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Added LM to VIO

This commit is contained in:
Vladyslav Usenko 2019-08-07 17:19:27 +02:00
parent 227694d572
commit f8285ccc64
2 changed files with 94 additions and 28 deletions
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2
include/basalt/vi_estimator/keypoint_vio.h
View File

@ -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;

84
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(1e-6),
min_lambda(1e-12),
max_lambda(10),
lambda_vee(2) {
this->obs_std_dev = config.vio_obs_std_dev;
this->huber_thresh = config.vio_obs_huber_thresh;
this->calib = calib;
@ -941,13 +945,26 @@ void KeypointVioEstimator::optimize() {
double error_total =
rld_error + imu_error + marg_prior_error + ba_error + bg_error;
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;
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-4) converged = true;
backup();
// apply increment to poses
for (auto& kv : frame_poses) {
@ -971,8 +988,56 @@ void KeypointVioEstimator::optimize() {
};
tbb::parallel_for(keys_range, update_points_func);
// backup();
// restore();
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) {
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 {
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--;
}
if (converged) {
std::cout << "[CONVERGED]" << std::endl;
}
if (config.vio_debug) {
double after_update_marg_prior_error = 0;
@ -1009,8 +1074,7 @@ 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;
@ -1024,7 +1088,7 @@ 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;