Tan Hua

University of Hong Kong, Shanghai Jiao Tong University

Papers

Total Citations

111

H-Index

About

Tan Hua is a leading researcher in the field of robotics and state estimation, with a primary focus on multi-sensor fusion for simultaneous localization and mapping (SLAM). His most significant contribution is the development of **FAST-LIVO2**, a groundbreaking fast and direct LiDAR-inertial-visual odometry framework. This work, which has already garnered **106 citations** since its 2024 publication, integrates IMU, LiDAR, and image data through an efficient error-state iterated Kalman filter, enabling highly accurate and robust real-time state estimation for autonomous systems. Hua’s research directly addresses critical challenges in SLAM, particularly the need for speed and reliability in dynamic environments. His more recent work, **InV2IWO** (2025), further advances the field by tackling observability degeneration in ground robots. By introducing invariant vanishing point-aided visual-inertial-wheel odometry, he provides a consistent solution to pose drift in structural environments. Through these innovations, Tan Hua is shaping the future of autonomous navigation, making his work essential reading for any student or researcher interested in the cutting edge of robotic perception and localization.