Sheng Tao

Yanshan University, National University of Defense Technology

Papers

Total Citations

H-Index

About

Sheng Tao is a robotics and autonomous systems researcher whose work spans two complementary domains: simultaneous localization and mapping (SLAM) and bipedal robot locomotion. His most recognized contribution, "Dynamic SLAM: A Visual SLAM in Outdoor Dynamic Scenes" (2023), has garnered over 56 citations across its publications and addresses one of the field's most persistent challenges — the assumption of static environments in traditional SLAM algorithms. By extending SLAM capabilities to handle dynamic outdoor scenes, Tao's research directly advances the practical deployment of autonomous vehicles, augmented reality systems, and mobile robots operating in real-world conditions. Earlier in his career, Tao demonstrated a strong foundation in biomechanical robotics with his 2009 work introducing the Loss Balance Degree (LBD), a novel stability criterion for underactuated biped robots — machines that walk without conventional feet, presenting unique control challenges. This contribution provided the field with a more rigorous framework for evaluating balance in complex locomotion systems. Together, his body of work reflects a sustained commitment to bridging theoretical robotics with practical, real-world applications, making him a meaningful contributor to both robot perception and embodied robot control research.