On fast object detection using single-photon lidar data

Julián Tachella, Yoann Altmann, Stephen McLaughlin, Jean-Yves Tourneret

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Light detection and ranging (Lidar) systems based on single-photon detection can be used to obtain range and reflectivity information from 3D scenes with high range resolution. However, reconstructing the 3D surfaces from the raw single-photon waveforms is challenging, in particular when a limited number of photons is detected and when the ratio of spurious background detection events is large. This paper reviews a set of fast detection algorithms, which can be used to assess the presence of objects/surfaces in each waveform, allowing only the histograms where the imaged surfaces are present to be further processed. The original method we recently proposed is extended here using a multiscale approach to further reduce the computational complexity of the detection process. The proposed methods are compared to state-of-the-art 3D reconstruction methods using synthetic and real single-photon data and the results illustrate their benefits for fast and robust target detection.

Original languageEnglish
Title of host publicationWavelets and Sparsity XVIII
EditorsDimitri Van De Ville, Dimitri Van De Ville, Manos Papadakis, Yue M. Lu
PublisherSPIE
ISBN (Electronic)9781510629707
ISBN (Print)9781510629691
DOIs
Publication statusPublished - 9 Sep 2019
EventSPIE Optical Engineering + Applications 2019 - San Diego, United States
Duration: 11 Aug 201912 Aug 2019

Publication series

NameProceedings of SPIE
Volume11138
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceSPIE Optical Engineering + Applications 2019
CountryUnited States
CitySan Diego
Period11/08/1912/08/19

Keywords

  • Bayesian statistics
  • detection
  • inverse problems
  • Lidar
  • low-photon imaging and sensing

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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