A Systematic Review of Agentic AI for Autonomous Navigation: SLAM-Based Intelligent Agents

Abstract

: Autonomous navigation poses a key challenge in Artificial Intelligence (AI), necessitating agents to plan and execute actions in complex, partially visible surroundings. Simultaneous Localization and Mapping (SLAM) facilitates autonomous navigation of robots and vehicle objects to construct an unfamiliar environment map while concurrently monitoring their inside position. This systematic review investigates the nascent convergence of agentic AI, defined by goal-oriented autonomy, with adaptive decision-making and reasoning, with SLAM-based navigation systems. This paper utilized Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, which concentrated on peer-reviewed articles published in (2017–2026), particularly in SLAM-based intelligent agents utilized for autonomous navigation. SLAM has transformed from a geometry-based localization framework into an advanced perceptual and reasoning paradigm for autonomous navigation. Recent advancements in agentic AI, semantic perception, multimodal learning, and embodied foundation models have facilitated autonomous agents in progressing from passive mapping to context-aware decision-making and goal-directed navigation. The emergence of agentic AI and embodied AI has revolutionized SLAM into a spatial world model that facilitates perception, memory, reasoning, and autonomous decision-making. Contemporary research emphasizes lifelong SLAM, collaborative multi-agent mapping, semantic world modelling, and the integration of Large Language Models (LLMs) and Vision-Language Models (VLMs) for intelligent autonomous agents. Consequently, SLAM has evolved from a localization instrument to an extensive cognitive framework facilitating advanced autonomous navigation systems.