HUST team introduces ROI-focused optimization for MEMS LiDAR
image: Fig. 1 LiDAR System Scheme Based on MEMS Scanning Mirrors
Credit: Copyright © 2025 Shaotang Wei et al.
Background
Micro-electromechanical system scanning mirrors (MEMS-SM) are pivotal for converting optical signals into digital images, thanks to their compactness, fast scanning capability, and programmable flexibility. They've become key components in technologies like LiDAR, medical imaging, and projection displays. Currently, Lissajous scanning—using sinusoidal trajectories—has become the standard approach, balancing efficiency and image stability while remaining compatible with most MEMS hardware.
Research Advance
Led by Professor Junya Wang at Huazhong University of Science and Technology, the team has pushed the envelope by introducing a method inspired by human visual “gaze” behavior. Instead of hardware upgrades, their solution dynamically adjusts the MEMS scanning trajectory so that, within a fixed sampling budget, more attention is directed toward regions of interest (ROIs). This clever strategy effectively improves local image clarity without disrupting overall coverage and avoids the complexities and costs of redesigning the hardware.
Specifically, the approach fine-tunes the phase and amplitude of horizontal and vertical scanning signals. The result? The MEMS LiDAR mimics human-like focusing—densely sampling ROIs while still maintaining baseline scans elsewhere—striking a smart balance between zoomed-in precision and contextual visibility.
Future Outlook
Though the technique may be less suited to equally scanning every part of a scene, it shines in settings where ROIs are distinct, such as autonomous vehicle target tracking, medical imaging of key tissue zones, and focused security surveillance. The research team envisions integrating real-time visual analysis tools to enable on-the-fly ROI detection and adaptive scanning—so the MEMS LiDAR could instinctively focus on sudden obstacles or active biological features in complex and dynamic environments.
Team and Publication Info
The study was conducted by Prof. Junya Wang's group at HUST's School of Mechanical Science and Engineering. Prof. Wang specializes in MEMS imaging and LiDAR systems. The findings are published in Research (2025, DOI: 10.34133/research.0756).
Sources: https://spj.science.org/doi/10.34133/research.0756