边缘化（marginalization ）和稀疏化(sparsification)---ceres-solver
我拿港科技沈老师的VINS中的BA优化来阐述ceres-solver怎么做边缘化和稀疏化.
代码如下：
void Estimator::optimization()
{
ceres::Problem problem;
ceres::LossFunction *loss_function;
//loss_function = new ceres::HuberLoss(1.0);
loss_function = new ceres::CauchyLoss(1.0);//添加需要优化的变量（camera的pose，imu的biases）
for (int i = 0; i < WINDOW_SIZE + 1; i++)
{
ceres::LocalParameterization *local_parameterization = new PoseLocalParameterization();
problem.AddParameterBlock(para_Pose[i], SIZE_POSE, local_parameterization);
problem.AddParameterBlock(para_SpeedBias[i], SIZE_SPEEDBIAS);
}//添加camera和IMU的坐标变换的变量
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)
{
ROS_DEBUG("fix extinsic param");
problem.SetParameterBlockConstant(para_Ex_Pose[i]);
}
else
ROS_DEBUG("estimate extinsic param");
}
TicToc t_whole, t_prepare;//把要优化的变量转成数组的形式
vector2double();
//添加上一次边缘化的parameter blocks
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);
}
//添加当前sliding window中的优化变量
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]);
}
int f_m_cnt = 0;
int feature_index = -1;
for (auto &it_per_id : f_manager.feature)//遍历sliding window的帧数
{
it_per_id.used_num = it_per_id.feature_per_frame.size();//每一帧图像feature的个数
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)//遍历每一帧图像所有features
{
imu_j++;
if (imu_i == imu_j)
{
continue;
}
Vector3d pts_j = it_per_frame.point;
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++;
}
}
relocalize = false;
//loop close factor//闭环检测，闭环这里就不说了
if(LOOP_CLOSURE)
{
int loop_constraint_num = 0;
for (int k = 0; k < (int)retrive_data_vector.size(); k++)
{
for(int i = 0; i < WINDOW_SIZE; i++)
{
if(retrive_data_vector[k].header == Headers[i].stamp.toSec())
{
relocalize = true;
ceres::LocalParameterization *local_parameterization = new PoseLocalParameterization();
problem.AddParameterBlock(retrive_data_vector[k].loop_pose, SIZE_POSE, local_parameterization);
loop_window_index = i;
loop_constraint_num++;
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 <= i)
{
while(retrive_data_vector[k].features_ids[retrive_feature_index] < it_per_id.feature_id)
{
retrive_feature_index++;
}
if(retrive_data_vector[k].features_ids[retrive_feature_index] == it_per_id.feature_id)
{
Vector3d pts_j = Vector3d(retrive_data_vector[k].measurements[retrive_feature_index].x, retrive_data_vector[k].measurements[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], retrive_data_vector[k].loop_pose, para_Ex_Pose[0], para_Feature[feature_index]);
retrive_feature_index++;
}
}
}
}
}
}
ROS_DEBUG("loop constraint num: %d", loop_constraint_num);
}
ROS_DEBUG("visual measurement count: %d", f_m_cnt);
ROS_DEBUG("prepare for ceres: %f", t_prepare.toc());
ceres::Solver::Options options;
options.linear_solver_type = ceres::DENSE_SCHUR;//线性求解类型 为什么是Dense schur？
//options.num_threads = 2;
options.trust_region_strategy_type = ceres::DOGLEG;//信頼域类型
options.max_num_iterations = NUM_ITERATIONS;
//options.use_explicit_schur_complement = true;//选择schur
//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;
ROS_DEBUG("Iterations : %d", static_cast(summary.iterations.size()));
ROS_DEBUG("solver costs: %f", t_solver.toc());
// relative info between two loop frame if(LOOP_CLOSURE && relocalize)//计算闭环后的相对位姿
{
for (int k = 0; k < (int)retrive_data_vector.size(); k++)
{
for(int i = 0; i< WINDOW_SIZE; i++)
{
if(retrive_data_vector[k].header == Headers[i].stamp.toSec())
{
retrive_data_vector[k].relative_pose = true;
Matrix3d Rs_i = Quaterniond(para_Pose[i][6], para_Pose[i][3], para_Pose[i][4], para_Pose[i][5]).normalized().toRotationMatrix();
Vector3d Ps_i = Vector3d(para_Pose[i][0], para_Pose[i][1], para_Pose[i][2]);
Quaterniond Qs_loop;
Qs_loop = Quaterniond(retrive_data_vector[k].loop_pose[6], retrive_data_vector[k].loop_pose[3], retrive_data_vector[k].loop_pose[4], retrive_data_vector[k].loop_pose[5]).normalized().toRotationMatrix();
Matrix3d Rs_loop = Qs_loop.toRotationMatrix();
Vector3d Ps_loop = Vector3d( retrive_data_vector[k].loop_pose[0], retrive_data_vector[k].loop_pose[1], retrive_data_vector[k].loop_pose[2]);
retrive_data_vector[k].relative_t = Rs_loop.transpose() * (Ps_i - Ps_loop);
retrive_data_vector[k].relative_q = Rs_loop.transpose() * Rs_i;
retrive_data_vector[k].relative_yaw = Utility::normalizeAngle(Utility::R2ypr(Rs_i).x() - Utility::R2ypr(Rs_loop).x());
if (abs(retrive_data_vector[k].relative_yaw) > 30.0 || retrive_data_vector[k].relative_t.norm() > 20.0)
retrive_data_vector[k].relative_pose = false;
}
}
}
}
double2vector();
//边缘化操作
TicToc t_whole_marginalization;//沈老师这里边缘化有两个策略，如果在sliding window中第二近的frame是关键帧则丢弃sliding window中最老的帧、否则丢弃该帧。无论丢弃哪一帧，都需要边缘化。
if (marginalization_flag == MARGIN_OLD)//丢弃老的一帧。
{
MarginalizationInfo *marginalization_info = new MarginalizationInfo();
vector2double();
if (last_marginalization_info)
{
vector drop_set;
for (int i = 0; i < static_cast(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);//添加上一次边缘化的变量
}
{
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{para_Pose[0], para_SpeedBias[0], para_Pose[1], para_SpeedBias[1]},
vector{0, 1});
marginalization_info->addResidualBlockInfo(residual_block_info);//添加优化变量
}
}
{
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;
ProjectionFactor *f = new ProjectionFactor(pts_i, pts_j);
ResidualBlockInfo *residual_block_info = new ResidualBlockInfo(f, loss_function,
vector{para_Pose[imu_i], para_Pose[imu_j], para_Ex_Pose[0], para_Feature[feature_index]},
vector{0, 3});
marginalization_info->addResidualBlockInfo(residual_block_info);//添加优化变量
}
}
}
TicToc t_pre_margin;
marginalization_info->preMarginalize();
ROS_DEBUG("pre marginalization %f ms", t_pre_margin.toc());
TicToc t_margin;
marginalization_info->marginalize();
ROS_DEBUG("marginalization %f ms", t_margin.toc());
std::unordered_map addr_shift;
for (int i = 1; i <= WINDOW_SIZE; i++)
{
addr_shift[reinterpret_cast(para_Pose[i])] = para_Pose[i - 1];
addr_shift[reinterpret_cast(para_SpeedBias[i])] = para_SpeedBias[i - 1];
}
for (int i = 0; i < NUM_OF_CAM; i++)
addr_shift[reinterpret_cast(para_Ex_Pose[i])] = para_Ex_Pose[i];
vector 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 drop_set;
for (int i = 0; i < static_cast(last_marginalization_parameter_blocks.size()); i++)
{
ROS_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;
ROS_DEBUG("begin marginalization");
marginalization_info->preMarginalize();
ROS_DEBUG("end pre marginalization, %f ms", t_pre_margin.toc());
TicToc t_margin;
ROS_DEBUG("begin marginalization");
marginalization_info->marginalize();
ROS_DEBUG("end marginalization, %f ms", t_margin.toc());
std::unordered_map 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(para_Pose[i])] = para_Pose[i - 1];
addr_shift[reinterpret_cast(para_SpeedBias[i])] = para_SpeedBias[i - 1];
}
else
{
addr_shift[reinterpret_cast(para_Pose[i])] = para_Pose[i];
addr_shift[reinterpret_cast(para_SpeedBias[i])] = para_SpeedBias[i];
}
}
for (int i = 0; i < NUM_OF_CAM; i++)
addr_shift[reinterpret_cast(para_Ex_Pose[i])] = para_Ex_Pose[i];
vector 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;
}
}
ROS_DEBUG("whole marginalization costs: %f", t_whole_marginalization.toc());
ROS_DEBUG("whole time for ceres: %f", t_whole.toc());
}