Mingyan Nie

Wuhan University, Hong Kong Polytechnic University

Papers

Total Citations

H-Index

About

Mingyan Nie is a robotics researcher specializing in multi-sensor fusion, LiDAR-based perception, and simultaneous localization and mapping (SLAM). Their work addresses critical challenges in autonomous navigation and mobile mapping, particularly in achieving robust and accurate sensor calibration and state estimation. Nie’s most-cited paper, “Automatic Extrinsic Calibration of Dual LiDARs With Adaptive Surface Normal Estimation” (2022, 15 citations), introduces a novel method for calibrating multiple LiDAR systems—a fundamental requirement for integrating point cloud data in complex environments. This contribution directly supports the reliability of autonomous systems in fields like robotics and surveying. More recently, Nie has advanced the field with “Degeneracy‐Resistant LiDAR‐SLAM Algorithm Based on Geometric and Visual Features' Fusion” (2025), which fuses geometric LiDAR data with visual features to overcome SLAM failures in challenging, feature-poor environments. By enhancing the robustness of LiDAR-SLAM against degeneracy, Nie’s work pushes toward more dependable robot autonomy. With a growing citation impact and a focus on practical, real-world solutions, Mingyan Nie is establishing themselves as a promising voice in the integration of perception and localization technologies.