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estimator.cpp
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estimator.cpp
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#include "estimator.h"
//#define LINEINCAM
Estimator::Estimator(): f_manager{Rs}
{
ROS_INFO("init begins");
clearState();
failure_occur = 0;
}
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();
lineProjectionFactor::sqrt_info = FOCAL_LENGTH / 1.5 * Matrix2d::Identity();
// lineProjectionFactor::sqrt_info = Matrix2d::Identity();
baseline_ = BASE_LINE;
}
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();
relocalize = false;
retrive_data_vector.clear();
relocalize_t = Eigen::Vector3d(0, 0, 0);
relocalize_r = Eigen::Matrix3d::Identity();
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;
}
// 如果 预积分数组pre_integrations 里还没有指向第frame_count帧的预积分量，那就创建
if (!pre_integrations[frame_count])
{
pre_integrations[frame_count] = new IntegrationBase{acc_0, gyr_0, Bas[frame_count], Bgs[frame_count]}; // acc_0, gyr_0 上一时刻的imu测量值
}
if (frame_count != 0)
{
pre_integrations[frame_count]->push_back(dt, linear_acceleration, angular_velocity); // 它会记录滑动窗口里两个关键帧之间的预积分量
//if(solver_flag != NON_LINEAR)
tmp_pre_integration->push_back(dt, linear_acceleration, angular_velocity); // 在每一个新的图像帧上，会创建这个东西。它只记录两个图像之间的预积分量
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);
// 用imu预测这个时刻的状态量p,q,v. 这些状态量会在swf里优化
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(); // R_{wi+1} = R_{wi} * R_{ii+1}
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;
}
//Eigen::Matrix<double, 5, 1>
void Estimator::processImage(const map<int, vector<pair<int, Eigen::Matrix<double, 5, 1>>>> &image, const map<int, vector<pair<int, Vector4d>>> &lines, const std_msgs::Header &header)
{
ROS_DEBUG("new image coming ------------------------------------------");
ROS_DEBUG("Adding feature points %lu", image.size());
// if (f_manager.addFeatureCheckParallax(frame_count, image)) // 当视差较大时，marg 老的关键帧
if(f_manager.addFeatureCheckParallax(frame_count, image, lines)) // 对检测到的特征进行存放处理
marginalization_flag = MARGIN_OLD;
else // 当视差较小时，比如静止，marg 新的图像帧
marginalization_flag = MARGIN_SECOND_NEW;
ROS_DEBUG("this frame is--------------------%s", marginalization_flag ? "reject" : "accept");
ROS_DEBUG("%s", marginalization_flag ? "Non-keyframe" : "Keyframe");
ROS_DEBUG("Solving %d", frame_count);
ROS_DEBUG("number of feature: %d", f_manager.getFeatureCount());
Headers[frame_count] = header;
ImageFrame imageframe(image, header.stamp.toSec());
imageframe.pre_integration = tmp_pre_integration; // tmp_pre_integration 记录了上次图像到这次图像之间的积分量
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]}; // 在新的图像上，重新开始一个预积分量
if(ESTIMATE_EXTRINSIC == 2)
{
ROS_INFO("calibrating extrinsic param, rotation movement is needed");
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))
{
ROS_WARN("initial extrinsic rotation calib success");
ROS_WARN_STREAM("initial extrinsic rotation: " << endl << calib_ric);
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(); // 三角化新特征 并 swf优化
slideWindow();
f_manager.removeFailures();
ROS_INFO("Initialization finish!");
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(); // 三角化新特征 并 swf优化
ROS_DEBUG("solver costs: %fms", t_solve.toc());
if (failureDetection())
{
ROS_WARN("failure detection!");
failure_occur = 1;
clearState();
setParameter();
ROS_WARN("system reboot!");
return;
}
TicToc t_margin;
slideWindow();
f_manager.removeFailures();
ROS_DEBUG("marginalization costs: %fms", 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];
}
}
// void Estimator::processImage(const map<int, vector<pair<int, Vector3d>>> &image, const map<int, vector<pair<int, Vector4d>>> &lines, const std_msgs::Header &header)
// // void Estimator::processImage(const map<int, vector<pair<int, Vector3d>>> &image,const map<int, vector<pair<int, Vector3d>>> &image1, const map<int, vector<pair<int, Vector4d>>> &lines, const std_msgs::Header &header)
// {
// ROS_DEBUG("new image coming ------------------------------------------");
// ROS_DEBUG("Adding feature points %lu", image.size());
// //if (f_manager.addFeatureCheckParallax(frame_count, image)) // 当视差较大时，marg 老的关键帧
// if(f_manager.addFeatureCheckParallax(frame_count, image, lines)) // 对检测到的特征进行存放处理
// marginalization_flag = MARGIN_OLD;
// else // 当视差较小时，比如静止，marg 新的图像帧
// marginalization_flag = MARGIN_SECOND_NEW;
// ROS_DEBUG("this frame is--------------------%s", marginalization_flag ? "reject" : "accept");
// ROS_DEBUG("%s", marginalization_flag ? "Non-keyframe" : "Keyframe");
// ROS_DEBUG("Solving %d", frame_count);
// ROS_DEBUG("number of feature: %d", f_manager.getFeatureCount());
// Headers[frame_count] = header;
// ImageFrame imageframe(image, header.stamp.toSec());
// imageframe.pre_integration = tmp_pre_integration; // tmp_pre_integration 记录了上次图像到这次图像之间的积分量
// 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]}; // 在新的图像上，重新开始一个预积分量
// if(ESTIMATE_EXTRINSIC == 2)
// {
// ROS_INFO("calibrating extrinsic param, rotation movement is needed");
// 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))
// {
// ROS_WARN("initial extrinsic rotation calib success");
// ROS_WARN_STREAM("initial extrinsic rotation: " << endl << calib_ric);
// 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(); // 三角化新特征 并 swf优化
// slideWindow();
// f_manager.removeFailures();
// ROS_INFO("Initialization finish!");
// 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(); // 三角化新特征 并 swf优化
// ROS_DEBUG("solver costs: %fms", t_solve.toc());
// if (failureDetection())
// {
// ROS_WARN("failure detection!");
// failure_occur = 1;
// clearState();
// setParameter();
// ROS_WARN("system reboot!");
// return;
// }
// TicToc t_margin;
// slideWindow();
// f_manager.removeFailures();
// ROS_DEBUG("marginalization costs: %fms", 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];
// }
// }
void Estimator::processImage(const map<int, vector<pair<int, Vector3d>>> &image, const map<int, vector<pair<int, Vector8d>>> &lines, const std_msgs::Header &header)
{
ROS_DEBUG("new image coming ------------------------------------------");
ROS_DEBUG("Adding feature points %lu", image.size());
//if (f_manager.addFeatureCheckParallax(frame_count, image)) // 当视差较大时，marg 老的关键帧
if(f_manager.addFeatureCheckParallax(frame_count, image, lines)) // 对检测到的特征进行存放处理
marginalization_flag = MARGIN_OLD;
else // 当视差较小时，比如静止，marg 新的图像帧
marginalization_flag = MARGIN_SECOND_NEW;
ROS_DEBUG("this frame is--------------------%s", marginalization_flag ? "reject" : "accept");
ROS_DEBUG("%s", marginalization_flag ? "Non-keyframe" : "Keyframe");
ROS_DEBUG("Solving %d", frame_count);
ROS_DEBUG("number of feature: %d", f_manager.getFeatureCount());
Headers[frame_count] = header;
ImageFrame imageframe(image, header.stamp.toSec());
imageframe.pre_integration = tmp_pre_integration; // tmp_pre_integration 记录了上次图像到这次图像之间的积分量
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]}; // 在新的图像上，重新开始一个预积分量
if(ESTIMATE_EXTRINSIC == 2)
{
ROS_INFO("calibrating extrinsic param, rotation movement is needed");
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))
{
ROS_WARN("initial extrinsic rotation calib success");
ROS_WARN_STREAM("initial extrinsic rotation: " << endl << calib_ric);
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(); // 三角化新特征 并 swf优化
slideWindow();
f_manager.removeFailures();
ROS_INFO("Initialization finish!");
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(); // 三角化新特征 并 swf优化
ROS_DEBUG("solver costs: %fms", t_solve.toc());
if (failureDetection())
{
ROS_WARN("failure detection!");
failure_occur = 1;
clearState();
setParameter();
ROS_WARN("system reboot!");
return;
}
TicToc t_margin;
slideWindow();
f_manager.removeFailures();
ROS_DEBUG("marginalization costs: %fms", 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];
}
}
void Estimator::processImage(const map<int, vector<pair<int, Vector3d>>> &image, const std_msgs::Header &header)
{
ROS_DEBUG("new image coming ------------------------------------------");
ROS_DEBUG("Adding feature points %lu", image.size());
if (f_manager.addFeatureCheckParallax(frame_count, image)) // 当视差较大时，marg 老的关键帧
marginalization_flag = MARGIN_OLD;
else // 当视差较小时，比如静止，marg 新的图像帧
marginalization_flag = MARGIN_SECOND_NEW;
ROS_DEBUG("this frame is--------------------%s", marginalization_flag ? "reject" : "accept");
ROS_DEBUG("%s", marginalization_flag ? "Non-keyframe" : "Keyframe");
ROS_DEBUG("Solving %d", frame_count);
ROS_DEBUG("number of feature: %d", f_manager.getFeatureCount());
Headers[frame_count] = header;
ImageFrame imageframe(image, header.stamp.toSec());
imageframe.pre_integration = tmp_pre_integration; // tmp_pre_integration 记录了上次图像到这次图像之间的积分量
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]}; // 在新的图像上，重新开始一个预积分量
if(ESTIMATE_EXTRINSIC == 2)
{
ROS_INFO("calibrating extrinsic param, rotation movement is needed");
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))
{
ROS_WARN("initial extrinsic rotation calib success");
ROS_WARN_STREAM("initial extrinsic rotation: " << endl << calib_ric);
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(); // 三角化新特征 并 swf优化
slideWindow();
f_manager.removeFailures();
ROS_INFO("Initialization finish!");
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(); // 三角化新特征 并 swf优化
ROS_DEBUG("solver costs: %fms", t_solve.toc());
if (failureDetection())
{
ROS_WARN("failure detection!");
failure_occur = 1;
clearState();
setParameter();
ROS_WARN("system reboot!");
return;
}
TicToc t_margin;
slideWindow();
f_manager.removeFailures();
ROS_DEBUG("marginalization costs: %fms", 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));
//ROS_WARN("IMU variation %f!", var);
if(var < 0.25)
{
ROS_INFO("IMU excitation not enouth!");
//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))
{
ROS_INFO("Not enough features or parallax; Move device around");
return false;
}
GlobalSFM sfm;
if(!sfm.construct(frame_count + 1, Q, T, l,
relative_R, relative_T,
sfm_f, sfm_tracked_points))
{
ROS_DEBUG("global SFM failed!");
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;
//cout << "feature id " << feature_id;
for (auto &i_p : id_pts.second)
{
//cout << " pts image_frame " << (i_p.second.head<2>() * 460 ).transpose() << endl;
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;
ROS_DEBUG("Not enough points for solve pnp !");
return false;
}
if (! cv::solvePnP(pts_3_vector, pts_2_vector, K, D, rvec, t, 1))
{
ROS_DEBUG("solve pnp fail!");
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
{
ROS_INFO("misalign visual structure with IMU");
return false;
}
}
bool Estimator::visualInitialAlign()
{
TicToc t_g;
VectorXd x;
//solve scale
bool result = VisualIMUAlignment(all_image_frame, Bgs, g, x);
if(!result)
{
ROS_WARN("solve g failed!");
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];
}
ROS_DEBUG_STREAM("g0 " << g.transpose());
ROS_DEBUG_STREAM("my R0 " << Utility::R2ypr(Rs[0]).transpose());
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);
//std::cout<<corres.size()<<"\n";
if (corres.size() > 20) // 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());
// std::cout << "average_parallax: "<<average_parallax * 460 << std::endl;
// if(average_parallax * 460 > 15 && m_estimator.solveRelativeRT(corres, relative_R, relative_T)) // 30
if(average_parallax * 460 > 30 && m_estimator.solveRelativeRT(corres, relative_R, relative_T))
{
l = i;
ROS_DEBUG("average_parallax %f choose l %d and newest frame to triangulate the whole structure", 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);
if(baseline_ > 0) //stereo
f_manager.triangulateLine(baseline_);
else
f_manager.triangulateLine(Ps, tic, ric);
ROS_DEBUG("triangulation costs %f", t_tri.toc());
// optimization();
onlyLineOpt(); // 三角化以后，优化一把
optimizationwithLine();
#ifdef LINEINCAM
LineBAincamera();
#else
// LineBA();
#endif
}
}
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);
#ifdef LINEINCAM
MatrixXd lineorth = f_manager.getLineOrthVectorInCamera();
#else
MatrixXd lineorth = f_manager.getLineOrthVector(Ps,tic,ric);
#endif
for (int i = 0; i < f_manager.getLineFeatureCount(); ++i) {
para_LineFeature[i][0] = lineorth.row(i)[0];
para_LineFeature[i][1] = lineorth.row(i)[1];
para_LineFeature[i][2] = lineorth.row(i)[2];
para_LineFeature[i][3] = lineorth.row(i)[3];
if(i > NUM_OF_F)
std::cerr << " 1000 1000 1000 1000 1000 \n\n";
}
}
void Estimator::double2vector()
{
// 相机姿态需要变化考虑优化以后，把yaw量旋转回去
Vector3d origin_R0 = Utility::R2ypr(Rs[0]); //优化之前的0th的姿态
Vector3d origin_P0 = Ps[0];
if (failure_occur)
{
origin_R0 = Utility::R2ypr(last_R0);
origin_P0 = last_P0;
failure_occur = 0;
}
// 优化以后的0th的姿态
Vector3d origin_R00 = Utility::R2ypr(Quaterniond(para_Pose[0][6],
para_Pose[0][3],
para_Pose[0][4],
para_Pose[0][5]).toRotationMatrix());
// 优化前后，yaw的变化
double y_diff = origin_R0.x() - origin_R00.x();
//TODO
Matrix3d rot_diff = Utility::ypr2R(Vector3d(y_diff, 0, 0));
// 由于VI系统的（绝对位置x,y,z,以及yaw）是不可观的。而优化过程中没有固定yaw角，因此yaw会朝着使得误差函数最小的方向优化，但这不一定是正确的。
// 所以这里把 yaw角的变化量给旋转回去。
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();
// Position 也转移到yaw角优化前的 0th坐在的世界坐标下
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]);
}
// 跟yaw没关系，所以不用管优化前后yaw的变化
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();
}
//std::cout <<"----------\n"<< Rwow1 <<"\n"<<twow1<<std::endl;
MatrixXd lineorth_vec(f_manager.getLineFeatureCount(),4);
for (int i = 0; i < f_manager.getLineFeatureCount(); ++i) {
Vector4d orth(para_LineFeature[i][0],
para_LineFeature[i][1],
para_LineFeature[i][2],
para_LineFeature[i][3]);
lineorth_vec.row(i) = orth;
}
#ifdef LINEINCAM
f_manager.setLineOrthInCamera(lineorth_vec);
#else
f_manager.setLineOrth(lineorth_vec,Ps,Rs,tic,ric);
#endif
VectorXd dep = f_manager.getDepthVector();
for (int i = 0; i < f_manager.getFeatureCount(); i++)
dep(i) = para_Feature[i][0];
f_manager.setDepth(dep);
// 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;
}
}
void Estimator::double2vector2()
{
// 六自由度优化的时候，整个窗口会在空间中任意优化，这时候我们需要把第一帧在yaw,position上的增量给去掉，因为vins在这几个方向上不可观，他们优化的增量也不可信。
// 所以这里的操作过程就相当于是 fix 第一帧的 yaw 和 postion, 使得整个轨迹不会在空间中任意飘。
// 相机姿态需要变化考虑优化以后，把yaw量旋转回去
Vector3d origin_R0 = Utility::R2ypr(Rs[0]); //优化之前的0th的姿态
Vector3d origin_P0 = Ps[0];
if (failure_occur)
{
origin_R0 = Utility::R2ypr(last_R0);
origin_P0 = last_P0;
failure_occur = 0;
}
// 优化以后的0th的姿态
Vector3d origin_R00 = Utility::R2ypr(Quaterniond(para_Pose[0][6],
para_Pose[0][3],
para_Pose[0][4],
para_Pose[0][5]).toRotationMatrix());
// 优化前后，yaw的变化
double y_diff = origin_R0.x() - origin_R00.x();
//TODO
Matrix3d rot_diff = Utility::ypr2R(Vector3d(y_diff, 0, 0));
// 由于VI系统的（绝对位置x,y,z,以及yaw）是不可观的。而优化过程中没有固定yaw角，因此yaw会朝着使得误差函数最小的方向优化，但这不一定是正确的。
// 所以这里把 yaw角的变化量给旋转回去。
for (int i = 0; i <= WINDOW_SIZE; i++)
{