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teaching:gsoc2017 [2017/03/05 19:10]
ahaidu [Topic 3: ROS with PR2 integration in Unreal Engine]
teaching:gsoc2017 [2017/03/17 18:21]
balintbe [Topic 1: Multi-modal Cluttered Scene Analysis in Knowledge Intensive Scenarios]
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 would start from (but not necessarly limit ourselves to) the implemen- would start from (but not necessarly limit ourselves to) the implemen-
 tation of two state-of-the-art algorithms described in recent papers: tation of two state-of-the-art algorithms described in recent papers:
-[1] Ilya Lysenkov, Victor Eruhimov, and Gary BradskiRecognition + 
-and Pose Estimation of Rigid Transparent Objects with a Kinect Sen- +[1] Aleksandrs EcinsCornelia Fermuller ​and Yiannis AloimonosCluttered Scene Segmentation Using the Symmetry ConstraintInternational Conference on Robotics and Automation(ICRA2016
-sor2013 Robotics: Science ​and Systems Conference ​(RSS), 2013.+
 [2] Richtsfeld A., M ̈ [2] Richtsfeld A., M ̈
 orwald T., Prankl J., Zillich M. and Vincze orwald T., Prankl J., Zillich M. and Vincze
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 **Task Difficulty:​** The task is considered to be challenging,​ as it is still a hot research topic where general solutions do not exist. **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 knowl- +**Requirements:​** Good programming skills in C++ and basic knowledge ​of CMake and ROS. Experience with PCL, OpenCV is prefered.
-edge of CMake. 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. **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.