Sebastian Eger
Papers
2
Total Citations
10
H-Index
2
About
Sebastian Eger is a robotics researcher whose work bridges the critical gap between robot perception and human-robot interaction. His primary research areas include visual SLAM (Simultaneous Localization and Mapping) and human-like motion planning, with a focus on making autonomous systems both perceptually robust and socially intuitive. Eger’s most notable contribution is **HPF-SLAM**, an efficient visual SLAM system that innovatively leverages hybrid point features—combining hand-crafted and learnable features—to overcome the limitations of traditional feature-based methods. This work, published in 2024, has already garnered 7 citations, signaling its growing influence in the fields of robot perception and extended reality. Earlier, Eger investigated how robots can move in a more humanlike manner by studying similarity measures for locomotor trajectories, grounded in human perception of motion differences. His 2015 paper on this topic, with 3 citations, laid foundational insights for validating human-like motion in human-populated environments. Through these contributions, Eger is advancing both the technical accuracy of robotic vision and the social acceptability of robot movement, making him a promising voice in modern robotics research.
Research Focus
Key Achievements
Top Papers
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- 2