Max Robotics — Global Service Robot Directory + US Certification

About

Xinlian Liang is a leading researcher at the intersection of robotics, autonomous driving, and environmental sensing, whose work is pioneering the use of LiDAR and deep learning for 3D mapping and forest monitoring. His most impactful contribution, the 2020 paper on under-canopy UAV laser scanning (182 citations), bridges robotic surveying and forestry, enabling accurate field measurements in challenging environments—a breakthrough for automated ecological data collection. In autonomous driving, Liang has advanced LiDAR odometry with CAE-LO (21 citations), introducing a fully unsupervised convolutional auto-encoder for robust interest point detection and feature description, enhancing navigation without manual labels. His recent work, 3D-SeqMOS (8 citations, 2024), tackles a critical SLAM challenge by segmenting moving objects in sequential 3D data, reducing drift errors for safer autonomous systems. By fusing unsupervised learning with geometric sensing, Liang’s research not only improves robot perception but also supports labor-intensive surveying tasks, demonstrating a unique ability to solve real-world problems from forest floors to city streets. His work is shaping the future of intelligent, autonomous environmental and vehicular systems.