Andreas Richtsfeld
Papers
9
Total Citations
223
H-Index
6
About
Andreas Richtsfeld is a leading researcher in robotic perception and computer vision, whose work has fundamentally advanced how robots understand and interact with cluttered, real-world environments. His primary research areas include object segmentation, attention-driven detection, and holistic scene understanding, with a particular focus on enabling robots to operate in complex domestic and indoor settings. Richtsfeld’s most impactful contribution is his framework for segmenting unknown objects in RGB-D images, detailed in his highly cited 2012 paper (156 citations), which directly addressed the critical challenge of clutter and occlusion in robotic grasping and manipulation tasks. He further pioneered the integration of attentional mechanisms with 2.5D symmetry for fast object detection in cluttered table scenes, as well as visual information abstraction for interactive robot learning. His work on combining plane estimation with shape detection for holistic scene understanding, and on coherent spatial abstraction for robotic visual attention, has been instrumental in moving beyond simple 2D cues to leverage 3D structure. With a publication record spanning from 2009 to 2014, Richtsfeld’s research has laid essential groundwork for cognitive robotics, providing the perceptual foundations that allow robots to learn from and safely navigate their surroundings.
Research Focus
Key Achievements
Top Papers
- 1Segmentation of unknown objects in indoor environments156 citations · 2012
- 2
- 3Visual information abstraction for interactive robot learning11 citations · 2011
- 4Incremental attention-driven object segmentation10 citations · 2014
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- 73D Shape Detection for Mobile Robot Learning5 citations · 2009
- 8
- 9Basic object shape detection and tracking using perceptual organization2 citations · 2009