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

Shuncong Shen; Mai Saito; Toshio Ito

doi:10.1109/ICSJ52620.2021.9648891

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 proceeding › Conference contribution

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 language	English
Title of host publication	2021 IEEE CPMT Symposium Japan, ICSJ 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	41-44
Number of pages	4
ISBN (Electronic)	9781665440790
DOIs	https://doi.org/10.1109/ICSJ52620.2021.9648891
Publication status	Published - 2021
Event	10th IEEE CPMT Symposium Japan, ICSJ 2021 - Kyoto, Japan Duration: 2021 Nov 10 → 2021 Nov 12

Publication series

Name	2021 IEEE CPMT Symposium Japan, ICSJ 2021

Conference

Conference	10th IEEE CPMT Symposium Japan, ICSJ 2021
Country/Territory	Japan
City	Kyoto
Period	21/11/10 → 21/11/12

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

Access to Document

10.1109/ICSJ52620.2021.9648891

Cite this

Feature Detection Based on Virtual Gradient Using Sensor Fusion for Low-resolution 3D LiDAR. / Shen, Shuncong; Saito, Mai; Ito, Toshio.

2021 IEEE CPMT Symposium Japan, ICSJ 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 41-44 (2021 IEEE CPMT Symposium Japan, ICSJ 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Shen, S, Saito, M & Ito, T 2021, Feature Detection Based on Virtual Gradient Using Sensor Fusion for Low-resolution 3D LiDAR. in 2021 IEEE CPMT Symposium Japan, ICSJ 2021. 2021 IEEE CPMT Symposium Japan, ICSJ 2021, Institute of Electrical and Electronics Engineers Inc., pp. 41-44, 10th IEEE CPMT Symposium Japan, ICSJ 2021, Kyoto, Japan, 21/11/10. https://doi.org/10.1109/ICSJ52620.2021.9648891

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

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

keywords = "3D LiDAR, Automotive, Gradient, Sensor fusion",

author = "Shuncong Shen and Mai Saito and Toshio Ito",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 10th IEEE CPMT Symposium Japan, ICSJ 2021 ; Conference date: 10-11-2021 Through 12-11-2021",

year = "2021",

doi = "10.1109/ICSJ52620.2021.9648891",

language = "English",

series = "2021 IEEE CPMT Symposium Japan, ICSJ 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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AU - Shen, Shuncong

AU - Saito, Mai

AU - Ito, Toshio

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

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KW - Automotive

KW - Gradient

KW - Sensor fusion

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M3 - Conference contribution

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T3 - 2021 IEEE CPMT Symposium Japan, ICSJ 2021

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BT - 2021 IEEE CPMT Symposium Japan, ICSJ 2021

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Y2 - 10 November 2021 through 12 November 2021

ER -

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

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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