Feature Detection Based on Virtual Gradient Using Sensor Fusion for Low-resolution 3D LiDAR

Shuncong Shen, Mai Saito, Toshio Ito

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

1 Citation (Scopus)

Abstract

LiDAR plays an indispensable role as recognition sensor for mobile devices, autonomous driving, etc. This paper aims to address Low-resolution LiDARs’ sparse data problem via fuse image feature point and pointcloud data.

Original languageEnglish
Title of host publication2021 IEEE CPMT Symposium Japan, ICSJ 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages41-44
Number of pages4
ISBN (Electronic)9781665440790
DOIs
Publication statusPublished - 2021
Event10th IEEE CPMT Symposium Japan, ICSJ 2021 - Kyoto, Japan
Duration: 2021 Nov 102021 Nov 12

Publication series

Name2021 IEEE CPMT Symposium Japan, ICSJ 2021

Conference

Conference10th IEEE CPMT Symposium Japan, ICSJ 2021
Country/TerritoryJapan
CityKyoto
Period21/11/1021/11/12

Keywords

  • 3D LiDAR
  • Automotive
  • Gradient
  • Sensor fusion

ASJC Scopus subject areas

  • Instrumentation
  • Atomic and Molecular Physics, and Optics
  • Computer Networks and Communications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

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