Haodong Jiang

Institute of Art, Chinese University of Hong Kong, Shenzhen

Papers

Total Citations

H-Index

About

Haodong Jiang is a robotics researcher whose work centers on state estimation, pose estimation, and sensor fusion for autonomous systems. His major contributions lie in advancing Ultra-Wideband (UWB) technology as a standalone solution for long-term drift correction, moving beyond its traditional role as a mere supplement to visual or inertial systems. His 2023 paper on efficient planar pose estimation via UWB has garnered 19 citations, demonstrating its impact on simplifying loop closure in autonomous navigation. Jiang has also made foundational theoretical contributions to filter-based estimation on matrix Lie groups. His 2022 work on closed-form error propagation for the Invariant Extended Kalman Filter (IEKF) on the \(SE_n(3)\) group, with 16 citations, provides a rigorous mathematical framework that enhances pose estimation accuracy in Visual-Inertial Navigation Systems (VINS). By bridging elegant Lie group theory with practical robotic perception, Jiang’s research offers both robust algorithms for real-world deployment and deeper insights into error dynamics, making his work essential reading for students and researchers tackling the challenges of autonomous state estimation.