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| teaching:gsoc2017 [2017/03/06 14:39] – [Topic 1: Multi-modal Cluttered Scene Analysis in Knowledge Intensive Scenarios] balintbe | teaching:gsoc2017 [2017/03/17 18:21] – [Topic 1: Multi-modal Cluttered Scene Analysis in Knowledge Intensive Scenarios] balintbe | ||
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| **Requirements: | **Requirements: | ||
| - | edge of CMake. Experience with PCL, OpenCV is prefered. | + | edge 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. | **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. | ||
Prof. Dr. hc. Michael Beetz PhD
Head of Institute
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Andrea Cowley
assistant to Prof. Beetz
ai-office@cs.uni-bremen.de
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