Material Recognition Using Time of Flight Lidar Surface Analysis

Daniel Tafone, Luke McEvoy, Yong Meng Sua, Patrick Rehain, Yuping Huang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

We are investigating a method for identifying materials from a distance, even when they are obscured, using a technique called Quantum Parametric Mode Sorting and single photons detection. By scanning a segment of the material, we are able to capture data on the relationships between the peak count of photons reflected at each position and the location of that reflection. This information allows us to measure the relative reflectance of the material and the texture of its surface, which enables us to achieve a material recognition accuracy of 99%, even maintaining 89.17% when materials are obscured by a lossy and multi-scattering obscurant that causes up to 15.2 round-trip optical depth.

Original languageEnglish
Title of host publicationQuantum Sensing, Imaging, and Precision Metrology
EditorsJacob Scheuer, Selim M. Shahriar
ISBN (Electronic)9781510659995
DOIs
StatePublished - 2023
EventQuantum Sensing, Imaging, and Precision Metrology 2023 - San Francisco, United States
Duration: 28 Jan 20232 Feb 2023

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12447
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceQuantum Sensing, Imaging, and Precision Metrology 2023
Country/TerritoryUnited States
CitySan Francisco
Period28/01/232/02/23

Keywords

  • Lidar
  • Machine Learning
  • Material Recognition

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