This package can segment object clusters on top of a table plane.

ROS API

table_object_cluster

The table_object_cluster node takes in sensor_msgs/PointCloud2 messages for the point map and the table plane and extracts objects clusters on top of the table. As output, bounding boxes for all the objects are provided.

Action Goal

table_object_cluster/goal (cob_3d_mapping_msgs/TableObjectClusterActionGoal)
  • contains the table plane and triggers cluster detection

Action Result

table_object_cluster/result (cob_3d_mapping_msgs/TableObjectClusterActionResult)
  • returns the bounding boxes for the object clusters

Action Feedback

table_object_cluster/feedback (cob_3d_mapping_msgs/TableObjectClusterActionFeedback)
  • empty

Subscribed Topics

/point_cloud (sensor_msgs/PointCloud2)
  • The input point cloud
/shape_array (cob_3d_mapping_msgs/ShapeArray)
  • The supporting plane(s)

Published Topics

/bb_marker (visualization_msgs/Marker)
  • Bounding boxes for object cluster
/cluster_array (cob_perception_msgs/PointCloud2Array)
  • The object clusters

Parameters

~height_min (double, default: -0.5)
  • lower boundary of cluster search area (relative to table plane, negative values for area on top of table)
~height_max (double, default: -0.03)
  • upper boundary of cluster search area (relative to table plane, negative values for area on top of table)
~min_cluster_size (double, default: 10)
  • minimum number of points for a valid cluster
~cluster_tolerance (double, default: 0.06)
  • threshold for separation of two clusters
~save (bool, default: false)
  • Flag for saving the results as PCD files (for debugging)
~file_path (string, default: /tmp)
  • File path for saving the results

Usage/Examples

Action mode and geometry map

Start the mapping pipeline in cob_3d_mapping_pipeline

roslaunch cob_3d_mapping_pipeline mapping.launch

Make sure that a table is in the field of view of the camera and trigger the mapping by

rosrun cob_3d_mapping_point_map trigger_mapping.py start

Check in rviz if the table is in the map (as a plane).
Start the semantic extraction of tables

roslaunch cob_3d_mapping_semantics extract_semantics.launch

Launch the cluster detection

roslaunch cob_table_object_cluster tabletop_object_cluster.launch

Run

rosrun cob_table_object_cluster table_object_cluster_action_client

This will extract object cluster on each table surface found. Bounding boxes for the cluster are published as visualization_msgs/Marker.

Topic mode

Run the segmentation from cob_3d_segmentation

roslaunch cob_3d_segmentation fast_segmentation.launch

Make sure to subscribe directly to the camera point cloud topic if you are not using registration. Launch the supporting plane extraction in cob_3d_mapping_semantics

roslaunch cob_3d_mapping_semantics extract_supporting_plane.launch

Launch the object cluster detection

roslaunch cob_table_object_cluster tabletop_object_cluster.launch

The object clusters are published as visualization_msgs/Marker for bounding boxes and cob_perception_msgs/PointCloud2Array.

Wiki: cob_table_object_cluster (last edited 2014-04-22 11:50:52 by GeorgArbeiter)