Lidar: a new self-driving vehicle for introducing optics to broader engineering and non-engineering audiences

Rablau, Corneliu

doi:10.1117/12.2523863

Cited by 53 publications

(28 citation statements)

References 3 publications

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“…Velodyne has been one of the first players in 3D automotive LiDAR [48]. They developed the first automotive 360 o mechanically rotating LiDAR for the second DARPA (Defense Advanced Research Projects Agency) grand challenge [49]. Similarly to US and RADAR, target range can be measured by pulsed signals and ToF, Eq.…”

Section: Radio Detection and Ranging (Radar)mentioning

confidence: 99%

“…In fact, 905 nm LiDAR systems can be built using off-the-shelf components (high power edge emitting pulsed diode lasers and detectors based on silicon photodiodes or photodiode arrays), therefore, reducing cost. This reduction in cost can be attributed to the compatibility with 905 nm CMOS detector arrays [49] and the implementation of optical phased array technology [60].…”

Section: A Wavelengthsmentioning

confidence: 99%

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A Review and Perspective on Optical Phased Array for Automotive LiDAR

Hsu

Solano-Rivas

et al. 2021

IEEE J. Select. Topics Quantum Electron.

176

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Section: Radio Detection and Ranging (Radar)mentioning

confidence: 99%

Section: A Wavelengthsmentioning

confidence: 99%

A Review and Perspective on Optical Phased Array for Automotive LiDAR

Hsu

Solano-Rivas

et al. 2021

IEEE J. Select. Topics Quantum Electron.

176

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“…For example, commercially available lidar systems designed for autonomous driving currently utilize high scanning rates and a large field-of-view, requiring high repetition rate lasers with moderate power and ∼200-m maximum distance. [44][45][46] In contrast, scanning linear-mode lidar in 3-D mapping remote sensing applications typically requires a higher power laser and operates at an altitude of ∼1000 to 5000 ft, 46 with operating ranges of ∼1 km or greater. In this section, we demonstrate the feasibility of the proposed architecture by presenting a notional implementation for a remote sensing application.…”

Section: Notional Systemmentioning

confidence: 99%

Hybrid passive polarimetric imager and lidar combination for material classification

et al. 2020

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We investigate the augmentation of active imaging with passive polarimetric imaging for material classification. Experiments are conducted to obtain a multimodal dataset of lidar reflectivity and polarimetric thermal self-emission measurements against a diverse set of material types. Using the assumption that active lidar imaging can provide high-resolution threedimensional spatial information, a known surface orientation is utilized to enable higher fidelity classification. Machine learning is applied to the dataset of monostatic lidar unidirectional reflectivity and passive longwave infrared degree of linear polarization features for material classification. The hybrid sensor technique can classify materials with 91.1% accuracy even with measurement noise resulting in a signal-to-noise ratio of only 6 dB. The application of the proposed technique is applicable for the classification of hidden objects or could assist existing spatial-based object classification. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…While the camera passively registers light, lidars emit laser light with wavelengths of typically 850, 905, 940 or 1550 nm (Hecht, 2018;Rablau, 2019); consequently, the camera detects its surrounding environment in terms of the time it takes for the light to return. Lidars can use rotating or stationary laser light, pulses, or continuous waves, but all lidars produce point clouds.…”

Section: Visionmentioning

confidence: 99%