Evidence map›Paper›PMID 39744589›Full record

ArticleThe International journal of robotics research2025

YUTO MMS: A comprehensive SLAM dataset for urban mobile mapping with tilted LiDAR and panoramic camera integration.

Yiujia Zhang, SeyedMostafa Ahmadi, Jungwon Kang, Zahra Arjmandi, Gunho Sohn

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Article in The International journal of robotics research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yiujia ZhangDepartment of Earth and Space Science and Engineering, Lassonde School of Engineering, York University, Toronto, ON, Canada.
SeyedMostafa AhmadiDepartment of Earth and Space Science and Engineering, Lassonde School of Engineering, York University, Toronto, ON, Canada.
Jungwon KangDepartment of Earth and Space Science and Engineering, Lassonde School of Engineering, York University, Toronto, ON, Canada.
Zahra ArjmandiDepartment of Earth and Space Science and Engineering, Lassonde School of Engineering, York University, Toronto, ON, Canada.
Gunho SohnDepartment of Earth and Space Science and Engineering, Lassonde School of Engineering, York University, Toronto, ON, Canada.ORCID https://orcid.org/0000-0002-5127-8358

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The York University Teledyne Optech (YUTO) Mobile Mapping System (MMS) Dataset, encompassing four sequences totaling 20.1 km, was thoroughly assembled through two data collection expeditions on August 12, 2020, and June 21, 2019. Acquisitions were performed using a uniquely equipped vehicle, fortified with a panoramic camera, a tilted LiDAR, a Global Positioning System (GPS), and an Inertial Measurement Unit (IMU), journeying through two strategic locations: the York University Keele Campus in Toronto and the Teledyne Optech headquarters in City of Vaughan, Canada. This paper not only delineates the comprehensive overview of the YUTO MMS dataset, delving into aspects such as the collection procedure, sensor configuration, synchronization, data structure and format but also presents a robust benchmark of prevailing Simultaneous Localization and Mapping (SLAM) systems. By subjecting them to analysis utilizing the introduced dataset, this research lays a foundational baseline for ensuing studies, thereby contributing to advancements and enhancements in the SLAM-integrated mobile mapping system. The dataset can be downloaded from: https://ausmlab.github.io/yutomms/.

Indexed as

computer visionDatasetLiDARmobile mapping systemodometrypanoramic cameraroboticssimultaneous localization and mapping

Identifiers

PMID39744589
PMCPMC11685038

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LicenceCC BY-NC
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.