-
Notifications
You must be signed in to change notification settings - Fork 0
/
cloudrunner.cpp
401 lines (329 loc) · 15.5 KB
/
cloudrunner.cpp
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
#include "cloudrunner.hpp"
#include "ui_cloudrunner.h"
#include <pcl/io/pcd_io.h>
#include <pcl/io/ply_io.h>
#include <pcl/point_cloud.h>
#include <pcl/console/parse.h>
#include <pcl/common/transforms.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <iostream>
#include <pcl/point_types.h>
#include <pcl/filters/passthrough.h>
#include <pcl/filters/statistical_outlier_removal.h>
#include <pcl/search/kdtree.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/segmentation/extract_clusters.h>
#include <jsoncpp/json/json.h>
#include <random>
#include <chrono>
#include <vector>
#include <algorithm>
#include <cmath>
#include <omp.h>
#include <pcl/registration/icp.h>
#include <pcl/common/common.h>
CloudRunner::CloudRunner (QWidget *parent) :
QMainWindow (parent),
ui (new Ui::CloudRunner)
{
ui->setupUi (this);
this->setWindowTitle ("Cloudrunner");
// Set up the QVTK window
viewer.reset (new pcl::visualization::PCLVisualizer ("viewer", false));
ui->qvtkWidget->SetRenderWindow (viewer->getRenderWindow ());
viewer->setupInteractor (ui->qvtkWidget->GetInteractor (), ui->qvtkWidget->GetRenderWindow ());
ui->qvtkWidget->update ();
// Seed the random number generator with the current time
srand(time(nullptr));
// Load file | Works with PLY files
pcl::PointCloud<pcl::PointXYZ>::Ptr global_cloud_filtered (new pcl::PointCloud<pcl::PointXYZ> ());
pcl::PointCloud<pcl::PointXYZ>::Ptr local_cloud2 (new pcl::PointCloud<pcl::PointXYZ> ());
pcl::PointCloud<pcl::PointXYZ>::Ptr local_cloud3 (new pcl::PointCloud<pcl::PointXYZ> ());
pcl::PointCloud<pcl::PointXYZ>::Ptr local_cloud4 (new pcl::PointCloud<pcl::PointXYZ> ());
pcl::PointCloud<pcl::PointXYZ>::Ptr local_cloud2_down (new pcl::PointCloud<pcl::PointXYZ> ());
pcl::PointCloud<pcl::PointXYZ>::Ptr local_cloud3_down (new pcl::PointCloud<pcl::PointXYZ> ());
pcl::PointCloud<pcl::PointXYZ>::Ptr local_cloud4_down (new pcl::PointCloud<pcl::PointXYZ> ());
pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZ>);
pcl::IterativeClosestPoint<pcl::PointXYZ, pcl::PointXYZ> icp;
Eigen::Affine3f transform = Eigen::Affine3f::Identity();
loadPointCloud("./../data/CL360global.ply", global_cloud_filtered);
// loadPointCloud("./../data/first_new_icp.ply", local_cloud2);
loadPointCloud("./../data/second_new_icp.ply", local_cloud3);
// loadPointCloud("./../data/third_new_icp.ply", local_cloud4);
// // loadPointCloud("./../data/first_new_downsampled_icp.ply", local_cloud2_down);
// loadPointCloud("./../data/second_new_downsampled_icp.ply", local_cloud3);
// loadPointCloud("./../data/third_new_downsampled_icp.ply", local_cloud4_down);
// icp.setInputSource(local_cloud2);
// icp.setInputTarget(global_cloud_filtered);
// pcl::PointCloud<pcl::PointXYZ>::Ptr aligned_cloud2(new pcl::PointCloud<pcl::PointXYZ>);
// icp.align(*aligned_cloud2);
drawPointCloud(ui, viewer, global_cloud_filtered, "global_cloud_filtered");
// // get the min/max bounds of the cloud
// pcl::PointXYZ global_cloud_min, global_cloud_max;
// pcl::getMinMax3D(*global_cloud_filtered, global_cloud_min, global_cloud_max);
// // print the min/max bounds
// std::cout << "Minimum x: " << global_cloud_min.x << std::endl;
// std::cout << "Minimum y: " << global_cloud_min.y << std::endl;
// std::cout << "Minimum z: " << global_cloud_min.z << std::endl;
// std::cout << "Maximum x: " << global_cloud_max.x << std::endl;
// std::cout << "Maximum y: " << global_cloud_max.y << std::endl;
// std::cout << "Maximum z: " << global_cloud_max.z << std::endl;
// get the min/max bounds of the cloud
pcl::PointXYZ local_cloud_min, local_cloud_max;
pcl::getMinMax3D(*local_cloud3, local_cloud_min, local_cloud_max);
// print the min/max bounds
std::cout << "Minimum x local: " << local_cloud_min.x << std::endl;
std::cout << "Minimum y local: " << local_cloud_min.y << std::endl;
std::cout << "Minimum z local: " << local_cloud_min.z << std::endl;
std::cout << "Maximum x local: " << local_cloud_max.x << std::endl;
std::cout << "Maximum y local: " << local_cloud_max.y << std::endl;
std::cout << "Maximum z local: " << local_cloud_max.z << std::endl;
// Create a KD tree for searching nearest neighbors
kdtree->setInputCloud(global_cloud_filtered);
// Initialize Monte Carlo Localization parameters
float y_deviation = 1.0;
double sigma = 0.05;
// Calculate the rmse_reference
double rmse_reference6 = calculateRMSE(kdtree, local_cloud3);
std::cout << "Reference RMSE: " << rmse_reference6 << endl;
// Define the range of x and y coordinates within which the local point cloud can be placed
// double x_min = global_cloud_min.x + local_cloud_size.x() / 2.0;
// double x_max = global_cloud_max.x - local_cloud_size.x() / 2.0;
// double y_min = (global_cloud_min.y + local_cloud_size.y()) / 2.0;
// double y_max = (global_cloud_max.y - local_cloud_size.y()) / 2.0;
double x_min = -60;
double x_max = 60;
double y_min = -60;
double y_max = 100;
std::random_device rd;
std::mt19937 generator(rd());
std::normal_distribution<double> x_distribution_gauss_robot(0.0, 0.03f);
std::normal_distribution<double> y_distribution_gauss_robot(0.0, y_deviation);
std::uniform_real_distribution<double> x_distribution(x_min, x_max);
std::uniform_real_distribution<double> y_distribution(y_min, y_max);
// // std::cout << "x_distribution: " << x_distribution << endl;
// // std::cout << "y_distribution: " << y_distribution << endl;
// // std::cout << "gauss_distribution: " << distribution_gauss_robot << endl;
int parameter = 0;
int num_particles = 10;
int total_particles = 30;
std::map<double, std::array<double, 4>> rmseCoords;
std::vector<double> x_coords(num_particles);
std::vector<double> y_coords(num_particles);
std::vector<pcl::PointCloud<pcl::PointXYZ>::Ptr> particles(num_particles);
std::vector<pcl::PointCloud<pcl::PointXYZ>::Ptr> new_particles;
int it_weight = 0;
// Generate random x and y coordinates within the defined range
// #pragma omp parallel for
for (int it = 0; it < 10; it++) {
for (int i = 0; i < num_particles; ++i) {
// UNIFORM
// x_coords[i] = x_distribution(generator);
// y_coords[i] = y_distribution(generator);
// GAUSS
x_coords[i] = x_distribution_gauss_robot(generator);
y_coords[i] = y_distribution_gauss_robot(generator);
}
}
omp_lock_t lock;
omp_init_lock(&lock);
// Initialize particle set with random poses
// #pragma omp parallel for
for (int it = 0; it < num_particles; it++) {
pcl::PointCloud<pcl::PointXYZ>::Ptr particle(new pcl::PointCloud<pcl::PointXYZ>);
transform.translation() << x_coords[it], y_coords[it], 0.0f; // set z coordinate to desired value
pcl::transformPointCloud(*local_cloud3, *particle, transform);
particles[it] = particle;
// Calculate rmse
double rmse = calculateRMSE(kdtree, particle);
std::array<double, 4> coords = {x_coords[it], y_coords[it], 0.0, 0.0};
omp_set_lock(&lock);
rmseCoords.insert(std::make_pair(rmse, coords));
omp_unset_lock(&lock);
}
omp_destroy_lock(&lock);
// Create a vector of pairs to store RMSE values and their coordinates
std::vector<std::pair<double, std::array<double, 4>>> rmseVector;
// Copy the contents of rmseCoords to rmseVector
for (const auto& entry : rmseCoords)
{
rmseVector.emplace_back(entry.first, entry.second);
}
// Sort rmseVector by the RMSE values in descending order
std::sort(rmseVector.begin(), rmseVector.end(), [](const auto& a, const auto& b) {
return a.first < b.first;
});
// Get the first 10 pairs in rmseVector
int numAreas = 0.2*num_particles;
std::vector<std::pair<double, std::array<double, 4>>> bestAreas(rmseVector.begin(), rmseVector.begin() + numAreas);
// Compute normalized weights
std::vector<double> normalized_weights;
double total_weight = std::accumulate(bestAreas.begin(), bestAreas.end(), 0.0,[](double sum, const std::pair<double, std::array<double, 4>>& pair) {
return sum + pair.first;
});
for (const auto& w : bestAreas) {
normalized_weights.push_back(w.first / total_weight);
}
for (const auto& area : bestAreas) {
// Generate a random sample from the Gaussian distribution
std::normal_distribution<double> x_distribution_gauss(area.second[0], sigma);
std::normal_distribution<double> y_distribution_gauss(area.second[1], sigma);
std::vector<pcl::PointCloud<pcl::PointXYZ>::Ptr> particles_new;
int num_particles2 = normalized_weights[it_weight]*total_particles;
for (int i = 0; i < num_particles2; ++i) {
// std::cout << "x_distribution: " << x_distribution_gauss << endl;
// std::cout << "y_distribution: " << y_distribution_gauss << endl;
// std::normal_distribution<double> z_distribution(mean.z(), sigma);
double x_particle = x_distribution_gauss(generator);
double y_particle = y_distribution_gauss(generator);
// Generate a new particle at the random sample location
pcl::PointCloud<pcl::PointXYZ>::Ptr particle_new(new pcl::PointCloud<pcl::PointXYZ>);
transform.translation() << x_particle, y_particle, 0.0f;
pcl::transformPointCloud(*local_cloud3, *particle_new, transform);
particles_new.push_back(particle_new);
// Calculate rmse
double rmse = calculateRMSE(kdtree, particle_new);
std::array<double, 4> coords = {x_particle, y_particle, 0.0, 0.0};
rmseCoords.insert(std::make_pair(rmse, coords));
}
it_weight++;
// Add the generated particles to the resampled particles vector
new_particles.insert(new_particles.end(), particles_new.begin(), particles_new.end());
}
auto lowest_rmse = rmseCoords.begin(); // iterator to the first key-value pair
double rmse = lowest_rmse->first; // rmse value
std::array<double, 4> coords_min = lowest_rmse->second; // corresponding coordinates
std::cout << rmse << "," << num_particles << "," << coords_min[0] << "," << coords_min[1] << "," << coords_min[2] << "," << coords_min[3] << "," << std::endl;
transform.translation() << coords_min[0], coords_min[1], coords_min[2];
std::cout << transform.matrix() << std::endl;
pcl::PointCloud<pcl::PointXYZ>::Ptr transformed_cloud (new pcl::PointCloud<pcl::PointXYZ> ());
pcl::transformPointCloud (*local_cloud3, *transformed_cloud, transform);
for (int j = 0; j < num_particles; j++) {
std::stringstream ss;
ss << "particle_" << j;
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> particle_color_handler (particles[j], 20, 20, 230); // Blue
viewer->addPointCloud(particles[j], particle_color_handler, ss.str());
}
for (int j = 0; j < new_particles.size(); j++) {
std::stringstream ss;
ss << "new_particle_" << j;
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> particle_color_handler (new_particles[j], 20, 230, 230); // Cyan
viewer->addPointCloud(new_particles[j], particle_color_handler, ss.str());
}
// Define R,G,B colors for the point cloud
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> local_cloud2_color_handler (local_cloud3, 230, 20, 20); // Red
viewer->addPointCloud (local_cloud3, local_cloud2_color_handler, "original_cloud");
// pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> local_cloud3_color_handler (aligned_cloud3, 20, 230, 20); // Red
// viewer->addPointCloud (aligned_cloud3, local_cloud3_color_handler, "original_cloud2");
// pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> local_cloud4_color_handler (aligned_cloud4, 20, 20, 230); // Red
// viewer->addPointCloud (aligned_cloud4, local_cloud4_color_handler, "original_cloud3");
pcl::visualization::PointCloudColorHandlerCustom<pcl::PointXYZ> transformed_cloud_color_handler (transformed_cloud, 20, 230, 20); // Green
viewer->addPointCloud (transformed_cloud, transformed_cloud_color_handler, "transformed_cloud");
}
CloudRunner::~CloudRunner ()
{
delete ui;
}
void CloudRunner::loadPointCloud(const std::string& filename, pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud)
{
// Check file extension
std::string extension = filename.substr(filename.find_last_of(".") + 1);
if (extension == "pcd") {
if (pcl::io::loadPCDFile(filename, *cloud) < 0) {
std::cerr << "Failed to load PCD file: " << filename << std::endl;
}
}
else if (extension == "ply") {
if (pcl::io::loadPLYFile(filename, *cloud) < 0) {
std::cerr << "Failed to load PLY file: " << filename << std::endl;
}
}
else {
std::cerr << "Unsupported file extension: " << extension << std::endl;
}
}
void CloudRunner::loadPointCloud(const std::string& filename, pcl::PointCloud<pcl::PointXYZ>::Ptr cloud)
{
// Check file extension
std::string extension = filename.substr(filename.find_last_of(".") + 1);
if (extension == "pcd") {
if (pcl::io::loadPCDFile(filename, *cloud) < 0) {
std::cerr << "Failed to load PCD file: " << filename << std::endl;
}
}
else if (extension == "ply") {
if (pcl::io::loadPLYFile(filename, *cloud) < 0) {
std::cerr << "Failed to load PLY file: " << filename << std::endl;
}
}
else {
std::cerr << "Unsupported file extension: " << extension << std::endl;
}
}
void CloudRunner::drawPointCloud(Ui::CloudRunner* ui, pcl::visualization::PCLVisualizer::Ptr viewer, pcl::PointCloud<pcl::PointXYZRGB>::Ptr cloud, char const* name)
{
viewer->addPointCloud (cloud, name);
viewer->resetCamera ();
ui->qvtkWidget->update ();
}
void CloudRunner::drawPointCloud(Ui::CloudRunner* ui, pcl::visualization::PCLVisualizer::Ptr viewer, pcl::PointCloud<pcl::PointXYZ>::Ptr cloud, char const* name)
{
viewer->addPointCloud (cloud, name);
viewer->resetCamera ();
ui->qvtkWidget->update ();
}
void CloudRunner::removeGround(std::shared_ptr<pcl::PointCloud<pcl::PointXYZ>> cloud, std::shared_ptr<pcl::PointCloud<pcl::PointXYZ>> cloud_filtered)
{
pcl::PassThrough<pcl::PointXYZ> pass;
pass.setInputCloud (cloud);
pass.setFilterFieldName ("z");
pass.setFilterLimits (0.0, 100.0);
// pass.setNegative (true);
pass.filter (*cloud_filtered);
}
void CloudRunner::removeOutliers(std::shared_ptr<pcl::PointCloud<pcl::PointXYZ>> cloud, std::shared_ptr<pcl::PointCloud<pcl::PointXYZ>> cloud_filtered)
{
pcl::StatisticalOutlierRemoval<pcl::PointXYZ> sor;
sor.setInputCloud (cloud);
sor.setMeanK (50);
sor.setStddevMulThresh (1.0);
sor.setNegative (true);
sor.filter (*cloud_filtered);
}
float CloudRunner::getMinJsonKey(Json::Value json)
{
// Find the lowest value of key in the JSON object
float min_key = std::numeric_limits<float>::max();
for (auto const& key : json.getMemberNames()) {
float f_key = std::stof(key);
if (f_key < min_key) {
min_key = f_key;
}
}
return min_key;
}
double CloudRunner::calculateRMSE(pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree, pcl::PointCloud<pcl::PointXYZ>::Ptr local_cloud)
{
double sum_squared_distance = 0.0;
// #pragma omp parallel for reduction(+:sum_squared_distance)
for (int i = 0; i < local_cloud->points.size(); i++)
{
std::vector<int> indices(1);
std::vector<float> distances(1);
if (kdtree->nearestKSearch(local_cloud->points[i], 1, indices, distances) > 0)
{
sum_squared_distance += distances[0] * distances[0];
}
}
// Calculate the average distance
double rmse_distance = sqrt(sum_squared_distance / local_cloud->points.size());
return rmse_distance;
}
float CloudRunner::generateRandomNumber()
{
std::random_device rd;
std::mt19937 gen(rd());
std::uniform_real_distribution<float> dist(-1.0f, 1.0f); // Generate a random number in the range from 0.01 to 0.5
float random_num = dist(gen);
return random_num;
}