robokudo.annotators.cluster_pose_pca
====================================

.. py:module:: robokudo.annotators.cluster_pose_pca

.. autoapi-nested-parse::

   3D pose estimation using principal component analysis.

   This module provides an annotator for:

   * Calculating 3D poses for object hypotheses
   * Using PCA for orientation estimation
   * Computing oriented bounding boxes
   * Generating visualization markers

   The module uses:

   * Principal component analysis for axes
   * Eigenvalue decomposition
   * Coordinate frame alignment
   * Open3D visualization tools

   .. note::
      Requires at least 10 points per object hypothesis.



Classes
-------

.. autoapisummary::

   robokudo.annotators.cluster_pose_pca.ClusterPosePCAAnnotator


Module Contents
---------------

.. py:class:: ClusterPosePCAAnnotator(name='ClusterPosePCAAnnotator')

   Bases: :py:obj:`robokudo.annotators.core.BaseAnnotator`


   3D pose estimation using principal component analysis.

   This annotator:

   * Calculates 3D poses for object point clusters
   * Uses PCA for orientation estimation
   * Aligns coordinate frames with principal axes
   * Creates pose annotations
   * Generates visualization markers

   .. note::
      Requires minimum 10 points per object hypothesis.


   .. py:method:: update()

      Process object hypotheses and estimate poses.

      The method:

      * Loads point cloud from CAS
      * For each object hypothesis:
        * Computes centroid and covariance
        * Performs eigenvalue decomposition
        * Aligns coordinate frame with principal axes
        * Creates pose annotations
        * Creates visualization markers

      :return: SUCCESS after processing
      :rtype: py_trees.Status