Evidence map›Paper›PMID 42616891›Full record

ArticleScience advances2026

Large-scale outdoor 3D trajectory measurements enabled by drone-assisted multicamera tracking.

Jungyeon Kim, Sangmin Lim, Pius Soh, Jin-Tae Kim

Abstract read
In one paragraph

Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

4 authors.

Jungyeon KimDepartment of Mechanical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea.ORCID 0009-0002-3453-6834
Sangmin LimDepartment of Mechanical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea.ORCID 0009-0007-4068-0134
Pius SohDepartment of Mechanical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea.ORCID 0009-0001-0407-7406
Jin-Tae KimDepartment of Mechanical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea.ORCID 0000-0001-9933-1931

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate measurement of three-dimensional (3D) trajectories in outdoor environments is essential for studying complex dynamic processes such as atmospheric transport, aerial vehicle motion, and collective behavior. However, obtaining quantitative trajectory measurements over large outdoor volumes remains challenging because multicamera systems require reliable calibration and synchronization under field conditions. Here, we introduce a portable and automated drone-assisted multicamera 3D tracking (DAM3T) framework for georeferenced trajectory reconstruction. The system estimates multicamera extrinsic parameters from an autonomous drone flight without dedicated calibration markers and aligns the reconstructed coordinate system with geographic coordinates (WGS84) using real-time kinematic Global Positioning System (RTK-GPS) measurements. Validation under indoor motion-capture and outdoor RTK-GPS conditions demonstrates trajectory errors below 2%. We further demonstrate the framework through Lagrangian analysis using passive tracers, georeferenced tracking of multiple unmanned aerial vehicles, and 3D motion analysis in sports scenes. This establishes DAM3T as a scalable measurement platform that extends outdoor tracking from local-coordinate reconstruction to georeferenced measurement of complex dynamic systems.

Identifiers

PMID42616891
PMCPMC13488927

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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.