindow();
f_manager.removeFailures();
printf("Initialization finish!\n");
last_R = Rs[WINDOW_SIZE];
last_P = Ps[WINDOW_SIZE];
last_R0 = Rs[0];
last_P0 = Ps[0];
}
else
slideWindow();
}
else
frame_count++;
}
else
{
TicToc t_solve;
solveOdometry();
printf("solver costs: %fms\n", t_solve.toc());
if (failureDetection())
{
printf("failure detection!\n");
failure_occur = 1;
clearState();
setParameter();
printf("system reboot!\n");
return;
}
TicToc t_margin;
slideWindow();
f_manager.removeFailures();
printf("marginalization costs: %fms\n", t_margin.toc());
// prepare output of VINS
key_poses.clear();
for (int i = 0; i <= WINDOW_SIZE; i++)
key_poses.push_back(Ps[i]);
last_R = Rs[WINDOW_SIZE];
last_P = Ps[WINDOW_SIZE];
last_R0 = Rs[0];
last_P0 = Ps[0];
}
}
bool Estimator::initialStructure()
{
TicToc t_sfm;
//check imu observibility
{
map<double, ImageFrame>::iterator frame_it;
Vector3d sum_g;
for (frame_it = all_image_frame.begin(), frame_it++; frame_it != all_image_frame.end(); frame_it++)
{
double dt = frame_it->second.pre_integration->sum_dt;
Vector3d tmp_g = frame_it->second.pre_integration->delta_v / dt;
sum_g += tmp_g;
}
Vector3d aver_g;
aver_g = sum_g * 1.0 / ((int)all_image_frame.size() - 1);
double var = 0;
for (frame_it = all_image_frame.begin(), frame_it++; frame_it != all_image_frame.end(); frame_it++)
{
double dt = frame_it->second.pre_integration->sum_dt;
Vector3d tmp_g = frame_it->second.pre_integration->delta_v / dt;
var += (tmp_g - aver_g).transpose() * (tmp_g - aver_g);
//cout << "frame g " << tmp_g.transpose() << endl;
}
var = sqrt(var / ((int)all_image_frame.size() - 1));
//printf("IMU variation %f!", var);
if(var < 0.25)
{
printf("IMU excitation not enouth!\n");
//return false;
}
}
// global sfm
Quaterniond Q[frame_count + 1];
Vector3d T[frame_count + 1];
map<int, Vector3d> sfm_tracked_points;
vector<SFMFeature> sfm_f;
for (auto &it_per_id : f_manager.feature)
{
int imu_j = it_per_id.start_frame - 1;
SFMFeature tmp_feature;
tmp_feature.state = false;
tmp_feature.id = it_per_id.feature_id;
for (auto &it_per_frame : it_per_id.feature_per_frame)
{
imu_j++;
Vector3d pts_j = it_per_frame.point;
tmp_feature.observation.push_back(make_pair(imu_j, Eigen::Vector2d{pts_j.x(), pts_j.y()}));
}
sfm_f.push_back(tmp_feature);
}
Matrix3d relative_R;
Vector3d relative_T;
int l;
if (!relativePose(relative_R, relative_T, l))
{
printf("Not enough features or parallax; Move device around\n");
return false;
}
GlobalSFM sfm;
if(!sfm.construct(frame_count + 1, Q, T, l,
relative_R, relative_T,
sfm_f, sfm_tracked_points))
{
printf("global SFM failed!\n");
marginalization_flag = MARGIN_OLD;
return false;
}
//solve pnp for all frame
map<double, ImageFrame>::iterator frame_it;
map<int, Vector3d>::iterator it;
frame_it = all_image_frame.begin( );
for (int i = 0; frame_it != all_image_frame.end( ); frame_it++)
{
// provide initial guess
cv::Mat r, rvec, t, D, tmp_r;
if((frame_it->first) == Headers[i].stamp.toSec())
{
frame_it->second.is_key_frame = true;
frame_it->second.R = Q[i].toRotationMatrix() * RIC[0].transpose();
frame_it->second.T = T[i];
i++;
continue;
}
if((frame_it->first) > Headers[i].stamp.toSec())
{
i++;
}
Matrix3d R_inital = (Q[i].inverse()).toRotationMatrix();
Vector3d P_inital = - R_inital * T[i];
cv::eigen2cv(R_inital, tmp_r);
cv::Rodrigues(tmp_r, rvec);
cv::eigen2cv(P_inital, t);
frame_it->second.is_key_frame = false;
vector<cv::Point3f> pts_3_vector;
vector<cv::Point2f> pts_2_vector;
for (auto &id_pts : frame_it->second.points)
{
int feature_id = id_pts.first;
for (auto &i_p : id_pts.second)
{
it = sfm_tracked_points.find(feature_id);
if(it != sfm_tracked_points.end())
{
Vector3d world_pts = it->second;
cv::Point3f pts_3(world_pts(0), world_pts(1), world_pts(2));
pts_3_vector.push_back(pts_3);
Vector2d img_pts = i_p.second.head<2>();
cv::Point2f pts_2(img_pts(0), img_pts(1));
pts_2_vector.push_back(pts_2);
}
}
}
cv::Mat K = (cv::Mat_<double>(3, 3) << 1, 0, 0, 0, 1, 0, 0, 0, 1);
if(pts_3_vector.size() < 6)
{
cout << "pts_3_vector size " << pts_3_vector.size() << endl;
printf("Not enough points for solve pnp !\n");
return false;
}
if (! cv::solvePnP(pts_3_vector, pts_2_vector, K, D, rvec, t, 1))
{
printf("solve pnp fail!\n");
return false;
}
cv::Rodrigues(rvec, r);
MatrixXd R_pnp,tmp_R_pnp;
cv::cv2eigen(r, tmp_R_pnp);
R_pnp = tmp_R_pnp.transpose();
MatrixXd T_pnp;
cv::cv2eigen(t, T_pnp);
T_pnp = R_pnp * (-T_pnp);
frame_it->second.R = R_pnp * RIC[0].transpose();
frame_it->second.T = T_pnp;
}
if (visualInitialAlign())
return true;
else
{
printf("misalign visual structure with IMU\n");
return false;
}
}
bool Estimator::visualInitialAlign()
{
TicToc t_g;
VectorXd x;
//solve scale
bool result = VisualIMUAlignment(all_image_frame, Bgs, g, x);
if(!result)
{
printf("solve g failed!\n");
return false;
}
// change state
for (int i = 0; i <= frame_count; i++)
{
Matrix3d Ri = all_image_frame[Headers[i].stamp.toSec()].R;
Vector3d Pi = all_image_frame[Headers[i].stamp.toSec()].T;
Ps[i] = Pi;
Rs[i] = Ri;
all_image_frame[Headers[i].stamp.toSec()].is_key_frame = true;
}
VectorXd dep = f_manager.getDepthVector();
for (int i = 0; i < dep.size(); i++)
dep[i] = -1;
f_manager.clearDepth(dep);
//triangulat on cam pose , no tic
Vector3d TIC_TMP[NUM_OF_CAM];
for(int i = 0; i < NUM_OF_CAM; i++)
TIC_TMP[i].setZero();
ric[0] = RIC[0];
f_manager.setRic(ric);
f_manager.triangulate(Ps, &(TIC_TMP[0]), &(RIC[0]));
double s = (x.tail<1>())(0);
for (int i = 0; i <= WINDOW_SIZE; i++)
{
pre_integrations[i]->repropagate(Vector3d::Zero(), Bgs[i]);
}
for (int i = frame_count; i >= 0; i--)
Ps[i] = s * Ps[i] - Rs[i] * TIC[0] - (s * Ps[0] - Rs[0] * TIC[0]);
int kv = -1;
map<double, ImageFrame>::iterator frame_i;
for (frame_i = all_image_frame.begin(); frame_i != all_image_frame.end(); frame_i++)
{
if(frame_i->second.is_key_frame)
{
kv++;
Vs[kv] = frame_i->second.R * x.segment<3>(kv * 3);
}
}
for (auto &it_per_id : f_manager.feature)
{
it_per_id.used_num = it_per_id.feature_per_frame.size();
if (!(it_per_id.used_num >= 2 && it_per_id.start_frame < WINDOW_SIZE - 2))
continue;
it_per_id.estimated_depth *= s;
}
Matrix3d R0 = Utility::g2R(g);
double yaw = Utility::R2ypr(R0 * Rs[0]).x();
R0 = Utility::ypr2R(Eigen::Vector3d{-yaw, 0, 0}) * R0;
g = R0 * g;
//Matrix3d rot_diff = R0 * Rs[0].transpose();
Matrix3d rot_diff = R0;
for (int i = 0; i <= frame_count; i++)
{
Ps[i] = rot_diff * Ps[i];
Rs[i] = rot_diff * Rs[i];
Vs[i] = rot_diff * Vs[i];
}
cout<<"g0 " << g.transpose()<<endl;
cout<<"my R0 " << Utility::R2ypr(Rs[0]).transpose()<<endl;
return true;
}
bool Estimator::relativePose(Matrix3d &relative_R, Vector3d &relative_T, int &l)
{
// find previous frame which contians enough correspondance and parallex with newest frame
for (int i = 0; i < WINDOW_SIZE; i++)
{
vector<pair<Vector3d, Vector3d>> corres;
corres = f_manager.getCorresponding(i, WINDOW_SIZE);
if (corres.size() > 20)
{
double sum_parallax = 0;
double average_parallax;
for (int j = 0; j < int(corres.size()); j++)
{
Vector2d pts_0(corres[j].first(0), corres[j].first(1));
Vector2d pts_1(corres[j].second(0), corres[j].second(1));
double parallax = (pts_0 - pts_1).norm();
sum_parallax = sum_parallax + parallax;
}
average_parallax = 1.0 * sum_parallax / int(corres.size());
if(average_parallax * 460 > 30 && m_estimator.solveRelativeRT(corres, relative_R, relative_T))
{
l = i;
printf("average_parallax %f choose l %d and newest frame to triangulate the whole structure\n", average_parallax * 460, l);
return true;
}
}
}
return false;
}
void Estimator::solveOdometry()
{
if (frame_count < WINDOW_SIZE)
return;
if (solver_flag == NON_LINEAR)
{
TicToc t_tri;
f_manager.triangulate(Ps, tic, ric);
printf("triangulation costs %f\n", t_tri.toc());
optimization();
}
}
void Estimator::vector2double()
{
for (int i = 0; i <= WINDOW_SIZE; i++)
{
para_Pose[i][0] = Ps[i].x();
para_Pose[i][1] = Ps[i].y();
para_Pose[i][2] = Ps[i].z();
Quaterniond q{Rs[i]};
para_Pose[i][3] = q.x();
para_Pose[i][4] = q.y();
para_Pose[i][5] = q.z();
para_Pose[i][6] = q.w();
para_SpeedBias[i][0] = Vs[i].x();
para_SpeedBias[i][1] = Vs[i].y();
para_SpeedBias[i][2] = Vs[i].z();
para_SpeedBias[i][3] = Bas[i].x();
para_SpeedBias[i][4] = Bas[i].y();
para_SpeedBias[i][5] = Bas[i].z();
para_SpeedBias[i][6] = Bgs[i].x();
para_SpeedBias[i][7] = Bgs[i].y();
para_SpeedBias[i][8] = Bgs[i].z();
}
for (int i = 0; i < NUM_OF_CAM; i++)
{
para_Ex_Pose[i][0] = tic[i].x();
para_Ex_Pose[i][1] = tic[i].y();
para_Ex_Pose[i][2] = tic[i].z();
Quaterniond q{ric[i]};
para_Ex_Pose[i][3] = q.x();
para_Ex_Pose[i][4] = q.y();
para_Ex_Pose[i][5] = q.z();
para_Ex_Pose[i][6] = q.w();
}
VectorXd dep = f_manager.getDepthVector();
for (int i = 0; i < f_manager.getFeatureCount(); i++)
para_Feature[i][0] = dep(i);
if (ESTIMATE_TD)
para_Td[0][0] = td;
}
void Estimator::double2vector()
{
Vector3d origin_R0 = Utility::R2ypr(Rs[0]);
Vector3d origin_P0 = Ps[0];
if (failure_occur)
{
origin_R0 = Utility::R2ypr(last_R0);
origin_P0 = last_P0;
failure_occur = 0;
}
Vector3d origin_R00 = Utility::R2ypr(Quaterniond(para_Pose[0][6],
para_Pose[0][3],
para_Pose[0][4],
para_Pose[0][5]).toRotationMatrix());
double y_diff = origin_R0.x() - origin_R00.x();
//TODO
Matrix3d rot_diff = Utility::ypr2R(Vector3d(y_diff, 0, 0));
if (abs(abs(origin_R0.y()) - 90) < 1.0 || abs(abs(origin_R00.y()) - 90) < 1.0)
{
printf("euler singular point!\n");
rot_diff = Rs[0] * Quaterniond(para_Pose[0][6],
para_Pose[0][3],
para_Pose[0][4],
para_Pose[0][5]).toRotationMatrix().transpose();
}
for (int i = 0; i <= WINDOW_SIZE; i++)
{
Rs[i] = rot_diff * Quaterniond(para_Pose[i][6], para_Pose[i][3], para_Pose[i][4], para_Pose[i][5]).normalized().toRotationMatrix();
Ps[i] = rot_diff * Vector3d(para_Pose[i][0] - para_Pose[0][0],
para_Pose[i][1] - para_Pose[0][1],
para_Pose[i][2] - para_Pose[0][2]) + origin_P0;
Vs[i] = rot_diff * Vector3d(para_SpeedBias[i][0],
para_SpeedBias[i][1],
para_SpeedBias[i][2]);
Bas[i] = Vector3d(para_SpeedBias[i][3],
para_SpeedBias[i][4],
para_SpeedBias[i][5]);
Bgs[i] = Vector3d(para_SpeedBias[i][6],
para_SpeedBias[i][7],
para_SpeedBias[i][8]);
}
for (int i = 0; i < NUM_OF_CAM; i++)
{
tic[i] = Vector3d(para_Ex_Pose[i][0],
para_Ex_Pose[i][1],
para_Ex_Pose[i][2]);
ric[i] = Quaterniond(para_Ex_Pose[i][6],
para_Ex_Pose[i][3],
para_Ex_Pose[i][4],
para_Ex_Pose[i][5]).toRotationMatrix();
}
VectorXd dep = f_manager.getDepthVector();
for (int i = 0; i < f_manager.getFeatureCount(); i++)
dep(i) = para_Feature[i][0];
f_manager.setDepth(dep);
if (ESTIMATE_TD)
td = para_Td[0][0];
// relative info between two loop frame
if(relocalization_info)
{
Matrix3d relo_r;
Vector3d relo_t;
relo_r = rot_diff * Quaterniond(relo_Pose[6], relo_Pose[3], relo_Pose[4], relo_Pose[5]).normalized().toRotationMatrix();
relo_t = rot_diff * Vector3d(relo_Pose[0] - para_Pose[0][0],
relo_Pose[1] - para_Pose[0][1],
relo_Pose[2] - para_Pose[0][2]) + origin_P0;
double drift_correct_yaw;
drift_correct_yaw = Utility::R2ypr(prev_relo_r).x() - Utility::R2ypr(relo_r).x();
drift_correct_r = Utility::ypr2R(Vector3d(drift_correct_yaw, 0, 0));
drift_correct_t = prev_relo_t - drift_correct_r * relo_t;
relo_relative_t = relo_r.transpose() * (Ps[relo_frame_local_index] - relo_t);
relo_relative_q = relo_r.transpose() * Rs[relo_frame_local_index];
relo_relative_yaw = Utility::normalizeAngle(Utility::R2ypr(Rs[relo_frame_local_index]).x() - Utility::R2ypr(relo_r).x());
//cout << "vins relo " << endl;
//cout << "vins relative_t " << relo_relative_t.transpose() << endl;
//cout << "vins relative_yaw " <<relo_relative_yaw << endl;
relocalization_info = 0;
}
}
bool Estimator::failureDetection()
{
if (f_manager.last_track_num < 2)
{
printf(" little feature %d\n", f_manager.last_track_num);
//return true;
}
if (Bas[WINDOW_SIZE].norm() > 2.5)
{
printf(" big IMU acc bias estimation %f\n", Bas[WINDOW_SIZE].norm());
return true;
}
if (Bgs[WINDOW_SIZE].norm() > 1.0)
{
printf(" big IMU gyr bias estimation %f\n", Bgs[WINDOW_SIZE].norm());
return true;
}
/*
if (tic(0) > 1)
{
printf(" big extri param estimation %d\n", tic(0) > 1);
return true;
}
*/
Vector3d tmp_P = Ps[WINDOW_SIZE];
if ((tmp_P - last_P).norm() > 5)
{
printf(" big translation\n");
return true;
}
if (abs(tmp_P.z() - last_P.z()) > 1)
{
printf(" big z translation\n");
return true;
}
Matrix3d tmp_R = Rs[WINDOW_SIZE];
Matrix3d delta_R = tmp_R.transpose() * last_R;
Quaterniond delta_Q(delta_R);
double delta_angle;
delta_angle = acos(delta_Q.w()) * 2.0 / 3.14 * 180.0;
if (delta_angle > 50)
{
printf(" big delta_angle \n");
//return true;
}
return false;
}
void Estimator::optimization()
{
ceres::Problem problem;
ceres::LossFunction *loss_function;
//loss_function = new ceres::HuberLoss(1.0);
loss_function = new ceres::CauchyLoss(1.0);
for (int i = 0; i < WINDOW_SIZE + 1; i++)
{
ceres::LocalParameterization *local_parameterization = new PoseLocalParameterization();
problem.AddParameterBlock(para_Pose[i], SIZE_POSE, local_parameterization);//位置、姿态（四元数）7维
problem.AddParameterBlock(para_SpeedBias[i], SIZE_SPEEDBIAS);//速度、陀螺bias、加表bias 9维
}
for (int i = 0; i < NUM_OF_CAM; i++)
{
ceres::LocalParameterization *local_parameterization = new PoseLocalParameterization();
problem.AddParameterBlock(para_Ex_Pose[i], SIZE_POSE, local_parameterization);
if (!ESTIMATE_EXTRINSIC)
{
printf("fix extinsic param\n");
problem.SetParameterBlockConstant(para_Ex_Pose[i]);//固定外参
}
else
printf("estimate extinsic param\n");
}
if (ESTIMATE_TD)
{
problem.AddParameterBlock(para_Td[0], 1);
//problem.SetParameterBlockConstant(para_Td[0]);
}
TicToc t_whole, t_prepare;
vector2double();
if (last_marginalization_info)
{
// construct new marginlization_factor
MarginalizationFactor *marginalization_factor = new MarginalizationFactor(last_marginalization_info);
problem.AddResidualBlock(marginalization_factor, NULL,
last_marginalization_parameter_blocks);
}
for (int i = 0; i < WINDOW_SIZE; i++)
{
int j = i + 1;
if (pre_integrations[j]->sum_dt > 10.0)
continue;
IMUFactor* imu_factor = new IMUFactor(pre_integrations[j]);
problem.AddResidualBlock(imu_factor, NULL, para_Pose[i], para_SpeedBias[i], para_Pose[j], para_SpeedBias[j]);
}
/*****************************************************GPS**********************************************************************
for(int i = 0; i < (WINDOW_SIZE + 1); i++)
{
for(int j=0;j<gpsvec.size();j++)
{
if(abs(Headers[i].stamp.toSec()-gpsvec[j].time)<0.00005)
{
GPSFactor* gpsfactor=new GPSFactor(gpsvec[j]);
problem.AddResidualBlock(gpsfactor,NULL,para_Pose[i]);
break;
}
}
}
/*****************************************************GPS**********************************************************************/
int f_m_cnt = 0;
int feature_index = -1;
for (auto &it_per_id : f_manager.feature)
{
it_per_id.used_num = it_per_id.feature_per_frame.size();
if (!(it_per_id.used_num >= 2 && it_per_id.start_frame < WINDOW_SIZE - 2))
continue;
++feature_index;
int imu_i = it_per_id.start_frame, imu_j = imu_i - 1;
Vector3d pts_i = it_per_id.feature_per_frame[0].point;
for (auto &it_per_frame : it_per_id.feature_per_frame)
{
imu_j++;
if (imu_i == imu_j)
{
continue;
}
Vector3d pts_j = it_per_frame.point;
if (ESTIMATE_TD)
{
ProjectionTdFactor *f_td = new ProjectionTdFactor(pts_i, pts_j, it_per_id.feature_per_frame[0].velocity, it_per_frame.velocity,
it_per_id.feature_per_frame[0].cur_td, it_per_frame.cur_td,
it_per_id.feature_per_frame[0].uv.y(), it_per_frame.uv.y());
problem.AddResidualBlock(f_td, loss_function, para_Pose[imu_i], para_Pose[imu_j], para_Ex_Pose[0], para_Feature[feature_index], para_Td[0]);
/*
double **para = new double *[5];
para[0] = para_Pose[imu_i];
para[1] = para_Pose[imu_j];
para[2] = para_Ex_Pose[0];
para[3] = para_Feature[feature_index];
para[4] = para_Td[0];
f_td->check(para);
*/
}
else
{
ProjectionFactor *f = new ProjectionFactor(pts_i, pts_j);
problem.AddResidualBlock(f, loss_function, para_Pose[imu_i], para_Pose[imu_j], para_Ex_Pose[0], para_Feature[feature_index]);
}
f_m_cnt++;
}
}
printf("visual measurement count: %d\n", f_m_cnt);
printf("prepare for ceres: %f\n", t_prepare.toc());
if(relocalization_info)
{
//printf("set relocalization factor! \n");
ceres::LocalParameterization *local_parameterization = new PoseLocalParameterization();
problem.AddParameterBlock(relo_Pose, SIZE_POSE, local_parameterization);
int retrive_feature_index = 0;
int feature_index = -1;
for (auto &it_per_id : f_manager.feature)
{
it_per_id.used_num = it_per_id.feature_per_frame.size();
if (!(it_per_id.used_num >= 2 && it_per_id.start_frame < WINDOW_SIZE - 2))
continue;
++feature_index;
int start = it_per_id.start_frame;
if(start <= relo_frame_local_index)
{
while((int)match_points[retrive_feature_index].z() < it_per_id.feature_id)
{
retrive_feature_index++;
}
if((int)match_points[retrive_feature_index].z() == it_per_id.feature_id)
{
Vector3d pts_j = Vector3d(match_points[retrive_feature_index].x(), match_points[retrive_feature_index].y(), 1.0);
Vector3d pts_i = it_per_id.feature_per_frame[0].point;
ProjectionFactor *f = new ProjectionFactor(pts_i, pts_j);
problem.AddResidualBlock(f, loss_function, para_Pose[start], relo_Pose, para_Ex_Pose[0], para_Feature[feature_index]);
retrive_feature_index++;
}
}
}
}
ceres::Solver::Options options;
options.linear_solver_type = ceres::DENSE_SCHUR;
//options.num_threads = 2;
options.trust_region_strategy_type = ceres::DOGLEG;
options.max_num_iterations = NUM_ITERATIONS;
//options.use_explicit_schur_complement = true;
//options.minimizer_progress_to_stdout = true;
//options.use_nonmonotonic_steps = true;
if (marginalization_flag == MARGIN_OLD)
options.max_solver_time_in_seconds = SOLVER_TIME * 4.0 / 5.0;
else
options.max_solver_time_in_seconds = SOLVER_TIME;
TicToc t_solver;
ceres::Solver::Summary summary;
ceres::Solve(options, &problem, &summary);
//cout << summary.BriefReport() << endl;
printf("Iterations : %d\n", static_cast<int>(summary.iterations.size()));
printf("solver costs: %f\n", t_solver.toc());
double2vector();
TicToc t_whole_marginalization;
if (marginalization_flag == MARGIN_OLD)
{
MarginalizationInfo *marginalization_info = new MarginalizationInfo();
vector2double();
if (last_marginalization_info)
{
vector<int> drop_set;
for (int i = 0; i < static_cast<int>(last_marginalization_parameter_blocks.size()); i++)
{
if (last_marginalization_parameter_blocks[i] == para_Pose[0] ||
last_marginalization_parameter_blocks[i] == para_SpeedBias[0])
drop_set.push_back(i);
}
// construct new marginlization_factor
MarginalizationFactor *marginalization_factor = new MarginalizationFactor(last_marginalization_info);
ResidualBlockInfo *residual_block_info = new ResidualBlockInfo(marginalization_factor, NULL,
last_marginalization_parameter_blocks,
drop_set);
marginalization_info->addResidualBlockInfo(residual_block_info);
}
{
if (pre_integrations[1]->sum_dt < 10.0)
{
IMUFactor* imu_factor = new IMUFactor(pre_integrations[1]);
ResidualBlockInfo *residual_block_info = new ResidualBlockInfo(imu_factor, NULL,
vector<double *>{para_Pose[0], para_SpeedBias[0], para_Pose[1], para_SpeedBias[1]},
vector<int>{0, 1});
marginalization_info->addResidualBlockInfo(residual_block_info);
}
}
{
int feature_index = -1;
for (auto &it_per_id : f_manager.feature)
{
it_per_id.used_num = it_per_id.feature_per_frame.size();
if (!(it_per_id.used_num >= 2 && it_per_id.start_frame < WINDOW_SIZE - 2))
continue;
++feature_index;
int imu_i = it_per_id.start_frame, imu_j = imu_i - 1;
if (imu_i != 0)
continue;
Vector3d pts_i = it_per_id.feature_per_frame[0].point;
for (auto &it_per_frame : it_per_id.feature_per_frame)
{
imu_j++;
if (imu_i == imu_j)
continue;
Vector3d pts_j = it_per_frame.point;
if (ESTIMATE_TD)
{
ProjectionTdFactor *f_td = new ProjectionTdFactor(pts_i, pts_j, it_per_id.feature_per_frame[0].velocity, it_per_frame.velocity,
it_per_id.feature_per_frame[0].cur_td, it_per_frame.cur_td,
it_per_id.feature_per_frame[0].uv.y(), it_per_frame.uv.y());
ResidualBlockInfo *residual_block_info = new ResidualBlockInfo(f_td, loss_function,
vector<double *>{para_Pose[imu_i], para_Pose[imu_j], para_Ex_Pose[0], para_Feature[feature_index], para_Td[0]},
vector<int>{0, 3});
marginalization_info->addResidualBlockInfo(residual_block_info);
}
else
{
ProjectionFactor *f = new ProjectionFactor(pts_i, pts_j);
ResidualBlockInfo *residual_block_info = new ResidualBlockInfo(f, loss_function,
vector<double *>{para_Pose[imu_i], para_Pose[imu_j], para_Ex_Pose[0], para_Feature[feature_index]},
vector<int>{0, 3});
marginalization_info->addResidualBlockInfo(residual_block_info);
}
}
}
}
TicToc t_pre_margin;
marginalization_info->preMarginalize();
printf("pre marginalization %f ms\n", t_pre_margin.toc());
TicToc t_margin;
marginalization_info->marginalize();
printf("marginalization %f ms\n", t_margin.toc());
std::unordered_map<long, double *> addr_shift;
for (int i = 1; i <= WINDOW_SIZE; i++)
{
addr_shift[reinterpret_cast<long>(para_Pose[i])] = para_Pose[i - 1];
addr_shift[reinterpret_cast<long>(para_SpeedBias[i])] = para_SpeedBias[i - 1];
}
for (int i = 0; i < NUM_OF_CAM; i++)
addr_shift[reinterpret_cast<long>(para_Ex_Pose[i])] = para_Ex_Pose[i];
if (ESTIMATE_TD)
{
addr_shift[reinterpret_cast<long>(para_Td[0])] = para_Td[0];
}
vector<double *> parameter_blocks = marginalization_info->getParameterBlocks(addr_shift);
if (last_marginalization_info)
delete last_marginalization_info;
last_marginalization_info = marginalization_info;
last_marginalization_parameter_blocks = parameter_blocks;
}
else
{
if (last_marginalization_info &&
std::count(std::begin(last_marginalization_parameter_blocks), std::end(last_marginalization_parameter_blocks), para_Pose[WINDOW_SIZE - 1]))
{
MarginalizationInfo *marginalization_info = new MarginalizationInfo();
vector2double();
if (last_marginalization_info)
{
vector<int> drop_set;
for (int i = 0; i < static_cast<int>(last_marginalization_parameter_blocks.size()); i++)
{
assert(last_marginalization_parameter_blocks[i] != para_SpeedBias[WINDOW_SIZE - 1]);
if (last_marginalization_parameter_blocks[i] == para_Pose[WINDOW_SIZE - 1])
drop_set.push_back(i);
}
// construct new marginlization_factor
MarginalizationFactor *marginalization_factor = new MarginalizationFactor(last_marginalization_info);
ResidualBlockInfo *residual_block_info = new ResidualBlockInfo(marginalization_factor, NULL,
last_marginalization_parameter_blocks,
drop_set);
marginalization_info->addResidualBlockInfo(residual_block_info);
}
TicToc t_pre_margin;
printf("begin marginalization\n");
marginalization_info->preMarginalize();
printf("end pre marginalization, %f ms\n", t_pre_margin.toc());
TicToc t_margin;
printf("begin marginalization\n");
marginalization_info->marginalize();
printf("end marginalization, %f ms\n", t_margin.toc());
std::unordered_map<long, double *> addr_shift;
for (int i = 0; i <= WINDOW_SIZE; i++)
{
if (i == WINDOW_SIZE - 1)
continue;
else if (i == WINDOW_SIZE)
{
addr_shift[reinterpret_cast<long>(para_Pose[i])] = para_Pose[i - 1];
addr_shift[reinterpret_cast<long>(para_SpeedBias[i])] = para_SpeedBias[i - 1];
}
else
{
addr_shift[reinterpret_cast<long>(para_Pose[i])] = para_Pose[i];
addr_shift[reinterpret_cast<long>(para_SpeedBias[i])] = para_SpeedBias[i];
}
}
for (int i = 0; i < NUM_OF_CAM; i++)
addr_shift[reinterpret_cast<long>(para_Ex_Pose[i])] = para_Ex_Pose[i];
if (ESTIMATE_TD)
{
addr_shift[reinterpret_cast<long>(para_Td[0])] = para_Td[0];
}
vector<double *> parameter_blocks = marginalization_info->getParameterBlocks(addr_shift);
if (last_marginalization_info)