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
|
/*
* Software License Agreement (BSD License)
*
* Point Cloud Library (PCL) - www.pointclouds.org
* Copyright (c) 2016-, Open Perception, Inc.
*
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the copyright holder(s) nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*
* $Id$
*
*/
#include <pcl/test/gtest.h>
#include <pcl/point_cloud.h>
#include <pcl/pcl_tests.h>
#include <pcl/features/normal_3d.h>
#include <pcl/io/pcd_io.h>
#include <pcl/features/flare.h>
using KdTreePtr = pcl::search::KdTree<pcl::PointXYZ>::Ptr;
using PointCloudPtr = pcl::PointCloud<pcl::PointXYZ>::Ptr;
PointCloudPtr cloud;
KdTreePtr tree;
//sampled surface for the computation of tangent X axis
PointCloudPtr sampled_cloud;
KdTreePtr sampled_tree;
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////
TEST (PCL, FLARELocalReferenceFrameEstimation)
{
pcl::PointCloud<pcl::Normal>::Ptr normals (new pcl::PointCloud<pcl::Normal> ());
pcl::PointCloud<pcl::ReferenceFrame> bunny_LRF;
constexpr float mesh_res = 0.005f;
// Compute normals
pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
ne.setRadiusSearch (2.0f*mesh_res);
ne.setViewPoint (1, 1, 10);
ne.setInputCloud (cloud);
ne.setSearchMethod (tree);
ne.compute (*normals);
// Compute FLARE LRF
pcl::FLARELocalReferenceFrameEstimation<pcl::PointXYZ, pcl::Normal, pcl::ReferenceFrame> lrf_estimator;
lrf_estimator.setRadiusSearch (5 * mesh_res);
lrf_estimator.setTangentRadius (20 * mesh_res);
lrf_estimator.setInputCloud (cloud);
lrf_estimator.setSearchSurface (cloud);
lrf_estimator.setInputNormals (normals);
lrf_estimator.setSearchMethod (tree);
lrf_estimator.setSearchMethodForSampledSurface (sampled_tree);
lrf_estimator.setSearchSampledSurface (sampled_cloud);
lrf_estimator.compute (bunny_LRF);
// TESTS
EXPECT_TRUE (bunny_LRF.is_dense);
// Expected Results
float score_15 = -0.0059431493f;
Eigen::Vector3f point_15_x (-0.46138301f, 0.75752199f, -0.46182927f);
Eigen::Vector3f point_15_y (-0.78785944f, -0.11049186f, 0.60586232f);
Eigen::Vector3f point_15_z (0.40792558f, 0.64339107f, 0.64779979f);
float score_45 = 0.018918669f;
Eigen::Vector3f point_45_x (0.63724411f, -0.74846953f, -0.18361199f);
Eigen::Vector3f point_45_y (0.76468521f, 0.58447874f, 0.27136898f);
Eigen::Vector3f point_45_z (-0.095794097f, -0.31333363f, 0.94479918f);
float score_163 = -0.050190225f;
Eigen::Vector3f point_163_x (-0.67064381f, 0.45722002f, 0.58411193f);
Eigen::Vector3f point_163_y (-0.58332449f, -0.81150508f, -0.034525186f);
Eigen::Vector3f point_163_z (0.45822418f, -0.36388087f, 0.81093854f);
float score_253 = -0.025943652f;
Eigen::Vector3f point_253_x (0.88240892f, -0.26585102f, 0.38817233f);
Eigen::Vector3f point_253_y (0.19853911f, 0.95840079f, 0.20506138f);
Eigen::Vector3f point_253_z (-0.42654046f, -0.10388060f, 0.89848322f);
//Test Results
for (int d = 0; d < 3; ++d)
{
EXPECT_NEAR (point_15_x[d], bunny_LRF.at (15).x_axis[d], 1E-3);
EXPECT_NEAR (point_15_y[d], bunny_LRF.at (15).y_axis[d], 1E-3);
EXPECT_NEAR (point_15_z[d], bunny_LRF.at (15).z_axis[d], 1E-3);
EXPECT_NEAR (point_45_x[d], bunny_LRF.at (45).x_axis[d], 1E-3);
EXPECT_NEAR (point_45_y[d], bunny_LRF.at (45).y_axis[d], 1E-3);
EXPECT_NEAR (point_45_z[d], bunny_LRF.at (45).z_axis[d], 1E-3);
EXPECT_NEAR (point_163_x[d], bunny_LRF.at (163).x_axis[d], 1E-3);
EXPECT_NEAR (point_163_y[d], bunny_LRF.at (163).y_axis[d], 1E-3);
EXPECT_NEAR (point_163_z[d], bunny_LRF.at (163).z_axis[d], 1E-3);
EXPECT_NEAR (point_253_x[d], bunny_LRF.at (253).x_axis[d], 1E-3);
EXPECT_NEAR (point_253_y[d], bunny_LRF.at (253).y_axis[d], 1E-3);
EXPECT_NEAR (point_253_z[d], bunny_LRF.at (253).z_axis[d], 1E-3);
}
EXPECT_NEAR (score_15, lrf_estimator.getSignedDistancesFromHighestPoints ()[15], 1E-4);
EXPECT_NEAR (score_45, lrf_estimator.getSignedDistancesFromHighestPoints ()[45], 1E-4);
EXPECT_NEAR (score_163, lrf_estimator.getSignedDistancesFromHighestPoints ()[163], 1E-4);
EXPECT_NEAR (score_253, lrf_estimator.getSignedDistancesFromHighestPoints ()[253], 1E-4);
}
/* ---[ */
int
main (int argc, char** argv)
{
if (argc < 2)
{
std::cerr << "No test file given. Please download `bun0.pcd` and pass its path to the test." << std::endl;
return (-1);
}
cloud.reset (new pcl::PointCloud<pcl::PointXYZ> ());
if (pcl::io::loadPCDFile<pcl::PointXYZ> (argv[1], *cloud) < 0)
{
std::cerr << "Failed to read test file. Please download `bun0.pcd` and pass its path to the test." << std::endl;
return (-1);
}
tree.reset (new pcl::search::KdTree<pcl::PointXYZ> (false));
tree->setInputCloud (cloud);
//create and set sampled point cloud for computation of X axis
constexpr float sampling_perc = 0.2f;
constexpr float sampling_incr = 1.0f / sampling_perc;
sampled_cloud.reset (new pcl::PointCloud<pcl::PointXYZ> ());
pcl::Indices sampled_indices;
for (float sa = 0.0f; sa < static_cast<float>(cloud->size ()); sa += sampling_incr)
sampled_indices.push_back (static_cast<int> (sa));
copyPointCloud (*cloud, sampled_indices, *sampled_cloud);
sampled_tree.reset (new pcl::search::KdTree<pcl::PointXYZ> (false));
sampled_tree->setInputCloud (sampled_cloud);
//start tests
testing::InitGoogleTest (&argc, argv);
return (RUN_ALL_TESTS ());
}
/* ]--- */
|
