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teaching:gsoc2017 [2017/02/08 08:09] – [openEASE -- Experiment Knowledge Database] liscateaching:gsoc2017 [2017/02/08 08:10] – [Proposed Topics] lisca
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 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]]. 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: Multi-modal Cluttered Scene Analysis in Knowledge Intensive Scenarios ====
 +
 +{{  :teaching:gsoc:topic1_rs.png?nolink&200|}}
 +
 +**Main Objective:** The main objective of this topic is to enable robots in a human environment to recognize objects in difficult and challenging scenarios. To achieve this the participant will develop software components for RoboSherlock, called annotators, that are aided by background knowledge in order to detect objects. These scenarios include stacked,occluded objects placed on shelves, objects in drawers, refrigerators, dishwashers, 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).
 +
 +**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. Experience with PCL, OpenCV is prefered. Knowledge of Prolog is a plus.
 +
 +**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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