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--- teaching:se-kiba:perception-assignment [2013/05/01 13:52] – [2. perception_group_msgs] balintbe
+++ teaching:se-kiba:perception-assignment [2016/05/19 09:19] (current) – external edit 127.0.0.1
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 ====== Homework: ROS packages for perception ======
-The aim is to create ros packages and set up a small pipeline for finding objects on top of a table and offer a service for returning the position and the volume of these. For the sake of simplicity we assume that these objects are clearly separated, meaning that they are spread around the table in an orderly manner, neiter  touching nor occluding each other.
+The aim is to create ros packages and set up a small pipeline for finding objects on top of a table offering a service for returning the position and the volume of these. For the sake of simplicity we assume that these objects are clearly separated, meaning that they are spread around the table in an orderly manner, neiter  touching nor occluding each other.
 {{ :teaching:se-kiba:ros-homework-perception.png?nolink&700 |}}
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 Create the three ROS packages and as a first step commit and push the empty packages to the repository.
+Download the bag file from this [[https://ai.uni-bremen.de/public/kiba_pointcloud.bag|link]].
 ====== Package Descriptions ======
 ===== 1. perception_pkg =====
-This package is the core of the pipeline. You need to implement three main parts here. First a subscriber to the topic where point clouds are being advertised, Then a processing module, where you work on the point cloud that you read in from the topic, and lastly the same node needs to offer a service for returning results.
+This package is the core of the pipeline. You need to implement three main parts here. First a subscriber to the topic where point clouds are being advertised, then a processing module, where you work on the point cloud that you read in from the topic, and lastly the same node needs to offer a service for returning results.
 ====Subscribe to the topic====
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 ====Processing the cloud====
+Point Clouds should be processed as they are read in, and a buffer of the results should be kept in memory at all time. The processing part of the package is to segment out objects lying on a table and separate each object in a separate cluster. After having done so, you are required to calculate the centroid of each cluster as well as the volume of it (note that all values in PCL are represented in meters.), and store these in a buffer. You will need to advertise a service, which, upon request, will always return the latest results.
+Notes on processing the input clouds:
+  * Start from the code presented in the tutorial ([[teaching:se-kiba:perception-tutorial|Perception Tutorial]])
+  * You are allowed to filter the cloud on the z axis only between 0 and 1.5 meters
+  * during plane fitting using RANSAC, make sure you find the correct plane.
+  * in order to find the objects on the table create a Convex Hull and use ExtractPolygonalPrism in order to find the points above the plane.(more on this [[http://pointclouds.org/documentation/tutorials/hull_2d.php#hull-2d|here]] and [[http://docs.pointclouds.org/trunk/classpcl_1_1_extract_polygonal_prism_data.html|here]])
+  * use Euclidean clustering with a distance threshold of 3 cm-s to find the point cluster of each object [[http://www.pointclouds.org/documentation/tutorials/cluster_extraction.php|link]]
 ====Service for advertising the result====
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 ===== 3. perception_client_pkg =====
+This package implements the client side of the pipeline. It is responsible for querying the service for the latest results, and for communicating them to the users. Besides serving as the bridge between users and processing, it is also the entry point for the pipeline, meaning that it needs to offer an easy interface for launching everything that is necessary for the pipeline (roscore, openni, service subscriber etc. )
+For questions and clarifications contact //balintbe **at** tzi **dot** de//