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teaching:gsoc2018 [2018/01/21 20:29] – balintbe | teaching:gsoc2018 [2018/01/22 09:27] – [RoboSherlock -- Framework for Cognitive Perception] ahaidu | ||
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RoboSherlock builds on top of the ROS ecosystem and is able to wrap almost any existing perception algorithm/ | RoboSherlock builds on top of the ROS ecosystem and is able to wrap almost any existing perception algorithm/ | ||
+ | ===== openEASE -- Web-based Robot Knowledge Service ===== | ||
+ | OpenEASE is a generic knowledge database for collecting and analyzing experiment data. Its foundation is the KnowRob knowledge processing system and ROS, enhanced by reasoning mechanisms and a web interface developed for inspecting comprehensive experiment logs. These logs can be recorded for example from complex CRAM plan executions, virtual reality experiments, | ||
+ | |||
+ | The OpenEASE web interface as well as further information and publication material can be accessed through its publicly available [[http:// | ||
===== Proposed Topics ===== | ===== Proposed Topics ===== | ||
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- | ==== Topic 1: Multi-modal Cluttered Scene Analysis in Knowledge Intensive Scenarios | + | ==== Topic 2: Felxible perception pipeline manipulation for RoboSherlock |
{{ : | {{ : | ||
- | **Main Objective: | + | **Main Objective: |
- | able robots | + | |
- | cult and challenging scenarios. To achieve this the participant will | + | |
- | develop annotators | + | |
- | object-hypotheses generation | + | |
- | essentially means to generate regions/ | + | |
- | 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 | + | |
- | 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 | + | |
- | + | ||
- | [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: | + | **Task Difficulty: |
| | ||
**Requirements: | **Requirements: | ||
- | **Expected Results: | + | **Expected Results: |
Contact: [[team/ | Contact: [[team/ | ||
Prof. Dr. hc. Michael Beetz PhD
Head of Institute
Contact via
Andrea Cowley
assistant to Prof. Beetz
ai-office@cs.uni-bremen.de
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