Xiao Huang

University of Arizona, Southern University of Science and Technology

Papers

Total Citations

H-Index

About

Xiao Huang is a robotics researcher whose work spans semantic perception, 3D mapping, and sensor fusion — areas critical to advancing autonomous and wearable robotic systems. His research addresses some of the most persistent challenges in robot vision and environmental understanding, particularly in scenarios involving difficult surfaces and complex spatial configurations. One of Huang's notable contributions is his investigation into polarization-based semantic perception for wearable robotics, recognizing that conventional vision systems struggle with specular surfaces such as water, transparent glass, and metallic materials. By moving beyond purely semantic approaches, his 2018 work opened new pathways for more robust environmental interpretation in real-world conditions, accumulating 19 citations in the field. More recently, Huang has advanced the state of 3D mapping technology through a coarse-to-fine hybrid system that integrates omnidirectional cameras with non-repetitive LiDAR sensors. His 2023 work introduces an automatic targetless co-calibration method that leverages the unique scanning properties of modern LiDAR systems, earning 18 citations and demonstrating practical impact for mobile mapping platforms. Collectively, Huang's research reflects a commitment to solving tangible perception and mapping challenges, making him a valuable contributor to the growing field of intelligent robotic systems.