Dongjiao He
Papers
12
Total Citations
465
H-Index
8
About
Dongjiao He is a robotics researcher whose work sits at the intersection of state estimation, simultaneous localization and mapping (SLAM), and autonomous aerial systems. He is best known for pioneering contributions to LiDAR-inertial and multi-sensor odometry frameworks, most notably Point-LIO (2023, 143 citations), which introduced a point-by-point processing paradigm enabling robust estimation of extremely aggressive robotic motions, and FAST-LIVO2 (2024, 106 citations), a tightly integrated LiDAR-inertial-visual odometry system designed for real-time SLAM applications. A unifying thread across his research is the rigorous mathematical treatment of Kalman filtering on differentiable manifolds, with several works formalizing symbolic and generic frameworks for error-state extended Kalman filters that spare researchers from repetitive case-by-case derivations. He has also advanced UAV platform design, contributing a self-rotating aerial vehicle with an extended sensor field of view (66 citations) and the novel HALO coaxial drone. His MARS-LVIG dataset further supports community benchmarking of LiDAR-visual-inertial-GNSS fusion. With over 450 cumulative citations across a concise body of work, He has established himself as a significant emerging voice in robot perception and navigation research.
Research Focus
Key Achievements
Top Papers
- 1Point‐LIO: Robust High‐Bandwidth Light Detection and Ranging Inertial Odometry143 citations · 2023
- 2FAST-LIVO2: Fast, Direct LiDAR–Inertial–Visual Odometry106 citations · 2024
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- 6Kalman Filters on Differentiable Manifolds27 citations · 2021
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- 8Embedding manifold structures into Kalman filters.18 citations · 2021
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- 10HALO: A Safe, Coaxial, and Dual-Ducted UAV Without Servo4 citations · 2023