Evidence map›Paper›PMID 40695862›Full record

ArticleScientific reports2025

Dynamic SLAM algorithm adopt with eliminating mismatched point chains in grid motion statistics.

Yong He, Jiangtao Yu, Xiaochuan He

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Yong HeSchool of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha, 41000, China.
Jiangtao YuSchool of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha, 41000, China. 444356847@qq.com.
Xiaochuan HeSchool of Electrical and Information Engineering, Changsha University of Science & Technology, Changsha, 41000, China.

Funding

Changsha University of Science and Technology major school-enterprise cooperation fund 30404022264
6 · The paper itself

Abstract

Feature matching is an essential part in areas such as target tracking, and three-dimensional reconstruction. In case of rotational motion in the image, the rotating exercise core 8 statistical motion support volume is applied, resulting in low matching accuracy and long time to eliminate mismatching. A principal component analysis method is proposed to calculate rotation angle, feature points are changed in the grid and its neighborhood grid, which sets Gaussian threshold according to Euclid distance between neighborhood feature point and the matching feature point. And a new fractional statistical model is proposed to increase the number of correct matching pairs, So as to improve the fastness and accuracy of characteristic matching. Aiming at the problem of mismatch caused by local similarity of images, a data set is proposed to determine the data set by using geometric relationship between feature points, which analyzes the similarity between the data by the Person correlation coefficient, and sets the threshold to remove the feature matching pairs with low confidence, so as to improve the accuracy of feature matching. Experimental results show that the feature matching speed of the improved GMS algorithm is 3 times that of original GMS algorithm, and the false matching is eliminated in local similar region, which improves the quality of feature matching.

Indexed as

Eliminate mismatchesGMSIndoor navigationSearch and rescueUAV

Identifiers

PMID40695862
PMCPMC12283918

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.