337 lines
16 KiB
HTML
337 lines
16 KiB
HTML
|
|
|
|
<!DOCTYPE html>
|
|
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
|
|
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
|
|
<head>
|
|
<meta charset="utf-8">
|
|
|
|
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
|
|
|
<title>Estimating VFH signatures for a set of points — Point Cloud Library 1.12.0 documentation</title>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
<script type="text/javascript" src="_static/js/modernizr.min.js"></script>
|
|
|
|
|
|
<script type="text/javascript" id="documentation_options" data-url_root="./" src="_static/documentation_options.js"></script>
|
|
<script type="text/javascript" src="_static/jquery.js"></script>
|
|
<script type="text/javascript" src="_static/underscore.js"></script>
|
|
<script type="text/javascript" src="_static/doctools.js"></script>
|
|
<script type="text/javascript" src="_static/language_data.js"></script>
|
|
|
|
<script type="text/javascript" src="_static/js/theme.js"></script>
|
|
|
|
|
|
|
|
|
|
<link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
|
|
<link rel="stylesheet" href="_static/pygments.css" type="text/css" />
|
|
<link rel="index" title="Index" href="genindex.html" />
|
|
<link rel="search" title="Search" href="search.html" />
|
|
</head>
|
|
|
|
<body class="wy-body-for-nav">
|
|
|
|
|
|
<div class="wy-grid-for-nav">
|
|
|
|
<nav data-toggle="wy-nav-shift" class="wy-nav-side">
|
|
<div class="wy-side-scroll">
|
|
<div class="wy-side-nav-search" >
|
|
|
|
|
|
|
|
<a href="index.html" class="icon icon-home"> Point Cloud Library
|
|
|
|
|
|
|
|
</a>
|
|
|
|
|
|
|
|
|
|
<div class="version">
|
|
1.12.0
|
|
</div>
|
|
|
|
|
|
|
|
|
|
<div role="search">
|
|
<form id="rtd-search-form" class="wy-form" action="search.html" method="get">
|
|
<input type="text" name="q" placeholder="Search docs" />
|
|
<input type="hidden" name="check_keywords" value="yes" />
|
|
<input type="hidden" name="area" value="default" />
|
|
</form>
|
|
</div>
|
|
|
|
|
|
</div>
|
|
|
|
<div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
|
|
|
|
|
|
|
|
|
|
|
|
|
|
<!-- Local TOC -->
|
|
<div class="local-toc"><ul>
|
|
<li><a class="reference internal" href="#">Estimating VFH signatures for a set of points</a></li>
|
|
<li><a class="reference internal" href="#theoretical-primer">Theoretical primer</a></li>
|
|
<li><a class="reference internal" href="#estimating-vfh-features">Estimating VFH features</a></li>
|
|
<li><a class="reference internal" href="#visualizing-vfh-signatures">Visualizing VFH signatures</a></li>
|
|
</ul>
|
|
</div>
|
|
|
|
|
|
</div>
|
|
</div>
|
|
</nav>
|
|
|
|
<section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">
|
|
|
|
|
|
<nav class="wy-nav-top" aria-label="top navigation">
|
|
|
|
<i data-toggle="wy-nav-top" class="fa fa-bars"></i>
|
|
<a href="index.html">Point Cloud Library</a>
|
|
|
|
</nav>
|
|
|
|
|
|
<div class="wy-nav-content">
|
|
|
|
<div class="rst-content">
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
<div role="navigation" aria-label="breadcrumbs navigation">
|
|
|
|
<ul class="wy-breadcrumbs">
|
|
|
|
<li><a href="index.html">Docs</a> »</li>
|
|
|
|
<li>Estimating VFH signatures for a set of points</li>
|
|
|
|
|
|
<li class="wy-breadcrumbs-aside">
|
|
|
|
|
|
|
|
</li>
|
|
|
|
</ul>
|
|
|
|
|
|
<hr/>
|
|
</div>
|
|
<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
|
|
<div itemprop="articleBody">
|
|
|
|
<div class="section" id="estimating-vfh-signatures-for-a-set-of-points">
|
|
<span id="vfh-estimation"></span><h1>Estimating VFH signatures for a set of points</h1>
|
|
<p>This document describes the Viewpoint Feature Histogram (<a class="reference internal" href="#vfh" id="id1">[VFH]</a>) descriptor, a
|
|
novel representation for point clusters for the problem of Cluster (e.g.,
|
|
Object) Recognition and 6DOF Pose Estimation.</p>
|
|
<p>The image below exhibits an example of VFH <em>recognition</em> and pose estimation.
|
|
Given a set of train data (top row, bottom row except the leftmost point
|
|
cloud), a model is learned and then a cloud (bottom left part) is used to
|
|
query/test the model. The matched results in order from best to worst go from
|
|
left to right starting at bottom left. For more information please see
|
|
<a class="reference internal" href="vfh_recognition.html#vfh-recognition"><span class="std std-ref">Cluster Recognition and 6DOF Pose Estimation using VFH descriptors</span></a> and/or <a class="reference internal" href="#vfh" id="id2">[VFH]</a>.</p>
|
|
<img alt="_images/vfh_example.jpg" class="align-center" src="_images/vfh_example.jpg" />
|
|
</div>
|
|
<div class="section" id="theoretical-primer">
|
|
<h1>Theoretical primer</h1>
|
|
<p>The Viewpoint Feature Histogram (or VFH) has its roots in the FPFH descriptor
|
|
(see <a class="reference internal" href="fpfh_estimation.html#fpfh-estimation"><span class="std std-ref">Fast Point Feature Histograms (FPFH) descriptors</span></a>). Due to its speed and discriminative power, we
|
|
decided to leverage the strong recognition results of FPFH, but to add in
|
|
viewpoint variance while retaining invariance to scale.</p>
|
|
<p>Our contribution to the problem of object recognition and pose identification
|
|
was to extend the FPFH to be estimated for the entire object cluster (as seen
|
|
in the figure below), and to compute additional statistics between the
|
|
viewpoint direction and the normals estimated at each point. To do this, we
|
|
used the key idea of mixing the viewpoint direction directly into the relative
|
|
normal angle calculation in the FPFH.</p>
|
|
<img alt="_images/first_component.jpg" class="align-center" src="_images/first_component.jpg" />
|
|
<p>The viewpoint component is computed by collecting a histogram of the angles
|
|
that the viewpoint direction makes with each normal. Note, we do not mean the
|
|
view angle to each normal as this would not be scale invariant, but instead we
|
|
mean the angle between the central viewpoint direction translated to each
|
|
normal. The second component measures the relative pan, tilt and yaw angles as
|
|
described in <a class="reference internal" href="fpfh_estimation.html#fpfh-estimation"><span class="std std-ref">Fast Point Feature Histograms (FPFH) descriptors</span></a> but now measured between the viewpoint
|
|
direction at the central point and each of the normals on the surface.</p>
|
|
<img alt="_images/second_component.jpg" class="align-center" src="_images/second_component.jpg" />
|
|
<p>The new assembled feature is therefore called the Viewpoint Feature Histogram (VFH). The figure below presents this idea with the new feature consisting of two parts:</p>
|
|
<blockquote>
|
|
<div><ol class="arabic simple">
|
|
<li>a viewpoint direction component and</li>
|
|
<li>a surface shape component comprised of an extended FPFH.</li>
|
|
</ol>
|
|
</div></blockquote>
|
|
<img alt="_images/vfh_histogram.jpg" class="align-center" src="_images/vfh_histogram.jpg" />
|
|
</div>
|
|
<div class="section" id="estimating-vfh-features">
|
|
<h1>Estimating VFH features</h1>
|
|
<p>The Viewpoint Feature Histogram is implemented in PCL as part of the
|
|
<a class="reference external" href="http://docs.pointclouds.org/trunk/a02944.html">pcl_features</a>
|
|
library.</p>
|
|
<p>The default VFH implementation uses 45 binning subdivisions for each of the
|
|
three extended FPFH values, plus another 45 binning subdivisions for the distances between each point and the centroid and 128 binning subdivisions for the viewpoint
|
|
component, which results in a 308-byte array of float values. These are stored
|
|
in a <strong>pcl::VFHSignature308</strong> point type.</p>
|
|
<p>The major difference between the PFH/FPFH descriptors and VFH, is that for a
|
|
given point cloud dataset, only a single VFH descriptor will be estimated,
|
|
while the resultant PFH/FPFH data will have the same number of entries as the
|
|
number of points in the cloud.</p>
|
|
<p>The following code snippet will estimate a set of VFH features for all the
|
|
points in the input dataset.</p>
|
|
<div class="highlight-cpp notranslate"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre> 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</pre></div></td><td class="code"><div class="highlight"><pre><span></span><span class="cp">#include</span> <span class="cpf"><pcl/point_types.h></span><span class="cp"></span>
|
|
<span class="cp">#include</span> <span class="cpf"><pcl/features/vfh.h></span><span class="cp"></span>
|
|
|
|
<span class="p">{</span>
|
|
<span class="n">pcl</span><span class="o">::</span><span class="n">PointCloud</span><span class="o"><</span><span class="n">pcl</span><span class="o">::</span><span class="n">PointXYZ</span><span class="o">>::</span><span class="n">Ptr</span> <span class="n">cloud</span> <span class="p">(</span><span class="k">new</span> <span class="n">pcl</span><span class="o">::</span><span class="n">PointCloud</span><span class="o"><</span><span class="n">pcl</span><span class="o">::</span><span class="n">PointXYZ</span><span class="o">></span><span class="p">);</span>
|
|
<span class="n">pcl</span><span class="o">::</span><span class="n">PointCloud</span><span class="o"><</span><span class="n">pcl</span><span class="o">::</span><span class="n">Normal</span><span class="o">>::</span><span class="n">Ptr</span> <span class="n">normals</span> <span class="p">(</span><span class="k">new</span> <span class="n">pcl</span><span class="o">::</span><span class="n">PointCloud</span><span class="o"><</span><span class="n">pcl</span><span class="o">::</span><span class="n">Normal</span><span class="o">></span> <span class="p">());</span>
|
|
|
|
<span class="p">...</span> <span class="n">read</span><span class="p">,</span> <span class="n">pass</span> <span class="n">in</span> <span class="n">or</span> <span class="n">create</span> <span class="n">a</span> <span class="n">point</span> <span class="n">cloud</span> <span class="n">with</span> <span class="n">normals</span> <span class="p">...</span>
|
|
<span class="p">...</span> <span class="p">(</span><span class="nl">note</span><span class="p">:</span> <span class="n">you</span> <span class="n">can</span> <span class="n">create</span> <span class="n">a</span> <span class="n">single</span> <span class="n">PointCloud</span><span class="o"><</span><span class="n">PointNormal</span><span class="o">></span> <span class="k">if</span> <span class="n">you</span> <span class="n">want</span><span class="p">)</span> <span class="p">...</span>
|
|
|
|
<span class="c1">// Create the VFH estimation class, and pass the input dataset+normals to it</span>
|
|
<span class="n">pcl</span><span class="o">::</span><span class="n">VFHEstimation</span><span class="o"><</span><span class="n">pcl</span><span class="o">::</span><span class="n">PointXYZ</span><span class="p">,</span> <span class="n">pcl</span><span class="o">::</span><span class="n">Normal</span><span class="p">,</span> <span class="n">pcl</span><span class="o">::</span><span class="n">VFHSignature308</span><span class="o">></span> <span class="n">vfh</span><span class="p">;</span>
|
|
<span class="n">vfh</span><span class="p">.</span><span class="n">setInputCloud</span> <span class="p">(</span><span class="n">cloud</span><span class="p">);</span>
|
|
<span class="n">vfh</span><span class="p">.</span><span class="n">setInputNormals</span> <span class="p">(</span><span class="n">normals</span><span class="p">);</span>
|
|
<span class="c1">// alternatively, if cloud is of type PointNormal, do vfh.setInputNormals (cloud);</span>
|
|
|
|
<span class="c1">// Create an empty kdtree representation, and pass it to the FPFH estimation object.</span>
|
|
<span class="c1">// Its content will be filled inside the object, based on the given input dataset (as no other search surface is given).</span>
|
|
<span class="n">pcl</span><span class="o">::</span><span class="n">search</span><span class="o">::</span><span class="n">KdTree</span><span class="o"><</span><span class="n">pcl</span><span class="o">::</span><span class="n">PointXYZ</span><span class="o">>::</span><span class="n">Ptr</span> <span class="n">tree</span> <span class="p">(</span><span class="k">new</span> <span class="n">pcl</span><span class="o">::</span><span class="n">search</span><span class="o">::</span><span class="n">KdTree</span><span class="o"><</span><span class="n">pcl</span><span class="o">::</span><span class="n">PointXYZ</span><span class="o">></span> <span class="p">());</span>
|
|
<span class="n">vfh</span><span class="p">.</span><span class="n">setSearchMethod</span> <span class="p">(</span><span class="n">tree</span><span class="p">);</span>
|
|
|
|
<span class="c1">// Output datasets</span>
|
|
<span class="n">pcl</span><span class="o">::</span><span class="n">PointCloud</span><span class="o"><</span><span class="n">pcl</span><span class="o">::</span><span class="n">VFHSignature308</span><span class="o">>::</span><span class="n">Ptr</span> <span class="n">vfhs</span> <span class="p">(</span><span class="k">new</span> <span class="n">pcl</span><span class="o">::</span><span class="n">PointCloud</span><span class="o"><</span><span class="n">pcl</span><span class="o">::</span><span class="n">VFHSignature308</span><span class="o">></span> <span class="p">());</span>
|
|
|
|
<span class="c1">// Compute the features</span>
|
|
<span class="n">vfh</span><span class="p">.</span><span class="n">compute</span> <span class="p">(</span><span class="o">*</span><span class="n">vfhs</span><span class="p">);</span>
|
|
|
|
<span class="c1">// vfhs->size () should be of size 1*</span>
|
|
<span class="p">}</span>
|
|
</pre></div>
|
|
</td></tr></table></div>
|
|
</div>
|
|
<div class="section" id="visualizing-vfh-signatures">
|
|
<h1>Visualizing VFH signatures</h1>
|
|
<p><em>libpcl_visualization</em> contains a special <strong>PCLHistogramVisualization</strong> class,
|
|
which is also used by <strong>pcl_viewer</strong> to automatically display the VFH
|
|
descriptors as a histogram of float values. For more information, please see
|
|
<a class="reference external" href="http://www.pointclouds.org/documentation/overview/visualization.php">http://www.pointclouds.org/documentation/overview/visualization.php</a>.</p>
|
|
<img alt="_images/vfh_histogram_visualized.jpg" class="align-center" src="_images/vfh_histogram_visualized.jpg" />
|
|
<table class="docutils citation" frame="void" id="vfh" rules="none">
|
|
<colgroup><col class="label" /><col /></colgroup>
|
|
<tbody valign="top">
|
|
<tr><td class="label">[VFH]</td><td><em>(<a class="fn-backref" href="#id1">1</a>, <a class="fn-backref" href="#id2">2</a>)</em> <a class="reference external" href="http://www.willowgarage.com/sites/default/files/Rusu10IROS.pdf">http://www.willowgarage.com/sites/default/files/Rusu10IROS.pdf</a></td></tr>
|
|
</tbody>
|
|
</table>
|
|
<div class="admonition note">
|
|
<p class="first admonition-title">Note</p>
|
|
<p class="last">@InProceedings{Rusu10IROS,
|
|
author = {Radu Bogdan Rusu and Gary Bradski and Romain Thibaux and John Hsu},
|
|
title = {Fast 3D Recognition and Pose Using the Viewpoint Feature Histogram},
|
|
booktitle = {Proceedings of the 23rd IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
|
|
year = {2010},
|
|
address = {Taipei, Taiwan},
|
|
month = {October}
|
|
}</p>
|
|
</div>
|
|
</div>
|
|
|
|
|
|
</div>
|
|
|
|
</div>
|
|
<footer>
|
|
|
|
|
|
<hr/>
|
|
|
|
<div role="contentinfo">
|
|
<p>
|
|
© Copyright
|
|
|
|
</p>
|
|
</div>
|
|
Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>.
|
|
|
|
</footer>
|
|
|
|
</div>
|
|
</div>
|
|
|
|
</section>
|
|
|
|
</div>
|
|
|
|
|
|
|
|
<script type="text/javascript">
|
|
jQuery(function () {
|
|
SphinxRtdTheme.Navigation.enable(true);
|
|
});
|
|
</script>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
</body>
|
|
</html> |