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Vision Gaze-Driven Micro-Electro-Mechanical Systems Light Detection and Ranging Optimization.

Shaotang Wei¹, Bo Gao¹, Junya Wang¹

¹School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.

Research (Washington, D.C.)

|June 24, 2025

Summary

This summary is machine-generated.

This study introduces a new programmable scanning method for micro-electro-mechanical systems (MEMS) light detection and ranging (LiDAR) to improve image quality. The technique enhances angular resolution and point cloud density in specific regions of interest (ROIs).

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Area of Science:

Optics and Photonics
Robotics and Automation
Computer Vision

Background:

Micro-electro-mechanical systems (MEMS) light detection and ranging (LiDAR) offer precise ranging and high-resolution imaging.
Conventional Lissajous scanning patterns in MEMS LiDAR face limitations in meeting advanced image quality demands.

Purpose of the Study:

To propose a novel programmable scanning method for MEMS LiDAR to enhance angular resolution in defined regions of interest (ROIs).
To establish an analytical link between scanning trajectory and ROI placement for precise resolution control.
To improve point cloud density and fidelity for 3D imaging applications.

Main Methods:

Utilizing parameter modulation techniques to link scanning trajectory with ROI placement.
Implementing a gaze-inspired trajectory modulation strategy.
Employing random modulation continuous wave ranging.
Developing a MEMS LiDAR prototype with programmable scanning capabilities.

Main Results:

Achieved localized point cloud density increases of 2 to 6 times (up to 650% improvement) within ROIs.
Reduced MEMS scanning mirror design complexity by 50% while maintaining high-resolution performance.
Demonstrated accurate 3D imaging within ROIs with a ranging accuracy of 2.4 cm (3σ).

Conclusions:

The proposed programmable scanning method significantly enhances angular resolution and point cloud fidelity in targeted ROIs for MEMS LiDAR.
This approach simplifies MEMS scanner design and extends the applicability of MEMS LiDAR to multitarget tracking and recognition.
A theoretical framework for ROI-based trajectory control is established, advancing next-generation high-resolution imaging systems.