Evidence map›Paper›PMID 40648358›Full record

ArticleSensors (Basel, Switzerland)2025

Mobile Ground-Truth 3D Detection Environment for Agricultural Robot Field Testing.

Daniel Barrelmeyer, Stefan Stiene, Jannik Jose, Mario Porrmann

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. 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
–field-weighted citation impact
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

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.

Daniel BarrelmeyerFaculty of Engineering and Computer Science, University of Applied Sciences Osnabrück, 49076 Osnabrück, Germany.ORCID 0009-0007-5233-5979
Stefan StieneFaculty of Engineering and Computer Science, University of Applied Sciences Osnabrück, 49076 Osnabrück, Germany.ORCID 0000-0003-3291-6323
Jannik JoseFaculty of Engineering and Computer Science, University of Applied Sciences Osnabrück, 49076 Osnabrück, Germany.ORCID 0009-0008-0426-785X
Mario PorrmannInstitute of Computer Science, Osnabrück University, 49090 Osnabrück, Germany.ORCID 0000-0003-1005-5753

Funding

Federal Ministry of Food and Agriculture 28DZI04A23
6 · The paper itself

Abstract

Safety and performance validation of autonomous agricultural robots is critically dependent on realistic, mobile test environments that provide high-fidelity ground truth. Existing infrastructures focus on either component-level sensor evaluation in fixed setups or system-level black-box testing under constrained conditions, lacking true mobility, multi-object capability and tracking or detecting objects in multiple Degrees Of Freedom (DOFs) in unstructured fields. In this paper, we present a sensor station network designed to overcome these limitations. Our mobile testbed consists of self-powered stations, each equipped with a high-resolution 3D-Light Detection And Ranging (LiDAR) sensor, dual-antenna Global Navigation Satellite System (GNSS) receivers and on-board edge computers. By synchronising over GNSS time and calibrating rigid LiDAR-to-LiDAR transformations, we fuse point clouds from multiple stations into a coherent geometric representation of a real agricultural environment, which we sample at up to 20 Hz. We demonstrate the performance of the system in field experiments with an autonomous robot traversing a 26,000 m

Indexed as

3D-LiDARagricultural roboticsautonomous field testinggeofencingGNSS time synchronisationground truthmulti-sensor fusion

Identifiers

PMID40648358
PMCPMC12251652

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.