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feature_matching.cpp
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feature_matching.cpp
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// Copyright 2024 Autoware Foundation
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "loam_feature_localization/feature_matching.hpp"
#include <pcl/common/eigen.h>
#include <pcl/common/angles.h>
#include <pcl/io/pcd_io.h>
#include <pcl_conversions/pcl_conversions.h>
#include <tf2/LinearMath/Quaternion.h>
#include <tf2_ros/transform_listener.h>
#include <tf2_ros/transform_broadcaster.h>
#include <tf2_geometry_msgs/tf2_geometry_msgs.hpp>
namespace loam_feature_localization
{
FeatureMatching::FeatureMatching(
int n_scan, int horizon_scan, std::string corner_map_path, std::string surface_map_path,
const rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr & pub_map_corner,
const rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr & pub_map_surface,
rclcpp::Time now, const Utils::SharedPtr & utils,
const std::string & odometry_frame, const std::string & base_link_frame,
double surrounding_key_frame_search_radius, double surrounding_key_frame_adding_angle_threshold,
double surrounding_key_frame_adding_dist_threshold, double surrounding_key_frame_density,
double mapping_corner_leaf_size, double mapping_surface_leaf_size,
int min_edge_feature_number, int min_surface_feature_number,
double rotation_tollerance, double z_tollerance, double imu_rpy_weight)
{
utils_ = utils;
odometry_frame_ = odometry_frame;
base_link_frame_ = base_link_frame;
n_scan_ = n_scan;
horizon_scan_ = horizon_scan;
surrounding_key_frame_search_radius_ = surrounding_key_frame_search_radius;
min_edge_feature_number_ = min_edge_feature_number;
min_surface_feature_number_ = min_surface_feature_number;
rotation_tollerance_ = rotation_tollerance;
z_tollerance_ = z_tollerance;
imu_rpy_weight_ = imu_rpy_weight;
surrounding_key_frame_adding_angle_threshold_ = surrounding_key_frame_adding_angle_threshold;
surrounding_key_frame_adding_dist_threshold_ = surrounding_key_frame_adding_dist_threshold;
surrounding_key_frame_density_ = surrounding_key_frame_density;
mapping_corner_leaf_size_ = mapping_corner_leaf_size;
mapping_surface_leaf_size_ = mapping_surface_leaf_size;
map_corner_path_ = corner_map_path;
map_surface_path_ = surface_map_path;
gtsam::ISAM2Params parameters;
parameters.relinearizeThreshold = 0.1;
parameters.relinearizeSkip = 1;
isam_ = new gtsam::ISAM2(parameters);
allocate_memory(pub_map_corner, pub_map_surface, now);
};
void FeatureMatching::allocate_memory(
const rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr & pub_map_corner,
const rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr & pub_map_surface,
rclcpp::Time now) {
cloud_key_poses_3d_.reset(new pcl::PointCloud<PointType>());
cloud_key_poses_6d_.reset(new pcl::PointCloud<PointTypePose>());
copy_cloud_key_poses_3d_.reset(new pcl::PointCloud<PointType>());
copy_cloud_key_poses_6d_.reset(new pcl::PointCloud<PointTypePose>());
kdtree_surrounding_key_poses_.reset(new pcl::KdTreeFLANN<PointType>());
kdtree_history_key_poses_.reset(new pcl::KdTreeFLANN<PointType>());
laser_cloud_corner_last_.reset(new pcl::PointCloud<PointType>()); // corner feature set from odoOptimization
laser_cloud_surface_last_.reset(new pcl::PointCloud<PointType>()); // surf feature set from odoOptimization
laser_cloud_corner_last_ds_.reset(new pcl::PointCloud<PointType>()); // downsampled corner featuer set from odoOptimization
laser_cloud_surface_last_ds_.reset(new pcl::PointCloud<PointType>()); // downsampled surf featuer set from odoOptimization
laser_cloud_ori_.reset(new pcl::PointCloud<PointType>());
coeff_sel_.reset(new pcl::PointCloud<PointType>());
laser_cloud_ori_corner_vec_.resize(n_scan_ * horizon_scan_);
coeff_sel_corner_vec_.resize(n_scan_ * horizon_scan_);
laser_cloud_ori_corner_flag_.resize(n_scan_ * horizon_scan_);
laser_cloud_ori_surface_vec_.resize(n_scan_ * horizon_scan_);
coeff_sel_surface_vec_.resize(n_scan_ * horizon_scan_);
laser_cloud_ori_surface_flag_.resize(n_scan_ * horizon_scan_);
std::fill(laser_cloud_ori_corner_flag_.begin(), laser_cloud_ori_corner_flag_.end(), false);
std::fill(laser_cloud_ori_surface_flag_.begin(), laser_cloud_ori_surface_flag_.end(), false);
laser_cloud_corner_from_map_.reset(new pcl::PointCloud<PointType>());
laser_cloud_surface_from_map_.reset(new pcl::PointCloud<PointType>());
laser_cloud_corner_from_map_ds_.reset(new pcl::PointCloud<PointType>());
laser_cloud_surface_from_map_ds_.reset(new pcl::PointCloud<PointType>());
map_corner_octree_.reset(new pcl::octree::OctreePointCloudSearch<PointType>(0.2));
map_surface_octree_.reset(new pcl::octree::OctreePointCloudSearch<PointType>(0.4));
down_size_filter_corner_.setLeafSize(mapping_corner_leaf_size_, mapping_corner_leaf_size_, mapping_corner_leaf_size_);
down_size_filter_surface_.setLeafSize(mapping_surface_leaf_size_, mapping_surface_leaf_size_, mapping_surface_leaf_size_);
down_size_filter_icp_.setLeafSize(mapping_surface_leaf_size_, mapping_surface_leaf_size_, mapping_surface_leaf_size_);
down_size_filter_surrounding_key_poses_.setLeafSize(
surrounding_key_frame_density_, surrounding_key_frame_density_, surrounding_key_frame_density_); // for surrounding key poses of scan-to-map optimization
kdtree_corner_from_map_.reset(new pcl::KdTreeFLANN<PointType>());
kdtree_surface_from_map_.reset(new pcl::KdTreeFLANN<PointType>());
for (int i = 0; i < 6; ++i){
transform_to_be_mapped_[i] = 0;
}
mat_p_.setZero();
map_corner_.reset(new pcl::PointCloud<PointType>());
if (pcl::io::loadPCDFile<PointType> (map_corner_path_, *map_corner_) == -1) //* load the file
{
PCL_ERROR ("Couldn't read corner cloud");
PCL_ERROR ("Couldn't read corner cloud");
PCL_ERROR ("Couldn't read corner cloud \n");
}
std::cout << "map_surface_path_: " << map_surface_path_ << std::endl;
map_surface_.reset(new pcl::PointCloud<PointType>());
if (pcl::io::loadPCDFile<PointType> (map_surface_path_, *map_surface_) == -1) //* load the file
{
PCL_ERROR ("Couldn't read surface cloud \n");
PCL_ERROR ("Couldn't read surface cloud \n");
PCL_ERROR ("Couldn't read surface cloud \n");
}
map_corner_clipped_.reset(new pcl::PointCloud<PointType>());
map_surface_clipped_.reset(new pcl::PointCloud<PointType>());
map_corner_octree_->setInputCloud(map_corner_);
map_corner_octree_->addPointsFromInputCloud();
map_surface_octree_->setInputCloud(map_surface_);
map_surface_octree_->addPointsFromInputCloud();
Eigen::Vector3f min_point(65464.3 - 200, 40904.08 - 200, 44.6 - 200);
Eigen::Vector3f max_point(65464.3 + 200, 40904.08 + 200, 44.6 + 200);
// -65464.3, -40904.08, -44.6
std::vector<int> point_indices_in_box_corner;
std::vector<int> point_indices_in_box_surface;
map_corner_octree_->boxSearch(min_point, max_point, point_indices_in_box_corner);
map_surface_octree_->boxSearch(min_point, max_point, point_indices_in_box_surface);
// pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_region (new pcl::PointCloud<pcl::PointXYZ>);
// laser_cloud_corner_from_map_ds_.reset(new pcl::PointCloud<PointType>());
// laser_cloud_surface_from_map_ds_.reset(new pcl::PointCloud<PointType>());
for (const auto& index : point_indices_in_box_corner) {
laser_cloud_corner_from_map_ds_->push_back(map_corner_->points[index]);
}
for (const auto& index : point_indices_in_box_surface) {
laser_cloud_surface_from_map_ds_->push_back(map_surface_->points[index]);
}
laser_cloud_corner_from_map_ds_num_ = laser_cloud_corner_from_map_ds_->size();
laser_cloud_surface_from_map_ds_num_ = laser_cloud_surface_from_map_ds_->size();
std::cout << "ALLOCATE MEMORY - laser_cloud_corner_from_map_ds_.size: " << laser_cloud_corner_from_map_ds_->size() << std::endl;
sensor_msgs::msg::PointCloud2 ros_cloud_corner;
pcl::toROSMsg(*map_corner_, ros_cloud_corner);
sensor_msgs::msg::PointCloud2 ros_cloud_surface;
pcl::toROSMsg(*map_surface_, ros_cloud_surface);
ros_cloud_corner.header.frame_id = "map";
ros_cloud_corner.header.stamp = now;
ros_cloud_surface.header.frame_id = "map";
ros_cloud_surface.header.stamp = now;
pub_map_corner->publish(ros_cloud_corner);
pub_map_surface->publish(ros_cloud_surface);
// RCLCPP_INFO(this->get_logger(), "\n\n\n\n\n\n\nPUBLISHED\n\n\n\n\n\n\n");
}
void FeatureMatching::laser_cloud_info_handler(
const Utils::CloudInfo msg_in, std_msgs::msg::Header header,
pcl::PointCloud<PointType>::Ptr cloud_corner, pcl::PointCloud<PointType>::Ptr cloud_surface,
const rclcpp::Publisher<nav_msgs::msg::Odometry>::SharedPtr & pub_odom_laser,
const rclcpp::Publisher<nav_msgs::msg::Odometry>::SharedPtr & pub_odom_laser_incremental,
const std::unique_ptr<tf2_ros::TransformBroadcaster> & br,
const rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr & pub_key_poses,
const rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr & pub_recent_key_frames,
// const rclcpp::Publisher<sensor_msgs::msg::PointCloud2>::SharedPtr & pub_cloud_registered,
const rclcpp::Publisher<nav_msgs::msg::Path>::SharedPtr & pub_path)
{
// extract time stamp
time_laser_info_stamp_ = header.stamp;
time_laser_info_cur_ = utils_->stamp2Sec(header.stamp);
// extract info and feature cloud
cloud_info_ = msg_in;
laser_cloud_corner_last_ = cloud_corner;
laser_cloud_surface_last_ = cloud_surface;
// pcl::fromROSMsg(cloud_corner, *laser_cloud_corner_last_);
// pcl::fromROSMsg(cloud_surface, *laserCloudSurfLast);
// std::lock_guard<std::mutex> lock(mtx);
double mapping_process_interval = 0.1;
static double timeLastProcessing = -1;
if (time_laser_info_cur_ - timeLastProcessing >= mapping_process_interval)
{
timeLastProcessing = time_laser_info_cur_;
std::cout << "-------------------------------------------------------------------" << std::endl;
auto start_update_initial_guess = std::chrono::high_resolution_clock::now();
update_initial_guess();
auto end_update_initial_guess = std::chrono::high_resolution_clock::now();
auto duration_update_initial_guess = std::chrono::duration_cast<std::chrono::milliseconds>(
end_update_initial_guess - start_update_initial_guess);
std::cout << "end_update_initial_guess time: " << std::setprecision(6) << duration_update_initial_guess.count() << std::endl;
auto start_extract_surrounding_key_frames = std::chrono::high_resolution_clock::now();
extract_surrounding_key_frames();
auto end_extract_surrounding_key_frames = std::chrono::high_resolution_clock::now();
auto duration_extract_surrounding_key_frames = std::chrono::duration_cast<std::chrono::milliseconds>(
end_extract_surrounding_key_frames - start_extract_surrounding_key_frames);
std::cout << "extract_surrounding_key_frames time: " << std::setprecision(6) << duration_extract_surrounding_key_frames.count() << std::endl;
auto start_downsample_current_scan = std::chrono::high_resolution_clock::now();
downsample_current_scan();
auto end_downsample_current_scan = std::chrono::high_resolution_clock::now();
auto duration_downsample_current_scan = std::chrono::duration_cast<std::chrono::milliseconds>(
end_downsample_current_scan - start_downsample_current_scan);
std::cout << "downsample_current_scan time: " << std::setprecision(6) << duration_downsample_current_scan.count() << std::endl;
auto start_scan_to_map_optimization = std::chrono::high_resolution_clock::now();
scan_to_map_optimization();
auto end_scan_to_map_optimization = std::chrono::high_resolution_clock::now();
auto duration_scan_to_map_optimization = std::chrono::duration_cast<std::chrono::milliseconds>(
end_scan_to_map_optimization - start_scan_to_map_optimization);
std::cout << "scan_to_map_optimization time: " << std::setprecision(6) << duration_scan_to_map_optimization.count() << std::endl;
auto start_save_key_frames_and_factor = std::chrono::high_resolution_clock::now();
save_key_frames_and_factor();
auto end_save_key_frames_and_factor = std::chrono::high_resolution_clock::now();
auto duration_save_key_frames_and_factor = std::chrono::duration_cast<std::chrono::milliseconds>(
end_save_key_frames_and_factor - start_save_key_frames_and_factor);
std::cout << "save_key_frames_and_factor time: " << std::setprecision(6) << duration_save_key_frames_and_factor.count() << std::endl;
auto start_correct_poses = std::chrono::high_resolution_clock::now();
correct_poses();
auto end_correct_poses = std::chrono::high_resolution_clock::now();
auto duration_correct_poses = std::chrono::duration_cast<std::chrono::milliseconds>(
end_correct_poses - start_correct_poses);
std::cout << "correct_poses time: " << std::setprecision(6) << duration_correct_poses.count() << std::endl;
auto start_publish_odometry = std::chrono::high_resolution_clock::now();
publish_odometry(pub_odom_laser, pub_odom_laser_incremental, br);
auto end_publish_odometry = std::chrono::high_resolution_clock::now();
auto duration_publish_odometry = std::chrono::duration_cast<std::chrono::milliseconds>(
end_publish_odometry - start_publish_odometry);
std::cout << "publish_odometry time: " << std::setprecision(6) << duration_publish_odometry.count() << std::endl;
auto start_publish_frames = std::chrono::high_resolution_clock::now();
publish_frames(pub_key_poses, pub_recent_key_frames, pub_path);
auto end_publish_frames = std::chrono::high_resolution_clock::now();
auto duration_publish_frames = std::chrono::duration_cast<std::chrono::milliseconds>(
end_publish_frames - start_publish_frames);
std::cout << "publish_frames time: " << std::setprecision(6) << duration_publish_frames.count() << std::endl;
}
std::cout << "-------------------------------------------------------------------\n\n" << std::endl;
}
void FeatureMatching::point_associate_to_map(PointType const * const pi, PointType * const po)
{
po->x = trans_point_associate_to_map_(0,0) * pi->x + trans_point_associate_to_map_(0,1) * pi->y + trans_point_associate_to_map_(0,2) * pi->z + trans_point_associate_to_map_(0,3);
po->y = trans_point_associate_to_map_(1,0) * pi->x + trans_point_associate_to_map_(1,1) * pi->y + trans_point_associate_to_map_(1,2) * pi->z + trans_point_associate_to_map_(1,3);
po->z = trans_point_associate_to_map_(2,0) * pi->x + trans_point_associate_to_map_(2,1) * pi->y + trans_point_associate_to_map_(2,2) * pi->z + trans_point_associate_to_map_(2,3);
po->intensity = pi->intensity;
}
pcl::PointCloud<PointType>::Ptr FeatureMatching::transform_point_cloud(
pcl::PointCloud<PointType>::Ptr cloud_in, PointTypePose* transform_in)
{
pcl::PointCloud<PointType>::Ptr cloudOut(new pcl::PointCloud<PointType>());
int cloudSize = cloud_in->size();
cloudOut->resize(cloudSize);
Eigen::Affine3f transCur = pcl::getTransformation(
transform_in->x, transform_in->y, transform_in->z,
transform_in->roll, transform_in->pitch, transform_in->yaw);
// #pragma omp parallel for num_threads(numberOfCores)
for (int i = 0; i < cloudSize; ++i)
{
const auto &pointFrom = cloud_in->points[i];
cloudOut->points[i].x = transCur(0,0) * pointFrom.x + transCur(0,1) * pointFrom.y + transCur(0,2) * pointFrom.z + transCur(0,3);
cloudOut->points[i].y = transCur(1,0) * pointFrom.x + transCur(1,1) * pointFrom.y + transCur(1,2) * pointFrom.z + transCur(1,3);
cloudOut->points[i].z = transCur(2,0) * pointFrom.x + transCur(2,1) * pointFrom.y + transCur(2,2) * pointFrom.z + transCur(2,3);
cloudOut->points[i].intensity = pointFrom.intensity;
}
return cloudOut;
}
gtsam::Pose3 FeatureMatching::pcl_point_to_gtsam_pose3(PointTypePose this_point)
{
return gtsam::Pose3(gtsam::Rot3::RzRyRx(double(this_point.roll), double(this_point.pitch), double(this_point.yaw)),
gtsam::Point3(double(this_point.x), double(this_point.y), double(this_point.z)));
}
gtsam::Pose3 FeatureMatching::trans_to_gtsam_pose(float transform_in[])
{
return gtsam::Pose3(gtsam::Rot3::RzRyRx(transform_in[0], transform_in[1], transform_in[2]),
gtsam::Point3(transform_in[3], transform_in[4], transform_in[5]));
}
Eigen::Affine3f FeatureMatching::pcl_point_to_affine3f(PointTypePose this_point)
{
return pcl::getTransformation(this_point.x, this_point.y, this_point.z,
this_point.roll, this_point.pitch, this_point.yaw);
}
Eigen::Affine3f FeatureMatching::trans_to_affine3f(float transform_in[])
{
return pcl::getTransformation(transform_in[3], transform_in[4], transform_in[5], transform_in[0], transform_in[1], transform_in[2]);
}
PointTypePose FeatureMatching::trans_to_point_type_pose(float transform_in[])
{
PointTypePose thisPose6D;
thisPose6D.x = transform_in[3];
thisPose6D.y = transform_in[4];
thisPose6D.z = transform_in[5];
thisPose6D.roll = transform_in[0];
thisPose6D.pitch = transform_in[1];
thisPose6D.yaw = transform_in[2];
return thisPose6D;
}
void FeatureMatching::update_initial_guess() {
// save current transformation before any processing
incremental_odometry_affine_front_= trans_to_affine3f(transform_to_be_mapped_);
static Eigen::Affine3f lastImuTransformation;
// initialization
if (cloud_key_poses_3d_->points.empty())
{
transform_to_be_mapped_[0] = cloud_info_.imu_roll_init;
transform_to_be_mapped_[1] = cloud_info_.imu_pitch_init;
transform_to_be_mapped_[2] = cloud_info_.imu_yaw_init;
// if (!useImuHeadingInitialization)
// transformTobeMapped[2] = 0;
// TODO: MAKE TOPIC HERE
// lastImuTransformation = pcl::getTransformation(-66458, -43619, -42, cloud_info_.imu_roll_init, cloud_info_.imu_pitch_init, cloud_info_.imu_yaw_init); // save imu before return;
lastImuTransformation = pcl::getTransformation(-65464.3, -40904.08, -44.6, cloud_info_.imu_roll_init, cloud_info_.imu_pitch_init, cloud_info_.imu_yaw_init); // save imu before return;
// lastImuTransformation = pcl::getTransformation(0, 0, 0, cloud_info_.imu_roll_init, cloud_info_.imu_pitch_init, cloud_info_.imu_yaw_init); // save imu before return;
return;
}
// use imu pre-integration estimation for pose guess
static bool lastImuPreTransAvailable = false;
static Eigen::Affine3f lastImuPreTransformation;
if (cloud_info_.odom_available == true)
{
Eigen::Affine3f transBack = pcl::getTransformation(
cloud_info_.initial_guess_x, cloud_info_.initial_guess_y, cloud_info_.initial_guess_z,
cloud_info_.initial_guess_roll, cloud_info_.initial_guess_pitch, cloud_info_.initial_guess_yaw);
if (lastImuPreTransAvailable == false)
{
lastImuPreTransformation = transBack;
lastImuPreTransAvailable = true;
} else {
Eigen::Affine3f transIncre = lastImuPreTransformation.inverse() * transBack;
Eigen::Affine3f transTobe = trans_to_affine3f(transform_to_be_mapped_);
Eigen::Affine3f transFinal = transTobe * transIncre;
pcl::getTranslationAndEulerAngles(transFinal, transform_to_be_mapped_[3], transform_to_be_mapped_[4],
transform_to_be_mapped_[5], transform_to_be_mapped_[0], transform_to_be_mapped_[1],
transform_to_be_mapped_[2]);
lastImuPreTransformation = transBack;
lastImuTransformation = pcl::getTransformation(0, 0, 0, cloud_info_.imu_roll_init, cloud_info_.imu_pitch_init, cloud_info_.imu_yaw_init); // save imu before return;
return;
}
}
// use imu incremental estimation for pose guess (only rotation)
if (cloud_info_.imu_available == true)
{
Eigen::Affine3f transBack = pcl::getTransformation(0, 0, 0, cloud_info_.imu_roll_init, cloud_info_.imu_pitch_init, cloud_info_.imu_yaw_init);
Eigen::Affine3f transIncre = lastImuTransformation.inverse() * transBack;
Eigen::Affine3f transTobe = trans_to_affine3f(transform_to_be_mapped_);
Eigen::Affine3f transFinal = transTobe * transIncre;
pcl::getTranslationAndEulerAngles(transFinal, transform_to_be_mapped_[3], transform_to_be_mapped_[4],
transform_to_be_mapped_[5], transform_to_be_mapped_[0], transform_to_be_mapped_[1],
transform_to_be_mapped_[2]);
lastImuTransformation = pcl::getTransformation(0, 0, 0, cloud_info_.imu_roll_init, cloud_info_.imu_pitch_init, cloud_info_.imu_yaw_init); // save imu before return;
return;
}
}
void FeatureMatching::extract_nearby()
{
pcl::PointCloud<PointType>::Ptr surroundingKeyPoses(new pcl::PointCloud<PointType>());
pcl::PointCloud<PointType>::Ptr surroundingKeyPosesDS(new pcl::PointCloud<PointType>());
std::vector<int> pointSearchInd;
std::vector<float> pointSearchSqDis;
// extract all the nearby key poses and downsample them
kdtree_surrounding_key_poses_->setInputCloud(cloud_key_poses_3d_); // create kd-tree
kdtree_surrounding_key_poses_->radiusSearch(cloud_key_poses_3d_->back(), (double)surrounding_key_frame_search_radius_, pointSearchInd, pointSearchSqDis);
for (int i = 0; i < (int)pointSearchInd.size(); ++i)
{
int id = pointSearchInd[i];
surroundingKeyPoses->push_back(cloud_key_poses_3d_->points[id]);
}
down_size_filter_surrounding_key_poses_.setInputCloud(surroundingKeyPoses);
down_size_filter_surrounding_key_poses_.filter(*surroundingKeyPosesDS);
for(auto& pt : surroundingKeyPosesDS->points)
{
kdtree_surrounding_key_poses_->nearestKSearch(pt, 1, pointSearchInd, pointSearchSqDis);
pt.intensity = cloud_key_poses_3d_->points[pointSearchInd[0]].intensity;
}
// also extract some latest key frames in case the robot rotates in one position
int numPoses = cloud_key_poses_3d_ ->size();
for (int i = numPoses-1; i >= 0; --i)
{
if (time_laser_info_cur_ - cloud_key_poses_6d_->points[i].time < 10.0)
surroundingKeyPosesDS->push_back(cloud_key_poses_3d_->points[i]);
else
break;
}
extract_cloud();
}
void FeatureMatching::extract_cloud()
{
float position_difference_from_last_map_load_point =
std::sqrt(std::pow(transform_to_be_mapped_[0] - transform_to_be_mapped_old_[0], 2) +
std::pow(transform_to_be_mapped_[1] - transform_to_be_mapped_old_[1], 2) +
std::pow(transform_to_be_mapped_[2] - transform_to_be_mapped_old_[2], 2));
if (position_difference_from_last_map_load_point >= 2){
Eigen::Vector3f min_point(
transform_to_be_mapped_[0] - 200, transform_to_be_mapped_[1] - 200,
transform_to_be_mapped_[2] - 200);
Eigen::Vector3f max_point(
transform_to_be_mapped_[0] + 200, transform_to_be_mapped_[1] + 200,
transform_to_be_mapped_[2] + 200);
std::cout << "transform_to_be_mapped_[0]: " << transform_to_be_mapped_[0] << std::endl;
std::cout << "transform_to_be_mapped_[1]: " << transform_to_be_mapped_[1] << std::endl;
std::cout << "transform_to_be_mapped_[2]: " << transform_to_be_mapped_[2] << std::endl;
//
// PointType min_point, max_point;
// min_point.x = transform_to_be_mapped_[0] - 200;
// min_point.y = transform_to_be_mapped_[1] - 200;
// min_point.z = transform_to_be_mapped_[2] - 200;
// min_point.intensity = 255.0f;
// max_point.x = transform_to_be_mapped_[0] + 200;
// max_point.y = transform_to_be_mapped_[1] + 200;
// max_point.z = transform_to_be_mapped_[2] + 200;
// max_point.intensity = 255.0f;
std::vector<int> point_indices_in_box_corner;
std::vector<int> point_indices_in_box_surface;
map_corner_octree_->boxSearch(min_point, max_point, point_indices_in_box_corner);
map_surface_octree_->boxSearch(min_point, max_point, point_indices_in_box_surface);
// pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_region (new pcl::PointCloud<pcl::PointXYZ>);
laser_cloud_corner_from_map_ds_.reset(new pcl::PointCloud<PointType>());
laser_cloud_surface_from_map_ds_.reset(new pcl::PointCloud<PointType>());
for (const auto& index : point_indices_in_box_corner) {
laser_cloud_corner_from_map_ds_->points.push_back(map_corner_->points[index]);
}
for (const auto& index : point_indices_in_box_surface) {
laser_cloud_surface_from_map_ds_->points.push_back(map_surface_->points[index]);
}
} else {
return;
}
laser_cloud_corner_from_map_ds_num_ = laser_cloud_corner_from_map_ds_->size();
laser_cloud_surface_from_map_ds_num_ = laser_cloud_surface_from_map_ds_->size();
std::cout << "\n\n\nEXTRACT CLOUD - laser_cloud_corner_from_map_ds_.size: " << laser_cloud_corner_from_map_ds_->size() << "\n\n\n" << std::endl;
if (laser_cloud_map_container_.size() > 1000)
laser_cloud_map_container_.clear();
transform_to_be_mapped_old_[0] = transform_to_be_mapped_[0];
transform_to_be_mapped_old_[1] = transform_to_be_mapped_[1];
transform_to_be_mapped_old_[2] = transform_to_be_mapped_[2];
}
void FeatureMatching::extract_surrounding_key_frames()
{
if (cloud_key_poses_3d_->points.empty() == true)
return;
// if (loopClosureEnableFlag == true)
// {
// extractForLoopClosure();
// } else {
// extractNearby();
// }
extract_nearby();
}
void FeatureMatching::downsample_current_scan()
{
// Downsample cloud from current scan
laser_cloud_corner_last_ds_->clear();
down_size_filter_corner_.setInputCloud(laser_cloud_corner_last_);
down_size_filter_corner_.filter(*laser_cloud_corner_last_ds_);
laser_cloud_corner_last_ds_num_ = laser_cloud_corner_last_ds_->size();
laser_cloud_surface_last_ds_->clear();
down_size_filter_surface_.setInputCloud(laser_cloud_surface_last_);
down_size_filter_surface_.filter(*laser_cloud_surface_last_ds_);
laser_cloud_surface_last_ds_num_ = laser_cloud_surface_last_ds_->size();
}
void FeatureMatching::update_point_associate_to_map()
{
trans_point_associate_to_map_ = trans_to_affine3f(transform_to_be_mapped_);
}
void FeatureMatching::corner_optimization()
{
update_point_associate_to_map();
// #pragma omp parallel for num_threads(numberOfCores)
for (int i = 0; i < laser_cloud_corner_last_ds_num_; i++)
{
PointType pointOri, pointSel, coeff;
std::vector<int> pointSearchInd;
std::vector<float> pointSearchSqDis;
pointOri = laser_cloud_corner_last_ds_->points[i];
point_associate_to_map(&pointOri, &pointSel);
kdtree_corner_from_map_->nearestKSearch(pointSel, 5, pointSearchInd, pointSearchSqDis);
cv::Mat matA1(3, 3, CV_32F, cv::Scalar::all(0));
cv::Mat matD1(1, 3, CV_32F, cv::Scalar::all(0));
cv::Mat matV1(3, 3, CV_32F, cv::Scalar::all(0));
if (pointSearchSqDis[4] < 1.0) {
float cx = 0, cy = 0, cz = 0;
for (int j = 0; j < 5; j++) {
cx += laser_cloud_corner_from_map_ds_->points[pointSearchInd[j]].x;
cy += laser_cloud_corner_from_map_ds_->points[pointSearchInd[j]].y;
cz += laser_cloud_corner_from_map_ds_->points[pointSearchInd[j]].z;
}
cx /= 5; cy /= 5; cz /= 5;
float a11 = 0, a12 = 0, a13 = 0, a22 = 0, a23 = 0, a33 = 0;
for (int j = 0; j < 5; j++) {
float ax = laser_cloud_corner_from_map_ds_->points[pointSearchInd[j]].x - cx;
float ay = laser_cloud_corner_from_map_ds_->points[pointSearchInd[j]].y - cy;
float az = laser_cloud_corner_from_map_ds_->points[pointSearchInd[j]].z - cz;
a11 += ax * ax; a12 += ax * ay; a13 += ax * az;
a22 += ay * ay; a23 += ay * az;
a33 += az * az;
}
a11 /= 5; a12 /= 5; a13 /= 5; a22 /= 5; a23 /= 5; a33 /= 5;
matA1.at<float>(0, 0) = a11; matA1.at<float>(0, 1) = a12; matA1.at<float>(0, 2) = a13;
matA1.at<float>(1, 0) = a12; matA1.at<float>(1, 1) = a22; matA1.at<float>(1, 2) = a23;
matA1.at<float>(2, 0) = a13; matA1.at<float>(2, 1) = a23; matA1.at<float>(2, 2) = a33;
cv::eigen(matA1, matD1, matV1);
if (matD1.at<float>(0, 0) > 3 * matD1.at<float>(0, 1)) {
float x0 = pointSel.x;
float y0 = pointSel.y;
float z0 = pointSel.z;
float x1 = cx + 0.1 * matV1.at<float>(0, 0);
float y1 = cy + 0.1 * matV1.at<float>(0, 1);
float z1 = cz + 0.1 * matV1.at<float>(0, 2);
float x2 = cx - 0.1 * matV1.at<float>(0, 0);
float y2 = cy - 0.1 * matV1.at<float>(0, 1);
float z2 = cz - 0.1 * matV1.at<float>(0, 2);
float a012 = sqrt(((x0 - x1)*(y0 - y2) - (x0 - x2)*(y0 - y1)) * ((x0 - x1)*(y0 - y2) - (x0 - x2)*(y0 - y1))
+ ((x0 - x1)*(z0 - z2) - (x0 - x2)*(z0 - z1)) * ((x0 - x1)*(z0 - z2) - (x0 - x2)*(z0 - z1))
+ ((y0 - y1)*(z0 - z2) - (y0 - y2)*(z0 - z1)) * ((y0 - y1)*(z0 - z2) - (y0 - y2)*(z0 - z1)));
float l12 = sqrt((x1 - x2)*(x1 - x2) + (y1 - y2)*(y1 - y2) + (z1 - z2)*(z1 - z2));
float la = ((y1 - y2)*((x0 - x1)*(y0 - y2) - (x0 - x2)*(y0 - y1))
+ (z1 - z2)*((x0 - x1)*(z0 - z2) - (x0 - x2)*(z0 - z1))) / a012 / l12;
float lb = -((x1 - x2)*((x0 - x1)*(y0 - y2) - (x0 - x2)*(y0 - y1))
- (z1 - z2)*((y0 - y1)*(z0 - z2) - (y0 - y2)*(z0 - z1))) / a012 / l12;
float lc = -((x1 - x2)*((x0 - x1)*(z0 - z2) - (x0 - x2)*(z0 - z1))
+ (y1 - y2)*((y0 - y1)*(z0 - z2) - (y0 - y2)*(z0 - z1))) / a012 / l12;
float ld2 = a012 / l12;
float s = 1 - 0.9 * fabs(ld2);
coeff.x = s * la;
coeff.y = s * lb;
coeff.z = s * lc;
coeff.intensity = s * ld2;
if (s > 0.1) {
laser_cloud_ori_corner_vec_[i] = pointOri;
coeff_sel_corner_vec_[i] = coeff;
laser_cloud_ori_corner_flag_[i] = true;
}
}
}
}
}
void FeatureMatching::surface_optimization()
{
update_point_associate_to_map();
// #pragma omp parallel for num_threads(10)
for (int i = 0; i < laser_cloud_surface_last_ds_num_; i++)
{
PointType pointOri, pointSel, coeff;
std::vector<int> pointSearchInd;
std::vector<float> pointSearchSqDis;
pointOri = laser_cloud_surface_last_ds_->points[i];
point_associate_to_map(&pointOri, &pointSel);
// auto start = std::chrono::high_resolution_clock::now();
kdtree_surface_from_map_->nearestKSearch(pointSel, 5, pointSearchInd, pointSearchSqDis);
// auto end = std::chrono::high_resolution_clock::now();
// auto duration = std::chrono::duration_cast<std::chrono::nanoseconds>(
// end - start);
// std::cout << "\t\tkdtree_surface_from_map_->nearestKSearch time: " << std::setprecision(10) << duration.count() << std::endl;
Eigen::Matrix<float, 5, 3> matA0;
Eigen::Matrix<float, 5, 1> matB0;
Eigen::Vector3f matX0;
matA0.setZero();
matB0.fill(-1);
matX0.setZero();
if (pointSearchSqDis[4] < 1.0) {
for (int j = 0; j < 5; j++) {
matA0(j, 0) = laser_cloud_surface_from_map_ds_->points[pointSearchInd[j]].x;
matA0(j, 1) = laser_cloud_surface_from_map_ds_->points[pointSearchInd[j]].y;
matA0(j, 2) = laser_cloud_surface_from_map_ds_->points[pointSearchInd[j]].z;
}
matX0 = matA0.colPivHouseholderQr().solve(matB0);
float pa = matX0(0, 0);
float pb = matX0(1, 0);
float pc = matX0(2, 0);
float pd = 1;
float ps = sqrt(pa * pa + pb * pb + pc * pc);
pa /= ps; pb /= ps; pc /= ps; pd /= ps;
bool planeValid = true;
for (int j = 0; j < 5; j++) {
if (fabs(pa * laser_cloud_surface_from_map_ds_->points[pointSearchInd[j]].x +
pb * laser_cloud_surface_from_map_ds_->points[pointSearchInd[j]].y +
pc * laser_cloud_surface_from_map_ds_->points[pointSearchInd[j]].z + pd) > 0.2) {
planeValid = false;
break;
}
}
if (planeValid) {
float pd2 = pa * pointSel.x + pb * pointSel.y + pc * pointSel.z + pd;
float s = 1 - 0.9 * fabs(pd2) / sqrt(sqrt(pointOri.x * pointOri.x
+ pointOri.y * pointOri.y + pointOri.z * pointOri.z));
coeff.x = s * pa;
coeff.y = s * pb;
coeff.z = s * pc;
coeff.intensity = s * pd2;
if (s > 0.1) {
laser_cloud_ori_surface_vec_[i] = pointOri;
coeff_sel_surface_vec_[i] = coeff;
laser_cloud_ori_surface_flag_[i] = true;
}
}
}
}
}
void FeatureMatching::combine_optimization_coeffs()
{
// combine corner coeffs
for (int i = 0; i < laser_cloud_corner_last_ds_num_; ++i){
if (laser_cloud_ori_corner_flag_[i] == true){
laser_cloud_ori_->push_back(laser_cloud_ori_corner_vec_[i]);
coeff_sel_->push_back(coeff_sel_corner_vec_[i]);
}
}
// combine surf coeffs
for (int i = 0; i < laser_cloud_surface_last_ds_num_; ++i){
if (laser_cloud_ori_surface_flag_[i] == true){
laser_cloud_ori_->push_back(laser_cloud_ori_surface_vec_[i]);
coeff_sel_->push_back(coeff_sel_surface_vec_[i]);
}
}
// reset flag for next iteration
std::fill(laser_cloud_ori_corner_flag_.begin(), laser_cloud_ori_corner_flag_.end(), false);
std::fill(laser_cloud_ori_surface_flag_.begin(), laser_cloud_ori_surface_flag_.end(), false);
}
bool FeatureMatching::lm_optimization(int iter_count)
{
// This optimization is from the original loam_velodyne by Ji Zhang, need to cope with coordinate transformation
// lidar <- camera --- camera <- lidar
// x = z --- x = y
// y = x --- y = z
// z = y --- z = x
// roll = yaw --- roll = pitch
// pitch = roll --- pitch = yaw
// yaw = pitch --- yaw = roll
// lidar -> camera
float srx = sin(transform_to_be_mapped_[1]);
float crx = cos(transform_to_be_mapped_[1]);
float sry = sin(transform_to_be_mapped_[2]);
float cry = cos(transform_to_be_mapped_[2]);
float srz = sin(transform_to_be_mapped_[0]);
float crz = cos(transform_to_be_mapped_[0]);
int laserCloudSelNum = laser_cloud_ori_->size();
if (laserCloudSelNum < 50) {
return false;
}
cv::Mat matA(laserCloudSelNum, 6, CV_32F, cv::Scalar::all(0));
cv::Mat matAt(6, laserCloudSelNum, CV_32F, cv::Scalar::all(0));
cv::Mat matAtA(6, 6, CV_32F, cv::Scalar::all(0));
cv::Mat matB(laserCloudSelNum, 1, CV_32F, cv::Scalar::all(0));
cv::Mat matAtB(6, 1, CV_32F, cv::Scalar::all(0));
cv::Mat matX(6, 1, CV_32F, cv::Scalar::all(0));
cv::Mat matP(6, 6, CV_32F, cv::Scalar::all(0));
PointType pointOri, coeff;
for (int i = 0; i < laserCloudSelNum; i++) {
// lidar -> camera
pointOri.x = laser_cloud_ori_->points[i].y;
pointOri.y = laser_cloud_ori_->points[i].z;
pointOri.z = laser_cloud_ori_->points[i].x;
// lidar -> camera
coeff.x = coeff_sel_->points[i].y;
coeff.y = coeff_sel_->points[i].z;
coeff.z = coeff_sel_->points[i].x;
coeff.intensity = coeff_sel_->points[i].intensity;
// in camera
float arx = (crx*sry*srz*pointOri.x + crx*crz*sry*pointOri.y - srx*sry*pointOri.z) * coeff.x
+ (-srx*srz*pointOri.x - crz*srx*pointOri.y - crx*pointOri.z) * coeff.y
+ (crx*cry*srz*pointOri.x + crx*cry*crz*pointOri.y - cry*srx*pointOri.z) * coeff.z;
float ary = ((cry*srx*srz - crz*sry)*pointOri.x
+ (sry*srz + cry*crz*srx)*pointOri.y + crx*cry*pointOri.z) * coeff.x
+ ((-cry*crz - srx*sry*srz)*pointOri.x
+ (cry*srz - crz*srx*sry)*pointOri.y - crx*sry*pointOri.z) * coeff.z;
float arz = ((crz*srx*sry - cry*srz)*pointOri.x + (-cry*crz-srx*sry*srz)*pointOri.y)*coeff.x
+ (crx*crz*pointOri.x - crx*srz*pointOri.y) * coeff.y
+ ((sry*srz + cry*crz*srx)*pointOri.x + (crz*sry-cry*srx*srz)*pointOri.y)*coeff.z;
// lidar -> camera
matA.at<float>(i, 0) = arz;
matA.at<float>(i, 1) = arx;
matA.at<float>(i, 2) = ary;
matA.at<float>(i, 3) = coeff.z;
matA.at<float>(i, 4) = coeff.x;
matA.at<float>(i, 5) = coeff.y;
matB.at<float>(i, 0) = -coeff.intensity;
}
cv::transpose(matA, matAt);
matAtA = matAt * matA;
matAtB = matAt * matB;
cv::solve(matAtA, matAtB, matX, cv::DECOMP_QR);
if (iter_count == 0) {
cv::Mat matE(1, 6, CV_32F, cv::Scalar::all(0));
cv::Mat matV(6, 6, CV_32F, cv::Scalar::all(0));
cv::Mat matV2(6, 6, CV_32F, cv::Scalar::all(0));
cv::eigen(matAtA, matE, matV);
matV.copyTo(matV2);
is_degenerate_ = false;
float eignThre[6] = {100, 100, 100, 100, 100, 100};
for (int i = 5; i >= 0; i--) {
if (matE.at<float>(0, i) < eignThre[i]) {
for (int j = 0; j < 6; j++) {
matV2.at<float>(i, j) = 0;
}
is_degenerate_ = true;
} else {
break;
}
}
matP = matV.inv() * matV2;
}
if (is_degenerate_)
{
cv::Mat matX2(6, 1, CV_32F, cv::Scalar::all(0));
matX.copyTo(matX2);
matX = matP * matX2;
}
transform_to_be_mapped_[0] += matX.at<float>(0, 0);
transform_to_be_mapped_[1] += matX.at<float>(1, 0);
transform_to_be_mapped_[2] += matX.at<float>(2, 0);
transform_to_be_mapped_[3] += matX.at<float>(3, 0);
transform_to_be_mapped_[4] += matX.at<float>(4, 0);
transform_to_be_mapped_[5] += matX.at<float>(5, 0);
float deltaR = sqrt(
std::pow(pcl::rad2deg(matX.at<float>(0, 0)), 2) +
std::pow(pcl::rad2deg(matX.at<float>(1, 0)), 2) +
std::pow(pcl::rad2deg(matX.at<float>(2, 0)), 2));
float deltaT = sqrt(
std::pow(matX.at<float>(3, 0) * 100, 2) +
std::pow(matX.at<float>(4, 0) * 100, 2) +
std::pow(matX.at<float>(5, 0) * 100, 2));
if (deltaR < 0.05 && deltaT < 0.05) {
return true; // converged
}
return false; // keep optimizing
}
void FeatureMatching::scan_to_map_optimization()
{
if (cloud_key_poses_3d_->points.empty())
return;
if (laser_cloud_corner_last_ds_num_ > min_edge_feature_number_ && laser_cloud_surface_last_ds_num_ > min_surface_feature_number_)
{
std::cout << "laser_cloud_corner_from_map_ds_.size: " << laser_cloud_corner_from_map_ds_->size() << std::endl;
kdtree_corner_from_map_->setInputCloud(laser_cloud_corner_from_map_ds_);
kdtree_surface_from_map_->setInputCloud(laser_cloud_surface_from_map_ds_);
for (int iterCount = 0; iterCount < 30; iterCount++)
{
laser_cloud_ori_->clear();
coeff_sel_->clear();
auto start_corner_optimization = std::chrono::high_resolution_clock::now();
corner_optimization();
auto end_corner_optimization = std::chrono::high_resolution_clock::now();
auto duration_corner_optimization = std::chrono::duration_cast<std::chrono::milliseconds>(
end_corner_optimization - start_corner_optimization);
std::cout << "\tcorner_optimization time: " << std::setprecision(6) << duration_corner_optimization.count() << std::endl;
auto start_surface_optimization = std::chrono::high_resolution_clock::now();
surface_optimization();
auto end_surface_optimization = std::chrono::high_resolution_clock::now();
auto duration_surface_optimization = std::chrono::duration_cast<std::chrono::milliseconds>(
end_surface_optimization - start_surface_optimization);
std::cout << "\tsurface_optimization time: " << std::setprecision(6) << duration_surface_optimization.count() << std::endl;
auto start_combine_optimization_coeffs = std::chrono::high_resolution_clock::now();
combine_optimization_coeffs();
auto end_combine_optimization_coeffs = std::chrono::high_resolution_clock::now();
auto duration_combine_optimization_coeffs = std::chrono::duration_cast<std::chrono::milliseconds>(
end_combine_optimization_coeffs - start_combine_optimization_coeffs);
std::cout << "\tcombine_optimization_coeffs time: " << std::setprecision(6) << duration_combine_optimization_coeffs.count() << std::endl;
if (lm_optimization(iterCount) == true)
break;
}
transform_update();
} else {
// RCLCPP_WARN(get_logger(), "Not enough features! Only %d edge and %d planar features available.", laserCloudCornerLastDSNum, laserCloudSurfLastDSNum);
std::cout << "Not enough features! Only " << laser_cloud_corner_last_ds_num_ << " edge and " << laser_cloud_surface_last_ds_num_ << "planar features available" << std::endl;
}
}
void FeatureMatching::transform_update()
{
if (cloud_info_.imu_available == true)
{
if (std::abs(cloud_info_.imu_pitch_init) < 1.4)
{
double imuWeight = imu_rpy_weight_;
tf2::Quaternion imuQuaternion;
tf2::Quaternion transformQuaternion;
double rollMid, pitchMid, yawMid;
// slerp roll
transformQuaternion.setRPY(transform_to_be_mapped_[0], 0, 0);
imuQuaternion.setRPY(cloud_info_.imu_roll_init, 0, 0);
tf2::Matrix3x3(transformQuaternion.slerp(imuQuaternion, imuWeight)).getRPY(rollMid, pitchMid, yawMid);
transform_to_be_mapped_[0] = rollMid;
// slerp pitch
transformQuaternion.setRPY(0, transform_to_be_mapped_[1], 0);
imuQuaternion.setRPY(0, cloud_info_.imu_pitch_init, 0);
tf2::Matrix3x3(transformQuaternion.slerp(imuQuaternion, imuWeight)).getRPY(rollMid, pitchMid, yawMid);
transform_to_be_mapped_[1] = pitchMid;
}
}
transform_to_be_mapped_[0] = constraint_transformation(transform_to_be_mapped_[0], rotation_tollerance_);
transform_to_be_mapped_[1] = constraint_transformation(transform_to_be_mapped_[1], rotation_tollerance_);
transform_to_be_mapped_[5] = constraint_transformation(transform_to_be_mapped_[5], z_tollerance_);
incremental_odometry_affine_back_ = trans_to_affine3f(transform_to_be_mapped_);
}
float FeatureMatching::constraint_transformation(float value, float limit)
{
if (value < -limit)
value = -limit;
if (value > limit)
value = limit;
return value;
}
bool FeatureMatching::save_frame() {
if (cloud_key_poses_3d_->points.empty())
return true;
// if (sensor == SensorType::LIVOX)
// {
// if (timeLaserInfoCur - cloudKeyPoses6D->back().time > 1.0)
// return true;
// }
Eigen::Affine3f transStart = pcl_point_to_affine3f(cloud_key_poses_6d_->back());
Eigen::Affine3f transFinal = pcl::getTransformation(
transform_to_be_mapped_[3], transform_to_be_mapped_[4], transform_to_be_mapped_[5],
transform_to_be_mapped_[0], transform_to_be_mapped_[1], transform_to_be_mapped_[2]);
Eigen::Affine3f transBetween = transStart.inverse() * transFinal;
float x, y, z, roll, pitch, yaw;
pcl::getTranslationAndEulerAngles(transBetween, x, y, z, roll, pitch, yaw);
if (abs(roll) < surrounding_key_frame_adding_angle_threshold_ &&
abs(pitch) < surrounding_key_frame_adding_angle_threshold_ &&
abs(yaw) < surrounding_key_frame_adding_angle_threshold_ &&
sqrt(x*x + y*y + z*z) < surrounding_key_frame_adding_dist_threshold_)
return false;
return true;
}
void FeatureMatching::add_odom_factor() {
if (cloud_key_poses_3d_->points.empty())
{
gtsam::noiseModel::Diagonal::shared_ptr priorNoise = gtsam::noiseModel::Diagonal::Variances((gtsam::Vector(6) << 1e-2, 1e-2, M_PI*M_PI, 1e8, 1e8, 1e8).finished()); // rad*rad, meter*meter
gtsam_graph_.add(gtsam::PriorFactor<gtsam::Pose3>(0, trans_to_gtsam_pose(transform_to_be_mapped_), priorNoise));
initial_estimate_.insert(0, trans_to_gtsam_pose(transform_to_be_mapped_));
}else{
gtsam::noiseModel::Diagonal::shared_ptr odometryNoise = gtsam::noiseModel::Diagonal::Variances((gtsam::Vector(6) << 1e-6, 1e-6, 1e-6, 1e-4, 1e-4, 1e-4).finished());
gtsam::Pose3 poseFrom = pcl_point_to_gtsam_pose3(cloud_key_poses_6d_->points.back());
gtsam::Pose3 poseTo = trans_to_gtsam_pose(transform_to_be_mapped_);
gtsam_graph_.add(gtsam::BetweenFactor<gtsam::Pose3>(cloud_key_poses_3d_->size()-1, cloud_key_poses_3d_->size(), poseFrom.between(poseTo), odometryNoise));
initial_estimate_.insert(cloud_key_poses_3d_->size(), poseTo);
}
}
void FeatureMatching::add_gps_factor()
{
if (gps_queue_.empty())
return;