InitResult KltHomographyInit::addSecondFrame(FramePtr frame_cur)
{
trackKlt(frame_ref_, frame_cur, px_ref_, px_cur_, f_ref_, f_cur_, disparities_);
SVO_INFO_STREAM("Init: KLT tracked "<< disparities_.size() <<" features");
if(disparities_.size() < Config::initMinTracked())
return FAILURE;
double disparity = vk::getMedian(disparities_);
SVO_INFO_STREAM("Init: KLT "<<disparity<<"px average disparity.");
if(disparity < Config::initMinDisparity())
return NO_KEYFRAME;
computeHomography(
f_ref_, f_cur_,
frame_ref_->cam_->errorMultiplier2(), Config::poseOptimThresh(),
inliers_, xyz_in_cur_, T_cur_from_ref_);
SVO_INFO_STREAM("Init: Homography RANSAC "<<inliers_.size()<<" inliers.");
if(inliers_.size() < Config::initMinInliers())
{
SVO_WARN_STREAM("Init WARNING: "<<Config::initMinInliers()<<" inliers minimum required.");
return FAILURE;
}
// Rescale the map such that the mean scene depth is equal to the specified scale
vector<double> depth_vec;
for(size_t i=0; i<xyz_in_cur_.size(); ++i)
depth_vec.push_back((xyz_in_cur_[i]).z());
double scene_depth_median = vk::getMedian(depth_vec);
double scale = Config::mapScale()/scene_depth_median;
frame_cur->T_f_w_ = T_cur_from_ref_ * frame_ref_->T_f_w_;
frame_cur->T_f_w_.translation() =
-frame_cur->T_f_w_.rotation_matrix()*(frame_ref_->pos() + scale*(frame_cur->pos() - frame_ref_->pos()));
// For each inlier create 3D point and add feature in both frames
SE3 T_world_cur = frame_cur->T_f_w_.inverse();
for(vector<int>::iterator it=inliers_.begin(); it!=inliers_.end(); ++it)
{
Vector2d px_cur(px_cur_[*it].x, px_cur_[*it].y);
Vector2d px_ref(px_ref_[*it].x, px_ref_[*it].y);
if(frame_ref_->cam_->isInFrame(px_cur.cast<int>(), 10) && frame_ref_->cam_->isInFrame(px_ref.cast<int>(), 10) && xyz_in_cur_[*it].z() > 0)
{
Vector3d pos = T_world_cur * (xyz_in_cur_[*it]*scale);
Point* new_point = new Point(pos);
Feature* ftr_cur(new Feature(frame_cur.get(), new_point, px_cur, f_cur_[*it], 0));
frame_cur->addFeature(ftr_cur);
new_point->addFrameRef(ftr_cur);
Feature* ftr_ref(new Feature(frame_ref_.get(), new_point, px_ref, f_ref_[*it], 0));
frame_ref_->addFeature(ftr_ref);
new_point->addFrameRef(ftr_ref);
}
}
return SUCCESS;
}
InitResult KltHomographyInit::addSecondFrame(FramePtr frame_cur, Matrix3d orient, Vector3d pos)
{
trackKlt(frame_ref_, frame_cur, px_ref_, px_cur_, f_ref_, f_cur_, disparities_);
SVO_INFO_STREAM("Init: KLT tracked "<< disparities_.size() <<" features");
if(disparities_.size() < Config::initMinTracked())
return FAILURE;
double disparity = vk::getMedian(disparities_);
SVO_INFO_STREAM("Init: KLT "<<disparity<<"px average disparity.");
if(disparity < Config::initMinDisparity())
return NO_KEYFRAME;
computeHomography(
f_ref_, f_cur_,
frame_ref_->cam_->errorMultiplier2(), Config::poseOptimThresh(),
inliers_, xyz_in_cur_, T_cur_from_ref_);
SVO_INFO_STREAM("Init: Homography RANSAC "<<inliers_.size()<<" inliers.");
if(inliers_.size() < Config::initMinInliers())
{
SVO_WARN_STREAM("Init WARNING: "<<Config::initMinInliers()<<" inliers minimum required.");
return FAILURE;
}
// Transformation in real world
T_cur_frame_real_scale = SE3(orient, pos);
Vector3d trans = T_cur_frame_real_scale.translation() - T_first_frame_real_scale.translation();
double length_real = sqrt(pow(trans[0],2) + pow(trans[1],2) + pow(trans[2],2));
SVO_INFO_STREAM("Real world transform x: " << trans[0] << " y:" << trans[1] << " z:" << trans[2] << " length:" << length_real);
double x = T_cur_from_ref_.translation()[0];
double y = T_cur_from_ref_.translation()[1];
double z = T_cur_from_ref_.translation()[2];
double length_svo = sqrt(pow(x,2) + pow(y,2) + pow(z,2));
SVO_INFO_STREAM("SVO transform x: " << x << " y:" << y << " z:" << z << " length:" << length_svo);
#ifdef USE_ASE_IMU
// Rescale the map such that the real length of the movement matches with the svo movement length
double scale =length_real / length_svo;
#else
// Rescale the map such that the mean scene depth is equal to the specified scale
vector<double> depth_vec;
for(size_t i=0; i<xyz_in_cur_.size(); ++i)
depth_vec.push_back((xyz_in_cur_[i]).z());
double scene_depth_median = vk::getMedian(depth_vec);
double scale = Config::mapScale()/scene_depth_median;
#endif
frame_cur->T_f_w_ = T_cur_from_ref_ * frame_ref_->T_f_w_;
frame_cur->T_f_w_.translation() =
-frame_cur->T_f_w_.rotation_matrix()*(frame_ref_->pos() + scale*(frame_cur->pos() - frame_ref_->pos()));
//frame_cur->T_f_w_ = T_cur_frame_real_scale;
//frame_ref_->T_f_w_ = T_first_frame_real_scale;
// // Rescale the map such that the mean scene depth is equal to the specified scale
// vector<double> depth_vec;
// for(size_t i=0; i<xyz_in_cur_.size(); ++i)
// depth_vec.push_back((xyz_in_cur_[i]).z());
// double scene_depth_median = vk::getMedian(depth_vec);
// double scale = Config::mapScale()/scene_depth_median;
// frame_cur->T_f_w_ = T_cur_from_ref_ * frame_ref_->T_f_w_;
// frame_cur->T_f_w_.translation() =
// -frame_cur->T_f_w_.rotation_matrix()*(frame_ref_->pos() + scale*(frame_cur->pos() - frame_ref_->pos()));
// For each inlier create 3D point and add feature in both frames
SE3 T_world_cur = frame_cur->T_f_w_.inverse();
for(vector<int>::iterator it=inliers_.begin(); it!=inliers_.end(); ++it)
{
Vector2d px_cur(px_cur_[*it].x, px_cur_[*it].y);
Vector2d px_ref(px_ref_[*it].x, px_ref_[*it].y);
if(frame_ref_->cam_->isInFrame(px_cur.cast<int>(), 10) && frame_ref_->cam_->isInFrame(px_ref.cast<int>(), 10) && xyz_in_cur_[*it].z() > 0)
{
Vector3d pos = T_world_cur * (xyz_in_cur_[*it]*scale);
Point* new_point = new Point(pos);
Feature* ftr_cur(new Feature(frame_cur.get(), new_point, px_cur, f_cur_[*it], 0));
frame_cur->addFeature(ftr_cur);
new_point->addFrameRef(ftr_cur);
Feature* ftr_ref(new Feature(frame_ref_.get(), new_point, px_ref, f_ref_[*it], 0));
frame_ref_->addFeature(ftr_ref);
new_point->addFrameRef(ftr_ref);
}
}
return SUCCESS;
}
InitResult Initialization::addSecondFrame(FramePtr frame_cur)
{
trackKlt(frame_ref_, frame_cur, px_ref_, px_cur_, f_ref_, f_cur_, disparities_);
std::cout << "Init: KLT tracked " << disparities_.size() << " features" << std::endl;
// 符合光流跟踪的特征数
if (disparities_.size() < 50)
return FAILURE;
// 对两帧光流跟踪之后像素差值的中值
double disparity = getMedian(disparities_);
std::cout << "Init: KLT " << disparity << "px average disparity." << std::endl;
// 如果中值小于给定配置参数，则表明这一帧不是关键帧，也就是刚开始的时候两帧不能太近
if (disparity < 50.0)
return NO_KEYFRAME;
// 计算单应矩阵
computeHomography(
f_ref_, f_cur_,
frame_ref_->cam_->getFocalLength(), 2.0,
inliers_, xyz_in_cur_, T_cur_from_ref_);
std::cout << "Init: Homography RANSAC " << inliers_.size() << " inliers." << std::endl;
// 根据计算单应矩阵之后，内点个数判断是否跟踪
if (inliers_.size() < 40)
{
std::cerr << "Init WARNING: 40 inliers minimum required." << std::endl;
return FAILURE;
}
// 通过单应矩阵，对两帧之间的特征形成的3d点进行计算，计算这些3d的深度中值，转换到指定的scale
std::vector<double> depth_vec;
for (size_t i = 0; i < xyz_in_cur_.size(); ++i)
depth_vec.push_back((xyz_in_cur_[i]).z());
double scene_depth_median = getMedian(depth_vec);
double scale = 1.0 / scene_depth_median;
// 计算相对变换SE3
frame_cur->T_f_w_ = T_cur_from_ref_ * frame_ref_->T_f_w_;
// 对位移变换添加尺度
frame_cur->T_f_w_.translation() =
-frame_cur->T_f_w_.rotation_matrix()*(frame_ref_->pos() + scale*(frame_cur->pos() - frame_ref_->pos()));
// 对每个内点创建3D点，设置特征，添加到这两帧中
SE3 T_world_cur = frame_cur->T_f_w_.inverse();
for (std::vector<int>::iterator it = inliers_.begin(); it != inliers_.end(); ++it)
{
Vector2d px_cur(px_cur_[*it].x, px_cur_[*it].y);
Vector2d px_ref(px_ref_[*it].x, px_ref_[*it].y);
if (frame_ref_->cam_->isInFrame(px_cur.cast<int>(), 10) && frame_ref_->cam_->isInFrame(px_ref.cast<int>(), 10) && xyz_in_cur_[*it].z() > 0)
{
Vector3d pos = T_world_cur * (xyz_in_cur_[*it] * scale);// 将相机下的点坐标转世界坐标
Point3D *new_point = new Point3D(pos);
Feature* ftr_cur = new Feature(frame_cur.get(), new_point, px_cur, f_cur_[*it], 0);
frame_cur->addFeature(ftr_cur);
// 将同一个点对应的特征保存起来，这样点删除了，对应的特征都可以删除
new_point->addFrameRef(ftr_cur);
Feature* ftr_ref = new Feature(frame_ref_.get(), new_point, px_ref, f_ref_[*it], 0);
frame_ref_->addFeature(ftr_ref);
new_point->addFrameRef(ftr_ref);
}
}
return SUCCESS;
}
