Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape

TL;DR

This survey reviews the current state of Collaborative SLAM, highlighting its importance for future multi-robot systems, discussing key challenges, and identifying promising research directions for robust, efficient multi-robot localization and mapping.

Contribution

It provides a comprehensive overview of C-SLAM, detailing fundamental concepts, challenges, and future research trends in the rapidly evolving field.

Findings

C-SLAM is crucial for future multi-robot applications.

Major challenges include robustness, communication, and resource management.

Emerging research trends focus on improving scalability and reliability.

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.