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teaching:gsoc2018 [2018/01/21 20:28] – balintbeteaching:gsoc2018 [2018/01/21 20:29] – balintbe
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 **Contact:** [[team/daniel_nyga|Daniel Nyga]] **Contact:** [[team/daniel_nyga|Daniel Nyga]]
 +
 +
 +==== Topic 1: Multi-modal Cluttered Scene Analysis in Knowledge Intensive Scenarios ====
 +
 +{{  :teaching:gsoc:topic1_rs.png?nolink&200|}}
 +
 +**Main Objective:** In this topic we will develop algorithms that en-
 +able robots in a human environment to recognize objects in diffi-
 +cult and challenging scenarios. To achieve this the participant will
 +develop annotators for RoboSherlock that are particularly aimed at
 +object-hypotheses generation and merging. Generating a hypotheses
 +essentially means to generate regions/clusters in our raw data that
 +form a single object or object-part. In particular this entails the de-
 +velopment of segmentation algorithms for visually challenging scenes
 +or object properties, as the likes of transparent objects, or cluttered,
 +occluded scenes. The addressed scenarios include stacked, occluded
 +objects placed on shelves, objects in drawers, refrigerators, dishwash-
 +ers, cupboards etc. In typical scenarios, these confined spaces also
 +bare an underlying structure, which will be exploited, and used as
 +background knowledge, to aid perception (e.g. stacked plates would
 +show up as parallel lines using an edge detection). Specifically we
 +would start from (but not necessarly limit ourselves to) the implemen-
 +tation of two state-of-the-art algorithms described in recent papers:
 +
 +[1] Aleksandrs Ecins, Cornelia Fermuller and Yiannis Aloimonos, Cluttered Scene Segmentation Using the Symmetry Constraint, International Conference on Robotics and Automation(ICRA) 2016
 +[2] Richtsfeld A., M ̈
 +orwald T., Prankl J., Zillich M. and Vincze
 +M. - Segmentation of Unknown Objects in Indoor Environments.
 +IEEE/RSJ International Conference on Intelligent Robots and Sys-
 +tems (IROS), 2012.
 +
 +**Task Difficulty:** The task is considered to be challenging, as it is still a hot research topic where general solutions do not exist.
 +  
 +**Requirements:** Good programming skills in C++ and basic knowledge of CMake and ROS. Experience with PCL, OpenCV is prefered.
 +
 +**Expected Results:** Currently the RoboSherlock framework lacks good perception algorithms that can generate object-hypotheses in challenging scenarios(clutter and/or occlusion). The expected results are several software components based on recent advances in cluttered scene analysis that are able to successfully recognized objects in the scenarios mentioned in the objectives, or a subset of these.
 +
 +Contact: [[team/ferenc_balint-benczedi|Ferenc Bálint-Benczédi]]
 +
  
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