robokudo.descriptors.analysis_engines.tiago_demo
================================================

.. py:module:: robokudo.descriptors.analysis_engines.tiago_demo

.. autoapi-nested-parse::

   Analysis engine for TIAGo robot perception pipeline.

   This module provides an analysis engine that demonstrates perception capabilities
   using the TIAGo robot's camera system. It implements a query-based pipeline for
   tabletop segmentation and object pose estimation.

   The pipeline implements the following functionality:

   * Query-based perception control
   * TIAGo camera data processing
   * Point cloud analysis and segmentation
   * Object pose estimation using PCA
   * Query result generation and response

   .. note::
       This engine is specifically designed for the TIAGo robot platform and uses
       its camera configuration. The pipeline can be extended with additional
       perception capabilities as needed.



Classes
-------

.. autoapisummary::

   robokudo.descriptors.analysis_engines.tiago_demo.AnalysisEngine


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

.. py:class:: AnalysisEngine

   Bases: :py:obj:`robokudo.analysis_engine.AnalysisEngineInterface`


   Analysis engine for TIAGo robot perception.

   This class implements a pipeline that processes camera data from the TIAGo
   robot to perform tabletop segmentation and object pose estimation. It uses
   a query-based approach to control perception tasks.

   The pipeline includes:

   * Query handling for perception control
   * TIAGo camera data collection and preprocessing
   * Point cloud analysis and segmentation
   * Object pose estimation using PCA
   * Query result generation and response

   .. note::
       The pipeline uses PCA-based pose estimation by default, but can be
       configured to use bounding box-based estimation by uncommenting the
       relevant annotator.


   .. py:method:: name()

      Get the name of the analysis engine.

      :return: The name identifier of this analysis engine
      :rtype: str



   .. py:method:: implementation()

      Create a pipeline for TIAGo robot perception.

      This method constructs a processing pipeline that handles perception
      tasks for the TIAGo robot. The pipeline processes camera data to
      perform tabletop segmentation and object pose estimation.

      Pipeline execution sequence:

      1. Initialize pipeline
      2. Wait for query
      3. Read TIAGo camera data
      4. Preprocess image
      5. Crop point cloud
      6. Detect table plane
      7. Extract object clusters
      8. Estimate object poses (PCA)
      9. Generate and send query response

      :return: The configured pipeline for TIAGo perception
      :rtype: robokudo.pipeline.Pipeline