Eisuke Terada
Papers
2
Total Citations
12
H-Index
2
About
Eisuke Terada is a researcher specializing in autonomous mobile robotics, with a primary focus on perception, traversability analysis, and long-range environmental estimation for unstructured and urban environments. His work bridges the gap between near-range sensor data and far-range visual understanding, enabling robots to navigate complex terrains with greater foresight and reliability. In his most-cited paper, "Vision Based Far-Range Perception and Traversability Analysis using Predictive Probability of Terrain Classification" (2010, 8 citations), Terada introduced a novel method for building long-range polar maps with multiple radial resolutions using stereo camera data, combining geometrical and appearance-based information to predict terrain traversability. This work laid the groundwork for safer autonomous navigation in unknown settings. Building on this, his 2012 paper "Self-Supervised Online Long-Range Road Estimation in Complicated Urban Environments" (4 citations) advanced the field by employing laser scanner remission values and graph cut algorithms to robustly estimate road surfaces in cluttered urban scenes. Though his citation counts are modest, Terada’s contributions are notable for their practical, self-supervised approaches that reduce reliance on pre-labeled data, making his methods valuable for real-world robotic deployment. His research remains relevant for students and engineers working on field robotics and autonomous vehicle perception.
Research Focus
Key Achievements
Top Papers
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