ReviewFrontiers in robotics and AI2024
A review of visual SLAM for robotics: evolution, properties, and future applications.
Review in Frontiers in robotics and AI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Feature-PLPD-Aided Visual-Inertial Odometry for Low-Cost Embedded Systems.Sensors (Basel, Switzerland) · 2026Article
- ROIV-SLAM: Rotation-Optimized Inertial-Visual SLAM for a Non-Coaxial Two-Wheeled Robot Under Roll Disturbances.Sensors (Basel, Switzerland) · 2026Article
- Video-Based 3D Reconstruction: A Review of Photogrammetry and Visual SLAM Approaches.Journal of imaging · 2026Review
- Improving ORB-SLAM3 Accuracy in Dynamic Scenes with YOLO11 Segmentation.Sensors (Basel, Switzerland) · 2026Article
- The ADePT framework for assessing autonomous laboratory robotics.Communications chemistry · 2026Review
- Mobile Robot Navigation with Enhanced 2D Mapping and Multi-Sensor Fusion.Sensors (Basel, Switzerland) · 2025Article
- SLAM Algorithm for Mobile Robots Based on Improved LVI-SAM in Complex Environments.Sensors (Basel, Switzerland) · 2024Article
- Enhancing Underwater SLAM Navigation and Perception: A Comprehensive Review of Deep Learning Integration.Sensors (Basel, Switzerland) · 2024Review
- Visual-Inertial RGB-D SLAM with Encoder Integration of ORB Triangulation and Depth Measurement Uncertainties.Sensors (Basel, Switzerland) · 2024Article
- YPL-SLAM: A Simultaneous Localization and Mapping Algorithm for Point-line Fusion in Dynamic Environments.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Visual simultaneous localization and mapping (V-SLAM) plays a crucial role in the field of robotic systems, especially for interactive and collaborative mobile robots. The growing reliance on robotics has increased complexity in task execution in real-world applications. Consequently, several types of V-SLAM methods have been revealed to facilitate and streamline the functions of robots. This work aims to showcase the latest V-SLAM methodologies, offering clear selection criteria for researchers and developers to choose the right approach for their robotic applications. It chronologically presents the evolution of SLAM methods, highlighting key principles and providing comparative analyses between them. The paper focuses on the integration of the robotic ecosystem with a robot operating system (ROS) as Middleware, explores essential V-SLAM benchmark datasets, and presents demonstrative figures for each method's workflow.
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Registered trials
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.