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Adding point cloud sampling examples (#770)
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gaussian_grid_map/gaussian_grid_map
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ray_casting_grid_map/ray_casting_grid_map
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lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial
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point_cloud_sampling/point_cloud_sampling
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k_means_object_clustering/k_means_object_clustering
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circle_fitting/circle_fitting
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rectangle_fitting/rectangle_fitting
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.. _point_cloud_sampling:
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Point cloud Sampling
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----------------------
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This sections explains point cloud sampling algorithms in PythonRobotics.
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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.
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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.
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Voxel Point Sampling
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~~~~~~~~~~~~~~~~~~~~~~~~
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.. figure:: voxel_point_sampling.png
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Voxel grid sampling is a method of reducing point cloud data by using the
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`Voxel grids <https://en.wikipedia.org/wiki/Voxel>`_ which is regular grids
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in three-dimensional space.
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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.
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API
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=====
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.. autofunction:: Mapping.point_cloud_sampling.point_cloud_sampling.voxel_point_sampling
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Farthest Point Sampling
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~~~~~~~~~~~~~~~~~~~~~~~~~
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.. figure:: farthest_point_sampling.png
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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.
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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.
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API
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=====
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.. autofunction:: Mapping.point_cloud_sampling.point_cloud_sampling.farthest_point_sampling
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Poisson Disk Sampling
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~~~~~~~~~~~~~~~~~~~~~~~~~
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.. figure:: poisson_disk_sampling.png
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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.
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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.
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API
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=====
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.. autofunction:: Mapping.point_cloud_sampling.point_cloud_sampling.poisson_disk_sampling
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