A review of research on slam technology based on the fusion of lidar and vision
Open accessAbstract
In recent years, simultaneous localization and mapping with the fusion of LiDAR and vision fusion has gained extensive attention in the field of autonomous navigation and environment sensing. However, its limitations in feature-scarce (low-texture, repetitive structure) environmental scenarios and dynamic environments have prompted researchers to investigate the use of combining LiDAR with other sensors, particularly the effective fusion with vision sensors. This technique has proven to be highly effective in handling a variety of situations by fusing deep learning with adaptive algorithms. LiDAR excels in complex environments, with its ability to acquire high-precision 3D spatial information, especially when dealing with complex and dynamic environments with high reliability. This paper analyzes the research status, including the main research results and findings, of the early single-sensor SLAM technology and the current stage of LiDAR and vision fusion SLAM. Specific solutions for current problems (complexity of data fusion, computational burden and real-time performance, multi-scenario data processing, etc.) are examined by categorizing and summarizing the body of the extant literature and, at the same time, discussing the trends and limitations of the current research by categorizing and summarizing the existing literature, as well as looks forward to the future research directions, including multi-sensor fusion, optimization of algorithms, improvement of real-time performance, and expansion of application scenarios. This review aims to provide guidelines and insights for the development of SLAM technology for LiDAR and vision fusion, with a view to providing a reference for further SLAM technology research.
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Relying on traditional single-modal approaches such as RGB images can encounter limitations in complex scenarios such as occlusion, lighting variations, and multi-object ambiguities.
“Relying on traditional single-modal approaches (e.g., solely using RGB images) can encounter various limitations in complex scenarios, such as occlusion, lighting variations, and multi-object ambiguities [32,33,34].”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [33]
The Introduction (page unavailable) says low light and moving objects can impair visual SLAM, and §5.1 (page unavailable) identifies occlusion and lighting changes as problems for SLAM sensor data. The paper does not specifically identify multi-object ambiguity as a limitation of RGB-only approaches; its discussion is about visual SLAM and SLAM systems more broadly. (LLM, conf=0.85)
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Cites
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Research on Environment Perception System of Quadruped Robots Based on LiDAR and Vision
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Tightly-Coupled LiDAR-Visual SLAM Based on Geometric Features for Mobile Agents
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A Survey of Simultaneous Localization and Mapping with an Envision in 6G Wireless Networks
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Flagged first, then by citations
Research works printed but not linked: 1
- [23]Liang T. Xie H. Yu K. Xia Z. Lin Z. Wang Y. Wang B. Tang Z. BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework arXiv 2019 10.13140/RG.2.2.31757.20966/1 2205.13790 · DOI
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“…[27] 2 ✗ ✗ ✗ Alaba et al. [28] 1 ✗ ✗ ✗ Li et al. [29] 1 ✓ ✗ ✗ Wu et al. [30] 1 ✗ ✗ ✗ Bordes et al. [31] 1 ✓ ✓ ✗ Lin et al. [32] 1 ✓ ✗ ✗ Chen et al. [33] 1 ✗ ✗ ✗ Abdulmaksoud et al. [34] 2 Although recent years have seen several surveys on multimodal fusion and vision-language models, significant…”
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- Authors
- Chen, P., Zhao, X., Zeng, L., Liu, L., Liu, S., Sun, L., Li, Z., Chen, H., Liu, G., Qiao, Z., Qu, Y., Xu, D., Li, L., & Li, L.
- Affiliations
- Hainan Normal University (CN)
- College of Physics and Electronic Engineering, Hainan Normal University, Haikou 571158, China
- Corresponding author
- Li, Lin
- ORCID
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2025-02-27
- Volume
- 25
- Issue
- 5
- Pages
- 1447
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC11902412
- OpenAlex
- W4408000594
- Cited by
- 46
- References
- 98
- Citations by year
- 2026: 34 · 2025: 12
- FWCI
- 4.08
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Finance Science and Technology Project of Hainan province (GHYF2025030, ZDYF2025GXJS007, 123MS035, 62174046, 12164016, 62274048, 62464006, 202411658025, 202411658027, 202311658002X, S202411658031)
- Hainan Provincial Natural Science Foundation of China (GHYF2025030, ZDYF2025GXJS007, 123MS035, 62174046, 12164016, 62274048, 62464006, 202411658025, 202411658027, 202311658002X, S202411658031)
- National Natural Science Foundation of China (GHYF2025030, ZDYF2025GXJS007, 123MS035, 62174046, 12164016, 62274048, 62464006, 202411658025, 202411658027, 202311658002X, S202411658031)
- Open Fund for Innovation Training Program of College Students of Hainan Normal University (GHYF2025030, ZDYF2025GXJS007, 123MS035, 62174046, 12164016, 62274048, 62464006, 202411658025, 202411658027, 202311658002X, S202411658031)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- SLAM, LiDAR, multi-sensor fusion, autonomous navigation, dynamic environments, real-time performance, computational load, 3D sensing
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