Post-integration based point-line feature visual SLAM in low-texture environments.

Yanli Liu, Zhengyuan Feng, Heng Zhang, Wang Dong

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.

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

RGB-D SLAM: A Review of Methods and Performance Trade-Offs for Different Requirements.Sensors (Basel, Switzerland) · 2026
Review

4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yanli LiuSchool of Electronic Information, Shanghai Dianji University, Shanghai, 201306, China.

Zhengyuan FengSchool of Electronic Information, Shanghai Dianji University, Shanghai, 201306, China.

Heng ZhangSchool of Electronic Information, Shanghai Dianji University, Shanghai, 201306, China. zhangheng@sdju.edu.cn.

Wang DongSchool of Electronic Information, Shanghai Dianji University, Shanghai, 201306, China.

Funding

Humanities and Social Sciences of Ministryof Education Planning Fund 22YJAZH145National Natural Science Foundation of China 61963017Shanghai Educational Science Research Project C2022056Shanghai Science and Technology Development Foundation 23010501000

6 · The paper itself

Abstract

To address the issues of weak robustness and low accuracy of traditional SLAM data processing algorithms in weak texture environments such as low light and low contrast, this paper first studies and improves the data feature extraction method, optimizing the AGAST-based feature extraction algorithm to adaptively adjust the extraction threshold according to the gradient size of different data features. Meanwhile, a fusion-based incremental loop closure detection method is proposed, which integrates the similarity scores of multi-dimensional data features based on the Borda counting strategy, thereby enhancing the accuracy of loop closure detection. The performance of loop closure detection was evaluated on public datasets (such as KITTI sequences 00, 05, and 06), achieving an average AP value of 92.03%. The overall system performance was evaluated on the EuRoC dataset, with the results showing a root mean square error range from 0.0061 to 0.0281 m, demonstrating the excellent accuracy and robustness of the proposed method in large-scale data processing.

Indexed as

Feature extractionLoop closure detectionLow-texture featuresPoint-line fusion SLAM

Identifiers

PMID40287455

DOI10.1038/s41598-025-97250-6

PMCPMC12033347

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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.