RGB-D SLAM: A Review of Methods and Performance Trade-Offs for Different Requirements.

Yixin Yan, Jinling Li, Qiuyang Liu, Siqian Lv, Qing Wu

Abstract readReview

In one paragraph

Review 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.

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0citing papers in PubMed

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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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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Yixin YanThe Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentation of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, China.

Jinling LiThe Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentation of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, China.

Qiuyang LiuThe Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentation of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, China.

Siqian LvThe Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentation of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, China.

Qing WuThe Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentation of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, China.ORCID 0000-0002-8716-7342

Funding

the National Key Research and Development Program of China 2023YFF1206100

6 · The paper itself

Abstract

The RGB-D (Red, Green, Blue plus Depth) camera is a widely used onboard sensor for Simultaneous Localization and Mapping (SLAM), as it captures RGB images and depth information simultaneously. This capability has made RGB-D SLAM a topic of broad research interest. We present a comprehensive introduction to the framework of RGB-D SLAM and illustrate representative approaches in each component. Furthermore, we examine the conflicts among diverse performance metrics and discuss performance trade-offs in cutting-edge RGB-D SLAM from a requirement perspective, as achieving a SLAM system with perfect performance is challenging in the present landscape. This manuscript merges theory and practice, serving as a reference for the selection of RGB-D SLAM methods under various practical applications and offering a structured perspective for future development of RGB-D SLAM.

Indexed as

deep learningmulti-sensor fusionperformance trade-offRGB-D SLAM

Identifiers

PMID42281027

DOI10.3390/s26113513

PMCPMC13258843

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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.