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. 2021 Dec 22;22(1):39.
doi: 10.3390/s22010039.

MEMS-Scanner Testbench for High Field of View LiDAR Applications

Valentin Baier  1   2 , Michael Schardt  2 , Maximilian Fink  1 , Martin Jakobi  1 , Alexander W Koch  1
Affiliations

MEMS-Scanner Testbench for High Field of View LiDAR Applications

Valentin Baier et al. Sensors (Basel). .

Abstract

LiDAR sensors are a key technology for enabling safe autonomous cars. For highway applications, such systems must have a long range, and the covered field of view (FoV) of >45° must be scanned with resolutions higher than 0.1°. These specifications can be met by modern MEMS scanners, which are chosen for their robustness and scalability. For the automotive market, these sensors, and especially the scanners within, must be tested to the highest standards. We propose a novel measurement setup for characterizing and validating these kinds of scanners based on a position-sensitive detector (PSD) by imaging a deflected laser beam from a diffuser screen onto the PSD. A so-called ray trace shifting technique (RTST) was used to minimize manual calibration effort, to reduce external mounting errors, and to enable dynamical one-shot measurements of the scanner's steering angle over large FoVs. This paper describes the overall setup and the calibration method according to a standard camera calibration. We further show the setup's capabilities by validating it with a statically set rotating stage and a dynamically oscillating MEMS scanner. The setup was found to be capable of measuring LiDAR MEMS scanners with a maximum FoV of 47° dynamically, with an uncertainty of less than 1%.

Keywords: LiDAR; MEMS; scanning; testbench.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
General setup of the testbench: (a) emission path; (b) detection path.
Figure 2
Figure 2
Setup for calibrating the testbench with two translation stages: (a) emission path; (b) detection path.
Figure 3
Figure 3
(a) Calibrated position on the screen (red) and the set pattern (black); (b) Deviations of the set values and the screen calibration (black) and the resulting standard deviation (red).
Figure 4
Figure 4
Concept of the ray trace shifting technique (RTST).
Figure 5
Figure 5
(a) Measured horizontal laser spot positions on the screen vs. the Z-axis stage position for several relative set rotation angles; a linear regression for each depicted set angle is included; (b) measured angles vs. the set angle including linear regressions for both the vector and trigonometric method of calculation.
Figure 6
Figure 6
Picture of the main configuration of the setup to characterize a MEMS scanner. The overlay shows the used components in the laser part (a) and the detection part (b).
Figure 7
Figure 7
(a) Measured steering angles for three actuation amplitudes of 300, 400, and 500 a.u. calculated with the vector and the trigonometric approaches; (b) angular difference of the vector and trigonometric methods in comparison to the reference FoV measurement.
See this image and copyright information in PMC

References

    1. Schwarz B. Mapping the world in 3D. Nat. Photonics. 2010;2010:429–430. doi: 10.1038/nphoton.2010.148. - DOI
    1. Thakur R. Scanning LIDAR in Advanced Driver Assistance Systems and Beyond: Building a road map for next-generation LIDAR technology. IEEE Consum. Electron. Mag. 2016;5:48–54. doi: 10.1109/MCE.2016.2556878. - DOI
    1. Wang D., Watkins C., Xie H. MEMS Mirrors for LiDAR: A review. Micromachines. 2020;11:456. doi: 10.3390/mi11050456. - DOI - PMC - PubMed
    1. Roriz R., Cabral J., Gomes T. Automotive LiDAR Technology: A Survey. IEEE Trans. Intell. Transport. Syst. 2021:1–16. doi: 10.1109/TITS.2021.3086804. - DOI
    1. Bastos D., Monteiro P.P., Oliveira A.S.R., Drummond M.V. An Overview of LiDAR Requirements and Techniques for Autonomous Driving; Proceedings of the 2021 Telecoms Conference (ConfTELE); Leiria, Portugal. 11–12 February 2021; pp. 1–6.

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