Haigen Min

China Mobile (China), Chang'an University

Papers

Total Citations

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About

Haigen Min is a researcher advancing the reliability of autonomous navigation through innovative visual perception and loop closure detection techniques. His work primarily focuses on solving the critical challenge of accurate, real-time localization for autonomous vehicles and mobile robots. A key contribution is the development of "LGD," a fast place recognition method that intelligently fuses local and global visual descriptors, achieving robust performance in complex environments. This work has garnered 9 citations since its 2024 publication. Min further addresses the persistent issue of measurement errors in autonomous driving with a modular, loosely coupled loop closure detection scheme, which enhances both efficiency and precision. Earlier foundational research includes a visual odometry method based on the trifocal tensor, designed to deliver high-precision positioning for autonomous robots using a single monocular camera. By tackling the fundamental problems of drift and misrecognition in visual SLAM systems, Min’s research provides practical, scalable solutions that are directly applicable to the next generation of self-driving cars and autonomous robots.