Literature DB >> 33916764

Sensing with Polarized LIDAR in Degraded Visibility Conditions Due to Fog and Low Clouds.

Ayala Ronen1, Eyal Agassi1, Ofer Yaron2.   

Abstract

LIDAR (Light Detection and Ranging) sensors are one of the leading technologies that are widely considered for autonomous navigation. However, foggy and cloudy conditions might pose a serious problem for a wide adoption of their use. Polarization is a well-known mechanism often applied to improve sensors' performance in a dense atmosphere, but is still not commonly applied, to the best of our knowledge, in self-navigated devices. This article explores this issue, both theoretically and experimentally, and focuses on the dependence of the expected performance on the atmospheric interference type. We introduce a model which combines the well-known LIDAR equation with Stocks vectors and the Mueller matrix formulations in order to assess the magnitudes of the true target signal loss as well as the excess signal that arises from the scattering medium radiance, by considering the polarization state of the E-M (Electro-Magnetic) waves. Our analysis shows that using the polarization state may recover some of the poor performance of such systems for autonomous platforms in low visibility conditions, but it depends on the atmospheric medium type. This conclusion is supported by measurements held inside an aerosol chamber within a well-controlled and monitored artificial degraded visibility atmospheric environment. The presented analysis tool can be used for the optimization of design and trade-off analysis of LIDAR systems, which allow us to achieve the best performance for self-navigation in all weather conditions.

Entities:  

Keywords:  automatic control; computer vision; driver assistance; environment perception; optimization; robotics; sensors

Year:  2021        PMID: 33916764     DOI: 10.3390/s21072510

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  2 in total

1.  Water Cloud Detection with Circular Polarization Lidar: A Semianalytic Monte Carlo Simulation Approach.

Authors:  Wiqas Ahmad; Kai Zhang; Yicheng Tong; Da Xiao; Lingyun Wu; Dong Liu
Journal:  Sensors (Basel)       Date:  2022-02-21       Impact factor: 3.576

2.  A Probabilistic Approach to Estimating Allowed SNR Values for Automotive LiDARs in "Smart Cities" under Various External Influences.

Authors:  Roman Meshcheryakov; Andrey Iskhakov; Mark Mamchenko; Maria Romanova; Saygid Uvaysov; Yedilkhan Amirgaliyev; Konrad Gromaszek
Journal:  Sensors (Basel)       Date:  2022-01-13       Impact factor: 3.576

  2 in total

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