Markus Suchi
Papers
8
Total Citations
133
H-Index
5
About
Markus Suchi is a leading researcher in robotic perception and human-robot interaction, with a focus on developing systems that enable robots to operate intelligently in real-world indoor environments. His work spans depth sensor evaluation, semantic scene understanding, and assistive robotics for older adults. Suchi’s most cited paper, “An Empirical Evaluation of Ten Depth Cameras” (81 citations), provides a critical benchmark for sensor performance under varied conditions, directly informing robot perception design. He has also pioneered tools for robotic vision, including the 3D-DAT annotation toolkit (11 citations) and EasyLabel (6 citations), which streamline the creation of high-quality RGB-D datasets essential for deep learning. Notably, Suchi’s user studies—such as “Setting Free a Service Robot for Older Adults at Home” (5 citations)—break new ground by deploying autonomous service robots in unscripted home settings, capturing genuine user experiences and preferences. His contributions to semantic web mining and situated robot perception (16 citations) further advance how robots interpret and navigate complex indoor spaces. Through open-source resources like the BURG-Toolkit, Suchi empowers the broader robotics community to benchmark and improve robotic grasping. His work has cumulatively garnered over 130 citations, reflecting its practical impact on both foundational sensing and applied assistive robotics.
Research Focus
Key Achievements
Top Papers
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- 33D-DAT: 3D-Dataset Annotation Toolkit for Robotic Vision11 citations · 2023
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