Min Lu

National University of Defense Technology

Papers

Total Citations

H-Index

About

Min Lu is a researcher whose work lies at the intersection of 3D computer vision and mobile robotics, with a particular focus on spatial perception and localization. Her most cited paper, "Benchmark datasets for 3D computer vision" (2014, 25 citations), established critical evaluation frameworks that have supported the rapid growth of 3D sensing technologies across robotics, biometrics, remote sensing, and medical applications. This foundational contribution helped standardize performance comparisons in a field experiencing explosive development. In her subsequent work, "Global localization in 3D maps for structured environment" (2016), Lu developed an innovative method enabling mobile robots to determine their position within known indoor environments using geometric information extracted from point clouds. Her approach leverages Hough transform to detect line features from projection maps, creating a robust global localization system that operates without prior pose estimates. This research addresses fundamental challenges in autonomous navigation for structured environments, contributing to the practical deployment of mobile robots in real-world settings. Lu's work continues to influence researchers working at the intersection of 3D perception and autonomous systems.