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diff --git a/‎tests/test_point_cloud_sampling.py‎
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@@ -0,0 +1,168 @@
+"""
+Point cloud sampling example codes. This code supports
+- Voxel point sampling
+- Farthest point sampling
+- Poisson disk sampling
+
+"""
+import matplotlib.pyplot as plt
+import numpy as np
+import numpy.typing as npt
+from collections import defaultdict
+
+do_plot = True
+
+
+def voxel_point_sampling(original_points: npt.NDArray, voxel_size: float):
+    """
+    Voxel Point Sampling function.
+    This function sample N-dimensional points with voxel grid.
+    Points in a same voxel grid will be merged by mean operation for sampling.
+
+    Parameters
+    ----------
+    original_points :  (M, N) N-dimensional points for sampling.
+                        The number of points is M.
+    voxel_size : voxel grid size
+
+    Returns
+    -------
+    sampled points (M', N)
+    """
+    voxel_dict = defaultdict(list)
+    for i in range(original_points.shape[0]):
+        xyz = original_points[i, :]
+        xyz_index = tuple(xyz // voxel_size)
+        voxel_dict[xyz_index].append(xyz)
+    points = np.vstack([np.mean(v, axis=0) for v in voxel_dict.values()])
+    return points
+
+
+def farthest_point_sampling(orig_points: npt.NDArray,
+                            n_points: int, seed: int):
+    """
+    Farthest point sampling function
+    This function sample N-dimensional points with the farthest point policy.
+
+    Parameters
+    ----------
+    orig_points :  (M, N) N-dimensional points for sampling.
+                    The number of points is M.
+    n_points : number of points for sampling
+    seed : random seed number
+
+    Returns
+    -------
+    sampled points (n_points, N)
+
+    """
+    rng = np.random.default_rng(seed)
+    n_orig_points = orig_points.shape[0]
+    first_point_id = rng.choice(range(n_orig_points))
+    min_distances = np.ones(n_orig_points) * float("inf")
+    selected_ids = [first_point_id]
+    while len(selected_ids) < n_points:
+        base_point = orig_points[selected_ids[-1], :]
+        distances = np.linalg.norm(orig_points[np.newaxis, :] - base_point,
+                                   axis=2).flatten()
+        min_distances = np.minimum(min_distances, distances)
+        distances_rank = np.argsort(-min_distances)  # Farthest order
+        for i in distances_rank:  # From the farthest point
+            if i not in selected_ids:  # if not selected yes, select it
+                selected_ids.append(i)
+                break
+    return orig_points[selected_ids, :]
+
+
+def poisson_disk_sampling(orig_points: npt.NDArray, n_points: int,
+                          min_distance: float, seed: int, MAX_ITER=1000):
+    """
+    Poisson disk sampling function
+    This function sample N-dimensional points randomly until the number of
+    points keeping minimum distance between selected points.
+
+    Parameters
+    ----------
+    orig_points :  (M, N) N-dimensional points for sampling.
+                    The number of points is M.
+    n_points : number of points for sampling
+    min_distance : minimum distance between selected points.
+    seed : random seed number
+    MAX_ITER : Maximum number of iteration. Default is 1000.
+
+    Returns
+    -------
+    sampled points (n_points or less, N)
+    """
+    rng = np.random.default_rng(seed)
+    selected_id = rng.choice(range(orig_points.shape[0]))
+    selected_ids = [selected_id]
+    loop = 0
+    while len(selected_ids) < n_points and loop <= MAX_ITER:
+        selected_id = rng.choice(range(orig_points.shape[0]))
+        base_point = orig_points[selected_id, :]
+        distances = np.linalg.norm(
+            orig_points[np.newaxis, selected_ids] - base_point,
+            axis=2).flatten()
+        if min(distances) >= min_distance:
+            selected_ids.append(selected_id)
+        loop += 1
+    if len(selected_ids) != n_points:
+        print("Could not find the specified number of points...")
+
+    return orig_points[selected_ids, :]
+
+
+def plot_sampled_points(original_points, sampled_points, method_name):
+    fig = plt.figure()
+    ax = fig.add_subplot(projection='3d')
+    ax.scatter(original_points[:, 0], original_points[:, 1],
+               original_points[:, 2], marker=".", label="Original points")
+    ax.scatter(sampled_points[:, 0], sampled_points[:, 1],
+               sampled_points[:, 2], marker="o",
+               label="Filtered points")
+    plt.legend()
+    plt.title(method_name)
+    plt.axis("equal")
+
+
+def main():
+    n_points = 1000
+    seed = 1234
+    rng = np.random.default_rng(seed)
+
+    x = rng.normal(0.0, 10.0, n_points)
+    y = rng.normal(0.0, 1.0, n_points)
+    z = rng.normal(0.0, 10.0, n_points)
+    original_points = np.vstack((x, y, z)).T
+    print(f"{original_points.shape=}")
+    print("Voxel point sampling")
+    voxel_size = 20.0
+    voxel_sampling_points = voxel_point_sampling(original_points, voxel_size)
+    print(f"{voxel_sampling_points.shape=}")
+
+    print("Farthest point sampling")
+    n_points = 20
+    farthest_sampling_points = farthest_point_sampling(original_points,
+                                                       n_points, seed)
+    print(f"{farthest_sampling_points.shape=}")
+
+    print("Poisson disk sampling")
+    n_points = 20
+    min_distance = 10.0
+    poisson_disk_points = poisson_disk_sampling(original_points, n_points,
+                                                min_distance, seed)
+    print(f"{poisson_disk_points.shape=}")
+
+    if do_plot:
+        plot_sampled_points(original_points, voxel_sampling_points,
+                            "Voxel point sampling")
+        plot_sampled_points(original_points, farthest_sampling_points,
+                            "Farthest point sampling")
+        plot_sampled_points(original_points, poisson_disk_points,
+                            "poisson disk sampling")
+        plt.show()
+
+
+if __name__ == '__main__':
+    main()
@@ -9,6 +9,7 @@ Mapping
    gaussian_grid_map/gaussian_grid_map
    ray_casting_grid_map/ray_casting_grid_map
    lidar_to_grid_map_tutorial/lidar_to_grid_map_tutorial
+   point_cloud_sampling/point_cloud_sampling
    k_means_object_clustering/k_means_object_clustering
    circle_fitting/circle_fitting
    rectangle_fitting/rectangle_fitting
 
@@ -0,0 +1,70 @@
+.. _point_cloud_sampling:
+
+Point cloud Sampling
+----------------------
+
+This sections explains point cloud sampling algorithms in PythonRobotics.
+
+Point clouds are two-dimensional and three-dimensional based data
+acquired by external sensors like LIDAR, cameras, etc.
+In general, Point Cloud data is very large in number of data.
+So, if you process all the data, computation time might become an issue.
+
+Point cloud sampling is a technique for solving this computational complexity
+issue by extracting only representative point data and thinning the point
+cloud data without compromising the performance of processing using the point
+cloud data.
+
+Voxel Point Sampling
+~~~~~~~~~~~~~~~~~~~~~~~~
+.. figure:: voxel_point_sampling.png
+
+Voxel grid sampling is a method of reducing point cloud data by using the
+`Voxel grids <https://en.wikipedia.org/wiki/Voxel>`_ which is regular grids
+in three-dimensional space.
+
+This method determines which each point is in a grid, and replaces the point
+clouds that are in the same Voxel with their average to reduce the number of
+points.
+
+API
+=====
+
+.. autofunction:: Mapping.point_cloud_sampling.point_cloud_sampling.voxel_point_sampling
+
+
+Farthest Point Sampling
+~~~~~~~~~~~~~~~~~~~~~~~~~
+.. figure:: farthest_point_sampling.png
+
+Farthest Point Sampling is a point cloud sampling method by a specified
+number of points so that the distance between points is as far from as
+possible.
+
+This method is useful for machine learning and other situations where
+you want to obtain a specified number of points from point cloud.
+
+API
+=====
+
+.. autofunction:: Mapping.point_cloud_sampling.point_cloud_sampling.farthest_point_sampling
+
+Poisson Disk Sampling
+~~~~~~~~~~~~~~~~~~~~~~~~~
+.. figure:: poisson_disk_sampling.png
+
+Poisson disk sample is a point cloud sampling method by a specified
+number of points so that the algorithm selects points where the distance
+from selected points is greater than a certain distance.
+
+Although this method does not have good performance comparing the Farthest
+distance sample where each point is distributed farther from each other,
+this is suitable for real-time processing because of its fast computation time.
+
+API
+=====
+
+.. autofunction:: Mapping.point_cloud_sampling.point_cloud_sampling.poisson_disk_sampling
+
+
+
@@ -0,0 +1,15 @@
+import conftest  # Add root path to sys.path
+from Mapping.point_cloud_sampling import point_cloud_sampling as m
+
+
+def test_1(capsys):
+    m.do_plot = False
+    m.main()
+    captured = capsys.readouterr()
+    assert "voxel_sampling_points.shape=(27, 3)" in captured.out
+    assert "farthest_sampling_points.shape=(20, 3)" in captured.out
+    assert "poisson_disk_points.shape=(20, 3)" in captured.out
+
+
+if __name__ == '__main__':
+    conftest.run_this_test(__file__)