A monocular visual SLAM system augmented by lightweight deep local feature extractor using in-house and low-cost LIDAR-camera integrated device-Reference-Cited by-同舟云学术

A monocular visual SLAM system augmented by lightweight deep local feature extractor using in-house and low-cost LIDAR-camera integrated device

Published:2022-11-06 Issue:1 Volume:15 Page:1929-1946
ISSN:1753-8947
Container-title:International Journal of Digital Earth
language:en
Short-container-title:International Journal of Digital Earth

Author:

Li Jing¹,Shi Chenhui¹,Chen Jun²^ORCID,Wang Ruisheng³,Yang Zhiyuan¹,Zhang Fan⁴,Gong Jianhua¹

Affiliation:

1. National Engineering Research Center for Geoinformatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People’s Republic of China

2. National Geomatics Center of China, Beijing, People’s Republic of China

3. Department of Geomatics Engineering, University of Calgary, Calgary, Canada

4. Department of Urban Studies and Planning, Massachusetts Institute of Technology, Cambridge, MA, USA

Funder

National Key Research and Development Program of China

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,Computer Science Applications,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/17538947.2022.2138591

Reference28 articles.

1. Campos, Carlos, Richard Elvira, Juan J. Gómez Rodríguez, José M. M. Montiel, and Juan D. Tardós. 2020. “ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM.” arXiv:2007.11898.

2. MonoSLAM: Real-Time Single Camera SLAM

3. ImageNet: A large-scale hierarchical image database

4. SuperPoint: Self-Supervised Interest Point Detection and Description

5. D2-Net: A Trainable CNN for Joint Description and Detection of Local Features

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