ROS package with launch files for pointcloud processing pipeline
The pc_filter node listens for raw pointclouds from the kinect and filters them using a passthrough filter. pc_scene_completion is constantly maintaining a list of the most recent filtered pointclouds published by pc_filter. pc_scene_completion also exposes an action server for scene completion, when hit by a client, pc_scene_completion takes the 5 most recent filtered clouds, concatenates them, segments them, and runs the individual segments through either pc_partial_mesh or shape_completion_server_v2 to generate meshes from the partial pointclouds. This list of meshes, their poses, and the partial_view pointcloud for each segment are all returned to the client.
This system has been tested on ubuntu 14.04 with ROS Indigo and ubuntu 16.04 with ROS Kinetic. Installation instructions can be found here:
https://github.com/CURG/pc_scene_completion_ws
Include the following in your launch file.
<launch>
<include file="$(find pc_pipeline_launch)/launch/pc_pipeline.launch">
<arg name="pc_filter/xpassthrough/filter_limit_min" value="0" />
<arg name="pc_filter/ypassthrough/filter_limit_min" value="0" />
<arg name="pc_filter/zpassthrough/filter_limit_min" value="0" />
<arg name="pc_filter/xpassthrough/filter_limit_max" value="1" />
<arg name="pc_filter/ypassthrough/filter_limit_max" value="1" />
<arg name="pc_filter/zpassthrough/filter_limit_max" value="1" />
<arg name="pc_filter/observed_frame_id" value="/camera_rgb_optical_frame" />
<arg name="pc_filter/filtered_frame_id" value="/ar_marker_8_filtered" />
<arg name="pc_filter/input_pc_topic" value="/camera/depth_registered/points" />
<arg name="pc_filter/output_pc_topic" value="/filtered_pc" />
<!--Set True if you want to run the pc_object_completion_partial which is simpler to debug-->
<!--Set False if you have shape completion cnn running on a machine with a GPU -->
<arg name="run_partial_mesh" value="True" />
</include>
</launch>
For example, my top level perception launch file looks like this:
<launch>
<include file="$(find freenect_launch)/launch/freenect.launch">
<arg name="depth_registration" value="true" />
<arg name="bond" value="true" />
<arg name="data_skip" value="5"/>
</include>
<include file="$(find pc_pipeline_launch)/launch/pc_pipeline.launch">
<arg name="pc_filter/xpassthrough/filter_limit_min" value="0" />
<arg name="pc_filter/ypassthrough/filter_limit_min" value="0" />
<arg name="pc_filter/zpassthrough/filter_limit_min" value="0.01" />
<arg name="pc_filter/xpassthrough/filter_limit_max" value="0.20" />
<arg name="pc_filter/ypassthrough/filter_limit_max" value="0.30" />
<arg name="pc_filter/zpassthrough/filter_limit_max" value="1" />
<arg name="pc_filter/observed_frame_id" value="/camera_rgb_optical_frame" />
<arg name="pc_filter/filtered_frame_id" value="/ar_marker_14_filtered" />
<arg name="pc_filter/input_pc_topic" value="/camera/depth_registered/points" />
<arg name="pc_filter/output_pc_topic" value="/filtered_pc" />
<arg name="run_partial_mesh" value="True" />
</include>
<include file="$(find ar_tracking)/launch/publish_transform.launch"/>
<include file="$(find ar_tracking)/launch/staubli_barrett_kinect360.launch"/>
<node pkg="filter_tf" type="filter_tf.py" name="filter_tf_marker8" args="/ar_marker_8 /camera_rgb_optical_frame"/>
<node pkg="filter_tf" type="filter_tf.py" name="filter_tf_marker14" args="/ar_marker_14 /camera_rgb_optical_frame"/>
</launch>
-
Verify the raw pointcloud is publishing in RVIZ:
-
Verify that the reference frame you want to filter the raw pointcloud in exists. Here we are going to filter our cloud in the frame of the AR marker tag using the package from https://github.com/ros-perception/ar_track_alvar. We also smooth the detected tf frame using https://github.com/CURG/filter_tf. The below image shows that these ar tags are being detected correctly.
-
Once the filtered frame is detected, we can verify that pc_filter is able to filter the raw pointcloud in the desired reference frame using the parameters from the launch file. In our example, the pointcloud is published in the frame of reference of the ar tag (/ar_marker_8_filtered), and the newly filtered pointcloud is published on the ROS topic /filtered_pc as shown below.
-
In order to get a set of meshes representing the scene, Use the action server provided by the pc_scene_completion package. You can easily try it out with the python client.
jvarley@skye:~$ ipython
Python 2.7.6 (default, Oct 26 2016, 20:30:19)
Type "copyright", "credits" or "license" for more information.
IPython 1.2.1 -- An enhanced Interactive Python.
? -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help -> Python's own help system.
object? -> Details about 'object', use 'object??' for extra details.
In [1]: import rospy
In [2]: import pc_scene_completion_client
In [3]: nh = rospy.init_node("scene_completion_client")
In [4]: result = pc_scene_completion_client.complete_scene()
[INFO] [WallTime: 1500928435.584586] waiting for scene_completion server...
[INFO] [WallTime: 1500928435.848621] scene_completion server started
[INFO] [WallTime: 1500928435.849404] about to send scene_completion goal
[INFO] [WallTime: 1500928435.849782] waiting for result
[INFO] [WallTime: 1500928436.118592] received result
In [5]: pc_scene_completion_client.ros_mesh_msg_to_plyfile(result.meshes[0], "/home/jvarley/out.ply")
In [6]: print result.poses[0]
header:
seq: 0
stamp:
secs: 0
nsecs: 0
frame_id: ''
pose:
position:
x: -0.17035150528
y: -0.00505908951163
z: 0.0
orientation:
x: 0.0
y: 0.0
z: 0.0
w: 1.0
- You can visualize the produced partial mesh with:
jvarley@skye:~$ meshlab out.ply
- Once the pc_pipeline is working for you, you can setup the pc_object_completion_cnn node to return meshes of completed objects rather than meshs of the partial view following: https://github.com/CURG/pc_object_completion_cnn
The pc_object_completion_cnn is a drop in replacement for the pc_object_completion_partial ROS package.