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About

Ayush Dewan is a robotics and autonomous systems researcher whose work centers on 3D LiDAR perception, scene understanding, and robot navigation in dynamic environments. His research has made significant contributions to the field of autonomous driving and mobile robotics, with a particular focus on enabling robots to perceive and interpret complex, real-world scenarios. Dewan's most impactful work includes his 2016 paper on motion-based detection and tracking in 3D LiDAR scans (181 citations), which addressed the critical challenge of identifying moving objects for safe autonomous navigation. Complementing this, his method for estimating dense rigid scene flow in 3D LiDAR scans (113 citations) offered a novel energy-based formulation for understanding environmental dynamics. Together, these two contributions established him as a notable voice in LiDAR-based perception research. Beyond motion estimation, Dewan has explored deep learning approaches for semantic segmentation of LiDAR data, including temporally consistent segmentation and pointwise semantic classification. His earlier work also touched on multi-robot collaboration and visual SLAM-based exploration, reflecting a broad foundation in autonomous systems. With his most-cited papers accumulating nearly 300 citations combined, Dewan's research continues to inform modern developments in autonomous vehicles and intelligent robotic platforms.