Xiaoping Hong
Papers
7
Total Citations
190
H-Index
5
About
Xiaoping Hong is a robotics researcher whose work spans autonomous perception, 3D mapping, and sensor fusion — areas that sit at the heart of modern intelligent systems. Hong's most influential contribution is the development of low-cost, retina-like robotic LiDARs based on incommensurable scanning, a landmark 2021 paper that has garnered over 108 citations. This work directly addresses a critical bottleneck in autonomous vehicle technology: the prohibitive cost of high-performance mechanical LiDARs, offering a compelling alternative that democratizes access to precision sensing. Building on this foundation, Hong has made significant strides in 3D place recognition, proposing the Binary Triangle Combined (BTC) descriptor — a novel approach achieving full pose invariance across diverse environments, already accumulating 49 citations since its 2024 publication. His research further extends to camera-LiDAR calibration and coarse-to-fine hybrid 3D mapping systems integrating omnidirectional cameras with non-repetitive LiDAR, underscoring a consistent focus on robust, practical sensor fusion. Notably, Hong has also ventured into the unconventional domain of robot hearing, exploring optical channels to tackle the notoriously difficult cocktail party problem, reflecting a broad and creative research vision across robotic perception.
Research Focus
Key Achievements
Top Papers
- 1Low-Cost Retina-Like Robotic Lidars Based on Incommensurable Scanning108 citations · 2021
- 2BTC: A Binary and Triangle Combined Descriptor for 3-D Place Recognition49 citations · 2024
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- 5Low-cost Retina-like Robotic Lidars Based on Incommensurable Scanning5 citations · 2020
- 6Robot Hearing Through Optical Channel in a Cocktail Party Environment2 citations · 2022
- 7Robot Hearing Through Optical Channel in a Cocktail Party Environment2 citations · 2022