Zhe Liu

National University of Defense Technology

Papers

Total Citations

H-Index

About

Zhe Liu is a robotics and computer vision researcher whose work centers on state estimation, autonomous navigation, and visual-inertial odometry (VIO) — the critical systems that enable robots and autonomous vehicles to perceive and navigate their environments with precision. His research pushes the boundaries of how machines understand motion and spatial context, even in challenging conditions. Liu's most recognized contribution, PLC-VIO (2021, 38 citations), introduced a tightly coupled monocular VIO system that cleverly combines point and line constraints to improve localization accuracy. By developing a novel line segment extraction and merging algorithm built on the EDLines method, he demonstrated that leveraging geometric structure beyond simple point features significantly enhances odometry robustness. This work has become a meaningful reference in the VIO community. Building on this foundation, Liu extended his research into the emerging domain of event cameras with ESVIO (2023, 22 citations), one of the first event-based stereo VIO systems. Event cameras offer extraordinary advantages in high-speed and low-light scenarios, and Liu's work positions him at the frontier of next-generation perception technology for autonomous robots. His growing citation record reflects both the timeliness and technical rigor of his contributions to intelligent robotic systems.