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<li class="toctree-l2 current"><a class="current reference internal" href="#">Point cloud Sampling</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="#voxel-point-sampling">Voxel Point Sampling</a><ul>
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<section id="point-cloud-sampling">
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<span id="id1"></span><h1>Point cloud Sampling<a class="headerlink" href="#point-cloud-sampling" title="Permalink to this headline"></a></h1>
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<p>This sections explains point cloud sampling algorithms in PythonRobotics.</p>
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<p>Point clouds are two-dimensional and three-dimensional based data
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acquired by external sensors like LIDAR, cameras, etc.
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In general, Point Cloud data is very large in number of data.
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So, if you process all the data, computation time might become an issue.</p>
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<p>Point cloud sampling is a technique for solving this computational complexity
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issue by extracting only representative point data and thinning the point
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cloud data without compromising the performance of processing using the point
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cloud data.</p>
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<section id="voxel-point-sampling">
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<h2>Voxel Point Sampling<a class="headerlink" href="#voxel-point-sampling" title="Permalink to this headline"></a></h2>
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<figure class="align-default">
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<img alt="../../../_images/voxel_point_sampling.png" src="../../../_images/voxel_point_sampling.png" />
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</figure>
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<p>Voxel grid sampling is a method of reducing point cloud data by using the
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<a class="reference external" href="https://en.wikipedia.org/wiki/Voxel">Voxel grids</a> which is regular grids
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in three-dimensional space.</p>
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<p>This method determines which each point is in a grid, and replaces the point
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clouds that are in the same Voxel with their average to reduce the number of
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points.</p>
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<section id="api">
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<h3>API<a class="headerlink" href="#api" title="Permalink to this headline"></a></h3>
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<dl class="py function">
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<dt class="sig sig-object py" id="Mapping.point_cloud_sampling.point_cloud_sampling.voxel_point_sampling">
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<span class="sig-prename descclassname"><span class="pre">Mapping.point_cloud_sampling.point_cloud_sampling.</span></span><span class="sig-name descname"><span class="pre">voxel_point_sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">original_points</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">numpy.dtype</span><span class="p"><span class="pre">[</span></span><span class="pre">numpy._typing._array_like._ScalarType_co</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">voxel_size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/Mapping/point_cloud_sampling/point_cloud_sampling.html#voxel_point_sampling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#Mapping.point_cloud_sampling.point_cloud_sampling.voxel_point_sampling" title="Permalink to this definition"></a></dt>
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<dd><p>Voxel Point Sampling function.
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This function sample N-dimensional points with voxel grid.
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Points in a same voxel grid will be merged by mean operation for sampling.</p>
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<dl class="field-list simple">
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||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
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||
<li><p><strong>original_points</strong> (<em>(</em><em>M</em><em>, </em><em>N</em><em>) </em><em>N-dimensional points for sampling.</em>) – The number of points is M.</p></li>
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<li><p><strong>voxel_size</strong> (<em>voxel grid size</em>) – </p></li>
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</ul>
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</dd>
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<dt class="field-even">Returns</dt>
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||
<dd class="field-even"><p></p>
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</dd>
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<dt class="field-odd">Return type</dt>
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<dd class="field-odd"><p>sampled points (M’, N)</p>
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</dd>
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</dl>
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</dd></dl>
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</section>
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</section>
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<section id="farthest-point-sampling">
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<h2>Farthest Point Sampling<a class="headerlink" href="#farthest-point-sampling" title="Permalink to this headline"></a></h2>
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||
<figure class="align-default">
|
||
<img alt="../../../_images/farthest_point_sampling.png" src="../../../_images/farthest_point_sampling.png" />
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||
</figure>
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<p>Farthest Point Sampling is a point cloud sampling method by a specified
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number of points so that the distance between points is as far from as
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possible.</p>
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<p>This method is useful for machine learning and other situations where
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you want to obtain a specified number of points from point cloud.</p>
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<section id="id2">
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<h3>API<a class="headerlink" href="#id2" title="Permalink to this headline"></a></h3>
|
||
<dl class="py function">
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<dt class="sig sig-object py" id="Mapping.point_cloud_sampling.point_cloud_sampling.farthest_point_sampling">
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||
<span class="sig-prename descclassname"><span class="pre">Mapping.point_cloud_sampling.point_cloud_sampling.</span></span><span class="sig-name descname"><span class="pre">farthest_point_sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">orig_points</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">numpy.dtype</span><span class="p"><span class="pre">[</span></span><span class="pre">numpy._typing._array_like._ScalarType_co</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_points</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/Mapping/point_cloud_sampling/point_cloud_sampling.html#farthest_point_sampling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#Mapping.point_cloud_sampling.point_cloud_sampling.farthest_point_sampling" title="Permalink to this definition"></a></dt>
|
||
<dd><p>Farthest point sampling function
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||
This function sample N-dimensional points with the farthest point policy.</p>
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||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>orig_points</strong> (<em>(</em><em>M</em><em>, </em><em>N</em><em>) </em><em>N-dimensional points for sampling.</em>) – The number of points is M.</p></li>
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||
<li><p><strong>n_points</strong> (<em>number of points for sampling</em>) – </p></li>
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<li><p><strong>seed</strong> (<em>random seed number</em>) – </p></li>
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</ul>
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</dd>
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<dt class="field-even">Returns</dt>
|
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<dd class="field-even"><p></p>
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</dd>
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<dt class="field-odd">Return type</dt>
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<dd class="field-odd"><p>sampled points (n_points, N)</p>
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</dd>
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</dl>
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</dd></dl>
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</section>
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</section>
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<section id="poisson-disk-sampling">
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<h2>Poisson Disk Sampling<a class="headerlink" href="#poisson-disk-sampling" title="Permalink to this headline"></a></h2>
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<figure class="align-default">
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||
<img alt="../../../_images/poisson_disk_sampling.png" src="../../../_images/poisson_disk_sampling.png" />
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</figure>
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<p>Poisson disk sample is a point cloud sampling method by a specified
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number of points so that the algorithm selects points where the distance
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from selected points is greater than a certain distance.</p>
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<p>Although this method does not have good performance comparing the Farthest
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distance sample where each point is distributed farther from each other,
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this is suitable for real-time processing because of its fast computation time.</p>
|
||
<section id="id3">
|
||
<h3>API<a class="headerlink" href="#id3" title="Permalink to this headline"></a></h3>
|
||
<dl class="py function">
|
||
<dt class="sig sig-object py" id="Mapping.point_cloud_sampling.point_cloud_sampling.poisson_disk_sampling">
|
||
<span class="sig-prename descclassname"><span class="pre">Mapping.point_cloud_sampling.point_cloud_sampling.</span></span><span class="sig-name descname"><span class="pre">poisson_disk_sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">orig_points</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">numpy.ndarray</span><span class="p"><span class="pre">[</span></span><span class="pre">Any</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">numpy.dtype</span><span class="p"><span class="pre">[</span></span><span class="pre">numpy._typing._array_like._ScalarType_co</span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_points</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_distance</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">seed</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">MAX_ITER</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/Mapping/point_cloud_sampling/point_cloud_sampling.html#poisson_disk_sampling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#Mapping.point_cloud_sampling.point_cloud_sampling.poisson_disk_sampling" title="Permalink to this definition"></a></dt>
|
||
<dd><p>Poisson disk sampling function
|
||
This function sample N-dimensional points randomly until the number of
|
||
points keeping minimum distance between selected points.</p>
|
||
<dl class="field-list simple">
|
||
<dt class="field-odd">Parameters</dt>
|
||
<dd class="field-odd"><ul class="simple">
|
||
<li><p><strong>orig_points</strong> (<em>(</em><em>M</em><em>, </em><em>N</em><em>) </em><em>N-dimensional points for sampling.</em>) – The number of points is M.</p></li>
|
||
<li><p><strong>n_points</strong> (<em>number of points for sampling</em>) – </p></li>
|
||
<li><p><strong>min_distance</strong> (<em>minimum distance between selected points.</em>) – </p></li>
|
||
<li><p><strong>seed</strong> (<em>random seed number</em>) – </p></li>
|
||
<li><p><strong>MAX_ITER</strong> (<em>Maximum number of iteration. Default is 1000.</em>) – </p></li>
|
||
</ul>
|
||
</dd>
|
||
<dt class="field-even">Returns</dt>
|
||
<dd class="field-even"><p></p>
|
||
</dd>
|
||
<dt class="field-odd">Return type</dt>
|
||
<dd class="field-odd"><p>sampled points (n_points or less, N)</p>
|
||
</dd>
|
||
</dl>
|
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</dd></dl>
|
||
|
||
</section>
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