void MonsterGroupManager::LoadJSONGroups() throw (std::string)
{
//open the file
std::ifstream file;
file.open(monGroupFilePath);
if(!file.good())
{
throw (std::string)"Unable to load file " + monGroupFilePath;
}
//load the data
picojson::value groupsRaw;
file >> groupsRaw;
/*std::string error = picojson::get_last_error();
if(! error.empty())
{
printf("'%s' : %s", monGroupFilePath, error.c_str());
return;
}*/
//check the data
if (! groupsRaw.is<picojson::array>()) {
throw (std::string)"The monster group file " + monGroupFilePath +
(std::string)" does not contain the expected JSON data";
}
init_translation();
picojson::object jsonobj;
MonsterGroup g;
const picojson::array& groups = groupsRaw.get<picojson::array>();
for (picojson::array::const_iterator it_groups = groups.begin(); it_groups != groups.end(); ++it_groups)
{
jsonobj = it_groups->get<picojson::object>();
g = GetMGroupFromJSON(&jsonobj);
monsterGroupMap[g.name] = g;
}
if(!json_good())
{
throw (std::string)"There was an error reading " + monGroupFilePath;
}
}
static MATRIX *
initialize_transform(MRI *mri_in, MRI *mri_ref, MP *parms) {
MATRIX *m_L ;
fprintf(stderr, "initializing alignment using PCA...\n") ;
if (Gdiag & DIAG_WRITE && parms->write_iterations > 0) {
MRIwriteImageViews(parms->mri_ref, "ref", IMAGE_SIZE) ;
MRIwriteImageViews(parms->mri_in, "before_pca", IMAGE_SIZE) ;
}
if (nopca)
m_L = MatrixIdentity(3, NULL) ;
else
m_L = compute_pca(mri_in, mri_ref) ;
init_scaling(mri_in, mri_ref, m_L) ;
#if 0
init_translation(mri_in, mri_ref, m_L) ; /* in case PCA failed */
#endif
/* convert it to RAS mm coordinates */
MRIvoxelXformToRasXform(mri_in, mri_ref, m_L, m_L) ;
if (Gdiag & DIAG_SHOW) {
printf("initial transform:\n") ;
MatrixPrint(stdout, m_L) ;
}
if (Gdiag & DIAG_WRITE && parms->write_iterations > 0) {
MRI *mri_aligned ;
mri_aligned = MRIapplyRASlinearTransform(parms->mri_in, NULL, m_L) ;
MRIwriteImageViews(mri_aligned, "after_pca", IMAGE_SIZE) ;
MRIfree(&mri_aligned) ;
}
return(m_L) ;
}
template<typename PointSource, typename PointTarget> void
pcl::NormalDistributionsTransform<PointSource, PointTarget>::computeTransformation (PointCloudSource &output, const Eigen::Matrix4f &guess)
{
nr_iterations_ = 0;
converged_ = false;
double gauss_c1, gauss_c2, gauss_d3;
// Initializes the guassian fitting parameters (eq. 6.8) [Magnusson 2009]
gauss_c1 = 10 * (1 - outlier_ratio_);
gauss_c2 = outlier_ratio_ / pow (resolution_, 3);
gauss_d3 = -log (gauss_c2);
gauss_d1_ = -log ( gauss_c1 + gauss_c2 ) - gauss_d3;
gauss_d2_ = -2 * log ((-log ( gauss_c1 * exp ( -0.5 ) + gauss_c2 ) - gauss_d3) / gauss_d1_);
if (guess != Eigen::Matrix4f::Identity ())
{
// Initialise final transformation to the guessed one
final_transformation_ = guess;
// Apply guessed transformation prior to search for neighbours
transformPointCloud (output, output, guess);
}
// Initialize Point Gradient and Hessian
point_gradient_.setZero ();
point_gradient_.block<3, 3>(0, 0).setIdentity ();
point_hessian_.setZero ();
Eigen::Transform<float, 3, Eigen::Affine, Eigen::ColMajor> eig_transformation;
eig_transformation.matrix () = final_transformation_;
// Convert initial guess matrix to 6 element transformation vector
Eigen::Matrix<double, 6, 1> p, delta_p, score_gradient;
Eigen::Vector3f init_translation = eig_transformation.translation ();
Eigen::Vector3f init_rotation = eig_transformation.rotation ().eulerAngles (0, 1, 2);
p << init_translation (0), init_translation (1), init_translation (2),
init_rotation (0), init_rotation (1), init_rotation (2);
Eigen::Matrix<double, 6, 6> hessian;
double score = 0;
double delta_p_norm;
// Calculate derivates of initial transform vector, subsequent derivative calculations are done in the step length determination.
score = computeDerivatives (score_gradient, hessian, output, p);
while (!converged_)
{
// Store previous transformation
previous_transformation_ = transformation_;
// Solve for decent direction using newton method, line 23 in Algorithm 2 [Magnusson 2009]
Eigen::JacobiSVD<Eigen::Matrix<double, 6, 6> > sv (hessian, Eigen::ComputeFullU | Eigen::ComputeFullV);
// Negative for maximization as opposed to minimization
delta_p = sv.solve (-score_gradient);
//Calculate step length with guarnteed sufficient decrease [More, Thuente 1994]
delta_p_norm = delta_p.norm ();
if (delta_p_norm == 0 || delta_p_norm != delta_p_norm)
{
trans_probability_ = score / static_cast<double> (input_->points.size ());
converged_ = delta_p_norm == delta_p_norm;
return;
}
delta_p.normalize ();
delta_p_norm = computeStepLengthMT (p, delta_p, delta_p_norm, step_size_, transformation_epsilon_ / 2, score, score_gradient, hessian, output);
delta_p *= delta_p_norm;
transformation_ = (Eigen::Translation<float, 3> (static_cast<float> (delta_p (0)), static_cast<float> (delta_p (1)), static_cast<float> (delta_p (2))) *
Eigen::AngleAxis<float> (static_cast<float> (delta_p (3)), Eigen::Vector3f::UnitX ()) *
Eigen::AngleAxis<float> (static_cast<float> (delta_p (4)), Eigen::Vector3f::UnitY ()) *
Eigen::AngleAxis<float> (static_cast<float> (delta_p (5)), Eigen::Vector3f::UnitZ ())).matrix ();
p = p + delta_p;
// Update Visualizer (untested)
if (update_visualizer_ != 0)
update_visualizer_ (output, std::vector<int>(), *target_, std::vector<int>() );
if (nr_iterations_ > max_iterations_ ||
(nr_iterations_ && (std::fabs (delta_p_norm) < transformation_epsilon_)))
{
converged_ = true;
}
nr_iterations_++;
}
// Store transformation probability. The realtive differences within each scan registration are accurate
// but the normalization constants need to be modified for it to be globally accurate
trans_probability_ = score / static_cast<double> (input_->points.size ());
}
void NDTScanMatching::scan_matching_callback(const sensor_msgs::PointCloud2::ConstPtr& points,
const geometry_msgs::PoseStamped::ConstPtr& pose)
{
ros::Time scan_time = ros::Time::now();
pcl::PointXYZI p;
pcl::PointCloud<pcl::PointXYZI> scan;
pcl::PointCloud<pcl::PointXYZI>::Ptr transformed_scan_ptr (new pcl::PointCloud<pcl::PointXYZI>());
tf::Quaternion q;
Eigen::Matrix4f t(Eigen::Matrix4f::Identity());
tf::Transform transform;
pcl::fromROSMsg(*points, scan);
pcl::PointCloud<pcl::PointXYZI>::Ptr scan_ptr(new pcl::PointCloud<pcl::PointXYZI>(scan));
if(initial_scan_loaded_ == 0)
{
last_scan_ = *scan_ptr;
last_pose_ = *pose;
initial_scan_loaded_ = 1;
return;
}
// Filtering input scan to roughly 10% of original size to increase speed of registration.
pcl::PointCloud<pcl::PointXYZI>::Ptr filtered_cloud_ptr (new pcl::PointCloud<pcl::PointXYZI>);
pcl::ApproximateVoxelGrid<pcl::PointXYZI> approximate_voxel_filter;
approximate_voxel_filter.setLeafSize (2.0, 2.0, 2.0);
approximate_voxel_filter.setInputCloud (scan_ptr);
approximate_voxel_filter.filter (*filtered_cloud_ptr);
// Setting scale dependent NDT parameters
// Setting minimum transformation difference for termination condition.
ndt_.setTransformationEpsilon (0.01);
// Setting maximum step size for More-Thuente line search.
ndt_.setStepSize (0.1);
//Setting Resolution of NDT grid structure (VoxelGridCovariance).
ndt_.setResolution (1.0);
// Setting max number of registration iterations.
ndt_.setMaximumIterations (30);
// Setting point cloud to be aligned.
ndt_.setInputSource (filtered_cloud_ptr);
pcl::PointCloud<pcl::PointXYZI>::Ptr last_scan_ptr(new pcl::PointCloud<pcl::PointXYZI>(last_scan_));
// Setting point cloud to be aligned to.
ndt_.setInputTarget (last_scan_ptr);
tf::Matrix3x3 init_rotation;
// 一個前のposeと引き算してx, y ,zの偏差を出す
offset_x_ = pose->pose.position.x - last_pose_.pose.position.x;
offset_y_ = pose->pose.position.y - last_pose_.pose.position.y;
double roll, pitch, yaw = 0;
getRPY(pose->pose.orientation, roll, pitch, yaw);
offset_yaw_ = yaw - last_yaw_;
guess_pos_.x = previous_pos_.x + offset_x_;
guess_pos_.y = previous_pos_.y + offset_y_;
guess_pos_.z = previous_pos_.z;
guess_pos_.roll = previous_pos_.roll;
guess_pos_.pitch = previous_pos_.pitch;
guess_pos_.yaw = previous_pos_.yaw + offset_yaw_;
Eigen::AngleAxisf init_rotation_x(guess_pos_.roll, Eigen::Vector3f::UnitX());
Eigen::AngleAxisf init_rotation_y(guess_pos_.pitch, Eigen::Vector3f::UnitY());
Eigen::AngleAxisf init_rotation_z(guess_pos_.yaw, Eigen::Vector3f::UnitZ());
Eigen::Translation3f init_translation(guess_pos_.x, guess_pos_.y, guess_pos_.z);
Eigen::Matrix4f init_guess = (init_translation * init_rotation_z * init_rotation_y * init_rotation_x).matrix();
pcl::PointCloud<pcl::PointXYZI>::Ptr output_cloud_ptr(new pcl::PointCloud<pcl::PointXYZI>);
ros::Time ndt_start = ros::Time::now();
ndt_.align (*output_cloud_ptr, init_guess);
ros::Duration ndt_delta_t = ros::Time::now() - ndt_start;
std::cout << "Normal Distributions Transform has converged:" << ndt_.hasConverged ()
<< " score: " << ndt_.getFitnessScore () << std::endl;
t = ndt_.getFinalTransformation();
// Transforming unfiltered, input cloud using found transform.
pcl::transformPointCloud (*scan_ptr, *output_cloud_ptr, t);
tf::Matrix3x3 tf3d;
tf3d.setValue(
static_cast<double>(t(0, 0)), static_cast<double>(t(0, 1)), static_cast<double>(t(0, 2)),
static_cast<double>(t(1, 0)), static_cast<double>(t(1, 1)), static_cast<double>(t(1, 2)),
static_cast<double>(t(2, 0)), static_cast<double>(t(2, 1)), static_cast<double>(t(2, 2)));
current_pos_.x = t(0, 3);
current_pos_.y = t(1, 3);
current_pos_.z = t(2, 3);
std::cout << "/////////////////////////////////////////////" << std::endl;
std::cout << "count : " << count_ << std::endl;
std::cout << "Process time : " << ndt_delta_t.toSec() << std::endl;
std::cout << "x : " << current_pos_.x << std::endl;
std::cout << "y : " << current_pos_.y << std::endl;
std::cout << "z : " << current_pos_.z << std::endl;
std::cout << "/////////////////////////////////////////////" << std::endl;
tf3d.getRPY(current_pos_.roll, current_pos_.pitch, current_pos_.yaw, 1);
transform.setOrigin(tf::Vector3(current_pos_.x, current_pos_.y, current_pos_.z));
q.setRPY(current_pos_.roll, current_pos_.pitch, current_pos_.yaw);
transform.setRotation(q);
// "map"に対する"base_link"の位置を発行する
br_.sendTransform(tf::StampedTransform(transform, scan_time, "map", "ndt_base_link"));
sensor_msgs::PointCloud2 scan_matched;
pcl::toROSMsg(*output_cloud_ptr, scan_matched);
scan_matched.header.stamp = scan_time;
scan_matched.header.frame_id = "matched_point_cloud";
point_cloud_pub_.publish(scan_matched);
// Update position and posture. current_pos -> previous_pos
previous_pos_ = current_pos_;
// save current scan
last_scan_ += *output_cloud_ptr;
last_pose_ = *pose;
last_yaw_ = yaw;
// geometry_msgs::TransformStamped ndt_trans;
// ndt_trans.header.stamp = scan_time;
// ndt_trans.header.frame_id = "map";
// ndt_trans.child_frame_id = "base_link";
// ndt_trans.transform.translation.x = curren_pos.x;
// ndt_trans.transform.translation.y = curren_pos.y;
// ndt_trans.transform.translation.z = curren_pos.z;
// ndt_trans.transform.rotation = q;
// ndt_broadcaster_.sendTransform(ndt_trans);
count_++;
}
void init_data_structures()
{
// all of the applicable types that can be loaded, along with their loading functions
// Add to this as needed with new StaticFunctionAccessors or new ClassFunctionAccessors for new applicable types
// Static Function Access
type_function_map["material"] = new StaticFunctionAccessor(&material_type::load_material);
type_function_map["bionic"] = new StaticFunctionAccessor(&load_bionic);
type_function_map["profession"] = new StaticFunctionAccessor(&profession::load_profession);
type_function_map["skill"] = new StaticFunctionAccessor(&Skill::load_skill);
type_function_map["dream"] = new StaticFunctionAccessor(&load_dream);
type_function_map["mutation"] = new StaticFunctionAccessor(&load_mutation);
type_function_map["lab_note"] = new StaticFunctionAccessor(&computer::load_lab_note);
type_function_map["hint"] = new StaticFunctionAccessor(&load_hint);
type_function_map["furniture"] = new StaticFunctionAccessor(&load_furniture);
type_function_map["terrain"] = new StaticFunctionAccessor(&load_terrain);
type_function_map["monstergroup"] = new StaticFunctionAccessor(&MonsterGroupManager::LoadMonsterGroup);
type_function_map["speech"] = new StaticFunctionAccessor(&load_speech);
//data/json/colors.json would be listed here, but it's loaded before the others (see curses_start_color())
// Non Static Function Access
type_function_map["snippet"] = new ClassFunctionAccessor<snippet_library>(&SNIPPET, &snippet_library::load_snippet);
type_function_map["item_group"] = new ClassFunctionAccessor<Item_factory>(item_controller, &Item_factory::load_item_group);
type_function_map["vehicle_part"] = new ClassFunctionAccessor<game>(g, &game::load_vehiclepart);
type_function_map["vehicle"] = new ClassFunctionAccessor<game>(g, &game::load_vehicle);
type_function_map["trap"] = new ClassFunctionAccessor<game>(g, &game::load_trap);
type_function_map["AMMO"] = new ClassFunctionAccessor<Item_factory>(item_controller, &Item_factory::load_ammo);
type_function_map["GUN"] = new ClassFunctionAccessor<Item_factory>(item_controller, &Item_factory::load_gun);
type_function_map["ARMOR"] = new ClassFunctionAccessor<Item_factory>(item_controller, &Item_factory::load_armor);
type_function_map["TOOL"] = new ClassFunctionAccessor<Item_factory>(item_controller, &Item_factory::load_tool);
type_function_map["BOOK"] = new ClassFunctionAccessor<Item_factory>(item_controller, &Item_factory::load_book);
type_function_map["COMESTIBLE"] = new ClassFunctionAccessor<Item_factory>(item_controller, &Item_factory::load_comestible);
type_function_map["CONTAINER"] = new ClassFunctionAccessor<Item_factory>(item_controller, &Item_factory::load_container);
type_function_map["GUNMOD"] = new ClassFunctionAccessor<Item_factory>(item_controller, &Item_factory::load_gunmod);
type_function_map["GENERIC"] = new ClassFunctionAccessor<Item_factory>(item_controller, &Item_factory::load_generic);
type_function_map["ITEM_CATEGORY"] = new ClassFunctionAccessor<Item_factory>(item_controller, &Item_factory::load_item_category);
type_function_map["MONSTER"] = new ClassFunctionAccessor<MonsterGenerator>(&MonsterGenerator::generator(), &MonsterGenerator::load_monster);
type_function_map["SPECIES"] = new ClassFunctionAccessor<MonsterGenerator>(&MonsterGenerator::generator(), &MonsterGenerator::load_species);
type_function_map["recipe_category"] = new StaticFunctionAccessor(&load_recipe_category);
type_function_map["recipe_subcategory"] = new StaticFunctionAccessor(&load_recipe_subcategory);
type_function_map["recipe"] = new StaticFunctionAccessor(&load_recipe);
type_function_map["tool_quality"] = new StaticFunctionAccessor(&load_quality);
type_function_map["technique"] = new StaticFunctionAccessor(&load_technique);
type_function_map["martial_art"] = new StaticFunctionAccessor(&load_martial_art);
type_function_map["effect_type"] = new StaticFunctionAccessor(&load_effect_type);
type_function_map["tutorial_messages"] =
new StaticFunctionAccessor(&load_tutorial_messages);
type_function_map["overmap_terrain"] =
new StaticFunctionAccessor(&load_overmap_terrain);
type_function_map["construction"] =
new StaticFunctionAccessor(&load_construction);
type_function_map["mapgen"] =
new StaticFunctionAccessor(&load_mapgen);
type_function_map["monitems"] = new ClassFunctionAccessor<game>(g, &game::load_monitem);
mutations_category[""].clear();
init_body_parts();
init_ter_bitflags_map();
init_vpart_bitflag_map();
init_translation();
init_martial_arts();
init_colormap();
init_artifacts();
init_mapgen_builtin_functions();
}
//static void velodyne_callback(const pcl::PointCloud<velodyne_pointcloud::PointXYZIR>::ConstPtr& input)
static void map_callback(const sensor_msgs::PointCloud2::ConstPtr& input)
{
if (map_loaded == 0) {
std::cout << "Loading map data... ";
map.header.frame_id = "/pointcloud_map_frame";
// Convert the data type(from sensor_msgs to pcl).
pcl::fromROSMsg(*input, map);
pcl::PointCloud<pcl::PointXYZI>::Ptr map_ptr(new pcl::PointCloud<pcl::PointXYZI>(map));
// Setting point cloud to be aligned to.
ndt.setInputTarget(map_ptr);
// Setting NDT parameters to default values
ndt.setMaximumIterations(iter);
ndt.setResolution(ndt_res);
ndt.setStepSize(step_size);
ndt.setTransformationEpsilon(trans_eps);
map_loaded = 1;
std::cout << "Map Loaded." << std::endl;
}
if (map_loaded == 1 && init_pos_set == 1) {
callback_start = ros::Time::now();
static tf::TransformBroadcaster br;
tf::Transform transform;
tf::Quaternion q;
tf::Quaternion q_control;
// 1 scan
/*
pcl::PointCloud<pcl::PointXYZI> scan;
pcl::PointXYZI p;
scan.header = input->header;
scan.header.frame_id = "velodyne_scan_frame";
*/
ros::Time scan_time;
scan_time.sec = additional_map.header.stamp / 1000000.0;
scan_time.nsec = (additional_map.header.stamp - scan_time.sec * 1000000.0) * 1000.0;
/*
std::cout << "scan.header.stamp: " << scan.header.stamp << std::endl;
std::cout << "scan_time: " << scan_time << std::endl;
std::cout << "scan_time.sec: " << scan_time.sec << std::endl;
std::cout << "scan_time.nsec: " << scan_time.nsec << std::endl;
*/
t1_start = ros::Time::now();
/*
for (pcl::PointCloud<velodyne_pointcloud::PointXYZIR>::const_iterator item = input->begin(); item != input->end(); item++) {
p.x = (double) item->x;
p.y = (double) item->y;
p.z = (double) item->z;
scan.points.push_back(p);
}
*/
// pcl::fromROSMsg(*input, scan);
t1_end = ros::Time::now();
d1 = t1_end - t1_start;
Eigen::Matrix4f t(Eigen::Matrix4f::Identity());
// pcl::PointCloud<pcl::PointXYZI>::Ptr scan_ptr(new pcl::PointCloud<pcl::PointXYZI>(scan));
pcl::PointCloud<pcl::PointXYZI>::Ptr filtered_additional_map_ptr(new pcl::PointCloud<pcl::PointXYZI>);
// Downsampling the velodyne scan using VoxelGrid filter
t2_start = ros::Time::now();
pcl::VoxelGrid<pcl::PointXYZI> voxel_grid_filter;
voxel_grid_filter.setLeafSize(voxel_leaf_size, voxel_leaf_size, voxel_leaf_size);
voxel_grid_filter.setInputCloud(additional_map_ptr);
voxel_grid_filter.filter(*filtered_additional_map_ptr);
t2_end = ros::Time::now();
d2 = t2_end - t2_start;
// Setting point cloud to be aligned.
ndt.setInputSource(filtered_additional_map_ptr);
// Guess the initial gross estimation of the transformation
t3_start = ros::Time::now();
tf::Matrix3x3 init_rotation;
guess_pos.x = previous_pos.x + offset_x;
guess_pos.y = previous_pos.y + offset_y;
guess_pos.z = previous_pos.z + offset_z;
guess_pos.roll = previous_pos.roll;
guess_pos.pitch = previous_pos.pitch;
guess_pos.yaw = previous_pos.yaw + offset_yaw;
Eigen::AngleAxisf init_rotation_x(guess_pos.roll, Eigen::Vector3f::UnitX());
Eigen::AngleAxisf init_rotation_y(guess_pos.pitch, Eigen::Vector3f::UnitY());
Eigen::AngleAxisf init_rotation_z(guess_pos.yaw, Eigen::Vector3f::UnitZ());
Eigen::Translation3f init_translation(guess_pos.x, guess_pos.y, guess_pos.z);
Eigen::Matrix4f init_guess = (init_translation * init_rotation_z * init_rotation_y * init_rotation_x).matrix();
t3_end = ros::Time::now();
d3 = t3_end - t3_start;
t4_start = ros::Time::now();
pcl::PointCloud<pcl::PointXYZI>::Ptr output_cloud(new pcl::PointCloud<pcl::PointXYZI>);
ndt.align(*output_cloud, init_guess);
t = ndt.getFinalTransformation();
pcl::PointCloud<pcl::PointXYZI>::Ptr transformed_additional_map_ptr (new pcl::PointCloud<pcl::PointXYZI>());
transformed_additional_map_ptr->header.frame_id = "/map";
pcl::transformPointCloud(*additional_map_ptr, *transformed_additional_map_ptr, t);
sensor_msgs::PointCloud2::Ptr msg_ptr(new sensor_msgs::PointCloud2);
pcl::toROSMsg(*transformed_additional_map_ptr, *msg_ptr);
msg_ptr->header.frame_id = "/map";
ndt_map_pub.publish(*msg_ptr);
// Writing Point Cloud data to PCD file
pcl::io::savePCDFileASCII("global_map.pcd", *transformed_additional_map_ptr);
std::cout << "Saved " << transformed_additional_map_ptr->points.size() << " data points to global_map.pcd." << std::endl;
pcl::PointCloud<pcl::PointXYZRGB> output;
output.width = transformed_additional_map_ptr->width;
output.height = transformed_additional_map_ptr->height;
output.points.resize(output.width * output.height);
for(size_t i = 0; i < output.points.size(); i++){
output.points[i].x = transformed_additional_map_ptr->points[i].x;
output.points[i].y = transformed_additional_map_ptr->points[i].y;
output.points[i].z = transformed_additional_map_ptr->points[i].z;
output.points[i].rgb = 255 << 8;
}
pcl::io::savePCDFileASCII("global_map_rgb.pcd", output);
std::cout << "Saved " << output.points.size() << " data points to global_map_rgb.pcd." << std::endl;
t4_end = ros::Time::now();
d4 = t4_end - t4_start;
t5_start = ros::Time::now();
/*
tf::Vector3 origin;
origin.setValue(static_cast<double>(t(0,3)), static_cast<double>(t(1,3)), static_cast<double>(t(2,3)));
*/
tf::Matrix3x3 tf3d;
tf3d.setValue(static_cast<double>(t(0, 0)), static_cast<double>(t(0, 1)), static_cast<double>(t(0, 2)), static_cast<double>(t(1, 0)), static_cast<double>(t(1, 1)), static_cast<double>(t(1, 2)), static_cast<double>(t(2, 0)), static_cast<double>(t(2, 1)), static_cast<double>(t(2, 2)));
// Update current_pos.
current_pos.x = t(0, 3);
current_pos.y = t(1, 3);
current_pos.z = t(2, 3);
tf3d.getRPY(current_pos.roll, current_pos.pitch, current_pos.yaw, 1);
// control_pose
current_pos_control.roll = current_pos.roll;
current_pos_control.pitch = current_pos.pitch;
current_pos_control.yaw = current_pos.yaw - angle / 180.0 * M_PI;
double theta = current_pos_control.yaw;
current_pos_control.x = cos(theta) * (-control_shift_x) + sin(theta) * (-control_shift_y) + current_pos.x;
current_pos_control.y = -sin(theta) * (-control_shift_x) + cos(theta) * (-control_shift_y) + current_pos.y;
current_pos_control.z = current_pos.z - control_shift_z;
// transform "/velodyne" to "/map"
#if 0
transform.setOrigin(tf::Vector3(current_pos.x, current_pos.y, current_pos.z));
q.setRPY(current_pos.roll, current_pos.pitch, current_pos.yaw);
transform.setRotation(q);
#else
//
// FIXME:
// We corrected the angle of "/velodyne" so that pure_pursuit
// can read this frame for the control.
// However, this is not what we want because the scan of Velodyne
// looks unmatched for the 3-D map on Rviz.
// What we really want is to make another TF transforming "/velodyne"
// to a new "/ndt_points" frame and modify pure_pursuit to
// read this frame instead of "/velodyne".
// Otherwise, can pure_pursuit just use "/ndt_frame"?
//
transform.setOrigin(tf::Vector3(current_pos_control.x, current_pos_control.y, current_pos_control.z));
q.setRPY(current_pos_control.roll, current_pos_control.pitch, current_pos_control.yaw);
transform.setRotation(q);
#endif
q_control.setRPY(current_pos_control.roll, current_pos_control.pitch, current_pos_control.yaw);
/*
std::cout << "ros::Time::now(): " << ros::Time::now() << std::endl;
std::cout << "ros::Time::now().sec: " << ros::Time::now().sec << std::endl;
std::cout << "ros::Time::now().nsec: " << ros::Time::now().nsec << std::endl;
*/
// br.sendTransform(tf::StampedTransform(transform, scan_time, "map", "velodyne"));
static tf::TransformBroadcaster pose_broadcaster;
tf::Transform pose_transform;
tf::Quaternion pose_q;
/* pose_transform.setOrigin(tf::Vector3(0, 0, 0));
pose_q.setRPY(0, 0, 0);
pose_transform.setRotation(pose_q);
pose_broadcaster.sendTransform(tf::StampedTransform(pose_transform, scan_time, "map", "ndt_frame"));
*/
// publish the position
// ndt_pose_msg.header.frame_id = "/ndt_frame";
ndt_pose_msg.header.frame_id = "/map";
ndt_pose_msg.header.stamp = scan_time;
ndt_pose_msg.pose.position.x = current_pos.x;
ndt_pose_msg.pose.position.y = current_pos.y;
ndt_pose_msg.pose.position.z = current_pos.z;
ndt_pose_msg.pose.orientation.x = q.x();
ndt_pose_msg.pose.orientation.y = q.y();
ndt_pose_msg.pose.orientation.z = q.z();
ndt_pose_msg.pose.orientation.w = q.w();
static tf::TransformBroadcaster pose_broadcaster_control;
tf::Transform pose_transform_control;
tf::Quaternion pose_q_control;
/* pose_transform_control.setOrigin(tf::Vector3(0, 0, 0));
pose_q_control.setRPY(0, 0, 0);
pose_transform_control.setRotation(pose_q_control);
pose_broadcaster_control.sendTransform(tf::StampedTransform(pose_transform_control, scan_time, "map", "ndt_frame"));
*/
// publish the position
// control_pose_msg.header.frame_id = "/ndt_frame";
control_pose_msg.header.frame_id = "/map";
control_pose_msg.header.stamp = scan_time;
control_pose_msg.pose.position.x = current_pos_control.x;
control_pose_msg.pose.position.y = current_pos_control.y;
control_pose_msg.pose.position.z = current_pos_control.z;
control_pose_msg.pose.orientation.x = q_control.x();
control_pose_msg.pose.orientation.y = q_control.y();
control_pose_msg.pose.orientation.z = q_control.z();
control_pose_msg.pose.orientation.w = q_control.w();
/*
std::cout << "ros::Time::now(): " << ros::Time::now() << std::endl;
std::cout << "ros::Time::now().sec: " << ros::Time::now().sec << std::endl;
std::cout << "ros::Time::now().nsec: " << ros::Time::now().nsec << std::endl;
*/
ndt_pose_pub.publish(ndt_pose_msg);
control_pose_pub.publish(control_pose_msg);
t5_end = ros::Time::now();
d5 = t5_end - t5_start;
#ifdef OUTPUT
// Writing position to position_log.txt
std::ofstream ofs("position_log.txt", std::ios::app);
if (ofs == NULL) {
std::cerr << "Could not open 'position_log.txt'." << std::endl;
exit(1);
}
ofs << current_pos.x << " " << current_pos.y << " " << current_pos.z << " " << current_pos.roll << " " << current_pos.pitch << " " << current_pos.yaw << std::endl;
#endif
// Calculate the offset (curren_pos - previous_pos)
offset_x = current_pos.x - previous_pos.x;
offset_y = current_pos.y - previous_pos.y;
offset_z = current_pos.z - previous_pos.z;
offset_yaw = current_pos.yaw - previous_pos.yaw;
// Update position and posture. current_pos -> previous_pos
previous_pos.x = current_pos.x;
previous_pos.y = current_pos.y;
previous_pos.z = current_pos.z;
previous_pos.roll = current_pos.roll;
previous_pos.pitch = current_pos.pitch;
previous_pos.yaw = current_pos.yaw;
callback_end = ros::Time::now();
d_callback = callback_end - callback_start;
std::cout << "-----------------------------------------------------------------" << std::endl;
std::cout << "Sequence number: " << input->header.seq << std::endl;
std::cout << "Number of scan points: " << additional_map_ptr->size() << " points." << std::endl;
std::cout << "Number of filtered scan points: " << filtered_additional_map_ptr->size() << " points." << std::endl;
std::cout << "NDT has converged: " << ndt.hasConverged() << std::endl;
std::cout << "Fitness score: " << ndt.getFitnessScore() << std::endl;
std::cout << "Number of iteration: " << ndt.getFinalNumIteration() << std::endl;
std::cout << "(x,y,z,roll,pitch,yaw):" << std::endl;
std::cout << "(" << current_pos.x << ", " << current_pos.y << ", " << current_pos.z << ", " << current_pos.roll << ", " << current_pos.pitch << ", " << current_pos.yaw << ")" << std::endl;
std::cout << "Transformation Matrix:" << std::endl;
std::cout << t << std::endl;
#ifdef VIEW_TIME
std::cout << "Duration of velodyne_callback: " << d_callback.toSec() << " secs." << std::endl;
std::cout << "Adding scan points: " << d1.toSec() << " secs." << std::endl;
std::cout << "VoxelGrid Filter: " << d2.toSec() << " secs." << std::endl;
std::cout << "Guessing the initial gross estimation: " << d3.toSec() << " secs." << std::endl;
std::cout << "NDT: " << d4.toSec() << " secs." << std::endl;
std::cout << "tf: " << d5.toSec() << " secs." << std::endl;
#endif
std::cout << "-----------------------------------------------------------------" << std::endl;
}
}
static void scan_callback(const sensor_msgs::PointCloud2::ConstPtr& input)
{
if (map_loaded == 1 && init_pos_set == 1) {
matching_start = std::chrono::system_clock::now();
static tf::TransformBroadcaster br;
tf::Transform transform;
tf::Quaternion predict_q, ndt_q, current_q, control_q, localizer_q;
pcl::PointXYZ p;
pcl::PointCloud<pcl::PointXYZ> scan;
current_scan_time = input->header.stamp;
pcl::fromROSMsg(*input, scan);
if(_localizer == "velodyne"){
pcl::PointCloud<velodyne_pointcloud::PointXYZIR> tmp;
pcl::fromROSMsg(*input, tmp);
scan.points.clear();
for (pcl::PointCloud<velodyne_pointcloud::PointXYZIR>::const_iterator item = tmp.begin(); item != tmp.end(); item++) {
p.x = (double) item->x;
p.y = (double) item->y;
p.z = (double) item->z;
if(item->ring >= min && item->ring <= max && item->ring % layer == 0 ){
scan.points.push_back(p);
}
}
}
pcl::PointCloud<pcl::PointXYZ>::Ptr scan_ptr(new pcl::PointCloud<pcl::PointXYZ>(scan));
pcl::PointCloud<pcl::PointXYZ>::Ptr filtered_scan_ptr(new pcl::PointCloud<pcl::PointXYZ>());
Eigen::Matrix4f t(Eigen::Matrix4f::Identity()); // base_link
Eigen::Matrix4f t2(Eigen::Matrix4f::Identity()); // localizer
// Downsampling the velodyne scan using VoxelGrid filter
pcl::VoxelGrid<pcl::PointXYZ> voxel_grid_filter;
voxel_grid_filter.setLeafSize(voxel_leaf_size, voxel_leaf_size, voxel_leaf_size);
voxel_grid_filter.setInputCloud(scan_ptr);
voxel_grid_filter.filter(*filtered_scan_ptr);
// Setting point cloud to be aligned.
ndt.setInputSource(filtered_scan_ptr);
// Guess the initial gross estimation of the transformation
predict_pose.x = previous_pose.x + offset_x;
predict_pose.y = previous_pose.y + offset_y;
predict_pose.z = previous_pose.z + offset_z;
predict_pose.roll = previous_pose.roll;
predict_pose.pitch = previous_pose.pitch;
predict_pose.yaw = previous_pose.yaw + offset_yaw;
Eigen::Translation3f init_translation(predict_pose.x, predict_pose.y, predict_pose.z);
Eigen::AngleAxisf init_rotation_x(predict_pose.roll, Eigen::Vector3f::UnitX());
Eigen::AngleAxisf init_rotation_y(predict_pose.pitch, Eigen::Vector3f::UnitY());
Eigen::AngleAxisf init_rotation_z(predict_pose.yaw, Eigen::Vector3f::UnitZ());
Eigen::Matrix4f init_guess = (init_translation * init_rotation_z * init_rotation_y * init_rotation_x) * tf_btol;
pcl::PointCloud<pcl::PointXYZ>::Ptr output_cloud(new pcl::PointCloud<pcl::PointXYZ>);
ndt.align(*output_cloud, init_guess);
t = ndt.getFinalTransformation(); // localizer
t2 = t * tf_ltob; // base_link
iteration = ndt.getFinalNumIteration();
score = ndt.getFitnessScore();
trans_probability = ndt.getTransformationProbability();
tf::Matrix3x3 mat_l; // localizer
mat_l.setValue(static_cast<double>(t(0, 0)), static_cast<double>(t(0, 1)), static_cast<double>(t(0, 2)),
static_cast<double>(t(1, 0)), static_cast<double>(t(1, 1)), static_cast<double>(t(1, 2)),
static_cast<double>(t(2, 0)), static_cast<double>(t(2, 1)), static_cast<double>(t(2, 2)));
// Update ndt_pose
localizer_pose.x = t(0, 3);
localizer_pose.y = t(1, 3);
localizer_pose.z = t(2, 3);
mat_l.getRPY(localizer_pose.roll, localizer_pose.pitch, localizer_pose.yaw, 1);
tf::Matrix3x3 mat_b; // base_link
mat_b.setValue(static_cast<double>(t2(0, 0)), static_cast<double>(t2(0, 1)), static_cast<double>(t2(0, 2)),
static_cast<double>(t2(1, 0)), static_cast<double>(t2(1, 1)), static_cast<double>(t2(1, 2)),
static_cast<double>(t2(2, 0)), static_cast<double>(t2(2, 1)), static_cast<double>(t2(2, 2)));
// Update ndt_pose
ndt_pose.x = t2(0, 3);
ndt_pose.y = t2(1, 3);
ndt_pose.z = t2(2, 3);
mat_b.getRPY(ndt_pose.roll, ndt_pose.pitch, ndt_pose.yaw, 1);
// Compute the velocity
scan_duration = current_scan_time - previous_scan_time;
double secs = scan_duration.toSec();
double distance = sqrt((ndt_pose.x - previous_pose.x) * (ndt_pose.x - previous_pose.x) +
(ndt_pose.y - previous_pose.y) * (ndt_pose.y - previous_pose.y) +
(ndt_pose.z - previous_pose.z) * (ndt_pose.z - previous_pose.z));
predict_pose_error = sqrt((ndt_pose.x - predict_pose.x) * (ndt_pose.x - predict_pose.x) +
(ndt_pose.y - predict_pose.y) * (ndt_pose.y - predict_pose.y) +
(ndt_pose.z - predict_pose.z) * (ndt_pose.z - predict_pose.z));
current_velocity = distance / secs;
current_velocity_smooth = (current_velocity + previous_velocity + second_previous_velocity) / 3.0;
if(current_velocity_smooth < 0.2){
current_velocity_smooth = 0.0;
}
current_acceleration = (current_velocity - previous_velocity) / secs;
estimated_vel_mps.data = current_velocity;
estimated_vel_kmph.data = current_velocity * 3.6;
estimated_vel_mps_pub.publish(estimated_vel_mps);
estimated_vel_kmph_pub.publish(estimated_vel_kmph);
if(predict_pose_error <= PREDICT_POSE_THRESHOLD){
use_predict_pose = 0;
}else{
use_predict_pose = 1;
}
use_predict_pose = 0;
if(use_predict_pose == 0){
current_pose.x = ndt_pose.x;
current_pose.y = ndt_pose.y;
current_pose.z = ndt_pose.z;
current_pose.roll = ndt_pose.roll;
current_pose.pitch = ndt_pose.pitch;
current_pose.yaw = ndt_pose.yaw;
}else{
current_pose.x = predict_pose.x;
current_pose.y = predict_pose.y;
current_pose.z = predict_pose.z;
current_pose.roll = predict_pose.roll;
current_pose.pitch = predict_pose.pitch;
current_pose.yaw = predict_pose.yaw;
}
control_pose.roll = current_pose.roll;
control_pose.pitch = current_pose.pitch;
control_pose.yaw = current_pose.yaw - angle / 180.0 * M_PI;
double theta = control_pose.yaw;
control_pose.x = cos(theta) * (-control_shift_x) + sin(theta) * (-control_shift_y) + current_pose.x;
control_pose.y = -sin(theta) * (-control_shift_x) + cos(theta) * (-control_shift_y) + current_pose.y;
control_pose.z = current_pose.z - control_shift_z;
// Set values for publishing pose
predict_q.setRPY(predict_pose.roll, predict_pose.pitch, predict_pose.yaw);
predict_pose_msg.header.frame_id = "/map";
predict_pose_msg.header.stamp = current_scan_time;
predict_pose_msg.pose.position.x = predict_pose.x;
predict_pose_msg.pose.position.y = predict_pose.y;
predict_pose_msg.pose.position.z = predict_pose.z;
predict_pose_msg.pose.orientation.x = predict_q.x();
predict_pose_msg.pose.orientation.y = predict_q.y();
predict_pose_msg.pose.orientation.z = predict_q.z();
predict_pose_msg.pose.orientation.w = predict_q.w();
ndt_q.setRPY(ndt_pose.roll, ndt_pose.pitch, ndt_pose.yaw);
ndt_pose_msg.header.frame_id = "/map";
ndt_pose_msg.header.stamp = current_scan_time;
ndt_pose_msg.pose.position.x = ndt_pose.x;
ndt_pose_msg.pose.position.y = ndt_pose.y;
ndt_pose_msg.pose.position.z = ndt_pose.z;
ndt_pose_msg.pose.orientation.x = ndt_q.x();
ndt_pose_msg.pose.orientation.y = ndt_q.y();
ndt_pose_msg.pose.orientation.z = ndt_q.z();
ndt_pose_msg.pose.orientation.w = ndt_q.w();
current_q.setRPY(current_pose.roll, current_pose.pitch, current_pose.yaw);
current_pose_msg.header.frame_id = "/map";
current_pose_msg.header.stamp = current_scan_time;
current_pose_msg.pose.position.x = current_pose.x;
current_pose_msg.pose.position.y = current_pose.y;
current_pose_msg.pose.position.z = current_pose.z;
current_pose_msg.pose.orientation.x = current_q.x();
current_pose_msg.pose.orientation.y = current_q.y();
current_pose_msg.pose.orientation.z = current_q.z();
current_pose_msg.pose.orientation.w = current_q.w();
control_q.setRPY(control_pose.roll, control_pose.pitch, control_pose.yaw);
control_pose_msg.header.frame_id = "/map";
control_pose_msg.header.stamp = current_scan_time;
control_pose_msg.pose.position.x = control_pose.x;
control_pose_msg.pose.position.y = control_pose.y;
control_pose_msg.pose.position.z = control_pose.z;
control_pose_msg.pose.orientation.x = control_q.x();
control_pose_msg.pose.orientation.y = control_q.y();
control_pose_msg.pose.orientation.z = control_q.z();
control_pose_msg.pose.orientation.w = control_q.w();
localizer_q.setRPY(localizer_pose.roll, localizer_pose.pitch, localizer_pose.yaw);
localizer_pose_msg.header.frame_id = "/map";
localizer_pose_msg.header.stamp = current_scan_time;
localizer_pose_msg.pose.position.x = localizer_pose.x;
localizer_pose_msg.pose.position.y = localizer_pose.y;
localizer_pose_msg.pose.position.z = localizer_pose.z;
localizer_pose_msg.pose.orientation.x = localizer_q.x();
localizer_pose_msg.pose.orientation.y = localizer_q.y();
localizer_pose_msg.pose.orientation.z = localizer_q.z();
localizer_pose_msg.pose.orientation.w = localizer_q.w();
predict_pose_pub.publish(predict_pose_msg);
ndt_pose_pub.publish(ndt_pose_msg);
current_pose_pub.publish(current_pose_msg);
control_pose_pub.publish(control_pose_msg);
localizer_pose_pub.publish(localizer_pose_msg);
// Send TF "/base_link" to "/map"
transform.setOrigin(tf::Vector3(current_pose.x, current_pose.y, current_pose.z));
transform.setRotation(current_q);
br.sendTransform(tf::StampedTransform(transform, current_scan_time, "/map", "/base_link"));
matching_end = std::chrono::system_clock::now();
exe_time = std::chrono::duration_cast<std::chrono::microseconds>(matching_end-matching_start).count()/1000.0;
time_ndt_matching.data = exe_time;
time_ndt_matching_pub.publish(time_ndt_matching);
// Set values for /estimate_twist
angular_velocity = (current_pose.yaw - previous_pose.yaw) / secs;
estimate_twist_msg.header.stamp = current_scan_time;
estimate_twist_msg.twist.linear.x = current_velocity;
estimate_twist_msg.twist.linear.y = 0.0;
estimate_twist_msg.twist.linear.z = 0.0;
estimate_twist_msg.twist.angular.x = 0.0;
estimate_twist_msg.twist.angular.y = 0.0;
estimate_twist_msg.twist.angular.z = angular_velocity;
estimate_twist_pub.publish(estimate_twist_msg);
geometry_msgs::Vector3Stamped estimate_vel_msg;
estimate_vel_msg.header.stamp = current_scan_time;
estimate_vel_msg.vector.x = current_velocity;
estimated_vel_pub.publish(estimate_vel_msg);
// Set values for /ndt_stat
ndt_stat_msg.header.stamp = current_scan_time;
ndt_stat_msg.exe_time = time_ndt_matching.data;
ndt_stat_msg.iteration = iteration;
ndt_stat_msg.score = score;
ndt_stat_msg.velocity = current_velocity;
ndt_stat_msg.acceleration = current_acceleration;
ndt_stat_msg.use_predict_pose = 0;
ndt_stat_pub.publish(ndt_stat_msg);
/* Compute NDT_Reliability */
ndt_reliability.data = Wa * (exe_time/100.0) * 100.0 + Wb * (iteration/10.0) * 100.0 + Wc * ((2.0-trans_probability)/2.0) * 100.0;
ndt_reliability_pub.publish(ndt_reliability);
#ifdef OUTPUT
// Output log.csv
std::ofstream ofs_log("log.csv", std::ios::app);
if (ofs_log == NULL) {
std::cerr << "Could not open 'log.csv'." << std::endl;
exit(1);
}
ofs_log << input->header.seq << ","
<< step_size << ","
<< trans_eps << ","
<< voxel_leaf_size << ","
<< current_pose.x << ","
<< current_pose.y << ","
<< current_pose.z << ","
<< current_pose.roll << ","
<< current_pose.pitch << ","
<< current_pose.yaw << ","
<< predict_pose.x << ","
<< predict_pose.y << ","
<< predict_pose.z << ","
<< predict_pose.roll << ","
<< predict_pose.pitch << ","
<< predict_pose.yaw << ","
<< current_pose.x - predict_pose.x << ","
<< current_pose.y - predict_pose.y << ","
<< current_pose.z - predict_pose.z << ","
<< current_pose.roll - predict_pose.roll << ","
<< current_pose.pitch - predict_pose.pitch << ","
<< current_pose.yaw - predict_pose.yaw << ","
<< predict_pose_error << ","
<< iteration << ","
<< score << ","
<< trans_probability << ","
<< ndt_reliability.data << ","
<< current_velocity << ","
<< current_velocity_smooth << ","
<< current_acceleration << ","
<< angular_velocity << ","
<< time_ndt_matching.data << ","
<< std::endl;
#endif
std::cout << "-----------------------------------------------------------------" << std::endl;
std::cout << "Sequence: " << input->header.seq << std::endl;
std::cout << "Timestamp: " << input->header.stamp << std::endl;
std::cout << "Frame ID: " << input->header.frame_id << std::endl;
std::cout << "Number of Scan Points: " << scan_ptr->size() << " points." << std::endl;
std::cout << "Number of Filtered Scan Points: " << filtered_scan_ptr->size() << " points." << std::endl;
std::cout << "Leaf Size: " << voxel_leaf_size << std::endl;
std::cout << "NDT has converged: " << ndt.hasConverged() << std::endl;
std::cout << "Fitness Score: " << ndt.getFitnessScore() << std::endl;
std::cout << "Transformation Probability: " << ndt.getTransformationProbability() << std::endl;
std::cout << "Execution Time: " << exe_time << " ms." << std::endl;
std::cout << "Number of Iterations: " << ndt.getFinalNumIteration() << std::endl;
std::cout << "NDT Reliability: " << ndt_reliability.data << std::endl;
std::cout << "(x,y,z,roll,pitch,yaw): " << std::endl;
std::cout << "(" << current_pose.x << ", " << current_pose.y << ", " << current_pose.z
<< ", " << current_pose.roll << ", " << current_pose.pitch << ", " << current_pose.yaw << ")" << std::endl;
std::cout << "Transformation Matrix: " << std::endl;
std::cout << t << std::endl;
std::cout << "-----------------------------------------------------------------" << std::endl;
// Update previous_***
offset_x = current_pose.x - previous_pose.x;
offset_y = current_pose.y - previous_pose.y;
offset_z = current_pose.z - previous_pose.z;
offset_yaw = current_pose.yaw - previous_pose.yaw;
previous_pose.x = current_pose.x;
previous_pose.y = current_pose.y;
previous_pose.z = current_pose.z;
previous_pose.roll = current_pose.roll;
previous_pose.pitch = current_pose.pitch;
previous_pose.yaw = current_pose.yaw;
previous_scan_time.sec = current_scan_time.sec;
previous_scan_time.nsec = current_scan_time.nsec;
second_previous_velocity = previous_velocity;
previous_velocity = current_velocity;
previous_acceleration = current_acceleration;
previous_estimated_vel_kmph.data = estimated_vel_kmph.data;
}
}
static void points_callback(const sensor_msgs::PointCloud2::ConstPtr& input)
{
if (map_loaded == 1 && init_pos_set == 1)
{
matching_start = std::chrono::system_clock::now();
static tf::TransformBroadcaster br;
tf::Transform transform;
tf::Quaternion predict_q, icp_q, current_q, localizer_q;
pcl::PointXYZ p;
pcl::PointCloud<pcl::PointXYZ> filtered_scan;
current_scan_time = input->header.stamp;
pcl::fromROSMsg(*input, filtered_scan);
pcl::PointCloud<pcl::PointXYZ>::Ptr filtered_scan_ptr(new pcl::PointCloud<pcl::PointXYZ>(filtered_scan));
int scan_points_num = filtered_scan_ptr->size();
Eigen::Matrix4f t(Eigen::Matrix4f::Identity()); // base_link
Eigen::Matrix4f t2(Eigen::Matrix4f::Identity()); // localizer
std::chrono::time_point<std::chrono::system_clock> align_start, align_end, getFitnessScore_start, getFitnessScore_end;
static double align_time, getFitnessScore_time = 0.0;
// Setting point cloud to be aligned.
// ndt.setInputSource(filtered_scan_ptr);
icp.setInputSource(filtered_scan_ptr);
// Guess the initial gross estimation of the transformation
predict_pose.x = previous_pose.x + offset_x;
predict_pose.y = previous_pose.y + offset_y;
predict_pose.z = previous_pose.z + offset_z;
predict_pose.roll = previous_pose.roll;
predict_pose.pitch = previous_pose.pitch;
predict_pose.yaw = previous_pose.yaw + offset_yaw;
Eigen::Translation3f init_translation(predict_pose.x, predict_pose.y, predict_pose.z);
Eigen::AngleAxisf init_rotation_x(predict_pose.roll, Eigen::Vector3f::UnitX());
Eigen::AngleAxisf init_rotation_y(predict_pose.pitch, Eigen::Vector3f::UnitY());
Eigen::AngleAxisf init_rotation_z(predict_pose.yaw, Eigen::Vector3f::UnitZ());
Eigen::Matrix4f init_guess = (init_translation * init_rotation_z * init_rotation_y * init_rotation_x) * tf_btol;
pcl::PointCloud<pcl::PointXYZ>::Ptr output_cloud(new pcl::PointCloud<pcl::PointXYZ>);
// ndt.align(*output_cloud, init_guess);
icp.setMaximumIterations(maximum_iterations);
icp.setTransformationEpsilon(transformation_epsilon);
icp.setMaxCorrespondenceDistance(max_correspondence_distance);
icp.setEuclideanFitnessEpsilon(euclidean_fitness_epsilon);
icp.setRANSACOutlierRejectionThreshold(ransac_outlier_rejection_threshold);
align_start = std::chrono::system_clock::now();
icp.align(*output_cloud, init_guess);
align_end = std::chrono::system_clock::now();
align_time = std::chrono::duration_cast<std::chrono::microseconds>(align_end - align_start).count() / 1000.0;
// t = ndt.getFinalTransformation(); // localizer
t = icp.getFinalTransformation(); // localizer
t2 = t * tf_ltob; // base_link
// iteration = ndt.getFinalNumIteration();
// score = ndt.getFitnessScore();
getFitnessScore_start = std::chrono::system_clock::now();
fitness_score = icp.getFitnessScore();
getFitnessScore_end = std::chrono::system_clock::now();
getFitnessScore_time = std::chrono::duration_cast<std::chrono::microseconds>(getFitnessScore_end - getFitnessScore_start).count() / 1000.0;
// trans_probability = ndt.getTransformationProbability();
tf::Matrix3x3 mat_l; // localizer
mat_l.setValue(static_cast<double>(t(0, 0)), static_cast<double>(t(0, 1)), static_cast<double>(t(0, 2)),
static_cast<double>(t(1, 0)), static_cast<double>(t(1, 1)), static_cast<double>(t(1, 2)),
static_cast<double>(t(2, 0)), static_cast<double>(t(2, 1)), static_cast<double>(t(2, 2)));
// Update localizer_pose
localizer_pose.x = t(0, 3);
localizer_pose.y = t(1, 3);
localizer_pose.z = t(2, 3);
mat_l.getRPY(localizer_pose.roll, localizer_pose.pitch, localizer_pose.yaw, 1);
tf::Matrix3x3 mat_b; // base_link
mat_b.setValue(static_cast<double>(t2(0, 0)), static_cast<double>(t2(0, 1)), static_cast<double>(t2(0, 2)),
static_cast<double>(t2(1, 0)), static_cast<double>(t2(1, 1)), static_cast<double>(t2(1, 2)),
static_cast<double>(t2(2, 0)), static_cast<double>(t2(2, 1)), static_cast<double>(t2(2, 2)));
// Update ndt_pose
icp_pose.x = t2(0, 3);
icp_pose.y = t2(1, 3);
icp_pose.z = t2(2, 3);
mat_b.getRPY(icp_pose.roll, icp_pose.pitch, icp_pose.yaw, 1);
// Calculate the difference between ndt_pose and predict_pose
predict_pose_error = sqrt((icp_pose.x - predict_pose.x) * (icp_pose.x - predict_pose.x) +
(icp_pose.y - predict_pose.y) * (icp_pose.y - predict_pose.y) +
(icp_pose.z - predict_pose.z) * (icp_pose.z - predict_pose.z));
if (predict_pose_error <= PREDICT_POSE_THRESHOLD)
{
use_predict_pose = 0;
}
else
{
use_predict_pose = 1;
}
use_predict_pose = 0;
if (use_predict_pose == 0)
{
current_pose.x = icp_pose.x;
current_pose.y = icp_pose.y;
current_pose.z = icp_pose.z;
current_pose.roll = icp_pose.roll;
current_pose.pitch = icp_pose.pitch;
current_pose.yaw = icp_pose.yaw;
}
else
{
current_pose.x = predict_pose.x;
current_pose.y = predict_pose.y;
current_pose.z = predict_pose.z;
current_pose.roll = predict_pose.roll;
current_pose.pitch = predict_pose.pitch;
current_pose.yaw = predict_pose.yaw;
}
// Compute the velocity and acceleration
scan_duration = current_scan_time - previous_scan_time;
double secs = scan_duration.toSec();
diff_x = current_pose.x - previous_pose.x;
diff_y = current_pose.y - previous_pose.y;
diff_z = current_pose.z - previous_pose.z;
diff_yaw = current_pose.yaw - previous_pose.yaw;
diff = sqrt(diff_x * diff_x + diff_y * diff_y + diff_z * diff_z);
current_velocity = diff / secs;
current_velocity_x = diff_x / secs;
current_velocity_y = diff_y / secs;
current_velocity_z = diff_z / secs;
angular_velocity = diff_yaw / secs;
current_velocity_smooth = (current_velocity + previous_velocity + previous_previous_velocity) / 3.0;
if (current_velocity_smooth < 0.2)
{
current_velocity_smooth = 0.0;
}
current_accel = (current_velocity - previous_velocity) / secs;
current_accel_x = (current_velocity_x - previous_velocity_x) / secs;
current_accel_y = (current_velocity_y - previous_velocity_y) / secs;
current_accel_z = (current_velocity_z - previous_velocity_z) / secs;
estimated_vel_mps.data = current_velocity;
estimated_vel_kmph.data = current_velocity * 3.6;
estimated_vel_mps_pub.publish(estimated_vel_mps);
estimated_vel_kmph_pub.publish(estimated_vel_kmph);
// Set values for publishing pose
predict_q.setRPY(predict_pose.roll, predict_pose.pitch, predict_pose.yaw);
predict_pose_msg.header.frame_id = "/map";
predict_pose_msg.header.stamp = current_scan_time;
predict_pose_msg.pose.position.x = predict_pose.x;
predict_pose_msg.pose.position.y = predict_pose.y;
predict_pose_msg.pose.position.z = predict_pose.z;
predict_pose_msg.pose.orientation.x = predict_q.x();
predict_pose_msg.pose.orientation.y = predict_q.y();
predict_pose_msg.pose.orientation.z = predict_q.z();
predict_pose_msg.pose.orientation.w = predict_q.w();
icp_q.setRPY(icp_pose.roll, icp_pose.pitch, icp_pose.yaw);
icp_pose_msg.header.frame_id = "/map";
icp_pose_msg.header.stamp = current_scan_time;
icp_pose_msg.pose.position.x = icp_pose.x;
icp_pose_msg.pose.position.y = icp_pose.y;
icp_pose_msg.pose.position.z = icp_pose.z;
icp_pose_msg.pose.orientation.x = icp_q.x();
icp_pose_msg.pose.orientation.y = icp_q.y();
icp_pose_msg.pose.orientation.z = icp_q.z();
icp_pose_msg.pose.orientation.w = icp_q.w();
current_q.setRPY(current_pose.roll, current_pose.pitch, current_pose.yaw);
current_pose_msg.header.frame_id = "/map";
current_pose_msg.header.stamp = current_scan_time;
current_pose_msg.pose.position.x = current_pose.x;
current_pose_msg.pose.position.y = current_pose.y;
current_pose_msg.pose.position.z = current_pose.z;
current_pose_msg.pose.orientation.x = current_q.x();
current_pose_msg.pose.orientation.y = current_q.y();
current_pose_msg.pose.orientation.z = current_q.z();
current_pose_msg.pose.orientation.w = current_q.w();
localizer_q.setRPY(localizer_pose.roll, localizer_pose.pitch, localizer_pose.yaw);
localizer_pose_msg.header.frame_id = "/map";
localizer_pose_msg.header.stamp = current_scan_time;
localizer_pose_msg.pose.position.x = localizer_pose.x;
localizer_pose_msg.pose.position.y = localizer_pose.y;
localizer_pose_msg.pose.position.z = localizer_pose.z;
localizer_pose_msg.pose.orientation.x = localizer_q.x();
localizer_pose_msg.pose.orientation.y = localizer_q.y();
localizer_pose_msg.pose.orientation.z = localizer_q.z();
localizer_pose_msg.pose.orientation.w = localizer_q.w();
predict_pose_pub.publish(predict_pose_msg);
icp_pose_pub.publish(icp_pose_msg);
current_pose_pub.publish(current_pose_msg);
localizer_pose_pub.publish(localizer_pose_msg);
// Send TF "/base_link" to "/map"
transform.setOrigin(tf::Vector3(current_pose.x, current_pose.y, current_pose.z));
transform.setRotation(current_q);
br.sendTransform(tf::StampedTransform(transform, current_scan_time, "/map", "/base_link"));
matching_end = std::chrono::system_clock::now();
exe_time = std::chrono::duration_cast<std::chrono::microseconds>(matching_end - matching_start).count() / 1000.0;
time_icp_matching.data = exe_time;
time_icp_matching_pub.publish(time_icp_matching);
// Set values for /estimate_twist
estimate_twist_msg.header.stamp = current_scan_time;
estimate_twist_msg.twist.linear.x = current_velocity;
estimate_twist_msg.twist.linear.y = 0.0;
estimate_twist_msg.twist.linear.z = 0.0;
estimate_twist_msg.twist.angular.x = 0.0;
estimate_twist_msg.twist.angular.y = 0.0;
estimate_twist_msg.twist.angular.z = angular_velocity;
estimate_twist_pub.publish(estimate_twist_msg);
geometry_msgs::Vector3Stamped estimate_vel_msg;
estimate_vel_msg.header.stamp = current_scan_time;
estimate_vel_msg.vector.x = current_velocity;
estimated_vel_pub.publish(estimate_vel_msg);
// Set values for /icp_stat
icp_stat_msg.header.stamp = current_scan_time;
icp_stat_msg.exe_time = time_icp_matching.data;
// icp_stat_msg.iteration = iteration;
icp_stat_msg.score = fitness_score;
icp_stat_msg.velocity = current_velocity;
icp_stat_msg.acceleration = current_accel;
icp_stat_msg.use_predict_pose = 0;
icp_stat_pub.publish(icp_stat_msg);
// Write log
if (!ofs)
{
std::cerr << "Could not open " << filename << "." << std::endl;
exit(1);
}
ofs << input->header.seq << "," << scan_points_num << ","
<< current_pose.x << "," << current_pose.y << "," << current_pose.z << "," << current_pose.roll << ","
<< current_pose.pitch << "," << current_pose.yaw << "," << predict_pose.x << "," << predict_pose.y << ","
<< predict_pose.z << "," << predict_pose.roll << "," << predict_pose.pitch << "," << predict_pose.yaw << ","
<< current_pose.x - predict_pose.x << "," << current_pose.y - predict_pose.y << ","
<< current_pose.z - predict_pose.z << "," << current_pose.roll - predict_pose.roll << ","
<< current_pose.pitch - predict_pose.pitch << "," << current_pose.yaw - predict_pose.yaw << ","
<< predict_pose_error << "," << "," << fitness_score << ","
<< "," << current_velocity << "," << current_velocity_smooth << "," << current_accel
<< "," << angular_velocity << "," << exe_time << "," << align_time << "," << getFitnessScore_time << std::endl;
std::cout << "-----------------------------------------------------------------" << std::endl;
std::cout << "Sequence: " << input->header.seq << std::endl;
std::cout << "Timestamp: " << input->header.stamp << std::endl;
std::cout << "Frame ID: " << input->header.frame_id << std::endl;
// std::cout << "Number of Scan Points: " << scan_ptr->size() << " points." << std::endl;
std::cout << "Number of Filtered Scan Points: " << scan_points_num << " points." << std::endl;
std::cout << "ICP has converged: " << icp.hasConverged() << std::endl;
std::cout << "Fitness Score: " << fitness_score << std::endl;
// std::cout << "Transformation Probability: " << ndt.getTransformationProbability() << std::endl;
std::cout << "Execution Time: " << exe_time << " ms." << std::endl;
// std::cout << "Number of Iterations: " << ndt.getFinalNumIteration() << std::endl;
// std::cout << "NDT Reliability: " << ndt_reliability.data << std::endl;
std::cout << "(x,y,z,roll,pitch,yaw): " << std::endl;
std::cout << "(" << current_pose.x << ", " << current_pose.y << ", " << current_pose.z << ", " << current_pose.roll
<< ", " << current_pose.pitch << ", " << current_pose.yaw << ")" << std::endl;
std::cout << "Transformation Matrix: " << std::endl;
std::cout << t << std::endl;
std::cout << "-----------------------------------------------------------------" << std::endl;
// Update offset
if (_offset == "linear")
{
offset_x = diff_x;
offset_y = diff_y;
offset_z = diff_z;
offset_yaw = diff_yaw;
}
else if (_offset == "quadratic")
{
offset_x = (current_velocity_x + current_accel_x * secs) * secs;
offset_y = (current_velocity_y + current_accel_y * secs) * secs;
offset_z = diff_z;
offset_yaw = diff_yaw;
}
else if (_offset == "zero")
{
offset_x = 0.0;
offset_y = 0.0;
offset_z = 0.0;
offset_yaw = 0.0;
}
// Update previous_***
previous_pose.x = current_pose.x;
previous_pose.y = current_pose.y;
previous_pose.z = current_pose.z;
previous_pose.roll = current_pose.roll;
previous_pose.pitch = current_pose.pitch;
previous_pose.yaw = current_pose.yaw;
previous_scan_time.sec = current_scan_time.sec;
previous_scan_time.nsec = current_scan_time.nsec;
previous_previous_velocity = previous_velocity;
previous_velocity = current_velocity;
previous_velocity_x = current_velocity_x;
previous_velocity_y = current_velocity_y;
previous_velocity_z = current_velocity_z;
previous_accel = current_accel;
previous_estimated_vel_kmph.data = estimated_vel_kmph.data;
}
}
int
main (int argc, char** argv)
{
// Loading first scan of room.
pcl::PointCloud<pcl::PointXYZ>::Ptr target_cloud (new pcl::PointCloud<pcl::PointXYZ>);
if (pcl::io::loadPCDFile<pcl::PointXYZ> ("room_scan1.pcd", *target_cloud) == -1)
{
PCL_ERROR ("Couldn't read file room_scan1.pcd \n");
return (-1);
}
std::cout << "Loaded " << target_cloud->size () << " data points from room_scan1.pcd" << std::endl;
// Loading second scan of room from new perspective.
pcl::PointCloud<pcl::PointXYZ>::Ptr input_cloud (new pcl::PointCloud<pcl::PointXYZ>);
if (pcl::io::loadPCDFile<pcl::PointXYZ> ("room_scan2.pcd", *input_cloud) == -1)
{
PCL_ERROR ("Couldn't read file room_scan2.pcd \n");
return (-1);
}
std::cout << "Loaded " << input_cloud->size () << " data points from room_scan2.pcd" << std::endl;
// Filtering input scan to roughly 10% of original size to increase speed of registration.
pcl::PointCloud<pcl::PointXYZ>::Ptr filtered_cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::ApproximateVoxelGrid<pcl::PointXYZ> approximate_voxel_filter;
approximate_voxel_filter.setLeafSize (0.2, 0.2, 0.2);
approximate_voxel_filter.setInputCloud (input_cloud);
approximate_voxel_filter.filter (*filtered_cloud);
std::cout << "Filtered cloud contains " << filtered_cloud->size ()
<< " data points from room_scan2.pcd" << std::endl;
// Initializing Normal Distributions Transform (NDT).
pcl::NormalDistributionsTransform<pcl::PointXYZ, pcl::PointXYZ> ndt;
// Setting scale dependent NDT parameters
// Setting minimum transformation difference for termination condition.
ndt.setTransformationEpsilon (0.01);
// Setting maximum step size for More-Thuente line search.
ndt.setStepSize (0.1);
//Setting Resolution of NDT grid structure (VoxelGridCovariance).
ndt.setResolution (1.0);
// Setting max number of registration iterations.
ndt.setMaximumIterations (35);
// Setting point cloud to be aligned.
ndt.setInputCloud (filtered_cloud);
// Setting point cloud to be aligned to.
ndt.setInputTarget (target_cloud);
// Set initial alignment estimate found using robot odometry.
Eigen::AngleAxisf init_rotation (0.6931, Eigen::Vector3f::UnitZ ());
Eigen::Translation3f init_translation (1.79387, 0.720047, 0);
Eigen::Matrix4f init_guess = (init_translation * init_rotation).matrix ();
// Calculating required rigid transform to align the input cloud to the target cloud.
pcl::PointCloud<pcl::PointXYZ>::Ptr output_cloud (new pcl::PointCloud<pcl::PointXYZ>);
ndt.align (*output_cloud, init_guess);
std::cout << "Normal Distributions Transform has converged:" << ndt.hasConverged ()
<< " score: " << ndt.getFitnessScore () << std::endl;
// Transforming unfiltered, input cloud using found transform.
pcl::transformPointCloud (*input_cloud, *output_cloud, ndt.getFinalTransformation ());
// Saving transformed input cloud.
pcl::io::savePCDFileASCII ("room_scan2_transformed.pcd", *output_cloud);
// Initializing point cloud visualizer
boost::shared_ptr<pcl::visualization::PCLVisualizer>
viewer_final (new pcl::visualization::PCLVisualizer ("3D Viewer"));
viewer_final->setBackgroundColor (0, 0, 0);
// Coloring and visualizing target cloud (red).
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ>
target_color (target_cloud, 255, 0, 0);
viewer_final->addPointCloud<pcl::PointXYZ> (target_cloud, target_color, "target cloud");
viewer_final->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE,
1, "target cloud");
// Coloring and visualizing transformed input cloud (green).
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ>
output_color (output_cloud, 0, 255, 0);
viewer_final->addPointCloud<pcl::PointXYZ> (output_cloud, output_color, "output cloud");
viewer_final->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE,
1, "output cloud");
// Starting visualizer
viewer_final->addCoordinateSystem (1.0);
viewer_final->initCameraParameters ();
// Wait until visualizer window is closed.
while (!viewer_final->wasStopped ())
{
viewer_final->spinOnce (100);
boost::this_thread::sleep (boost::posix_time::microseconds (100000));
}
return (0);
}