beluga_tools.conversion_utils module
- class beluga_tools.conversion_utils.DiscreteCell3D(x: int, y: int, z: int)
Bases:
object- x: int
- y: int
- z: int
- class beluga_tools.conversion_utils.NDTMap2D(resolution: float)
Bases:
object- add_distribution(cell: DiscreteCell2D, ndt: NormalDistribution)
Add a new cell with its distribution.
- classmethod from_hdf5(hdf5_file: Path)
Create an NDTMap instance from a path to a hdf5 file.
See ‘to_hdf5’ docstring for details on the hdf5 format.
- is_close(other: NDTMap2D, abs_tol: float = 1e-08) bool
Equality with tolerance between two NDT maps.
- plot(show: bool = False) matplotlib.pyplot.figure
Plot the map using contour plots into the current figure and returns it.
Optionally show the plot based on the ‘show’ parameter.
- to_hdf5(output_file_path: Path)
Serialize the NDT map into an HDF5 format.
See https://docs.hdfgroup.org/hdf5/develop/_intro_h_d_f5.html for details on the format. It’ll create 4 datasets:
“resolution”: () resolution for the discrete grid (cells are resolution x resolution m^2 squares).
“cells”: (NUM_CELLS, 2) that contains the cell coordinates.
“means”: (NUM_CELLS, 2) that contains the 2d mean of the normal distribution of the cell.
“covariances”: (NUM_CELLS, 2, 2) Contains the covariance for each cell.
- class beluga_tools.conversion_utils.NDTMap3D(resolution: float)
Bases:
object- add_distribution(cell: DiscreteCell3D, ndt: NormalDistribution)
Add a new cell with its distribution.
- classmethod from_hdf5(hdf5_file: Path)
Create an NDTMap3D instance from a path to a hdf5 file.
See ‘to_hdf5’ docstring for details on the hdf5 format.
- is_close(other: NDTMap3D, abs_tol: float = 1e-08) bool
Equality with tolerance between two NDT maps.
- to_hdf5(output_file_path: Path)
Serialize the NDT map into an HDF5 format.
See https://docs.hdfgroup.org/hdf5/develop/_intro_h_d_f5.html for details on the format. It’ll create 4 datasets:
“resolution”: () resolution for the discrete grid (cells are resolution x resolution x resolution m^3 cubes).
“cells”: (NUM_CELLS, 3) that contains the cell coordinates.
“means”: (NUM_CELLS, 3) that contains the 3d mean of the normal distribution of the cell.
“covariances”: (NUM_CELLS, 3, 3) Contains the covariance for each cell.
- class beluga_tools.conversion_utils.NormalDistribution(mean: numpy.ndarray, covariance: numpy.ndarray)
Bases:
objectWrapper around scipy’s multivariate normal distribution implementation.
Allows for equality checks and typing hints.
- covariance: numpy.ndarray
- is_close(other: NormalDistribution, abs_tol: float = 1e-08) bool
Element-wise equality with tolerance.
- mean: numpy.ndarray
- to_scipy()
Get its scipy representation.
- class beluga_tools.conversion_utils.OccupancyGrid(resolution: float, origin: numpy.ndarray, grid: numpy.ndarray)
Bases:
object- grid: numpy.ndarray
- static load_from_file(yaml_path: Path) OccupancyGrid
Create an grid from a yaml file describing a ROS style occupancy grid.
- origin: numpy.ndarray
- resolution: float
- beluga_tools.conversion_utils.fit_normal_distribution(points: numpy.ndarray, min_variance: float = 0.005) NormalDistribution | None
Fit a normal distribution to a set of 2D or 3D points.
A minimum variance in each dimension will be enforced to avoid singularities.
- beluga_tools.conversion_utils.grid_to_point_cloud(occupancy_grid: OccupancyGrid) numpy.ndarray
Convert an OccupancyGrid’s occupied cells to a 2D point cloud.
Uses the center of the cell for the conversion to reduce max error.