diff --git a/octreelib/grid/grid_base.py b/octreelib/grid/grid_base.py
index 606c60b..2f03349 100644
--- a/octreelib/grid/grid_base.py
+++ b/octreelib/grid/grid_base.py
@@ -157,15 +157,15 @@ def map_leaf_points(self, function: Callable[[PointCloud], PointCloud]):
     @abstractmethod
     def map_leaf_points_cuda_ransac(
         self,
-        n_poses_per_batch: int = 10,
-        threshold: int = 0.01,
-        n_iterations: int = 1024,
+        poses_per_batch: int = 1,
+        threshold: float = 0.01,
+        hypotheses_number: int = 1024,
     ):
         """
         transform point cloud in the node using the function
-        :param n_poses_per_batch: Number of poses per batch.
+        :param poses_per_batch: Number of poses per batch.
         :param threshold: Distance threshold.
-        :param n_iterations: Number of RANSAC iterations (<= 1024).
+        :param hypotheses_number: Number of RANSAC iterations (<= 1024).
         """
         pass
 
diff --git a/octreelib/octree/octree.py b/octreelib/octree/octree.py
index 3fb570c..d0266c6 100644
--- a/octreelib/octree/octree.py
+++ b/octreelib/octree/octree.py
@@ -269,9 +269,9 @@ def apply_mask(self, mask: np.ndarray):
         """
         start_index = 0
         for leaf in filter(lambda v: v.n_points != 0, self._cached_leaves):
-            n_points = leaf.n_points
-            leaf.apply_mask(mask[start_index : start_index + n_points])
-            start_index += n_points
+            points_number = leaf.n_points
+            leaf.apply_mask(mask[start_index : start_index + points_number])
+            start_index += points_number
 
     @property
     def n_points(self):
diff --git a/octreelib/ransac/util.py b/octreelib/ransac/util.py
index b2d9818..3d0e45a 100644
--- a/octreelib/ransac/util.py
+++ b/octreelib/ransac/util.py
@@ -45,32 +45,34 @@ def generate_random_int(rng_states, lower_bound, upper_bound):
 
 
 @cuda.jit(device=True, inline=True)
-def generate_random_indices(initial_point_indices, rng_states, block_size, n_points):
+def generate_random_indices(
+    initial_point_indices, rng_states, block_size, points_number
+):
     """
     Generate random points from the given block.
     :param initial_point_indices: Array to store the initial point indices.
     :param rng_states: Random number generator states.
     :param block_size: Size of the block.
-    :param n_points: Number of points to generate.
+    :param points_number: Number of points to generate.
     """
 
-    for i in range(n_points):
+    for i in range(points_number):
         initial_point_indices[i] = generate_random_int(rng_states, 0, block_size)
     return initial_point_indices
 
 
 @cuda.jit(device=True, inline=True)
 def generate_unique_random_indices(
-    initial_point_indices, rng_states, block_size, n_points
+    initial_point_indices, rng_states, block_size, points_number
 ):
     """
     Generate unique random points from the given block.
     :param initial_point_indices: Array to store the initial point indices.
     :param rng_states: Random number generator states.
     :param block_size: Size of the block.
-    :param n_points: Number of points to generate.
+    :param points_number: Number of points to generate.
     """
-    for ii in range(n_points):
+    for ii in range(points_number):
         initial_point_indices[ii] = generate_random_int(rng_states, 0, block_size)
         unique = False
         while not unique:
diff --git a/test/grid/test_cuda_ransac.py b/test/grid/test_cuda_ransac.py
index 51c1ba2..038e9fd 100644
--- a/test/grid/test_cuda_ransac.py
+++ b/test/grid/test_cuda_ransac.py
@@ -7,14 +7,14 @@
 @pytest.fixture()
 def generated_grid_with_planar_clouds():
     def generate_planar_cloud(
-        n_points, plane_coefficients, voxel_corner, edge_length, sigma
+        points_number, plane_coefficients, voxel_corner, edge_length, sigma
     ):
         voxel_points = (
-            np.random.rand(n_points, 3) * np.array([edge_length - 6 * sigma] * 3)
+            np.random.rand(points_number, 3) * np.array([edge_length - 6 * sigma] * 3)
             + voxel_corner
             + 3 * sigma
         )
-        noise = np.random.normal(0, sigma, (n_points,))
+        noise = np.random.normal(0, sigma, (points_number,))
         plane_points_z = (
             -plane_coefficients[0] * voxel_points[:, 0]
             - plane_coefficients[1] * voxel_points[:, 1]
@@ -23,7 +23,7 @@ def generate_planar_cloud(
         noisy_plane_points_z = plane_points_z + noise
         return np.column_stack((voxel_points[:, :2], noisy_plane_points_z))
 
-    n_points = 10
+    points_number = 10
     corner = np.array([0, 0, 0])
     edge_length = 5
     sigma = 0.1
@@ -33,7 +33,7 @@ def generate_planar_cloud(
     grid.insert_points(
         0,
         generate_planar_cloud(
-            n_points=n_points,
+            points_number=points_number,
             plane_coefficients=(1, 2, 3, 0.5),
             voxel_corner=corner,
             edge_length=edge_length,
@@ -43,7 +43,7 @@ def generate_planar_cloud(
     grid.insert_points(
         1,
         generate_planar_cloud(
-            n_points=n_points,
+            points_number=points_number,
             plane_coefficients=(-1, 2, 3, 0.5),
             voxel_corner=corner,
             edge_length=edge_length,