Papers
4
Total Citations
40
H-Index
3
About
Zhuqing Zhang is a leading researcher in robotics and autonomous navigation, specializing in visual-inertial odometry (VIO), simultaneous localization and mapping (SLAM), and legged locomotion. His most impactful contribution is the development of FEJ-VIRO (First-Estimate Jacobian Visual-Inertial-Ranging Odometry), a landmark 2022 work with 21 citations that addresses a critical limitation in VIO systems—localization drift over long trajectories—by fusing ranging measurements with visual-inertial data using a consistent first-estimate Jacobian approach. Zhang also advanced robust SLAM through his 2019 work on variational Bayesian adaptive cubature Kalman filtering, which handles heavy-tailed noise in real-world environments. His 2023 research on fusing multiple isolated maps into VIO systems online provides a practical solution for large-scale mapping without requiring a globally consistent prior map. Beyond perception, Zhang has contributed to control systems for quadruped robots, developing a nonlinear MPC-based framework for precise foot placement on complex terrain. With a growing citation record and a focus on bridging theoretical consistency with practical robustness, Zhang’s work is shaping the next generation of autonomous mobile robots capable of long-term, reliable operation in challenging environments.
Research Focus
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Top Papers
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