Papers
5
Total Citations
129
H-Index
4
About
Hexiang Wei is an emerging robotics and autonomous systems researcher whose work centers on multi-sensor perception, simultaneous localization and mapping (SLAM), and sensor calibration for mobile robots. He is perhaps best known for spearheading the **FusionPortable** benchmark series — a rigorously designed, multi-sensor dataset enabling evaluation of localization and mapping algorithms across diverse robotic platforms and environments. The original FusionPortable paper (2022) has garnered 50 citations, while its expanded successor, FusionPortableV2 (2024), has already accumulated 33, underscoring the research community's sustained appetite for generalized, scalable SLAM benchmarks. Wei has also made notable contributions to sensor calibration, with his LCE-Calib framework (2023, 31 citations) offering a globally optimal solution for extrinsic calibration between LiDARs and event cameras — addressing a critical gap in robust perception under challenging lighting conditions. His TAIL dataset (2024) further extends his impact into terrain-aware SLAM for deformable granular environments, tackling real-world off-road locomotion challenges. Early work in quantum-inspired path planning hints at his broad optimization interests. Collectively, Wei's research equips the next generation of autonomous robots with the perceptual infrastructure needed to navigate complex, unpredictable worlds.
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