void ndtTransformAndAdd(pcl::PointCloud<pcl::PointXYZRGB>::Ptr& A,pcl::PointCloud<pcl::PointXYZRGB>::Ptr& B)
{
pcl::NormalDistributionsTransform<pcl::PointXYZRGB, pcl::PointXYZRGB> 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.setInputSource (B);
// Setting point cloud to be aligned to.
ndt.setInputTarget (A);
pcl::PointCloud<pcl::PointXYZRGB>::Ptr output_cloud (new pcl::PointCloud<pcl::PointXYZRGB>);
Eigen::Affine3f estimate;
estimate.setIdentity();
ndt.align (*output_cloud, estimate.matrix());
std::cout << "Normal Distributions Transform has converged:" << ndt.hasConverged ()
<< " score: " << ndt.getFitnessScore () << std::endl;
// Transforming unfiltered, input cloud using found transform.
pcl::transformPointCloud (*B, *output_cloud, ndt.getFinalTransformation ());
*A=*A+*output_cloud;
}
int
main (int, char**)
{
typedef pcl::PointCloud<pcl::PointXYZ> CloudType;
CloudType::Ptr cloud (new CloudType);
cloud->is_dense = false;
CloudType::Ptr output_cloud (new CloudType);
CloudType::PointType p_nan;
p_nan.x = std::numeric_limits<float>::quiet_NaN();
p_nan.y = std::numeric_limits<float>::quiet_NaN();
p_nan.z = std::numeric_limits<float>::quiet_NaN();
cloud->push_back(p_nan);
CloudType::PointType p_valid;
p_valid.x = 1.0f;
cloud->push_back(p_valid);
std::cout << "size: " << cloud->points.size () << std::endl;
std::vector<int> indices;
pcl::removeNaNFromPointCloud(*cloud, *output_cloud, indices);
std::cout << "size: " << output_cloud->points.size () << std::endl;
return 0;
}
// CALLBACKS
void pointcloudCallback(const pcl::PointCloud<pcl::PointXYZ>::ConstPtr& cloud)
{
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr output_cloud (new pcl::PointCloud<pcl::PointXYZ>);
// Create the filtering objects
pcl::PassThrough<pcl::PointXYZ> pass;
pass.setInputCloud (cloud);
pass.setFilterFieldName (fieldName_);
if(numBands_ <= 1){
// Just center it to be nice
pass.setFilterLimits ((startPoint_+endPoint_)/2.0, (startPoint_+endPoint_)/2.0+bandWidth_);
pass.filter(*cloud_filtered);
*output_cloud = *cloud_filtered;
} else {
// Do the first one manually so that certain parameters like frame_id always match
pass.setFilterLimits (startPoint_, startPoint_+bandWidth_);
pass.filter(*cloud_filtered);
*output_cloud = *cloud_filtered;
float dL = endPoint_-startPoint_;
for(int i = 1; i < numBands_; i++){
float sLine = startPoint_ + i*dL/(float)(numBands_-1);
float eLine = sLine + bandWidth_;
pass.setFilterLimits (sLine, eLine);
pass.filter(*cloud_filtered);
*output_cloud += *cloud_filtered;
}
}
cloudout_pub_.publish(*output_cloud);
}
void pcl::SACSoftSegmentation<PointT>::segment(pcl::PointIndices &inliers, pcl::ModelCoefficients &model_coefficients)
{
typedef std::hash_set<int, hash_compare<int, greater<int>>> index_hash;
typedef std::hash_set<int, hash_compare<int, greater<int>>>::iterator index_hash_itr;
pcl::SACSegmentation<PointT>::segment (inliers, model_coefficients);
pcl::PointIndices outliers;
index_hash inliers_hash;
for( int i=0;i<inliers.indices.size();i++)
{
inliers_hash.insert(inliers.indices.at(i));
}
//iterate over all point in the point cloud
for (int i = 0; i < this->input_->size(); i++)
{
if (inliers_hash.find(i) == inliers_hash.end())
{
outliers.indices.push_back(i);
}
}
index_hash outliers_hash;
for (int i = 0; i < outliers.indices.size(); i++)
{
outliers_hash.insert(outliers.indices.at(i));
}
index_hash current_segment;
index_hash_itr itr;
this->findPointNeighbours();
while (!outliers_hash.empty())
{
int seed = outliers_hash.pop_back();
current_segment.insert(seed);
std::vector<int> seed_neighbours = point_neighbours_[seed];
{
}
}
pcl::PointCloud<PointT>::Ptr output_cloud(new pcl::PointCloud<PointT>());
pcl::copyPointCloud(*this->input_,outliers,*output_cloud);
pcl::PCDWriter writer;
writer.write("output.pcd", *output_cloud);
}
void ColorizeDistanceFromPlane::colorize(
const sensor_msgs::PointCloud2::ConstPtr& cloud_msg,
const jsk_recognition_msgs::ModelCoefficientsArray::ConstPtr& coefficients_msg,
const jsk_recognition_msgs::PolygonArray::ConstPtr& polygons)
{
boost::mutex::scoped_lock lock(mutex_);
if (coefficients_msg->coefficients.size() == 0) {
return;
}
// convert all the data into pcl format
pcl::PointCloud<PointT>::Ptr cloud
(new pcl::PointCloud<PointT>);
pcl::fromROSMsg(*cloud_msg, *cloud);
std::vector<pcl::ModelCoefficients::Ptr> coefficients
= pcl_conversions::convertToPCLModelCoefficients(
coefficients_msg->coefficients);
// first, build ConvexPolygon::Ptr
std::vector<ConvexPolygon::Ptr> convexes;
for (size_t i = 0; i < polygons->polygons.size(); i++) {
if (polygons->polygons[i].polygon.points.size() > 0) {
ConvexPolygon convex =
ConvexPolygon::fromROSMsg(polygons->polygons[i].polygon);
ConvexPolygon::Ptr convex_ptr
= boost::make_shared<ConvexPolygon>(convex);
convexes.push_back(convex_ptr);
}
else {
JSK_NODELET_ERROR_STREAM(__PRETTY_FUNCTION__ << ":: there is no points in the polygon");
}
}
pcl::PointCloud<pcl::PointXYZRGB>::Ptr output_cloud
(new pcl::PointCloud<pcl::PointXYZRGB>);
for (size_t i = 0; i < cloud->points.size(); i++) {
PointT p = cloud->points[i];
pcl::PointXYZRGB p_output;
pointFromXYZToXYZ<PointT, pcl::PointXYZRGB>(p, p_output);
double d = distanceToConvexes(p, convexes);
if (d != DBL_MAX) {
uint32_t color = colorForDistance(d);
p_output.rgb = *reinterpret_cast<float*>(&color);
output_cloud->points.push_back(p_output);
}
}
sensor_msgs::PointCloud2 ros_output;
pcl::toROSMsg(*output_cloud, ros_output);
ros_output.header = cloud_msg->header;
pub_.publish(ros_output);
}
template <typename PointT> void
pcl::people::GroundBasedPeopleDetectionApp<PointT>::swapDimensions (pcl::PointCloud<pcl::RGB>::Ptr& cloud)
{
pcl::PointCloud<pcl::RGB>::Ptr output_cloud(new pcl::PointCloud<pcl::RGB>);
output_cloud->points.resize(cloud->height*cloud->width);
output_cloud->width = cloud->height;
output_cloud->height = cloud->width;
for (int i = 0; i < cloud->width; i++)
{
for (int j = 0; j < cloud->height; j++)
{
(*output_cloud)(j,i) = (*cloud)(cloud->width - i - 1, j);
}
}
cloud = output_cloud;
}
void VoxelGridDownsampleManager::pointCB(const sensor_msgs::PointCloud2ConstPtr &input)
{
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::PointXYZ>::Ptr output_cloud (new pcl::PointCloud<pcl::PointXYZ>);
if (grid_.size() == 0) {
ROS_INFO("the number of registered grids is 0, skipping");
return;
}
fromROSMsg(*input, *cloud);
for (size_t i = 0; i < grid_.size(); i++)
{
visualization_msgs::Marker::ConstPtr target_grid = grid_[i];
// not yet tf is supported
int id = target_grid->id;
// solve tf with ros::Time 0.0
// transform pointcloud to the frame_id of target_grid
pcl::PointCloud<pcl::PointXYZ>::Ptr transformed_cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl_ros::transformPointCloud(target_grid->header.frame_id,
*cloud,
*transformed_cloud,
tf_listener);
double center_x = target_grid->pose.position.x;
double center_y = target_grid->pose.position.y;
double center_z = target_grid->pose.position.z;
double range_x = target_grid->scale.x * 1.0; // for debug
double range_y = target_grid->scale.y * 1.0;
double range_z = target_grid->scale.z * 1.0;
double min_x = center_x - range_x / 2.0;
double max_x = center_x + range_x / 2.0;
double min_y = center_y - range_y / 2.0;
double max_y = center_y + range_y / 2.0;
double min_z = center_z - range_z / 2.0;
double max_z = center_z + range_z / 2.0;
double resolution = target_grid->color.r;
// filter order: x -> y -> z -> downsample
pcl::PassThrough<pcl::PointXYZ> pass_x;
pass_x.setFilterFieldName("x");
pass_x.setFilterLimits(min_x, max_x);
pcl::PassThrough<pcl::PointXYZ> pass_y;
pass_y.setFilterFieldName("y");
pass_y.setFilterLimits(min_y, max_y);
pcl::PassThrough<pcl::PointXYZ> pass_z;
pass_z.setFilterFieldName("z");
pass_z.setFilterLimits(min_z, max_z);
ROS_INFO_STREAM(id << " filter x: " << min_x << " - " << max_x);
ROS_INFO_STREAM(id << " filter y: " << min_y << " - " << max_y);
ROS_INFO_STREAM(id << " filter z: " << min_z << " - " << max_z);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_after_x (new pcl::PointCloud<pcl::PointXYZ>);
pass_x.setInputCloud (transformed_cloud);
pass_x.filter(*cloud_after_x);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_after_y (new pcl::PointCloud<pcl::PointXYZ>);
pass_y.setInputCloud (cloud_after_x);
pass_y.filter(*cloud_after_y);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_after_z (new pcl::PointCloud<pcl::PointXYZ>);
pass_z.setInputCloud (cloud_after_y);
pass_z.filter(*cloud_after_z);
// downsample
pcl::VoxelGrid<pcl::PointXYZ> sor;
sor.setInputCloud (cloud_after_z);
sor.setLeafSize (resolution, resolution, resolution);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>);
sor.filter (*cloud_filtered);
// reverse transform
pcl::PointCloud<pcl::PointXYZ>::Ptr reverse_transformed_cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl_ros::transformPointCloud(input->header.frame_id,
*cloud_filtered,
*reverse_transformed_cloud,
tf_listener);
// adding the output into *output_cloud
// tmp <- cloud_filtered + output_cloud
// output_cloud <- tmp
//pcl::PointCloud<pcl::PointXYZ>::Ptr tmp (new pcl::PointCloud<pcl::PointXYZ>);
//pcl::concatenatePointCloud (*cloud_filtered, *output_cloud, tmp);
//output_cloud = tmp;
ROS_INFO_STREAM(id << " includes " << reverse_transformed_cloud->points.size() << " points");
for (size_t i = 0; i < reverse_transformed_cloud->points.size(); i++) {
output_cloud->points.push_back(reverse_transformed_cloud->points[i]);
}
}
sensor_msgs::PointCloud2 out;
toROSMsg(*output_cloud, out);
out.header = input->header;
pub_.publish(out); // for debugging
// for concatenater
size_t cluster_num = output_cloud->points.size() / max_points_ + 1;
ROS_INFO_STREAM("encoding into " << cluster_num << " clusters");
for (size_t i = 0; i < cluster_num; i++) {
size_t start_index = max_points_ * i;
size_t end_index = max_points_ * (i + 1) > output_cloud->points.size() ?
output_cloud->points.size(): max_points_ * (i + 1);
sensor_msgs::PointCloud2 cluster_out_ros;
pcl::PointCloud<pcl::PointXYZ>::Ptr
cluster_out_pcl(new pcl::PointCloud<pcl::PointXYZ>);
cluster_out_pcl->points.resize(end_index - start_index);
// build cluster_out_pcl
ROS_INFO_STREAM("make cluster from " << start_index << " to " << end_index);
for (size_t j = start_index; j < end_index; j++) {
cluster_out_pcl->points[j - start_index] = output_cloud->points[j];
}
// conevrt cluster_out_pcl into ros msg
toROSMsg(*cluster_out_pcl, cluster_out_ros);
cluster_out_ros.header = input->header;
cluster_out_ros.header.frame_id = (boost::format("%s %d %d") % (cluster_out_ros.header.frame_id) % (i) % (sequence_id_)).str();
pub_encoded_.publish(cluster_out_ros);
ros::Duration(1.0 / rate_).sleep();
}
}
void walkl3 (FILE *fp, struct node *n)
{
struct node *l2 ;
struct node *m ;
char cloudname [MAXCLOUDNAME] ;
int l1count, l3count ;
struct node *directl1 [2], *directl3 [2] ;
int selected ;
/*
* Reset all non-L3 nodes
*/
for (m = mobj_head (nodemobj) ; m != NULL ; m = m->next)
{
if (m->nodetype == NT_L3)
MK_CLEAR (m, MK_L2TRANSPORT) ;
else
{
MK_CLEAR (m, MK_L2TRANSPORT) ;
MK_CLEAR (m, MK_IPMATCH) ;
MK_CLEAR (m, MK_ISROUTER) ;
MK_CLEAR (m, MK_VLANTRAVERSAL) ;
MK_CLEAR (m, MK_PROCESSED) ;
}
vlan_zero (m->vlanset) ;
}
l2 = get_neighbour (n, NT_L2) ;
if (l2 != NULL)
transport_vlan_on_L2 (l2, l2->u.l2.vlan) ;
/*
* All reachable L2 nodes are marked.
*
* Count physical interfaces (L1 nodes) to see if this equipement
* is connected via a direct link to another node, or if this
* equipement is connected to a broadcast domain.
*
* Count marked L1 nodes. If there is only 2 of them, check if
* linkname matches (if not "X", or EXTLINK).
*/
l1count = 0 ;
l3count = 0 ;
selected = 0 ;
for (m = mobj_head (nodemobj) ; m != NULL ; m = m->next)
{
if (m->nodetype == NT_L1 && MK_ISSET (m, MK_L2TRANSPORT))
{
if (MK_ISSELECTED (m) || MK_ISSELECTED (m->eq))
selected = 1 ;
if (l1count < 2)
directl1 [l1count] = m ;
l1count++ ;
}
if (m->nodetype == NT_L3 && MK_ISSET (m, MK_L2TRANSPORT)
&& MK_ISSET (m, MK_IPMATCH))
{
if (MK_ISSELECTED (m) || MK_ISSELECTED (m->eq))
selected = 1 ;
if (l3count < 2)
directl3 [l3count] = m ;
l3count++ ;
}
}
if (! selected)
return ;
if (l1count == 2 && l3count == 2 &&
directl1 [0]->u.l1.link == directl1 [1]->u.l1.link)
{
/*
* This is a direct link between two L3 nodes
*/
output_direct (fp, directl3) ;
MK_SET (directl3 [0], MK_PROCESSED) ;
MK_SET (directl3 [1], MK_PROCESSED) ;
}
else
{
/*
* This is a cloud
*/
for (m = mobj_head (nodemobj) ; m != NULL ; m = m->next)
{
if (m->nodetype == NT_L2 && MK_ISSET (m, MK_L2TRANSPORT))
{
struct linklist *ll ;
struct node *r ;
for (ll = m->linklist ; ll != NULL ; ll = ll->next)
{
r = getlinkpeer (ll->link, m) ;
if (r->nodetype == NT_L3 && MK_ISSET (r, MK_IPMATCH))
MK_SET (r, MK_VLANTRAVERSAL) ;
}
}
}
/*
* Output cloud
*/
output_cloud (fp, n, cloudname, sizeof cloudname) ;
/*
* Output links to this cloud
*/
for (m = mobj_head (nodemobj) ; m != NULL ; m = m->next)
{
if (m->nodetype == NT_L3 && MK_ISSET (m, MK_VLANTRAVERSAL))
{
output_link (fp, m, cloudname) ;
MK_CLEAR (m, MK_VLANTRAVERSAL) ;
MK_SET (m, MK_PROCESSED) ;
}
}
}
}
//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;
}
}
void HintedHandleEstimator::estimate(
const sensor_msgs::PointCloud2::ConstPtr& cloud_msg,
const geometry_msgs::PointStampedConstPtr &point_msg)
{
boost::mutex::scoped_lock lock(mutex_);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::Normal>::Ptr cloud_normals(new pcl::PointCloud<pcl::Normal>);
pcl::PassThrough<pcl::PointXYZ> pass;
int K = 1;
std::vector<int> pointIdxNKNSearch(K);
std::vector<float> pointNKNSquaredDistance(K);
pcl::search::KdTree<pcl::PointXYZ>::Ptr kd_tree(new pcl::search::KdTree<pcl::PointXYZ>);
pcl::fromROSMsg(*cloud_msg, *cloud);
geometry_msgs::PointStamped transed_point;
ros::Time now = ros::Time::now();
try
{
listener_.waitForTransform(cloud->header.frame_id, point_msg->header.frame_id, now, ros::Duration(1.0));
listener_.transformPoint(cloud->header.frame_id, now, *point_msg, point_msg->header.frame_id, transed_point);
}
catch(tf::TransformException ex)
{
JSK_ROS_ERROR("%s", ex.what());
return;
}
pcl::PointXYZ searchPoint;
searchPoint.x = transed_point.point.x;
searchPoint.y = transed_point.point.y;
searchPoint.z = transed_point.point.z;
//remove too far cloud
pass.setInputCloud(cloud);
pass.setFilterFieldName("x");
pass.setFilterLimits(searchPoint.x - 3*handle.arm_w, searchPoint.x + 3*handle.arm_w);
pass.filter(*cloud);
pass.setInputCloud(cloud);
pass.setFilterFieldName("y");
pass.setFilterLimits(searchPoint.y - 3*handle.arm_w, searchPoint.y + 3*handle.arm_w);
pass.filter(*cloud);
pass.setInputCloud(cloud);
pass.setFilterFieldName("z");
pass.setFilterLimits(searchPoint.z - 3*handle.arm_w, searchPoint.z + 3*handle.arm_w);
pass.filter(*cloud);
if(cloud->points.size() < 10){
JSK_ROS_INFO("points are too small");
return;
}
if(1){ //estimate_normal
pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
ne.setInputCloud(cloud);
ne.setSearchMethod(kd_tree);
ne.setRadiusSearch(0.02);
ne.setViewPoint(0, 0, 0);
ne.compute(*cloud_normals);
}
else{ //use normal of msg
}
if(! (kd_tree->nearestKSearch (searchPoint, K, pointIdxNKNSearch, pointNKNSquaredDistance) > 0)){
JSK_ROS_INFO("kdtree failed");
return;
}
float x = cloud->points[pointIdxNKNSearch[0]].x;
float y = cloud->points[pointIdxNKNSearch[0]].y;
float z = cloud->points[pointIdxNKNSearch[0]].z;
float v_x = cloud_normals->points[pointIdxNKNSearch[0]].normal_x;
float v_y = cloud_normals->points[pointIdxNKNSearch[0]].normal_y;
float v_z = cloud_normals->points[pointIdxNKNSearch[0]].normal_z;
double theta = acos(v_x);
// use normal for estimating handle direction
tf::Quaternion normal(0, v_z/NORM(0, v_y, v_z) * cos(theta/2), -v_y/NORM(0, v_y, v_z) * cos(theta/2), sin(theta/2));
tf::Quaternion final_quaternion = normal;
double min_theta_index = 0;
double min_width = 100;
tf::Quaternion min_qua(0, 0, 0, 1);
visualization_msgs::Marker debug_hand_marker;
debug_hand_marker.header = cloud_msg->header;
debug_hand_marker.ns = string("debug_grasp");
debug_hand_marker.id = 0;
debug_hand_marker.type = visualization_msgs::Marker::LINE_LIST;
debug_hand_marker.pose.orientation.w = 1;
debug_hand_marker.scale.x=0.003;
tf::Matrix3x3 best_mat;
//search 180 degree and calc the shortest direction
for(double theta_=0; theta_<3.14/2;
theta_+=3.14/2/30){
tf::Quaternion rotate_(sin(theta_), 0, 0, cos(theta_));
tf::Quaternion temp_qua = normal * rotate_;
tf::Matrix3x3 temp_mat(temp_qua);
geometry_msgs::Pose pose_respected_to_tf;
pose_respected_to_tf.position.x = x;
pose_respected_to_tf.position.y = y;
pose_respected_to_tf.position.z = z;
pose_respected_to_tf.orientation.x = temp_qua.getX();
pose_respected_to_tf.orientation.y = temp_qua.getY();
pose_respected_to_tf.orientation.z = temp_qua.getZ();
pose_respected_to_tf.orientation.w = temp_qua.getW();
Eigen::Affine3d box_pose_respected_to_cloud_eigend;
tf::poseMsgToEigen(pose_respected_to_tf, box_pose_respected_to_cloud_eigend);
Eigen::Affine3d box_pose_respected_to_cloud_eigend_inversed
= box_pose_respected_to_cloud_eigend.inverse();
Eigen::Matrix4f box_pose_respected_to_cloud_eigen_inversed_matrixf;
Eigen::Matrix4d box_pose_respected_to_cloud_eigen_inversed_matrixd
= box_pose_respected_to_cloud_eigend_inversed.matrix();
jsk_pcl_ros::convertMatrix4<Eigen::Matrix4d, Eigen::Matrix4f>(
box_pose_respected_to_cloud_eigen_inversed_matrixd,
box_pose_respected_to_cloud_eigen_inversed_matrixf);
Eigen::Affine3f offset = Eigen::Affine3f(box_pose_respected_to_cloud_eigen_inversed_matrixf);
pcl::PointCloud<pcl::PointXYZ>::Ptr output_cloud(new pcl::PointCloud<pcl::PointXYZ>);
pcl::transformPointCloud(*cloud, *output_cloud, offset);
pcl::PassThrough<pcl::PointXYZ> pass;
pcl::PointCloud<pcl::PointXYZ>::Ptr points_z(new pcl::PointCloud<pcl::PointXYZ>), points_yz(new pcl::PointCloud<pcl::PointXYZ>), points_xyz(new pcl::PointCloud<pcl::PointXYZ>);
pass.setInputCloud(output_cloud);
pass.setFilterFieldName("y");
pass.setFilterLimits(-handle.arm_w*2, handle.arm_w*2);
pass.filter(*points_z);
pass.setInputCloud(points_z);
pass.setFilterFieldName("z");
pass.setFilterLimits(-handle.finger_d, handle.finger_d);
pass.filter(*points_yz);
pass.setInputCloud(points_yz);
pass.setFilterFieldName("x");
pass.setFilterLimits(-(handle.arm_l-handle.finger_l), handle.finger_l);
pass.filter(*points_xyz);
pcl::KdTreeFLANN<pcl::PointXYZ> kdtree;
for(size_t index=0; index<points_xyz->size(); index++){
points_xyz->points[index].x = points_xyz->points[index].z = 0;
}
if(points_xyz->points.size() == 0){JSK_ROS_INFO("points are empty");return;}
kdtree.setInputCloud(points_xyz);
std::vector<int> pointIdxRadiusSearch;
std::vector<float> pointRadiusSquaredDistance;
pcl::PointXYZ search_point_tree;
search_point_tree.x=search_point_tree.y=search_point_tree.z=0;
if( kdtree.radiusSearch(search_point_tree, 10, pointIdxRadiusSearch, pointRadiusSquaredDistance) > 0 ){
double before_w=10, temp_w;
for(size_t index = 0; index < pointIdxRadiusSearch.size(); ++index){
temp_w =sqrt(pointRadiusSquaredDistance[index]);
if(temp_w - before_w > handle.finger_w*2){
break; // there are small space for finger
}
before_w=temp_w;
}
if(before_w < min_width){
min_theta_index = theta_;
min_width = before_w;
min_qua = temp_qua;
best_mat = temp_mat;
}
//for debug view
geometry_msgs::Point temp_point;
std_msgs::ColorRGBA temp_color;
temp_color.r=0; temp_color.g=0; temp_color.b=1; temp_color.a=1;
temp_point.x=x-temp_mat.getColumn(1)[0] * before_w;
temp_point.y=y-temp_mat.getColumn(1)[1] * before_w;
temp_point.z=z-temp_mat.getColumn(1)[2] * before_w;
debug_hand_marker.points.push_back(temp_point);
debug_hand_marker.colors.push_back(temp_color);
temp_point.x+=2*temp_mat.getColumn(1)[0] * before_w;
temp_point.y+=2*temp_mat.getColumn(1)[1] * before_w;
temp_point.z+=2*temp_mat.getColumn(1)[2] * before_w;
debug_hand_marker.points.push_back(temp_point);
debug_hand_marker.colors.push_back(temp_color);
}
}
geometry_msgs::PoseStamped handle_pose_stamped;
handle_pose_stamped.header = cloud_msg->header;
handle_pose_stamped.pose.position.x = x;
handle_pose_stamped.pose.position.y = y;
handle_pose_stamped.pose.position.z = z;
handle_pose_stamped.pose.orientation.x = min_qua.getX();
handle_pose_stamped.pose.orientation.y = min_qua.getY();
handle_pose_stamped.pose.orientation.z = min_qua.getZ();
handle_pose_stamped.pose.orientation.w = min_qua.getW();
std_msgs::Float64 min_width_msg;
min_width_msg.data = min_width;
pub_pose_.publish(handle_pose_stamped);
pub_debug_marker_.publish(debug_hand_marker);
pub_debug_marker_array_.publish(make_handle_array(handle_pose_stamped, handle));
jsk_recognition_msgs::SimpleHandle simple_handle;
simple_handle.header = handle_pose_stamped.header;
simple_handle.pose = handle_pose_stamped.pose;
simple_handle.handle_width = min_width;
pub_handle_.publish(simple_handle);
}
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);
}