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Single Photon LiDAR Compression: An Overview

  • Abdullah H. Al-Shabili
  • , Hashan K. Weerasooriya
  • , Harshana Weligampola
  • , Prateek Chennuri
  • , Pamela Abshire
  • , Stanley Chan
  • Purdue University

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

Abstract

Real-time mapping of the surroundings plays a pivotal role in ensuring the success of autonomous vehicles. Among the technologies showing promise for depth mapping is Single Photon Light Detection and Ranging (SPL) which offers impressive long-range capabilities, superior depth resolution, and energy-efficient laser sources. However, the adoption of high-resolution, high frame-rate SPL systems results in significant data volumes, often reaching several Gbit/s. This creates a throughput bottleneck that impedes real-time applications and necessitates the development of effective compression schemes. This paper addresses the challenges associated with SPL compression and provides a comprehensive overview of recent advancements in this domain. The review encompasses various compression methods, including algorithmic and/or hardware-based approaches, spatial and/or temporal compression techniques. We also discuss future directions, such as integrating deep learning and fusion techniques with other sensing modalities.

Original languageEnglish
Title of host publication2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages952-956
Number of pages5
ISBN (Electronic)9798350302103
DOIs
StatePublished - 2023
Event2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023 - Tempe, United States
Duration: Aug 6 2023Aug 9 2023

Publication series

NameMidwest Symposium on Circuits and Systems
ISSN (Print)1548-3746

Conference

Conference2023 IEEE 66th International Midwest Symposium on Circuits and Systems, MWSCAS 2023
Country/TerritoryUnited States
CityTempe
Period08/6/2308/9/23

Keywords

  • compression
  • compressive learning
  • compressive sensing
  • deep learning
  • depth imaging
  • Single Photon LiDAR (SPL)

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