Robust depth imaging in adverse scenarios using single-photon Lidar and beta-divergences

Q. Legros, S. McLaughlin, Y. Altmann, S. Meignen, Mike E. Davies

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

1 Citation (Scopus)

Abstract

This paper addresses the problem of robust estimation of range profiles from single-photon Lidar waveforms associated with single surfaces using a simple model. In contrast to existing methods explicitly modeling nuisance photon detection events, the observation model considered neglects such events and the depth parameters are instead estimated using a cost function which is robust to model mismatch. More precisely, the family of \beta-divergences is considered instead of the classical likelihood function. This reformulation allows the weights of the observations to be balanced depending on the amount of robustness required. The performance of our approach is assessed through a series of experiments using synthetic data under different observation scenarios. The obtained results demonstrate a significant improvement of the robustness of the estimation compared to state-of-The-Art pixelwise methods, for different background illumination and imaging scenarios.

Original languageEnglish
Title of host publication2020 Sensor Signal Processing for Defence Conference (SSPD)
PublisherIEEE
ISBN (Electronic)9781728138107
DOIs
Publication statusPublished - 30 Nov 2020
Event9th Sensor Signal Processing for Defence 2020: from Sensor to Decision -
Duration: 15 Sep 202016 Sep 2020

Conference

Conference9th Sensor Signal Processing for Defence 2020
Abbreviated titleSSPD 2020
Period15/09/2016/09/20

Keywords

  • 3D reconstruction
  • Robust estimation
  • Single-photon lidar

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing

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