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Improved sampling scheme for LiDAR in Lissajous scanning mode.

Junya Wang1, Gaofei Zhang2,3, Zheng You1,2,3

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

Microsystems & Nanoengineering
|June 20, 2022
PubMed
Summary

This study enhances Micro-Electro-Mechanical Systems (MEMS) light detection and ranging (LiDAR) imaging by improving the sampling scheme. The new method doubles point cloud resolution and density for MEMS LiDAR systems.

Keywords:
Electrical and electronic engineeringOptical sensors

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Area of Science:

  • Optoelectronics
  • Robotics
  • Sensor Technology

Background:

  • Micro-Electro-Mechanical Systems (MEMS) light detection and ranging (LiDAR) is crucial for vehicle environment sensing.
  • Key technologies for MEMS LiDAR imaging include beam scanning trajectory, sampling schemes, and gridding.

Purpose of the Study:

  • To improve the sampling scheme in Lissajous scanning mode for MEMS LiDAR.
  • To develop a general sampling scheme independent of specific beam scanning trajectories.
  • To enhance the point cloud imaging performance of MEMS LiDAR systems.

Main Methods:

  • Developed an improved sampling scheme for Lissajous scanning MEMS LiDAR.
  • Summarized rules of the Cartesian grid to create a trajectory-independent sampling scheme.
  • Validated through simulations and experimental results using a MEMS scanning mirror (MEMS-SM).

Main Results:

  • Achieved a denser Cartesian grid of point cloud data at the same scanning frequency.
  • Increased resolution by 2 times compared to existing sampling schemes.
  • Doubled the number of points per frame with identical hardware and scanning frequencies.

Conclusions:

  • The proposed sampling scheme significantly enhances MEMS LiDAR imaging performance.
  • The general sampling scheme offers flexibility across different MEMS scanning trajectories.
  • This advancement is vital for improving the overall effectiveness of MEMS LiDAR in sensing applications.