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k.koide 2024-05-04 13:06:54 +09:00
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@ -254,6 +254,7 @@ Example A : Perform registration with numpy arrays
```python
# Align two point clouds using various ICP-like algorithms.
#
# Parameters
# ----------
# target_points : NDArray[np.float64]
@ -272,6 +273,7 @@ Example A : Perform registration with numpy arrays
# Maximum distance for matching points between point clouds.
# num_threads : int = 1
# Number of threads to use for parallel processing.
#
# Returns
# -------
# RegistrationResult
@ -291,6 +293,7 @@ Example B : Perform preprocessing and registration separately
```python
# Preprocess point cloud (downsampling, kdtree construction, and normal/covariance estimation)
#
# Parameters
# ----------
# points : NDArray[np.float64]
@ -301,6 +304,7 @@ Example B : Perform preprocessing and registration separately
# Number of neighbor points to usefor point normal/covariance estimation.
# num_threads : int = 1
# Number of threads to use for parallel processing.
#
# Returns
# -------
# PointCloud
@ -316,29 +320,29 @@ target.points() # Nx4 numpy array [x, y, z, 1] x N
target.normals() # Nx4 numpy array [nx, ny, nz, 0] x N
target.covs() # Array of 4x4 covariance matrices
# Align two point clouds using various ICP-like algorithms.
# Parameters
# ----------
# target : PointCloud
# Target point cloud.
# source : PointCloud
# Source point cloud
# target_tree : PointCloud
# KdTree for the target point cloud (optional).
# init_T_target_source : np.ndarray[np.float64]
# 4x4 matrix representing the initial transformation from target to source.
# registration_type : str = 'GICP'
# Type of registration algorithm to use ('ICP', 'PLANE_ICP', 'GICP', 'VGICP').
# downsampling_resolution : float = 0.25
# Resolution for downsampling the point clouds.
# max_correspondence_distance : float = 1.0
# Maximum distance for matching points between point clouds.
# num_threads : int = 1
# Number of threads to use for parallel processing.
# Returns
# -------
# RegistrationResult
# Object containing the final transformation matrix and convergence status.
# Align two point clouds using specified ICP-like algorithms, utilizing point cloud and KD-tree inputs.
#
# Parameters
# ----------
# target : PointCloud::ConstPtr
# Pointer to the target point cloud.
# source : PointCloud::ConstPtr
# Pointer to the source point cloud.
# target_tree : KdTree<PointCloud>::ConstPtr, optional
# Pointer to the KD-tree of the target for nearest neighbor search. If nullptr, a new tree is built.
# init_T_target_source : NDArray[np.float64]
# 4x4 matrix representing the initial transformation from target to source.
# registration_type : str = 'GICP'
# Type of registration algorithm to use ('ICP', 'PLANE_ICP', 'GICP').
# max_correspondence_distance : float = 1.0
# Maximum distance for corresponding point pairs.
# num_threads : int = 1
# Number of threads to use for computation.
#
# Returns
# -------
# RegistrationResult
# Object containing the final transformation matrix and convergence status.
result = small_gicp.align(target, source, target_tree)
```