A Monocular LiDAR Fusion SLAM for Indoor Environment

Bingxin Zi; Haiying Wang; Jose Santos; Huiru Zheng

doi:10.1007/978-3-031-78857-4_11

A Monocular LiDAR Fusion SLAM for Indoor Environment

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

Abstract

The research on SLAM (simultaneous localization and mapping) has greatly progressed in recent years. However, research gaps remain because of the nature of different sensors in SLAM applications. This paper proposes a fusion SLAM method for the robot platform in the indoor environment, equipped with a LiDAR (light detecting and ranging) and a monocular camera. The proposed method extracts point, line, and plane features from monocular images and LiDAR scans and uses points and line features for pose estimation and optimization. The experiment result on the recently released challenge dataset showed that the proposed method would recover the scale for monocular SLAM by 32-scan LiDAR with almost no accuracy loss and that it was more robust than state-of-the-art algorithms.

Original language	English
Title of host publication	UK Workshop on Computational Intelligence (UKCI 2024)
Publisher	Springer Cham
Pages	129-139
Volume	1462
ISBN (Electronic)	978-3-031-78857-4
ISBN (Print)	978-3-031-78856-7
DOIs	https://doi.org/10.1007/978-3-031-78857-4_11
Publication status	Published (in print/issue) - 8 Jan 2025
Event	23rd Annual UK Workshop on Computational Intelligence 2024 - Ulster University, Belfast, Belfast, Northern Ireland Duration: 2 Sept 2024 → 4 Sept 2024 https://computing.ulster.ac.uk/ZhengLab/UKCI2024/

Workshop

Workshop	23rd Annual UK Workshop on Computational Intelligence 2024
Abbreviated title	UKCI 2024
Country/Territory	Northern Ireland
City	Belfast
Period	2/09/24 → 4/09/24
Internet address	https://computing.ulster.ac.uk/ZhengLab/UKCI2024/

Keywords

Fusion SLAM
LiDAR
Monocular Camera
Scale

Access to Document

10.1007/978-3-031-78857-4_11

Cite this

@inproceedings{936be3075a384491ad56b5ab45256bb7,

title = "A Monocular LiDAR Fusion SLAM for Indoor Environment",

abstract = "The research on SLAM (simultaneous localization and mapping) has greatly progressed in recent years. However, research gaps remain because of the nature of different sensors in SLAM applications. This paper proposes a fusion SLAM method for the robot platform in the indoor environment, equipped with a LiDAR (light detecting and ranging) and a monocular camera. The proposed method extracts point, line, and plane features from monocular images and LiDAR scans and uses points and line features for pose estimation and optimization. The experiment result on the recently released challenge dataset showed that the proposed method would recover the scale for monocular SLAM by 32-scan LiDAR with almost no accuracy loss and that it was more robust than state-of-the-art algorithms.",

keywords = "Fusion SLAM, LiDAR, Monocular Camera, Scale",

author = "Bingxin Zi and Haiying Wang and Jose Santos and Huiru Zheng",

year = "2025",

month = jan,

day = "8",

doi = "10.1007/978-3-031-78857-4\_11",

language = "English",

isbn = "978-3-031-78856-7",

volume = "1462",

pages = "129--139",

booktitle = "UK Workshop on Computational Intelligence (UKCI 2024)",

publisher = "Springer Cham",

note = "23rd Annual UK Workshop on Computational Intelligence 2024, UKCI 2024 ; Conference date: 02-09-2024 Through 04-09-2024",

url = "https://computing.ulster.ac.uk/ZhengLab/UKCI2024/",

}

TY - GEN

T1 - A Monocular LiDAR Fusion SLAM for Indoor Environment

AU - Zi, Bingxin

AU - Wang, Haiying

AU - Santos, Jose

AU - Zheng, Huiru

PY - 2025/1/8

Y1 - 2025/1/8

N2 - The research on SLAM (simultaneous localization and mapping) has greatly progressed in recent years. However, research gaps remain because of the nature of different sensors in SLAM applications. This paper proposes a fusion SLAM method for the robot platform in the indoor environment, equipped with a LiDAR (light detecting and ranging) and a monocular camera. The proposed method extracts point, line, and plane features from monocular images and LiDAR scans and uses points and line features for pose estimation and optimization. The experiment result on the recently released challenge dataset showed that the proposed method would recover the scale for monocular SLAM by 32-scan LiDAR with almost no accuracy loss and that it was more robust than state-of-the-art algorithms.

AB - The research on SLAM (simultaneous localization and mapping) has greatly progressed in recent years. However, research gaps remain because of the nature of different sensors in SLAM applications. This paper proposes a fusion SLAM method for the robot platform in the indoor environment, equipped with a LiDAR (light detecting and ranging) and a monocular camera. The proposed method extracts point, line, and plane features from monocular images and LiDAR scans and uses points and line features for pose estimation and optimization. The experiment result on the recently released challenge dataset showed that the proposed method would recover the scale for monocular SLAM by 32-scan LiDAR with almost no accuracy loss and that it was more robust than state-of-the-art algorithms.

KW - Fusion SLAM

KW - LiDAR

KW - Monocular Camera

KW - Scale

U2 - 10.1007/978-3-031-78857-4_11

DO - 10.1007/978-3-031-78857-4_11

M3 - Conference contribution

SN - 978-3-031-78856-7

VL - 1462

SP - 129

EP - 139

BT - UK Workshop on Computational Intelligence (UKCI 2024)

PB - Springer Cham

T2 - 23rd Annual UK Workshop on Computational Intelligence 2024

Y2 - 2 September 2024 through 4 September 2024

ER -

A Monocular LiDAR Fusion SLAM for Indoor Environment

Abstract

Workshop

Keywords

Access to Document

Fingerprint

Cite this