Skip to main navigation Skip to search Skip to main content

Resolution Limit of Single-Photon LiDAR

  • Stanley H. Chan
  • , Hashan K. Weerasooriya
  • , Weijian Zhang
  • , Pamela Abshire
  • , Istvan Gyongy
  • , Robert K. Henderson
  • Purdue University
  • University of Edinburgh

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

10 Scopus citations

Abstract

Single-photon Light Detection and Ranging (LiDAR) systems are often equipped with an array of detectors for improved spatial resolution and sensing speed. However, given a fixed amount of flux produced by the laser transmitter across the scene, the per-pixel Signal-to-Noise Ratio (SNR) will decrease when more pixels are packed in a unit space. This presents a fundamental trade-off between the spatial resolution of the sensor array and the SNR received at each pixel. Theoretical characterization of this fundamental limit is explored. By deriving the photon arrival statistics and introducing a series of new approximation techniques, the Mean Squared Error (MSE) of the maximum-likelihood estimator of the time delay is derived. The theoretical predictions align well with simulations and real data.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024
PublisherIEEE Computer Society
Pages25307-25316
Number of pages10
ISBN (Electronic)9798350353006
ISBN (Print)9798350353006
DOIs
StatePublished - 2024
Event2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024 - Seattle, United States
Duration: Jun 16 2024Jun 22 2024

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Conference

Conference2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024
Country/TerritoryUnited States
CitySeattle
Period06/16/2406/22/24

Fingerprint

Dive into the research topics of 'Resolution Limit of Single-Photon LiDAR'. Together they form a unique fingerprint.

Cite this