Advancing Frontiers in SLAM: A Survey of Symbolic Representation and Human-Machine Teaming in Environmental Mapping

TL;DR

This survey reviews recent progress in SLAM, emphasizing symbolic representations, multi-agent systems, and human-machine teaming to improve environmental mapping accuracy and efficiency.

Contribution

It provides a comprehensive overview of advancements in symbolic SLAM, multi-agent architectures, and human collaboration, highlighting new trends and design strategies.

Findings

Symbolic representations enhance map interpretability.

Multi-agent systems improve mapping robustness.

Human-machine teaming increases efficiency in environmental mapping.

Abstract

This survey paper presents a comprehensive overview of the latest advancements in the field of Simultaneous Localization and Mapping (SLAM) with a focus on the integration of symbolic representation of environment features. The paper synthesizes research trends in multi-agent systems (MAS) and human-machine teaming, highlighting their applications in both symbolic and sub-symbolic SLAM tasks. The survey emphasizes the evolution and significance of ontological designs and symbolic reasoning in creating sophisticated 2D and 3D maps of various environments. Central to this review is the exploration of different architectural approaches in SLAM, with a particular interest in the functionalities and applications of edge and control agent architectures in MAS settings. This study acknowledges the growing demand for enhanced human-machine collaboration in mapping tasks and examines how these collaborative efforts improve the accuracy and efficiency of environmental mapping