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Merge branch 'LM' into 'master' 

Added Levengert-Marquadt support

See merge request basalt/basalt!32
This commit is contained in:
Vladyslav Usenko 2019-08-13 10:03:14 +00:00
parent de3e23ce2a a2eeb00290
commit 397ddc159c
14 changed files with 436 additions and 115 deletions
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8
data/euroc_config.json
View File

@ -20,6 +20,9 @@
"config.vio_filter_iteration": 4,
"config.vio_max_iterations": 7,
"config.vio_enforce_realtime": false,
"config.vio_use_lm": false,
"config.vio_lm_lambda_min": 1e-32,
"config.vio_lm_lambda_max": 1e2,
"config.mapper_obs_std_dev": 0.25,
"config.mapper_obs_huber_thresh": 1.5,
@ -33,6 +36,9 @@
"config.mapper_second_best_test_ratio": 1.2,
"config.mapper_bow_num_bits": 16,
"config.mapper_min_triangulation_dist": 0.07,
"config.mapper_no_factor_weights": false
"config.mapper_no_factor_weights": false,
"config.mapper_use_lm": false,
"config.mapper_lm_lambda_min": 1e-32,
"config.mapper_lm_lambda_max": 1e3
}
}

8
data/euroc_config_no_weights.json
View File

@ -20,6 +20,9 @@
"config.vio_filter_iteration": 4,
"config.vio_max_iterations": 7,
"config.vio_enforce_realtime": false,
"config.vio_use_lm": false,
"config.vio_lm_lambda_min": 1e-32,
"config.vio_lm_lambda_max": 1e2,
"config.mapper_obs_std_dev": 0.25,
"config.mapper_obs_huber_thresh": 1.5,
@ -33,6 +36,9 @@
"config.mapper_second_best_test_ratio": 1.2,
"config.mapper_bow_num_bits": 16,
"config.mapper_min_triangulation_dist": 0.07,
"config.mapper_no_factor_weights": true
"config.mapper_no_factor_weights": true,
"config.mapper_use_lm": false,
"config.mapper_lm_lambda_min": 1e-32,
"config.mapper_lm_lambda_max": 1e3
}
}

8
data/tumvi_512_config.json
View File

@ -21,6 +21,9 @@
"config.vio_filter_iteration": 4,
"config.vio_max_iterations": 7,
"config.vio_enforce_realtime": false,
"config.vio_use_lm": false,
"config.vio_lm_lambda_min": 1e-32,
"config.vio_lm_lambda_max": 1e2,
"config.mapper_obs_std_dev": 0.25,
"config.mapper_obs_huber_thresh": 1.5,
@ -34,6 +37,9 @@
"config.mapper_second_best_test_ratio": 1.2,
"config.mapper_bow_num_bits": 16,
"config.mapper_min_triangulation_dist": 0.07,
"config.mapper_no_factor_weights": false
"config.mapper_no_factor_weights": false,
"config.mapper_use_lm": false,
"config.mapper_lm_lambda_min": 1e-32,
"config.mapper_lm_lambda_max": 1e3
}
}

103
include/basalt/utils/imu_types.h
View File

@ -57,7 +57,9 @@ static const Eigen::Vector3d g(0, 0, -9.81);
static const Eigen::Vector3d g_dir(0, 0, -1);
} // namespace constants
struct PoseVelBiasStateWithLin : private PoseVelBiasState {
struct PoseVelBiasStateWithLin {
using VecN = PoseVelBiasState::VecN;
PoseVelBiasStateWithLin() {
linearized = false;
delta.setZero();
@ -67,14 +69,14 @@ struct PoseVelBiasStateWithLin : private PoseVelBiasState {
const Eigen::Vector3d& vel_w_i,
const Eigen::Vector3d& bias_gyro,
const Eigen::Vector3d& bias_accel, bool linearized)
: PoseVelBiasState(t_ns, T_w_i, vel_w_i, bias_gyro, bias_accel),
linearized(linearized) {
: linearized(linearized),
state_linearized(t_ns, T_w_i, vel_w_i, bias_gyro, bias_accel) {
delta.setZero();
state_current = *this;
state_current = state_linearized;
}
PoseVelBiasStateWithLin(const PoseVelBiasState& other)
: PoseVelBiasState(other), linearized(false) {
: linearized(false), state_linearized(other) {
delta.setZero();
state_current = other;
}
@ -88,115 +90,152 @@ struct PoseVelBiasStateWithLin : private PoseVelBiasState {
void applyInc(const VecN& inc) {
if (!linearized) {
PoseVelBiasState::applyInc(inc);
state_linearized.applyInc(inc);
} else {
delta += inc;
state_current = *this;
state_current = state_linearized;
state_current.applyInc(delta);
}
}
inline const PoseVelBiasState& getState() const {
if (!linearized) {
return *this;
return state_linearized;
} else {
return state_current;
}
}
inline const PoseVelBiasState& getStateLin() const { return *this; }
inline const PoseVelBiasState& getStateLin() const {
return state_linearized;
}
inline bool isLinearized() const { return linearized; }
inline const VecN& getDelta() const { return delta; }
inline int64_t getT_ns() const { return t_ns; }
inline int64_t getT_ns() const { return state_linearized.t_ns; }
friend struct PoseStateWithLin;
inline void backup() {
backup_delta = delta;
backup_state_linearized = state_linearized;
backup_state_current = state_current;
}
inline void restore() {
delta = backup_delta;
state_linearized = backup_state_linearized;
state_current = backup_state_current;
}
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
private:
bool linearized;
VecN delta;
PoseVelBiasState state_linearized, state_current;
PoseVelBiasState state_current;
VecN backup_delta;
PoseVelBiasState backup_state_linearized, backup_state_current;
friend class cereal::access;
template <class Archive>
void serialize(Archive& ar) {
ar(T_w_i);
ar(vel_w_i);
ar(bias_gyro);
ar(bias_accel);
ar(state_linearized.T_w_i);
ar(state_linearized.vel_w_i);
ar(state_linearized.bias_gyro);
ar(state_linearized.bias_accel);
ar(state_current.T_w_i);
ar(state_current.vel_w_i);
ar(state_current.bias_gyro);
ar(state_current.bias_accel);
ar(delta);
ar(linearized);
ar(t_ns);
ar(state_linearized.t_ns);
}
};
struct PoseStateWithLin : private PoseState {
struct PoseStateWithLin {
using VecN = PoseState::VecN;
PoseStateWithLin() {
linearized = false;
delta.setZero();
};
PoseStateWithLin(int64_t t_ns, const Sophus::SE3d& T_w_i)
: PoseState(t_ns, T_w_i), linearized(false) {
: linearized(false), pose_linearized(t_ns, T_w_i) {
delta.setZero();
T_w_i_current = T_w_i;
}
PoseStateWithLin(const PoseVelBiasStateWithLin& other)
: PoseState(other.t_ns, other.T_w_i),
linearized(other.linearized),
delta(other.delta.head<6>()) {
T_w_i_current = T_w_i;
incPose(delta, T_w_i_current);
: linearized(other.linearized),
delta(other.delta.head<6>()),
pose_linearized(other.state_linearized.t_ns,
other.state_linearized.T_w_i) {
T_w_i_current = pose_linearized.T_w_i;
PoseState::incPose(delta, T_w_i_current);
}
inline const Sophus::SE3d& getPose() const {
if (!linearized) {
return T_w_i;
return pose_linearized.T_w_i;
} else {
return T_w_i_current;
}
}
inline const Sophus::SE3d& getPoseLin() const { return T_w_i; }
inline const Sophus::SE3d& getPoseLin() const {
return pose_linearized.T_w_i;
}
inline void applyInc(const VecN& inc) {
if (!linearized) {
incPose(inc, T_w_i);
PoseState::incPose(inc, pose_linearized.T_w_i);
} else {
delta += inc;
T_w_i_current = T_w_i;
incPose(delta, T_w_i_current);
T_w_i_current = pose_linearized.T_w_i;
PoseState::incPose(delta, T_w_i_current);
}
}
inline bool isLinearized() const { return linearized; }
inline const VecN& getDelta() const { return delta; }
inline int64_t getT_ns() const { return t_ns; }
inline int64_t getT_ns() const { return pose_linearized.t_ns; }
inline void backup() {
backup_delta = delta;
backup_pose_linearized = pose_linearized;
backup_T_w_i_current = T_w_i_current;
}
inline void restore() {
delta = backup_delta;
pose_linearized = backup_pose_linearized;
T_w_i_current = backup_T_w_i_current;
}
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
private:
bool linearized;
VecN delta;
PoseState pose_linearized;
Sophus::SE3d T_w_i_current;
VecN backup_delta;
PoseState backup_pose_linearized;
Sophus::SE3d backup_T_w_i_current;
friend class cereal::access;
template <class Archive>
void serialize(Archive& ar) {
ar(T_w_i);
ar(pose_linearized.T_w_i);
ar(T_w_i_current);
ar(delta);
ar(linearized);
ar(t_ns);
ar(pose_linearized.t_ns);
}
};

8
include/basalt/utils/vio_config.h
View File

@ -68,6 +68,10 @@ struct VioConfig {
bool vio_enforce_realtime;
bool vio_use_lm;
double vio_lm_lambda_min;
double vio_lm_lambda_max;
double mapper_obs_std_dev;
double mapper_obs_huber_thresh;
int mapper_detection_num_points;
@ -81,5 +85,9 @@ struct VioConfig {
int mapper_bow_num_bits;
double mapper_min_triangulation_dist;
bool mapper_no_factor_weights;
bool mapper_use_lm;
double mapper_lm_lambda_min;
double mapper_lm_lambda_max;
};
} // namespace basalt

12
include/basalt/vi_estimator/ba_base.h
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@ -368,6 +368,18 @@ class BundleAdjustmentBase {
AccumT accum;
};
inline void backup() {
for (auto& kv : frame_states) kv.second.backup();
for (auto& kv : frame_poses) kv.second.backup();
lmdb.backup();
}
inline void restore() {
for (auto& kv : frame_states) kv.second.restore();
for (auto& kv : frame_poses) kv.second.backup();
lmdb.restore();
}
// protected:
PoseStateWithLin getPoseStateWithLin(int64_t t_ns) const {
auto it = frame_poses.find(t_ns);

2
include/basalt/vi_estimator/keypoint_vio.h
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@ -228,6 +228,8 @@ class KeypointVioEstimator : public VioEstimatorBase,
VioConfig config;
double lambda, min_lambda, max_lambda, lambda_vee;
int64_t msckf_kf_id;
std::shared_ptr<std::thread> processing_thread;

22
include/basalt/vi_estimator/landmark_database.h
View File

@ -44,7 +44,21 @@ struct KeypointPosition {
Eigen::Vector2d dir;
double id;
inline void backup() {
backup_dir = dir;
backup_id = id;
}
inline void restore() {
dir = backup_dir;
id = backup_id;
}
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
private:
Eigen::Vector2d backup_dir;
double backup_id;
};
struct KeypointObservation {
@ -92,6 +106,14 @@ class LandmarkDatabase {
void removeObservations(int lm_id, const std::set<TimeCamId>& obs);
inline void backup() {
for (auto& kv : kpts) kv.second.backup();
}
inline void restore() {
for (auto& kv : kpts) kv.second.restore();
}
private:
Eigen::unordered_map<int, KeypointPosition> kpts;
Eigen::map<TimeCamId,

2
include/basalt/vi_estimator/nfr_mapper.h
View File

@ -205,5 +205,7 @@ class NfrMapper : public BundleAdjustmentBase {
std::shared_ptr<HashBow<256>> hash_bow_database;
VioConfig config;
double lambda, min_lambda, max_lambda, lambda_vee;
};
} // namespace basalt

16
src/utils/vio_config.cpp
View File

@ -68,6 +68,10 @@ VioConfig::VioConfig() {
vio_enforce_realtime = false;
vio_use_lm = false;
vio_lm_lambda_min = 1e-32;
vio_lm_lambda_max = 1e2;
mapper_obs_std_dev = 0.25;
mapper_obs_huber_thresh = 1.5;
mapper_detection_num_points = 800;
@ -81,6 +85,10 @@ VioConfig::VioConfig() {
mapper_bow_num_bits = 16;
mapper_min_triangulation_dist = 0.07;
mapper_no_factor_weights = false;
mapper_use_lm = false;
mapper_lm_lambda_min = 1e-32;
mapper_lm_lambda_max = 1e2;
}
void VioConfig::save(const std::string& filename) {
@ -132,6 +140,10 @@ void serialize(Archive& ar, basalt::VioConfig& config) {
ar(CEREAL_NVP(config.vio_enforce_realtime));
ar(CEREAL_NVP(config.vio_use_lm));
ar(CEREAL_NVP(config.vio_lm_lambda_min));
ar(CEREAL_NVP(config.vio_lm_lambda_max));
ar(CEREAL_NVP(config.mapper_obs_std_dev));
ar(CEREAL_NVP(config.mapper_obs_huber_thresh));
ar(CEREAL_NVP(config.mapper_detection_num_points));
@ -145,5 +157,9 @@ void serialize(Archive& ar, basalt::VioConfig& config) {
ar(CEREAL_NVP(config.mapper_bow_num_bits));
ar(CEREAL_NVP(config.mapper_min_triangulation_dist));
ar(CEREAL_NVP(config.mapper_no_factor_weights));
ar(CEREAL_NVP(config.mapper_use_lm));
ar(CEREAL_NVP(config.mapper_lm_lambda_min));
ar(CEREAL_NVP(config.mapper_lm_lambda_max));
}
} // namespace cereal

11
src/vi_estimator/ba_base.cpp
View File

@ -465,8 +465,9 @@ void BundleAdjustmentBase::filterOutliers(double outlier_threshold,
std::map<int, std::vector<std::pair<TimeCamId, double>>> outliers;
computeError(error, &outliers, outlier_threshold);
// std::cout << "============================================" << std::endl;
// std::cout << "Num landmarks: " << lmdb.numLandmarks() << " with outliners
// std::cout << "============================================" <<
// std::endl; std::cout << "Num landmarks: " << lmdb.numLandmarks() << "
// with outliners
// "
// << outliers.size() << std::endl;
@ -498,7 +499,8 @@ void BundleAdjustmentBase::filterOutliers(double outlier_threshold,
}
}
// std::cout << "============================================" << std::endl;
// std::cout << "============================================" <<
// std::endl;
}
void BundleAdjustmentBase::marginalizeHelper(Eigen::MatrixXd& abs_H,
@ -552,7 +554,8 @@ void BundleAdjustmentBase::marginalizeHelper(Eigen::MatrixXd& abs_H,
// H_mm_inv = abs_H.bottomRightCorner(marg_size, marg_size)
// .fullPivHouseholderQr()
// .solve(Eigen::MatrixXd::Identity(marg_size, marg_size));
// .solve(Eigen::MatrixXd::Identity(marg_size,
// marg_size));
abs_H.topRightCorner(keep_size, marg_size) *= H_mm_inv;

158
src/vi_estimator/keypoint_vio.cpp
View File

@ -53,7 +53,11 @@ KeypointVioEstimator::KeypointVioEstimator(
frames_after_kf(0),
g(g),
initialized(false),
config(config) {
config(config),
lambda(config.vio_lm_lambda_min),
min_lambda(config.vio_lm_lambda_min),
max_lambda(config.vio_lm_lambda_max),
lambda_vee(2) {
this->obs_std_dev = config.vio_obs_std_dev;
this->huber_thresh = config.vio_obs_huber_thresh;
this->calib = calib;
@ -949,35 +953,136 @@ void KeypointVioEstimator::optimize() {
double error_total =
rld_error + imu_error + marg_prior_error + ba_error + bg_error;
if (config.vio_debug)
std::cout << "[LINEARIZE] Error: " << error_total << " num points "
<< std::endl;
lopt.accum.setup_solver();
Eigen::VectorXd Hdiag = lopt.accum.Hdiagonal();
Hdiag *= 1e-12;
for (int i = 0; i < Hdiag.size(); i++)
Hdiag[i] = std::max(Hdiag[i], 1e-12);
const Eigen::VectorXd inc = lopt.accum.solve(&Hdiag);
bool converged = false;
// apply increment to poses
for (auto& kv : frame_poses) {
int idx = aom.abs_order_map.at(kv.first).first;
kv.second.applyInc(-inc.segment<POSE_SIZE>(idx));
}
if (config.vio_use_lm) { // Use Levenberg–Marquardt
bool step = false;
int max_iter = 10;
// apply increment to states
for (auto& kv : frame_states) {
int idx = aom.abs_order_map.at(kv.first).first;
kv.second.applyInc(-inc.segment<POSE_VEL_BIAS_SIZE>(idx));
}
while (!step && max_iter > 0 && !converged) {
Eigen::VectorXd Hdiag_lambda = Hdiag * lambda;
for (int i = 0; i < Hdiag_lambda.size(); i++)
Hdiag_lambda[i] = std::max(Hdiag_lambda[i], min_lambda);
// Update points
tbb::blocked_range<size_t> keys_range(0, rld_vec.size());
auto update_points_func = [&](const tbb::blocked_range<size_t>& r) {
for (size_t i = r.begin(); i != r.end(); ++i) {
const auto& rld = rld_vec[i];
updatePoints(aom, rld, inc);
const Eigen::VectorXd inc = lopt.accum.solve(&Hdiag_lambda);
double max_inc = inc.array().abs().maxCoeff();
if (max_inc < 1e-4) converged = true;
backup();
// apply increment to poses
for (auto& kv : frame_poses) {
int idx = aom.abs_order_map.at(kv.first).first;
kv.second.applyInc(-inc.segment<POSE_SIZE>(idx));
}
// apply increment to states
for (auto& kv : frame_states) {
int idx = aom.abs_order_map.at(kv.first).first;
kv.second.applyInc(-inc.segment<POSE_VEL_BIAS_SIZE>(idx));
}
// Update points
tbb::blocked_range<size_t> keys_range(0, rld_vec.size());
auto update_points_func = [&](const tbb::blocked_range<size_t>& r) {
for (size_t i = r.begin(); i != r.end(); ++i) {
const auto& rld = rld_vec[i];
updatePoints(aom, rld, inc);
}
};
tbb::parallel_for(keys_range, update_points_func);
double after_update_marg_prior_error = 0;
double after_update_vision_error = 0, after_update_imu_error = 0,
after_bg_error = 0, after_ba_error = 0;
computeError(after_update_vision_error);
computeImuError(aom, after_update_imu_error, after_bg_error,
after_ba_error, frame_states, imu_meas,
gyro_bias_weight, accel_bias_weight, g);
computeMargPriorError(after_update_marg_prior_error);
double after_error_total =
after_update_vision_error + after_update_imu_error +
after_update_marg_prior_error + after_bg_error + after_ba_error;
double f_diff = (error_total - after_error_total);
double l_diff = 0.5 * inc.dot(inc * lambda + lopt.accum.getB());
// std::cout << "f_diff " << f_diff << " l_diff " << l_diff <<
// std::endl;
double step_quality = f_diff / l_diff;
if (step_quality < 0) {
if (config.vio_debug)
std::cout << "\t[REJECTED] lambda:" << lambda
<< " step_quality: " << step_quality
<< " max_inc: " << max_inc
<< " Error: " << after_error_total << std::endl;
lambda = std::min(max_lambda, lambda_vee * lambda);
lambda_vee *= 2;
restore();
} else {
if (config.vio_debug)
std::cout << "\t[ACCEPTED] lambda:" << lambda
<< " step_quality: " << step_quality
<< " max_inc: " << max_inc
<< " Error: " << after_error_total << std::endl;
lambda = std::max(
min_lambda,
lambda *
std::max(1.0 / 3, 1 - std::pow(2 * step_quality - 1, 3.0)));
lambda_vee = 2;
step = true;
}
max_iter--;
}
};
tbb::parallel_for(keys_range, update_points_func);
if (config.vio_debug && converged) {
std::cout << "[CONVERGED]" << std::endl;
}
} else { // Use Gauss-Newton
Eigen::VectorXd Hdiag_lambda = Hdiag * min_lambda;
for (int i = 0; i < Hdiag_lambda.size(); i++)
Hdiag_lambda[i] = std::max(Hdiag_lambda[i], min_lambda);
const Eigen::VectorXd inc = lopt.accum.solve(&Hdiag_lambda);
double max_inc = inc.array().abs().maxCoeff();
if (max_inc < 1e-4) converged = true;
// apply increment to poses
for (auto& kv : frame_poses) {
int idx = aom.abs_order_map.at(kv.first).first;
kv.second.applyInc(-inc.segment<POSE_SIZE>(idx));
}
// apply increment to states
for (auto& kv : frame_states) {
int idx = aom.abs_order_map.at(kv.first).first;
kv.second.applyInc(-inc.segment<POSE_VEL_BIAS_SIZE>(idx));
}
// Update points
tbb::blocked_range<size_t> keys_range(0, rld_vec.size());
auto update_points_func = [&](const tbb::blocked_range<size_t>& r) {
for (size_t i = r.begin(); i != r.end(); ++i) {
const auto& rld = rld_vec[i];
updatePoints(aom, rld, inc);
}
};
tbb::parallel_for(keys_range, update_points_func);
}
if (config.vio_debug) {
double after_update_marg_prior_error = 0;
@ -1014,8 +1119,7 @@ void KeypointVioEstimator::optimize() {
<< " bg_error: " << after_bg_error
<< " ba_error: " << after_ba_error
<< " marg prior: " << after_update_marg_prior_error
<< " total: " << after_error_total << " max_inc "
<< inc.array().abs().maxCoeff() << " error_diff "
<< " total: " << after_error_total << " error_diff "
<< error_diff << " time : " << elapsed.count()
<< "(us), num_states " << frame_states.size()
<< " num_poses " << frame_poses.size() << std::endl;
@ -1029,7 +1133,7 @@ void KeypointVioEstimator::optimize() {
filterOutliers(config.vio_outlier_threshold, 4);
}
if (inc.array().abs().maxCoeff() < 1e-4) break;
if (converged) break;
// std::cerr << "LT\n" << LT << std::endl;
// std::cerr << "z_p\n" << z_p.transpose() << std::endl;
@ -1040,7 +1144,7 @@ void KeypointVioEstimator::optimize() {
if (config.vio_debug) {
std::cout << "=================================" << std::endl;
}
}
} // namespace basalt
void KeypointVioEstimator::computeProjections(
std::vector<Eigen::vector<Eigen::Vector4d>>& data) const {

19
src/vi_estimator/landmark_database.cpp
View File

@ -155,6 +155,8 @@ void LandmarkDatabase::removeLandmark(int lm_id) {
host_to_kpts.at(it->second.kf_id).erase(lm_id);
std::set<TimeCamId> to_remove;
for (auto &kv : obs.at(it->second.kf_id)) {
int idx = -1;
for (size_t i = 0; i < kv.second.size(); ++i) {
@ -165,14 +167,22 @@ void LandmarkDatabase::removeLandmark(int lm_id) {
}
if (idx >= 0) {
BASALT_ASSERT(kv.second.size() > 0);
std::swap(kv.second[idx], kv.second[kv.second.size() - 1]);
kv.second.resize(kv.second.size() - 1);
num_observations--;
kpts_num_obs.at(lm_id)--;
if (kv.second.size() == 0) to_remove.insert(kv.first);
}
}
for (const auto &v : to_remove) {
obs.at(it->second.kf_id).erase(v);
}
BASALT_ASSERT_STREAM(kpts_num_obs.at(lm_id) == 0, kpts_num_obs.at(lm_id));
kpts_num_obs.erase(lm_id);
kpts.erase(lm_id);
@ -183,6 +193,8 @@ void LandmarkDatabase::removeObservations(int lm_id,
auto it = kpts.find(lm_id);
BASALT_ASSERT(it != kpts.end());
std::set<TimeCamId> to_remove;
for (auto &kv : obs.at(it->second.kf_id)) {
if (outliers.count(kv.first) > 0) {
int idx = -1;
@ -193,14 +205,21 @@ void LandmarkDatabase::removeObservations(int lm_id,
}
}
BASALT_ASSERT(idx >= 0);
BASALT_ASSERT(kv.second.size() > 0);
std::swap(kv.second[idx], kv.second[kv.second.size() - 1]);
kv.second.resize(kv.second.size() - 1);
num_observations--;
kpts_num_obs.at(lm_id)--;
if (kv.second.size() == 0) to_remove.insert(kv.first);
}
}
for (const auto &v : to_remove) {
obs.at(it->second.kf_id).erase(v);
}
}
} // namespace basalt

174
src/vi_estimator/nfr_mapper.cpp
View File

@ -44,7 +44,11 @@ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
namespace basalt {
NfrMapper::NfrMapper(const Calibration<double>& calib, const VioConfig& config)
: config(config) {
: config(config),
lambda(config.mapper_lm_lambda_min),
min_lambda(config.mapper_lm_lambda_min),
max_lambda(config.mapper_lm_lambda_max),
lambda_vee(2) {
this->calib = calib;
this->obs_std_dev = config.mapper_obs_std_dev;
this->huber_thresh = config.mapper_obs_huber_thresh;
@ -274,7 +278,7 @@ void NfrMapper::optimize(int num_iterations) {
double error_total = rld_error + lopt.rel_error + lopt.roll_pitch_error;
std::cout << "iter " << iter
std::cout << "[LINEARIZE] iter " << iter
<< " before_update_error: vision: " << rld_error
<< " rel_error: " << lopt.rel_error
<< " roll_pitch_error: " << lopt.roll_pitch_error
@ -284,61 +288,132 @@ void NfrMapper::optimize(int num_iterations) {
lopt.accum.print_info = true;
lopt.accum.setup_solver();
Eigen::VectorXd Hdiag = lopt.accum.Hdiagonal();
Hdiag.setConstant(Hdiag.size(), 1e-6);
const Eigen::VectorXd Hdiag = lopt.accum.Hdiagonal();
const Eigen::VectorXd inc = lopt.accum.solve(&Hdiag);
bool converged = false;
// apply increment to poses
for (auto& kv : frame_poses) {
int idx = aom.abs_order_map.at(kv.first).first;
BASALT_ASSERT(!kv.second.isLinearized());
kv.second.applyInc(-inc.segment<POSE_SIZE>(idx));
if (config.vio_use_lm) { // Use Levenberg–Marquardt
bool step = false;
int max_iter = 10;
while (!step && max_iter > 0 && !converged) {
Eigen::VectorXd Hdiag_lambda = Hdiag * lambda;
for (int i = 0; i < Hdiag_lambda.size(); i++)
Hdiag_lambda[i] = std::max(Hdiag_lambda[i], min_lambda);
const Eigen::VectorXd inc = lopt.accum.solve(&Hdiag_lambda);
double max_inc = inc.array().abs().maxCoeff();
if (max_inc < 1e-5) converged = true;
backup();
// apply increment to poses
for (auto& kv : frame_poses) {
int idx = aom.abs_order_map.at(kv.first).first;
BASALT_ASSERT(!kv.second.isLinearized());
kv.second.applyInc(-inc.segment<POSE_SIZE>(idx));
}
// Update points
tbb::blocked_range<size_t> keys_range(0, rld_vec.size());
auto update_points_func = [&](const tbb::blocked_range<size_t>& r) {
for (size_t i = r.begin(); i != r.end(); ++i) {
const auto& rld = rld_vec[i];
updatePoints(aom, rld, inc);
}
};
tbb::parallel_for(keys_range, update_points_func);
double after_update_vision_error = 0;
double after_rel_error = 0;
double after_roll_pitch_error = 0;
computeError(after_update_vision_error);
computeRelPose(after_rel_error);
computeRollPitch(after_roll_pitch_error);
double after_error_total = after_update_vision_error + after_rel_error +
after_roll_pitch_error;
double f_diff = (error_total - after_error_total);
double l_diff = 0.5 * inc.dot(inc * lambda + lopt.accum.getB());
std::cout << "f_diff " << f_diff << " l_diff " << l_diff << std::endl;
double step_quality = f_diff / l_diff;
if (step_quality < 0) {
std::cout << "\t[REJECTED] lambda:" << lambda
<< " step_quality: " << step_quality
<< " max_inc: " << max_inc
<< " vision_error: " << after_update_vision_error
<< " rel_error: " << after_rel_error
<< " roll_pitch_error: " << after_roll_pitch_error
<< " total: " << after_error_total << std::endl;
lambda = std::min(max_lambda, lambda_vee * lambda);
lambda_vee *= 2;
restore();
} else {
std::cout << "\t[ACCEPTED] lambda:" << lambda
<< " step_quality: " << step_quality
<< " max_inc: " << max_inc
<< " vision_error: " << after_update_vision_error
<< " rel_error: " << after_rel_error
<< " roll_pitch_error: " << after_roll_pitch_error
<< " total: " << after_error_total << std::endl;
lambda = std::max(
min_lambda,
lambda *
std::max(1.0 / 3, 1 - std::pow(2 * step_quality - 1, 3.0)));
lambda_vee = 2;
step = true;
}
max_iter--;
if (after_error_total > error_total) {
std::cout << "increased error after update!!!" << std::endl;
}
}
} else { // Use Gauss-Newton
Eigen::VectorXd Hdiag_lambda = Hdiag * min_lambda;
for (int i = 0; i < Hdiag_lambda.size(); i++)
Hdiag_lambda[i] = std::max(Hdiag_lambda[i], min_lambda);
const Eigen::VectorXd inc = lopt.accum.solve(&Hdiag_lambda);
double max_inc = inc.array().abs().maxCoeff();
if (max_inc < 1e-5) converged = true;
// apply increment to poses
for (auto& kv : frame_poses) {
int idx = aom.abs_order_map.at(kv.first).first;
BASALT_ASSERT(!kv.second.isLinearized());
kv.second.applyInc(-inc.segment<POSE_SIZE>(idx));
}
// Update points
tbb::blocked_range<size_t> keys_range(0, rld_vec.size());
auto update_points_func = [&](const tbb::blocked_range<size_t>& r) {
for (size_t i = r.begin(); i != r.end(); ++i) {
const auto& rld = rld_vec[i];
updatePoints(aom, rld, inc);
}
};
tbb::parallel_for(keys_range, update_points_func);
}
// Update points
tbb::blocked_range<size_t> keys_range(0, rld_vec.size());
auto update_points_func = [&](const tbb::blocked_range<size_t>& r) {
for (size_t i = r.begin(); i != r.end(); ++i) {
const auto& rld = rld_vec[i];
updatePoints(aom, rld, inc);
}
};
tbb::parallel_for(keys_range, update_points_func);
double after_update_vision_error = 0;
double after_rel_error = 0;
double after_roll_pitch_error = 0;
computeError(after_update_vision_error);
computeRelPose(after_rel_error);
computeRollPitch(after_roll_pitch_error);
double after_error_total =
after_update_vision_error + after_rel_error + after_roll_pitch_error;
double error_diff = error_total - after_error_total;
auto t2 = std::chrono::high_resolution_clock::now();
auto elapsed =
std::chrono::duration_cast<std::chrono::microseconds>(t2 - t1);
std::cout << "iter " << iter
<< " after_update_error: vision: " << after_update_vision_error
<< " rel_error: " << after_rel_error
<< " roll_pitch_error: " << after_roll_pitch_error
<< " total: " << after_error_total << " max_inc "
<< inc.array().abs().maxCoeff() << " error_diff " << error_diff
<< " time : " << elapsed.count() << "(us), num_states "
<< frame_states.size() << " num_poses " << frame_poses.size()
<< std::endl;
std::cout << "iter " << iter << " time : " << elapsed.count()
<< "(us), num_states " << frame_states.size() << " num_poses "
<< frame_poses.size() << std::endl;
if (after_error_total > error_total) {
std::cout << "increased error after update!!!" << std::endl;
}
if (inc.array().abs().maxCoeff() < 1e-4) break;
if (converged) break;
// std::cerr << "LT\n" << LT << std::endl;
// std::cerr << "z_p\n" << z_p.transpose() << std::endl;
@ -508,7 +583,8 @@ void NfrMapper::match_all() {
// std::cout << "Closest frames for " << tcid << ": ";
for (const auto& otcid_score : results) {
// std::cout << otcid_score.first << "(" << otcid_score.second << ") ";
// std::cout << otcid_score.first << "(" << otcid_score.second << ")
// ";
if (otcid_score.first.frame_id != tcid.frame_id &&
otcid_score.second > config.mapper_frames_to_match_threshold) {
match_pair m;