A catkin workspace in ROS which uses DBSCAN to identify which points in a point cloud belong to the same object.
I apply a clustering technique called DBSCAN to identify which points in a point cloud belong to the same object.
This is the second perception exercise from Udacity's RoboND.
This builds upon my solution for the first perception exercise where I apply techniques to separate our objects of interest.
You can learn more about PCL here. You can learn more about DBSCAN in the following links:
$ rosdep install --from-paths src --ignore-src --rosdistro=kinetic -y
$ catkin_make
.bashrc
file:export GAZEBO_MODEL_PATH=~/catkin_ws/src/sensor_stick/models
source ~/catkin_ws/devel/setup.bash
$ roslaunch sensor_stick robot_spawn.launch
/src/sensor_stick/scripts/
folder in this repository$ python clustering.py
RViz
should run, select the /pcl_cluster
from the Topics dropdown