Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape
arXiv:2108.08325 · doi:10.55417/fr.2022032
Abstract
Motivated by the tremendous progress we witnessed in recent years, this paper presents a survey of the scientific literature on the topic of Collaborative Simultaneous Localization and Mapping (C-SLAM), also known as multi-robot SLAM. With fleets of self-driving cars on the horizon and the rise of multi-robot systems in industrial applications, we believe that Collaborative SLAM will soon become a cornerstone of future robotic applications. In this survey, we introduce the basic concepts of C-SLAM and present a thorough literature review. We also outline the major challenges and limitations of C-SLAM in terms of robustness, communication, and resource management. We conclude by exploring the area's current trends and promising research avenues.
arXiv comment44 pages, 3 figures
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Cited by in corpus (13)
- Swarm-SLAM : Sparse Decentralized Collaborative Simultaneous Localization and Mapping Framework for Multi-Robot Systems
- Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape
- DCL-SLAM: A Distributed Collaborative LiDAR SLAM Framework for a Robotic Swarm
- Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems
- S3E: A Multi-Robot Multimodal Dataset for Collaborative SLAM
- A Robot Web for Distributed Many-Device Localisation
- Message Flow Analysis with Complex Causal Links for Distributed ROS 2 Systems
- Self-Supervised Domain Calibration and Uncertainty Estimation for Place Recognition
- Multi-Robot Decentralized Collaborative SLAM in Planetary Analogue Environments: Dataset, Challenges, and Lessons Learned
- A Benchmark Dataset for Collaborative SLAM in Service Environments
- ConfidentSplat: Confidence-Weighted Depth Fusion for Accurate 3D Gaussian Splatting SLAM
- Map as a By-product: Collective Landmark Mapping from IMU Data and User-provided Texts in Situated Tasks
- SKiD-SLAM: Resource-Aware Distributed Multi-Robot LiDAR SLAM for Planetary Missions with Field Deployments at Analog Sites