Shaoshu Su

Papers

Total Citations

H-Index

About

Shaoshu Su is an emerging researcher at the forefront of robot perception and autonomous navigation, with a particular focus on Simultaneous Localization and Mapping (SLAM) and data-driven approaches to robotic systems. His most recognized work, "iSLAM: Imperative SLAM" (2024), has already garnered 25 citations within its first year of publication — a strong indicator of its immediate impact on the robotics community. In this work, Su advances the integration of deep learning with classical SLAM pipelines, addressing the longstanding challenge of bridging front-end motion estimation with back-end drift correction through imperative learning frameworks. This contribution represents a meaningful step toward more robust, generalizable robot navigation systems that can operate reliably in complex, real-world environments. Su's research sits at a critical intersection of computer vision, machine learning, and robotics, areas that are rapidly shaping the future of autonomous vehicles, drones, and service robots. For students and researchers exploring next-generation perception systems, Su's work offers a compelling example of how principled algorithmic thinking combined with modern data-driven techniques can push the boundaries of what autonomous systems can achieve.