Evidence map›Paper›PMID 41600274›Full record

ArticleSensors (Basel, Switzerland)2026

Low-Cost Optical-Inertial Point Cloud Acquisition and Sketch System.

Tung-Chen Chao, Hsi-Fu Shih, Chuen-Lin Tien, Han-Yen Tu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 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
–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.

Tung-Chen ChaoDepartment of Mechanical Engineering, National Chung Hsing University, Taichung 40227, Taiwan.
Hsi-Fu ShihDepartment of Mechanical Engineering, National Chung Hsing University, Taichung 40227, Taiwan.ORCID 0000-0003-4013-8605
Chuen-Lin TienDepartment of Electrical Engineering, Feng Chia University, Taichung 40724, Taiwan.ORCID 0000-0002-5610-6010
Han-Yen TuDepartment of Electrical Engineering, Chinese Culture University, Taipei 11114, Taiwan.

Funding

National Science and Technology Council NSTC 111-2221-E-005-031-
6 · The paper itself

Abstract

This paper proposes an optical three-dimensional (3D) point cloud acquisition and sketching system, which is not limited by the measurement size, unlike traditional 3D object measurement techniques. The system employs an optical displacement sensor for surface displacement scanning and a six-axis inertial sensor (accelerometer and gyroscope) for spatial attitude perception. A microprocessor control unit (MCU) is responsible for acquiring, merging, and calculating data from the sensors, converting it into 3D point clouds. Butterworth filtering and Mahoney complementary filtering are used for sensor signal preprocessing and calculation, respectively. Furthermore, a human-machine interface is designed to visualize the point cloud and display the scanning path and measurement trajectory in real time. Compared to existing works in the literature, this system has a simpler hardware architecture, more efficient algorithms, and better operation, inspection, and observation features. The experimental results show that the maximum measurement error on 2D planes is 4.7% with a root mean square (RMS) error of 2.1%, corresponding to the reference length of 10.3 cm. For 3D objects, the maximum measurement error is 5.3% with the RMS error of 2.4%, corresponding to the reference length of 9.3 cm. Finally, it was verified that this system can also be applied to large-sized 3D objects for outlines.

Indexed as

accelerometerattitude anglegyroscopeinertial sensorMahoney complementary filteringoptical displacement sensorpoint cloudthree-dimensional (3D) measurement

Identifiers

PMID41600274
PMCPMC12845970

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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.