| .. _object_recognition: | |
| Object recognition | |
| ================== | |
| Our object recognition comes from two different modalities, namely 3D based object recognition and | |
| 2D object detection and recognition. | |
| .. _3d_object_recognition_model: | |
| 3D object recognition models | |
| ---------------------------- | |
| `Our 3D object recognition node <https://github.com/b-it-bots/mas_industrial_robotics/blob/melodic/mir_perception/mir_object_recognition/ros/script/pc_object_recognizer_node>`_ | |
| uses segmented point clouds described in :ref:`3d_object_segmentation` as the input | |
| to the models. These segemented point clouds are published from | |
| `mir_object_recognition node <https://github.com/b-it-bots/mas_industrial_robotics/blob/melodic/mir_perception/mir_object_recognition/ros/src/multimodal_object_recognition_node.cpp>`_. | |
| The tutorial for training the model is described in :ref:`training`. | |
| We use two models for the 3D object recognition, namely: | |
| * Random forest with Radial density distribution and 3D modified Fisher vector | |
| (3DmFV) as features as described in `our paper <https://link.springer.com/chapter/10.1007/978-3-030-35699-6_48>`_. | |
| * `Dynamic Graph CNN <https://github.com/WangYueFt/dgcnn>`_: an end-to-end point | |
| cloud classification. However, in addition to points, we also incorporate colors as inputs. | |
| You can change the classifier in the launch file | |
| .. literalinclude:: ../../../mir_perception/mir_object_recognition/ros/launch/pc_object_recognition.launch | |
| :language: xml | |
| :lineno-start: 0 | |
| :linenos: | |
| Where: | |
| * `model`: whether it is CNN based (`cnn_based`) or traditional ML estimators (`feature_based`) | |
| * `model_id`: the actual name of the model, available model ids: | |
| * `cnn_based`: `dgcnn` | |
| * `feature_based`: `fvrdd` | |
| * `dataset`: the dataset name where the model was trained on | |
| .. _2d_object_recognition_model: | |
| 2D object recognition models | |
| ---------------------------- | |
| We use `squeezeDet <https://github.com/BichenWuUCB/squeezeDet>`_ for out 2D object detection model. | |
| This is lightweight, one-shot object detection and classification. | |
| The model can be changed in the `rgb_object_recognition.launch` | |
| .. literalinclude:: ../../../mir_perception/mir_object_recognition/ros/launch/rgb_object_recognition.launch | |
| :language: xml | |
| :lineno-start: 0 | |
| :linenos: | |
| Where: | |
| * `classifier`: the model used to detect and classify objects | |
| * `dataset`: the dataset used to train the model | |
| .. _mutlimodal_object_recognition: | |
| Multimodal object recognition | |
| ----------------------------- | |
| `multimodal_object_recognition_node <https://github.com/b-it-bots/mas_industrial_robotics/blob/melodic/mir_perception/mir_object_recognition/ros/src/multimodal_object_recognition_node.cpp>`_ | |
| coordinates the whole perception pipeline as described in the following items: | |
| * Subscribes to rgb and point cloud topics | |
| * Transforms point cloud to the target fram | |
| * Finds 3D object clusters from the point cloud using `mir_object_segementation` | |
| * Sends the 3D clusters to point cloud object recognizer (`pc_object_recognizer_node`) | |
| * Sends the image to rgb object detection and recognition node (`rgb_object_recognized_node`) | |
| * Waits until it gets results from both classifiers or if the timeout is reached | |
| * Posts processing of the recognized objects | |
| * Applies filters for the objects | |
| * Sends object_list to object_list_merger | |
| **Trigger multimodal_object_recognition** | |
| .. code-block:: bash | |
| rostopic pub /mir_perception/multimodal_object_recognition/event_in std_msgs/String e_start | |
| **Outputs** | |
| .. code-block:: bash | |
| /mcr_perception/object_detector/object_list | |
| /mir_perception/multimodal_object_recognition/output/workspace_height | |
| **Visualization outputs** | |
| .. code-block:: bash | |
| /mir_perception/multimodal_object_recognition/output/bounding_boxes | |
| /mir_perception/multimodal_object_recognition/output/debug_cloud_plane | |
| /mir_perception/multimodal_object_recognition/output/pc_labels | |
| /mir_perception/multimodal_object_recognition/output/pc_object_pose_array | |
| /mir_perception/multimodal_object_recognition/output/rgb_labels | |
| /mir_perception/multimodal_object_recognition/output/rgb_object_pose_array | |
| /mir_perception/multimodal_object_recognition/output/tabletop_cluster_pc | |
| /mir_perception/multimodal_object_recognition/output/tabletop_cluster_rgb |