Octree-Based Level Progressive Point Cloud Registration Framework

Kaiming Hou; Junyu Liang; Zheng Cai; Shiyue Liu

doi:10.23919/CCC58697.2023.10240242

Octree-Based Level Progressive Point Cloud Registration Framework

Kaiming Hou, Junyu Liang, Zheng Cai, Shiyue Liu

School of Automation

Beijing Institute of Technology

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

Abstract

Among many 2D and 3D SLAM algorithms, scan matching and map merging are extremely critical steps. Among these steps, the registration of point cloud plays the most important role, which directly affects the localization and mapping accuracy. Aiming at the conflict between accuracy and efficiency caused by the huge amount of data in dense point cloud registration, we propose a Level Progressive Registration framework based on Octree structure, which is used for the registration processing of dense point clouds, and has been verified by experiments. The aim of this work is to accelerate the process of dense point cloud registration and utilize the organizational role of the Octree in space to handle point cloud registration issues in a more robust manner.

Original language	English
Title of host publication	2023 42nd Chinese Control Conference, CCC 2023
Publisher	IEEE Computer Society
Pages	3708-3713
Number of pages	6
ISBN (Electronic)	9789887581543
DOIs	https://doi.org/10.23919/CCC58697.2023.10240242
Publication status	Published - 2023
Event	42nd Chinese Control Conference, CCC 2023 - Tianjin, China Duration: 24 Jul 2023 → 26 Jul 2023

Publication series

Name	Chinese Control Conference, CCC
Volume	2023-July
ISSN (Print)	1934-1768
ISSN (Electronic)	2161-2927

Conference

Conference	42nd Chinese Control Conference, CCC 2023
Country/Territory	China
City	Tianjin
Period	24/07/23 → 26/07/23

Keywords

Octree
Point Cloud Registration
SLAM

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10.23919/CCC58697.2023.10240242

Cite this

@inproceedings{c5173c37cfa44fb6a5227a1649a355d3,

title = "Octree-Based Level Progressive Point Cloud Registration Framework",

abstract = "Among many 2D and 3D SLAM algorithms, scan matching and map merging are extremely critical steps. Among these steps, the registration of point cloud plays the most important role, which directly affects the localization and mapping accuracy. Aiming at the conflict between accuracy and efficiency caused by the huge amount of data in dense point cloud registration, we propose a Level Progressive Registration framework based on Octree structure, which is used for the registration processing of dense point clouds, and has been verified by experiments. The aim of this work is to accelerate the process of dense point cloud registration and utilize the organizational role of the Octree in space to handle point cloud registration issues in a more robust manner.",

keywords = "Octree, Point Cloud Registration, SLAM",

author = "Kaiming Hou and Junyu Liang and Zheng Cai and Shiyue Liu",

note = "Publisher Copyright: {\textcopyright} 2023 Technical Committee on Control Theory, Chinese Association of Automation.; 42nd Chinese Control Conference, CCC 2023 ; Conference date: 24-07-2023 Through 26-07-2023",

year = "2023",

doi = "10.23919/CCC58697.2023.10240242",

language = "English",

series = "Chinese Control Conference, CCC",

publisher = "IEEE Computer Society",

pages = "3708--3713",

booktitle = "2023 42nd Chinese Control Conference, CCC 2023",

address = "United States",

}

TY - GEN

T1 - Octree-Based Level Progressive Point Cloud Registration Framework

AU - Hou, Kaiming

AU - Liang, Junyu

AU - Cai, Zheng

AU - Liu, Shiyue

PY - 2023

Y1 - 2023

N2 - Among many 2D and 3D SLAM algorithms, scan matching and map merging are extremely critical steps. Among these steps, the registration of point cloud plays the most important role, which directly affects the localization and mapping accuracy. Aiming at the conflict between accuracy and efficiency caused by the huge amount of data in dense point cloud registration, we propose a Level Progressive Registration framework based on Octree structure, which is used for the registration processing of dense point clouds, and has been verified by experiments. The aim of this work is to accelerate the process of dense point cloud registration and utilize the organizational role of the Octree in space to handle point cloud registration issues in a more robust manner.

AB - Among many 2D and 3D SLAM algorithms, scan matching and map merging are extremely critical steps. Among these steps, the registration of point cloud plays the most important role, which directly affects the localization and mapping accuracy. Aiming at the conflict between accuracy and efficiency caused by the huge amount of data in dense point cloud registration, we propose a Level Progressive Registration framework based on Octree structure, which is used for the registration processing of dense point clouds, and has been verified by experiments. The aim of this work is to accelerate the process of dense point cloud registration and utilize the organizational role of the Octree in space to handle point cloud registration issues in a more robust manner.

KW - Octree

KW - Point Cloud Registration

KW - SLAM

UR - http://www.scopus.com/inward/record.url?scp=85175563461&partnerID=8YFLogxK

U2 - 10.23919/CCC58697.2023.10240242

DO - 10.23919/CCC58697.2023.10240242

M3 - Conference contribution

AN - SCOPUS:85175563461

T3 - Chinese Control Conference, CCC

SP - 3708

EP - 3713

BT - 2023 42nd Chinese Control Conference, CCC 2023

PB - IEEE Computer Society

T2 - 42nd Chinese Control Conference, CCC 2023

Y2 - 24 July 2023 through 26 July 2023

ER -

Octree-Based Level Progressive Point Cloud Registration Framework

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