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teaching:gsoc2018 [2018/01/17 17:48] – [Topic 1: Markov logic networks in Python] nygateaching:gsoc2018 [2018/01/21 20:29] – balintbe
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 package in the Python package index ([[https://pypi.python.org/pypi/pracmln|PyPI]]). package in the Python package index ([[https://pypi.python.org/pypi/pracmln|PyPI]]).
  
 +
 +===== RoboSherlock -- Framework for Cognitive Perception =====
 +
 +RoboSherlock is a common framework for cognitive perception, based on the principle of unstructured information management (UIM). UIM has proven itself to be a powerful paradigm for scaling intelligent information and question answering systems towards real-world complexity (i.e. the Watson system from IBM). Complexity in UIM is handled by identifying (or hypothesizing) pieces of
 +structured information in unstructured documents, by applying ensembles of experts for annotating information pieces, and by testing and integrating these isolated annotations into a comprehensive interpretation of the document.
 +
 +RoboSherlock builds on top of the ROS ecosystem and is able to wrap almost any existing perception algorithm/framework, and allows easy and coherent combination of the results of these. The framework has a close integration with two of the most popular libraries used in robotic perception, namely OpneCV and PCL. More details about RoboSherlock can be found on the project [[http://robosherlock.org/|webpage]].
 +
 +
 +===== Proposed Topics =====
 +
 +In the following, we list our proposals for the Google Summer of Code topics that contribute to the aforementioned open-source projects.
 ==== Topic 1: Markov logic networks in Python ==== ==== Topic 1: Markov logic networks in Python ====
  
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 **Contact:** [[team/daniel_nyga|Daniel Nyga]] **Contact:** [[team/daniel_nyga|Daniel Nyga]]
-===== Proposed Topics ===== 
  
-In the following, we list our proposals for the Google Summer of Code topics that contribute to the aforementioned open-source projects.+ 
 +==== 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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